
Gli strumenti di intelligenza artificiale online stanno rapidamente trasformando l'ingegneria meccanica aumentando le capacità umane di progettazione e analisi, produzionee manutenzione. Questi sistemi di intelligenza artificiale sono in grado di elaborare grandi quantità di dati, identificare modelli complessi e generare soluzioni innovative molto più rapidamente dei metodi tradizionali. Ad esempio, l'IA può aiutarvi a ottimizzare i progetti per le prestazioni e la producibilità, accelerare simulazioni complesse, prevedere le proprietà dei materiali e automatizzare un'ampia gamma di attività analitiche.
I suggerimenti forniti qui di seguito aiuteranno, ad esempio, a progettare in modo generativo, ad accelerare le simulazioni (FEA/CFD), ad aiutare nella manutenzione predittiva, dove l'intelligenza artificiale analizza i dati dei sensori dei macchinari per prevedere potenziali guasti, consentendo un'assistenza proattiva e riducendo al minimo i tempi di fermo, a selezionare i materiali e molto altro ancora.
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- Analisi della causa principale
- Ingegneria meccanica
Prompt AI per Analisi dell'albero dei guasti Impostazione dell'evento top
- Progettazione per la produzione (DfM), Convalida del progetto, Analisi dei guasti, Analisi dell'albero dei guasti (FTA), Industria meccanica, Gestione della qualità, Analisi del rischio, Gestione del rischio, Sicurezza
Aiuta ad avviare un'analisi dell'albero dei guasti (FTA) definendo l'evento indesiderato principale e suggerendo i guasti dei sottosistemi o gli eventi di base che vi contribuiscono immediatamente per un sistema meccanico descritto. Questo prompt fornisce un punto di partenza per una dettagliata valutazione quantitativa o qualitativa del rischio. L'output è una struttura ad albero in formato markdown.
Uscita:
- Markdown
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- Campi: {testo_descrizione_del_sistema} {descrizione_di_guasto_di_top_evento_indesiderato} {elenco_di_chiave_sottosistemi_o_componenti_csv}
Act as a System Safety Engineer specializing in Fault Tree Analysis (FTA).
Your TASK is to help set up the initial levels of a Fault Tree for the `{system_description_text}`.
The TOP EVENT (the main undesired failure) is: `{undesired_top_event_failure_description}`.
Consider the `{key_subsystems_or_components_list_csv}` (CSV: 'Subsystem_Or_Component_Name
Brief_Function') as potential contributors.
You should propose immediate contributing events (intermediate events or basic events) and the logical gates (AND
OR) that connect them to the Top Event or to each other at the first couple of levels.
**FAULT TREE ANALYSIS - INITIAL STRUCTURE (MUST be Markdown format):**
**System Under Analysis**: `{system_description_text}`
**Top Undesired Event**: `{undesired_top_event_failure_description}`
**Level 0: Top Event**
```mermaid
graph TD
TE(`{undesired_top_event_failure_description}`")
```
**Level 1: Immediate Contributing Events / Sub-System Failures**
* **Guidance**: Think about the major ways the Top Event could occur. These could be failures of major subsystems listed in `{key_subsystems_or_components_list_csv}` or general failure categories. Determine if these immediate causes need to ALL occur (AND gate) or if ANY ONE of them occurring is sufficient (OR gate) to cause the Top Event.
**Proposed Structure (Example - AI to generate based on inputs):**
* *If the Top Event can be caused by failure of Subsystem A OR Subsystem B OR an External Event:*
```mermaid
graph TD
TE("`{undesired_top_event_failure_description}`") -->|OR Gate G1| IE1("Failure of [Subsystem A Name from CSV]")
TE -->|OR Gate G1| IE2("Failure of [Subsystem B Name from CSV]")
TE -->|OR Gate G1| IE3("Relevant External Event Causing Failure
e.g.
Power Loss")
```
* *If the Top Event occurs only if Component X AND Component Y fail simultaneously:*
```mermaid
graph TD
TE("`{undesired_top_event_failure_description}`") -->|AND Gate G2| BE1("Failure of [Component X Name from CSV]")
TE -->|AND Gate G2| BE2("Failure of [Component Y Name from CSV]")
```
**Level 2: Further Breakdown of Level 1 Intermediate Events (Illustrative for one branch)**
* **Guidance**: Take ONE of the Intermediate Events (IE) from Level 1 and break it down further. Identify how that specific subsystem or intermediate event could fail.
* **Example (Continuing from OR Gate G1
focusing on IE1 'Failure of Subsystem A'):**
* *If 'Failure of Subsystem A' can be caused by 'Component A1 Failure' OR 'Component A2 Failure':*
```mermaid
graph TD
TE("`{undesired_top_event_failure_description}`") -->|OR Gate G1| IE1("Failure of [Subsystem A Name]")
TE -->|OR Gate G1| IE2("Failure of [Subsystem B Name]")
TE -->|OR Gate G1| IE3("External Event")
IE1 -->|OR Gate G1A| BE_A1("Failure of [Component A1 of Subsystem A]")
IE1 -->|OR Gate G1A| BE_A2("Failure of [Component A2 of Subsystem A]")
```
* The events BE_A1
BE_A2 would be ""Basic Events"" if they represent the limit of resolution (e.g.
a specific part failing
human error
software glitch) for this initial setup
or they could be further developed Intermediate Events.
**Key Considerations for Further Development by User:**
* **Basic Events**: These are typically failures of individual components
human errors
or external events that require no further decomposition. Their probabilities of occurrence are often estimated from historical data
handbook data
or expert judgment.
* **Gate Logic**: Carefully determine if contributing events need an AND gate (all must occur) or an OR gate (any one can cause the higher-level event).
* **Mutual Exclusivity**: Assume events are independent unless otherwise specified.
* **Data Requirements**: For a quantitative FTA
failure probabilities for all basic events are needed.
* **Common Cause Failures**: Consider if a single event could cause multiple basic events to fail simultaneously (this adds complexity beyond this initial setup but is important for full FTA).
**AI's Proposed Initial Breakdown (specific to your inputs):**
*(The AI should now provide a concrete proposed Mermaid diagram snippet for Level 0 and Level 1
and one branch of Level 2
based on the user's specific `{system_description_text}`
`{undesired_top_event_failure_description}`
and `{key_subsystems_or_components_list_csv}`. It should make reasonable assumptions about how these subsystems might contribute to the top event
stating the gate logic clearly.)*
```mermaid
graph TD
TE("`{undesired_top_event_failure_description}`")
// AI will populate the connections and Level 1 / Level 2 events here
// Example: If key_subsystems_or_components_list_csv includes 'Hydraulic_Pump
Provides_Pressure' and 'Control_Valve
Directs_Flow'
// and top event is 'System_Fails_to_Actuate'
// TE -->|OR Gate G_Main| Pump_Failure("Hydraulic Pump Fails")
// TE -->|OR Gate G_Main| Valve_Failure("Control Valve Fails")
// TE -->|OR Gate G_Main| Electrical_Failure("Control System Electrical Failure")
// Pump_Failure -->|OR Gate G_Pump| Motor_Fails("Pump Motor Fails (Basic Event)")
// Pump_Failure -->|OR Gate G_Pump| Pump_Internal_Leak("Pump Internal Leakage (Basic Event)")
```
**IMPORTANT**: This prompt generates a STARTING POINT for an FTA. A complete FTA is a detailed and iterative process. The Mermaid syntax is provided to suggest a visual structure; the user would use FTA software or draw this out. The AI's main role here is to structure the initial decomposition logically."
- Ideale per: Aiutare gli ingegneri meccanici a iniziare un'analisi dell'albero dei guasti (FTA) definendo l'evento principale e suggerendo i rami iniziali e le porte logiche per i guasti del sottosistema.
- Analisi della causa principale
- Ingegneria meccanica
Prompt AI per Comparative RCA for Repetitive Failures
- Miglioramento continuo, Azione correttiva, Progettazione per la produzione (DfM), Analisi dei guasti, Produzione snella, Industria meccanica, Miglioramento dei processi, Gestione della qualità, Analisi della causa principale
Analyzes textual descriptions from multiple incident reports of a repetitive failure in a mechanical system. This prompt aims to identify common patterns potential shared root causes and any differentiating factors across incidents helping to solve persistent issues. The output is a markdown formatted comparative analysis.
Uscita:
- Markdown
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- Fields: {system_or_component_name} {common_failure_description} {multiple_failure_incident_reports_text}
Act as a Senior Reliability Engineer conducting a Root Cause Analysis (RCA) on REPETITIVE failures.
Your TASK is to analyze the information provided in `{multiple_failure_incident_reports_text}` concerning recurring instances of '`{common_failure_description}`' affecting the `{system_or_component_name}`.
The goal is to identify common patterns
potential shared root causes
and any significant differentiating factors or unique conditions across the incidents.
The `{multiple_failure_incident_reports_text}` is a single block of text where each incident report is clearly demarcated (e.g.
by '---INCIDENT REPORT X START---' and '---INCIDENT REPORT X END---'
or user ensures separation). Each report may contain details like date
operator
specific symptoms
environmental conditions
immediate actions taken
and initial findings.
**COMPARATIVE ROOT CAUSE ANALYSIS REPORT (MUST be Markdown format):**
**1. Overview of Repetitive Failure:**
* **System/Component**: `{system_or_component_name}`
* **Common Failure Mode**: `{common_failure_description}`
* **Number of Incident Reports Analyzed**: [AI to count based on demarcations in `{multiple_failure_incident_reports_text}`]
**2. Data Extraction and Tabulation (Conceptual - AI to perform this internally):**
* For each incident report
extract key information such as:
* Incident ID/Date
* Specific symptoms observed (beyond the `{common_failure_description}`)
* Operating conditions at time of failure (load
speed
temperature
etc.)
* Environmental conditions
* Maintenance history just prior
* Operator actions or comments
* Any parts replaced or immediate fixes tried.
* *(AI should internally process this information to find patterns. A table won't be in the final output unless it's a summary table
but the AI's logic should be based on this kind of structured comparison.)*
**3. Identification of Common Patterns and Themes Across Incidents:**
* **Symptomology**: Are there consistent preceding symptoms or secondary effects noted across multiple reports before or during the `{common_failure_description}`?
* **Operating Conditions**: Do failures tend to occur under specific loads
speeds
temperatures
or during particular phases of operation (startup
shutdown
steady-state)?
* **Environmental Factors**: Is there a correlation with specific environmental conditions (e.g.
high humidity
dusty environment
specific time of day/year)?
* **Maintenance Activities**: Do failures cluster after certain maintenance activities
or if maintenance is overdue?
* **Component Batch/Supplier (if mentioned in reports)**: Is there any indication of issues related to specific batches or suppliers of the `{system_or_component_name}` or its sub-parts?
* **Human Factors**: Any patterns related to operator experience
shift changes
or specific procedures being followed/not followed?
**4. Identification of Differentiating Factors and Unique Conditions:**
* Are there any incidents that stand out as different in terms
of symptoms
conditions
or severity?
* What unique factors were present in these outlier incidents?
* Could these differences point to multiple root causes or aggravating factors for the `{common_failure_description}`?
**5. Hypothesis Generation for Potential Shared Root Cause(s):**
Based on the common patterns
propose 2-3 primary hypotheses for the underlying root cause(s) of the repetitive '`{common_failure_description}`'. For each hypothesis:
* **Hypothesis Statement**: (e.g.
'Material fatigue due to cyclic loading under X condition'
'Inadequate lubrication leading to premature wear'
'Sensor malfunction providing incorrect feedback to control system').
* **Supporting Evidence from Reports**: Briefly list the common patterns from section 3 that support this hypothesis.
**6. Recommended Next Steps for Investigation / Verification:**
* What specific data collection
tests
or analyses should be performed to confirm or refute the proposed hypotheses? Examples:
* `Detailed metallurgical analysis of failed components from multiple incidents.`
* `Targeted inspection of [specific sub-component] across all similar units.`
* `Review of design specifications vs. actual operating conditions.`
* `Interviews with operators and maintenance staff involved in the incidents.`
* `Monitoring specific parameters (e.g.
vibration
temperature) that might be precursors.`
**7. Interim Containment or Mitigation Actions (if obvious from analysis):**
* Are there any immediate actions that could be taken to potentially reduce the frequency or severity of the failures while the full RCA is ongoing
based on the patterns identified?
**IMPORTANT**: The analysis should focus on synthesizing information from MULTIPLE reports to find trends that might not be obvious from a single incident. The AI should clearly articulate the logic connecting observed patterns to potential root causes.
- Best for: Assisting mechanical engineers in diagnosing recurrent system failures by comparatively analyzing multiple incident reports to identify common patterns and potential shared root causes.
- Ottimizzazione del disegno sperimentale
- Ingegneria meccanica
Prompt AI per Critica del piano DOE per l'esperimento fattoriale
- Industria meccanica, Ottimizzazione del processo, Controllo di qualità, Gestione della qualità, Analisi statistica, Test statistici
Critica una proposta di disegno degli esperimenti (DOE) per un esperimento fattoriale, suggerendo miglioramenti per quanto riguarda il livello di selezione dei fattori, l'adeguatezza del confondimento e la potenza statistica. Questo suggerimento aiuta gli ingegneri meccanici a ottimizzare i loro progetti sperimentali in termini di robustezza ed efficienza. L'output è una critica in formato markdown.
Uscita:
- Markdown
- non richiede Internet in diretta
- Campi: {testo_obiettivo_sperimentale} {fattori_e_livelli_json} {tabella_sperimentale_proposta_di_runs_csv} {elenco_di_variabili_di_risposta_csv}
Act as a Statistical Consultant specializing in Design of Experiments (DOE) for engineering applications.
Your TASK is to critique the proposed DOE plan for a factorial experiment
based on the following inputs:
* `{experimental_objective_text}`: Clear statement of what the experiment aims to achieve (e.g.
'To determine the main effects and two-factor interactions of cutting speed
feed rate
and depth of cut on surface roughness and tool wear in milling 6061 Aluminum.').
* `{factors_and_levels_json}`: A JSON string defining factors and their levels (e.g.
`{"CuttingSpeed_m_min": [100
150
200]
"FeedRate_mm_rev": [0.1
0.2]
"DepthOfCut_mm": [0.5
1.0]}`). The actual JSON will be standard.
* `{proposed_experimental_runs_table_csv}`: A CSV string of the proposed experimental runs
showing combinations of factor levels (e.g.
'Run
CuttingSpeed
FeedRate
DepthOfCut
...'). If it's a standard design (e.g.
full factorial
fractional factorial)
this might be implied or the user might just state the design type.
* `{response_variables_list_csv}`: CSV string listing the output variables to be measured (e.g.
'SurfaceRoughness_Ra_microns
ToolWear_VB_mm').
**CRITIQUE OF DOE PLAN (MUST be Markdown format):**
**1. Alignment with Objective:**
* **Assessment**: Does the selection of factors
levels
and responses in `{factors_and_levels_json}` and `{response_variables_list_csv}` directly support achieving the `{experimental_objective_text}`?
* **Recommendations**: [e.g.
'The objective mentions interactions
ensure the design specified in `{proposed_experimental_runs_table_csv}` allows estimation of these (e.g.
full factorial or appropriate fractional factorial).' or 'Consider if [Additional Factor] might be relevant to the objective.']
**2. Factor Selection and Levels:**
* **Assessment**: Are the factors in `{factors_and_levels_json}` truly independent and controllable? Are the chosen levels appropriate (e.g.
spanning a reasonable range
not too close
not too far apart to cause process instability)? Are there enough levels to detect non-linearity if expected (more than 2 for a factor)?
* **Recommendations**: [e.g.
'For Factor X
the levels [L1
L2] are very close; consider widening the range if feasible to better observe its effect.' or 'If quadratic effects are suspected for Factor Y
three levels would be necessary.']
**3. Experimental Design Choice (based on `{proposed_experimental_runs_table_csv}` or implied design):**
* **Assessment**:
* **Type of Design**: (e.g.
Full factorial
Fractional factorial
other). Is it clearly stated or inferable?
* **Resolution (for fractional factorials)**: If fractional
what is its resolution and what interactions are confounded? Is this acceptable given the `{experimental_objective_text}`?
* **Number of Runs**: Is it practical? Is it sufficient for the effects being estimated?
* **Randomization**: Is randomization of run order planned? (CRITICAL - should be mentioned as essential).
* **Replication**: Are replications planned
especially at center points (if any) or for key runs
to estimate pure error?
* **Recommendations**: [e.g.
'The proposed fractional factorial design (if identified) confounds main effects with two-factor interactions involving Factor Z. If Factor Z interactions are critical
a higher resolution design or full factorial is needed.' or 'Strongly recommend randomizing the run order to mitigate effects of lurking variables.' or 'Include 3-5 replications at the center point (if applicable) to check for curvature and get a robust estimate of error.']
**4. Response Variables:**
* **Assessment**: Are the `{response_variables_list_csv}` well-defined
measurable
and relevant to the objective? Is the measurement system capable (repeatable and reproducible)? (Latter is an assumption
but good to mention).
* **Recommendations**: [e.g.
'Ensure a consistent measurement protocol for [Response Y].']
**5. Statistical Power and Analysis:**
* **Assessment**: While a full power analysis is complex
comment qualitatively if the design seems underpowered for detecting effects of practical importance
especially if there are few runs or high expected variability.
* **Recommendations**: [e.g.
'With only N runs
detecting small but significant interactions might be challenging. Consider if effect sizes are expected to be large.' or 'Plan for ANOVA and regression analysis. Check model assumptions (normality
constant variance of residuals) post-experiment.']
**Summary of Key Recommendations:**
* [List the top 3-4 most critical suggestions for improving the DOE plan.]
**IMPORTANT**: The critique should be constructive and provide actionable advice. If the `{proposed_experimental_runs_table_csv}` is not detailed
critique based on the likely design implied by factors/levels and objective.
- Ideale per: Criticare i piani di progettazione degli esperimenti (DOE) per esperimenti fattoriali in ingegneria meccanica per migliorare l'adeguatezza del livello di selezione dei fattori e la validità statistica.
- Ottimizzazione del disegno sperimentale
- Ingegneria meccanica
Prompt AI per Gruppo di controllo Suggerimento per il test sui materiali
- Materiali, Industria meccanica, Proprietà meccaniche, Monitoraggio delle prestazioni, Garanzia di qualità, Controllo di qualità, Analisi statistica, Trattamento della superficie, Metodi di prova
Suggerisce gruppi di controllo e misure di riferimento adeguati per uno studio sperimentale su un nuovo materiale o trattamento superficiale in un'applicazione meccanica, garantendo confronti validi e conclusioni affidabili. Questo prompt aiuta gli ingegneri a progettare protocolli di prova dei materiali più solidi. Il risultato è una raccomandazione basata sul testo.
Uscita:
- Testo
- non richiede Internet in diretta
- Campi: {descrizione_materiale_di_prova_o_trattamento} {testo_condizioni_sperimentali} {metrica_di_performance_da_misurare_elenco_csv}
Act as an Experimental Design Specialist in Materials Science and Engineering.
Your TASK is to recommend appropriate control groups and baseline measurements for an experimental study involving `{test_material_or_treatment_description}` under `{experimental_conditions_text}`
where `{performance_metrics_to_be_measured_list_csv}` (CSV: 'Metric_Name
Units') are the key outputs.
The goal is to ensure that any observed changes in performance can be confidently attributed to the `{test_material_or_treatment_description}`.
**RECOMMENDATIONS FOR CONTROL GROUPS AND BASELINE MEASUREMENTS:**
**1. Understanding the Core Investigation:**
* The primary goal is to evaluate the effect of `{test_material_or_treatment_description}`.
* The `{experimental_conditions_text}` (e.g.
'High-temperature tensile testing at 600°C'
'Cyclic fatigue testing under 200 MPa load for 10^6 cycles'
'Wear testing against a steel counterface with 10N load for 5 hours') define the environment.
* The `{performance_metrics_to_be_measured_list_csv}` (e.g.
'Ultimate_Tensile_Strength_MPa
Elongation_Percent'
'Fatigue_Life_Cycles'
'Wear_Rate_mm3_Nm') are the indicators of performance.
**2. Recommended Control Group(s):**
* **A. Untreated/Standard Material Control:**
* **Description**: Samples made from the SAME BASE MATERIAL as the `{test_material_or_treatment_description}` but WITHOUT the specific new material feature or treatment being tested. If the test involves a new alloy
the control might be the conventional alloy it aims to replace or a version of the new alloy without a critical processing step.
* **Justification**: This is the MOST CRITICAL control. It allows for direct comparison to determine if the `{test_material_or_treatment_description}` provides any benefit (or detriment) over the standard or untreated state.
* **Processing**: These control samples should
as much as possible
undergo all other processing steps (e.g.
heat treatments
machining) that the test samples experience
EXCEPT for the specific treatment/feature being evaluated.
* **B. (Optional
if applicable) Benchmark/Reference Material Control:**
* **Description**: Samples made from a well-characterized
industry-standard benchmark material that is commonly used in similar applications or for which extensive performance data exists.
* **Justification**: This allows comparison against a known quantity and can help validate the testing procedure if the benchmark material behaves as expected. It also positions the performance of the `{test_material_or_treatment_description}` within the broader field.
* **C. (Optional
if treatment involves application) Placebo/Sham Treatment Control:**
* **Description**: If the treatment involves a complex application process (e.g.
a coating applied via a specific sequence of steps
some of which might independently affect the material)
a sham control experiences all application steps EXCEPT the active treatment ingredient/process.
* **Justification**: Helps to isolate the effect of the active treatment component from the effects of the application process itself.
**3. Baseline Measurements (Pre-Test Characterization):**
* For ALL samples (both test and control groups)
consider performing and recording the following baseline measurements BEFORE subjecting them to the main `{experimental_conditions_text}`:
* **Initial Microstructure Analysis**: (e.g.
Optical microscopy
SEM) To document the starting state
grain size
presence of defects
or treatment-induced surface changes.
* **Initial Hardness Testing**: A quick way to check for consistency or initial effects of a surface treatment.
* **Precise Dimensional Measurements**: Especially important for wear or deformation studies.
* **Surface Roughness**: If surface properties are critical or affected by the treatment.
* **Compositional Analysis (Spot Checks)**: To verify material or coating composition if it's a key variable.
* **Justification**: Baseline data helps confirm initial sample consistency
can reveal pre-existing flaws
and provides a reference point for assessing changes after testing.
**4. Experimental Considerations:**
* **Sample Size**: Ensure a sufficient number of samples in each group (test and control) for statistical validity.
* **Randomization**: If there are variations in the testing apparatus or over time
randomize the testing order of samples from different groups.
* **Identical Test Conditions**: CRITICAL - All groups (test and control) MUST be subjected to the EXACT SAME `{experimental_conditions_text}` and measurement procedures for the `{performance_metrics_to_be_measured_list_csv}`.
**Summary**: By including these control groups and baseline measurements
the experiment will be better able to isolate the true effect of the `{test_material_or_treatment_description}` and produce more reliable and defensible conclusions.
- Ideale per: Guidare gli ingegneri meccanici nella selezione di gruppi di controllo e di misure di base appropriate per i test sui materiali, garantendo la validità sperimentale e l'interpretazione affidabile dei risultati.
- Ottimizzazione del disegno sperimentale
- Ingegneria meccanica
Prompt AI per Posizionamento dei sensori per le prove di vibrazione
- Industria meccanica, Garanzia di qualità, Controllo di qualità, Sensori, Elaborazione del segnale, Ingegneria strutturale, Metodi di prova, Analisi delle vibrazioni
Raccomanda i tipi di sensori e le strategie di posizionamento ottimali per le prove di vibrazione su una struttura meccanica per catturare i modi rilevanti e garantire la qualità dei dati in base alla descrizione della struttura e agli obiettivi della prova. Questo prompt aiuta a pianificare un'efficace analisi modale sperimentale o il monitoraggio delle vibrazioni. L'output è una raccomandazione in formato markdown.
Uscita:
- Markdown
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- Campi: {descrizione_struttura_e_materiale} {testo_prova_di_vibrazione_obiettivi} {testo_di_frequenza_di_interesse} {lista_dei_tipi_di_sensori_disponibili_csv}
Act as a Vibration Testing and Modal Analysis Expert.
Your TASK is to recommend optimal sensor types and placement strategies for vibration testing on the `{structure_description_and_material}`.
The recommendations should align with the `{vibration_test_objectives_text}`
consider the `{frequency_range_of_interest_text}`
and select from the `{available_sensor_types_list_csv}` (CSV: 'SensorType
KeySpecification_e.g._Sensitivity_FrequencyRange_Weight').
**SENSOR PLACEMENT AND TYPE RECOMMENDATIONS (MUST be Markdown format):**
**1. Analysis of Inputs:**
* **Structure**: `{structure_description_and_material}` (e.g.
'Cantilevered steel beam
1m long
5cm x 1cm cross-section'
'Aluminum plate
50cm x 50cm x 0.5cm
simply supported on four edges'
'Complex welded frame assembly').
* **Objectives**: `{vibration_test_objectives_text}` (e.g.
'Identify first 5 natural frequencies and mode shapes'
'Monitor operational vibration levels at critical bearing locations'
'Assess damping effectiveness of a new treatment').
* **Frequency Range**: `{frequency_range_of_interest_text}` (e.g.
'0-500 Hz'
'Up to 2 kHz').
* **Available Sensors**: `{available_sensor_types_list_csv}` (e.g.
'Accelerometer_A
100mV/g
1-5000Hz
10grams'
'Displacement_Sensor_B
non-contact_eddy
DC-1000Hz
50grams_probe').
**2. Recommended Sensor Type(s):**
* **Selection Rationale**: Based on the `{vibration_test_objectives_text}`
`{frequency_range_of_interest_text}`
and characteristics of the `{structure_description_and_material}` (e.g.
size
stiffness
expected displacement/acceleration levels).
* **Primary Choice(s) from `{available_sensor_types_list_csv}`**:
* [Sensor Type 1]: Justify why it's suitable (e.g.
'Accelerometer_A is suitable due to its wide frequency range covering the interest area
good sensitivity
and relatively low mass which minimizes mass loading on lighter structures.').
* [Sensor Type 2 (if needed or alternative)]: Justify.
* **Considerations**:
* **Mass Loading**: Ensure sensor mass is significantly less than the dynamic mass of the structure at the attachment point (typically <10%).
* **Dynamic Range**: Sensor must handle expected vibration amplitudes without clipping or poor signal-to-noise.
* **Environmental Conditions**: Temperature
humidity (if relevant).
**3. Sensor Placement Strategy:**
* **Goal**: To adequately capture the modes of interest (if modal analysis) or critical operational responses.
* **General Principles**:
* Place sensors where significant motion is expected for the modes of interest.
* Avoid placing sensors at nodal points/lines of key modes if those modes are to be measured.
* Ensure good mechanical coupling between sensor and structure (e.g.
stud mount
rigid adhesive
magnetic base on suitable surface).
* Consider sensor orientation to capture motion in relevant directions (uniaxial
triaxial sensors).
* **Specific Recommendations for `{structure_description_and_material}`**:
* **Driving Point (for modal testing with shaker)**: Place a sensor near the excitation point to measure input (if not using an impedance head).
* **Response Points (General Grid/Targeted)**:
* If identifying mode shapes: Distribute sensors across the structure to provide sufficient spatial resolution. A preliminary Finite Element Analysis (FEA) model
if available
can guide optimal placement by showing high displacement areas for different modes.
* If simple structure (e.g.
beam
plate): Suggest a grid or specific points (e.g.
'For the cantilever beam
place sensors at L/4
L/2
3L/4
and L from the fixed end to capture bending modes.').
* If monitoring specific locations (from `{vibration_test_objectives_text}`
e.g.
'bearing housings'): Prioritize these locations.
* **Number of Sensors**: Based on objectives
complexity of modes
and available channels. Suggest a minimum or typical number.
* **Pre-Test Checks**:
* Perform a "tap test" or preliminary sweep to ensure sensors are working and capturing signals as expected.
**4. Data Acquisition Considerations (Briefly):**
* Ensure sampling rate is adequate (at least 2.56 times the max `{frequency_range_of_interest_text}`
ideally 5-10 times for better time domain representation).
* Anti-aliasing filters are essential.
* Cable routing to minimize noise.
**IMPORTANT**: These are general guidelines. The optimal setup can depend on subtle details of the structure and test. If a pre-test FEA modal analysis is feasible for the user
it's highly recommended for guiding sensor placement for complex mode shapes.
- Ideale per: Guidare gli ingegneri meccanici nella scelta e nel posizionamento dei sensori per le prove di vibrazione, garantendo l'acquisizione ottimale dei dati per l'analisi modale o il monitoraggio della salute.
- Ottimizzazione del disegno sperimentale
- Ingegneria meccanica
Prompt AI per Ottimizzazione delle variabili del protocollo di prova dell'usura
- Scienza dei materiali, Industria meccanica, Ottimizzazione del processo, Controllo di qualità, Analisi statistica, Metodi di prova, Tribologia, Tecnologia indossabile
Analizza un protocollo di test di usura per un componente meccanico, suggerendo modi per ridurre il numero di variabili o migliorare il controllo dei parametri per isolare effetti specifici e migliorare la ripetibilità e l'affidabilità del test. Questo prompt aiuta a perfezionare le impostazioni sperimentali per gli studi tribologici. L'output è un elenco di raccomandazioni in formato markdown.
Uscita:
- Markdown
- non richiede Internet in diretta
- Campi: {testo_di_protocollo_di_usura} {materiale_componente_e_controfaccia_materiale} {lista_chiave_variabili_da_indagare_csv}
Act as a Tribology Specialist with expertise in wear testing methodologies.
Your TASK is to analyze the provided `{wear_test_protocol_description_text}` for testing a component made of `{component_material_and_counterface_material}` (specify both
e.g.
'Component: Bearing Steel
Counterface: Stainless Steel 304'). The aim is to suggest improvements for reducing uncontrolled variables
isolating the effects of `{key_variables_being_investigated_list_csv}` (CSV: 'Variable_Name
Range_or_Levels')
and enhancing overall test repeatability and reliability.
**RECOMMENDATIONS FOR WEAR TESTING PROTOCOL OPTIMIZATION (MUST be Markdown format):**
**1. Review of Current Protocol and Objectives:**
* **Understanding the Protocol**: Briefly summarize the core elements of the `{wear_test_protocol_description_text}` (e.g.
'Pin-on-disk test
10N load
0.5 m/s sliding speed
1000m distance
ambient temperature
dry contact').
* **Investigated Variables**: Clarify the specific variables from `{key_variables_being_investigated_list_csv}` that the protocol aims to study (e.g.
'Effect of Load (5N
10N
15N)'
'Effect of Lubricant Type (Oil A
Oil B
Dry)').
* **Materials**: `{component_material_and_counterface_material}`.
**2. Identification of Potential Uncontrolled or Confounding Variables:**
Based on the protocol description
identify factors that might not be explicitly controlled or could interfere with isolating the effects of the `{key_variables_being_investigated_list_csv}`. Examples:
* **Environmental Factors**:
* Temperature fluctuations (ambient vs. localized heating due to friction).
* Humidity variations.
* Contamination (dust
debris from previous tests).
* **Specimen Preparation Inconsistencies**:
* Surface finish variations (initial roughness of component and counterface).
* Cleaning procedures before test.
* Specimen alignment and clamping.
* **Test Rig / Operational Factors**:
* Actual load application (static vs. dynamic components
precise load control).
* Speed fluctuations.
* Vibration from the test rig or surroundings.
* Wear debris accumulation or removal during the test.
* **Measurement Inconsistencies (for wear quantification)**:
* Method of wear measurement (mass loss
profilometry
wear scar dimensions) and its precision/repeatability.
* Timing of measurements.
**3. Recommendations for Improving Control and Isolation of Variables:**
* **For each identified potential issue
suggest specific improvements:**
* **Environmental Control**:
* `e.g.
Consider conducting tests in a temperature and humidity controlled chamber if feasible
or at least monitor and record ambient conditions.`
* `e.g.
Implement strict cleaning protocols for the test chamber and specimens between runs.`
* **Specimen Preparation Standardization**:
* `e.g.
Define and adhere to a specific surface preparation procedure (e.g.
grinding
polishing to a consistent Ra value). Verify roughness before each test.`
* `e.g.
Use standardized cleaning solvents and drying methods.`
* `e.g.
Develop a fixture or procedure for consistent alignment.`
* **Test Rig Calibration and Monitoring**:
* `e.g.
Regularly calibrate load cells
speed sensors
and environmental sensors.`
* `e.g.
Monitor key parameters like load
speed
and friction coefficient IN-SITU if possible.`
* **Wear Debris Management**:
* `e.g.
Decide on a strategy: either allow debris to accumulate naturally (if studying three-body wear is intended) or implement a method for controlled removal (e.g.
periodic cleaning
inert gas flow) if two-body abrasion is the focus. Document the choice.`
* **Standardized Wear Measurement**:
* `e.g.
Clearly define the wear measurement technique
including specific locations for profilometry scans or number of mass measurements. Calibrate measurement instruments.`
* **Isolating `{key_variables_being_investigated_list_csv}`**:
* `e.g.
When investigating 'Load'
ensure ALL other parameters (speed
environment
lubricant if any
material batch
surface prep) are kept as constant as possible across different load levels.`
* `e.g.
Use a full factorial or well-designed fractional factorial approach if multiple variables from the list are changed simultaneously to understand interactions.`
**4. Enhancing Repeatability and Reliability:**
* **Replicates**: `Perform multiple test runs (e.g.
3-5 replicates) for each unique test condition to assess variability and calculate confidence intervals.`
* **Randomization**: `Randomize the order of test runs for different conditions to minimize systematic errors related to time or drift.`
* **Reference Runs**: `Periodically run a test with a standard reference material pair under fixed conditions to check for drift in the test rig performance.`
**IMPORTANT**: The goal is to make the experimental results more attributable to the `{key_variables_being_investigated_list_csv}` by minimizing other sources of variation.
- Ideale per: Migliorare l'affidabilità e la precisione dei protocolli di prova dell'usura, identificando e controllando le variabili che garantiscono un migliore isolamento degli effetti indagati negli studi tribologici.
- Modellazione predittiva
- Ingegneria meccanica
Prompt AI per Strategia del modello di previsione delle proprietà dei materiali
- Apprendimento automatico, Materiali, Industria meccanica, Proprietà meccaniche, Algoritmi di manutenzione predittiva, Ottimizzazione del processo, Gestione della qualità, Analisi statistica
Delinea una strategia per lo sviluppo di un modello predittivo per una specifica proprietà del materiale basato su parametri di composizione e lavorazione utilizzando un set di dati descritto. Questo prompt aiuta gli ingegneri meccanici ad avviare la progettazione o la selezione dei materiali in base ai dati. L'output è un documento strategico formattato in markdown.
Uscita:
- Markdown
- non richiede Internet in diretta
- Campi: {target_material_property_to_predict} {dataset_dimensioni_e_caratteristiche_testo_dataset_disponibile_input_features_csv_description}
Act as a Materials Informatics Specialist.
Your TASK is to outline a strategy for developing a predictive model for `{target_material_property_to_predict}` (e.g.
'Tensile Strength in MPa'
'Hardness in HRC'
'Fatigue Life in cycles').
The model will be based on input features described in `{available_input_features_csv_description}` (CSV string detailing feature names
types (numeric/categorical)
and example ranges
e.g.
'FeatureName
DataType
ExampleRange
CarbonContent_Numeric_0.1-1.0%
HeatTreatmentTemp_Numeric_800-1200C
AlloyingElementX_Categorical_Present/Absent').
Consider the `{dataset_size_and_characteristics_text}` (e.g.
'Approximately 500 data points
some missing values
potential outliers noted in preliminary analysis
data from various literature sources').
**PREDICTIVE MODEL DEVELOPMENT STRATEGY (MUST be Markdown format):**
**1. Project Goal:**
* To develop a predictive model for `{target_material_property_to_predict}` using the features outlined in `{available_input_features_csv_description}`.
* Potential use cases: Material screening
alloy design optimization
property estimation where experimental data is scarce.
**2. Data Preprocessing and Exploration (Key Steps):**
* **2.1. Data Loading and Initial Inspection**:
* Load the dataset.
* Verify feature names
data types against `{available_input_features_csv_description}`.
* Initial check for gross errors or inconsistencies.
* **2.2. Handling Missing Values**: Based on `{dataset_size_and_characteristics_text}` if it mentions missing data.
* Strategy: (e.g.
Imputation using mean/median/mode
K-Nearest Neighbors imputation
or model-based imputation if complex. Justify choice).
* Consider adding an indicator column for imputed values.
* **2.3. Outlier Detection and Treatment**: Based on `{dataset_size_and_characteristics_text}` if it mentions outliers.
* Strategy: (e.g.
Z-score
IQR method
Isolation Forest). Decide whether to cap
transform
or remove outliers
and justify.
* **2.4. Feature Engineering (if applicable)**:
* Transformations: (e.g.
Logarithmic
square root transformations for skewed data; Polynomial features if non-linear relationships are suspected).
* Categorical Encoding: For features identified as categorical in `{available_input_features_csv_description}` (e.g.
One-Hot Encoding
Label Encoding).
* Interaction Terms: Consider creating interaction terms between key features if domain knowledge suggests their importance.
* **2.5. Feature Scaling/Normalization**:
* Strategy: (e.g.
StandardScaler
MinMaxScaler) especially important for distance-based algorithms or neural networks.
* **2.6. Exploratory Data Analysis (EDA)**:
* Visualize distributions of individual features and the `{target_material_property_to_predict}`.
* Plot relationships between input features and the target property (scatter plots
correlation matrix).
* Identify potential correlations or patterns.
**3. Model Selection and Training:**
* **3.1. Splitting the Dataset**:
* Training set (e.g.
70-80%)
Validation set (e.g.
10-15%)
Test set (e.g.
10-15%). Stratified splitting if the target variable is highly imbalanced or for classification tasks (though this is regression).
* **3.2. Candidate Model Algorithms (Suggest 2-3 to explore)**:
* **Baseline Model**: Simple Linear Regression or a decision tree regressor.
* **More Complex Models**:
* Ensemble methods (Random Forest Regressor
Gradient Boosting Regressor like XGBoost
LightGBM) - often perform well on tabular data.
* Support Vector Regression (SVR).
* Neural Networks (Multilayer Perceptron - MLP) - consider if dataset is large enough and complex non-linearities are expected.
* Justify choices based on `{dataset_size_and_characteristics_text}` and nature of features.
* **3.3. Hyperparameter Tuning**:
* Strategy: (e.g.
GridSearchCV
RandomizedSearchCV
Bayesian Optimization) using the validation set.
* **3.4. Training**: Train candidate models on the training set using optimized hyperparameters.
**4. Model Evaluation:**
* **4.1. Performance Metrics (for regression)**:
* Primary metrics: Root Mean Squared Error (RMSE)
Mean Absolute Error (MAE).
* Secondary metrics: R-squared (Coefficient of Determination)
Mean Absolute Percentage Error (MAPE).
* **4.2. Evaluation on Test Set**: Assess final model performance on the unseen test set to estimate generalization ability.
* **4.3. Residual Analysis**: Plot residuals to check for patterns
homoscedasticity
and normality.
* **4.4. Feature Importance Analysis**: (e.g.
from tree-based models) to understand which features are most influential.
**5. Iteration and Refinement:**
* Based on evaluation
iterate on feature engineering
model selection
or hyperparameter tuning.
* Consider ensemble stacking if individual models show complementary strengths.
**6. Deployment Considerations (Briefly
if applicable):**
* How will the model be used? API
embedded system
standalone tool?
* Monitoring model performance over time if new data comes in.
**IMPORTANT**: This strategy should be comprehensive yet adaptable. The specific choices for methods will depend on the detailed exploration of the data. Emphasize the iterative nature of model development.
- Ideale per: Guidare gli ingegneri meccanici attraverso il processo di sviluppo di un modello predittivo delle proprietà dei materiali basato sui dati, dall'elaborazione iniziale dei dati alla valutazione del modello.
- Modellazione predittiva
- Ingegneria meccanica
Prompt AI per Modello RUL Identificazione dei parametri chiave di ingresso
- Corrosione, Progettazione per la produzione (DfM), Analisi delle modalità e degli effetti dei guasti (FMEA), Industria meccanica, Algoritmi di manutenzione predittiva, Ottimizzazione del processo, Sensori, Analisi delle vibrazioni
Identifica ed elenca i parametri di ingresso chiave e i tipi di dati dei sensori più importanti per lo sviluppo di un modello predittivo di vita utile residua (RUL) per un tipo specifico di apparecchiatura meccanica rotante. Questo prompt aiuta a selezionare i dati appropriati per la prognostica. L'output è un elenco in formato CSV.
Uscita:
- CSV
- non richiede Internet in diretta
- Campi: {tipo_di_equipaggiamento_e_funzione} {modalità_di_fallimento_conosciute_elenco_csv} {testo_disponibile_dei_flussi_di_dati_di_sensori}
Act as a Prognostics and Health Management (PHM) Specialist.
Your TASK is to identify key input parameters and sensor data types that would be MOST RELEVANT for developing a Remaining Useful Life (RUL) predictive model for `{equipment_type_and_function}`.
Consider the `{known_failure_modes_list_csv}` (CSV: 'FailureMode_ID
Description
AffectedComponents') and the `{available_sensor_data_streams_description_text}` (e.g.
'Vibration (accelerometers on bearings
10kHz sampling)
Temperature (thermocouples on casing
motor winding
1Hz)
Oil pressure (transducer
10Hz)
Rotational speed (encoder
1kHz)
Acoustic emission (sensor on housing
1MHz range)
Load current (motor control unit
50Hz)').
**KEY INPUT PARAMETERS FOR RUL MODEL (MUST be CSV format):**
**CSV Header**: `Parameter_Rank
Parameter_Name_Or_Feature
Sensor_Or_Data_Source
Relevance_To_Failure_Modes
Potential_Feature_Engineering_Notes`
**Analysis Logic to Generate Rows:**
1. **Understand Equipment and Failure Modes**:
* Analyze `{equipment_type_and_function}` (e.g.
'Centrifugal Pump for corrosive fluids'
'High-speed gearbox for wind turbine'
'Aircraft engine bearing assembly').
* Review each failure mode in `{known_failure_modes_list_csv}`. For each mode
think about what physical parameters would change as degradation progresses towards that failure.
2. **Map Sensor Data to Degradation Indicators**:
* For each sensor stream in `{available_sensor_data_streams_description_text}`
assess its potential to capture indicators of the identified failure modes.
3. **Prioritize Parameters**: Rank parameters based on their likely sensitivity to degradation and relevance to the most critical or common failure modes.
4. **Suggest Feature Engineering**: For raw sensor data
suggest derived features that are often more informative for RUL models.
**Example Parameters to Consider (AI to generate specific to inputs):**
* **Vibration-based features**:
* RMS
Kurtosis
Crest Factor
Skewness (overall or in specific frequency bands).
* Spectral power in bands around bearing defect frequencies
gear mesh frequencies
unbalance frequencies.
* Cepstral analysis features for identifying harmonics/sidebands.
* **Temperature-based features**:
* Absolute temperature levels.
* Temperature trends/rate of change.
* Temperature differentials between components.
* **Process/Operational Parameters**:
* Load levels
torque
current drawn (can indicate increased friction or effort).
* Pressure drops
flow rates (for pumps
hydraulic systems).
* Speed variations.
* **Oil/Lubricant Related (if applicable & sensors exist)**:
* Particle count
viscosity
water content (if online sensors
otherwise lab data).
* **Acoustic Emission features**:
* Hit counts
energy levels
peak amplitudes (good for crack detection
early wear).
* **Usage/Cycle Counters**:
* Number of starts/stops
operating hours
load cycles.
**Output CSV Content Example (Conceptual - AI generates actuals):**
`1
Vibration RMS (Bearing Housing Z-axis)
Accelerometer
Bearing wear
Spalling
Calculate rolling window RMS
Trend analysis`
`2
Oil Temperature Trend
Thermocouple (Oil Sump)
Lubricant degradation
Overheating
Slope of temperature over time
Threshold alerts`
`3
Spectral Peak at 2x RPM
Accelerometer (Motor Shaft)
Misalignment
Unbalance
FFT analysis
Monitor amplitude growth`
`4
Motor Current Mean
Motor Control Unit
Increased friction
Winding fault
Filter out transients
Compare to baseline`
`5
Acoustic Emission Hit Rate
AE Sensor
Crack initiation
Severe wear
Denoise signal
Detect sudden increases`
**IMPORTANT**: The list should be prioritized
with the most promising parameters ranked higher. The 'Relevance_To_Failure_Modes' column should specifically link the parameter to failure modes from `{known_failure_modes_list_csv}` if possible. 'Potential_Feature_Engineering_Notes' gives hints for data processing.
- Ideale per: Assistere gli ingegneri meccanici nella selezione dei dati dei sensori e dei parametri operativi più rilevanti per lo sviluppo di modelli predittivi di vita utile residua (RUL) per le apparecchiature rotanti.
- Modellazione predittiva
- Ingegneria meccanica
Prompt AI per Estrapolazione del trend di degrado delle prestazioni
- Analisi dei guasti, Industria meccanica, Monitoraggio delle prestazioni, Algoritmi di manutenzione predittiva, Ottimizzazione del processo, Gestione della qualità, Controllo statistico del processo (SPC), Analisi delle vibrazioni
Analizza le serie temporali di dati sulle prestazioni di un sistema meccanico per identificare le tendenze al degrado ed estrapolarle per prevedere quando potrebbe essere raggiunta una soglia di guasto predefinita. Questo prompt aiuta nelle attività di prognosi, suggerendo un modello matematico adeguato per la tendenza e stimando il tempo al guasto. L'output è un oggetto JSON contenente la previsione del tipo di modello e la confidenza.
Uscita:
- JSON
- non richiede Internet in diretta
- Campi: {nome_metrico_di_performance_e_unità} {serie_di_tempo_dati_csv} {valore_soglia_di_fallimento}
Act as a Reliability Analyst specializing in trend analysis and prognostics.
Your TASK is to analyze a time-series dataset of a `{performance_metric_name_and_units}` (e.g.
'Vibration_Amplitude_mm_s'
'Efficiency_Percent'
'Crack_Length_mm') for a mechanical system.
The data is provided as `{time_series_data_csv}` (CSV string with two columns: 'Timestamp_or_Cycle' and 'Metric_Value').
Your goal is to:
1. Identify a suitable mathematical model for the degradation trend.
2. Extrapolate this trend to predict when the metric will reach the `{failure_threshold_value}`.
3. Provide an estimate of this prediction.
**ANALYSIS AND PREDICTION STEPS:**
1. **Data Loading and Preparation:**
* Parse `{time_series_data_csv}` into time/cycle (X) and metric value (Y). Ensure time is monotonically increasing.
* Visualize the data to observe the trend (e.g.
increasing
decreasing
linear
exponential).
2. **Trend Model Selection and Fitting:**
* Based on the visual trend and common degradation patterns
select potential models. Suggest AT LEAST TWO plausible models:
* **Linear Model**: `Y = aX + b`
* **Exponential Model**: `Y = a * exp(bX) + c` or `Y = a * X^b + c` (Power Law). If using log-transform for fitting
note this.
* **Polynomial Model (e.g.
quadratic)**: `Y = aX^2 + bX + c` (Use with caution
can be poor for extrapolation if not well-justified).
* Fit the selected models to the data using appropriate regression techniques (e.g.
least squares).
3. **Model Goodness-of-Fit Assessment:**
* For each fitted model
calculate a goodness-of-fit metric (e.g.
R-squared
RMSE).
* Select the BEST FITTING model that also makes SENSE from a physical degradation perspective (e.g.
avoid overly complex models that fit noise).
4. **Extrapolation and Time-to-Threshold Prediction:**
* Using the equation of the best-fitting model
solve for the 'Timestamp_or_Cycle' (X) when the 'Metric_Value' (Y) equals the `{failure_threshold_value}`.
* This predicted X is the estimated time/cycles to reach the threshold.
5. **Confidence Assessment (Qualitative or Simplified Quantitative):**
* Acknowledge the uncertainty in extrapolation.
* Qualitatively state confidence (e.g.
'High' if data shows a very clear
stable trend and threshold is not too far;
'Medium' or 'Low' if data is noisy
trend is less clear
or extrapolation is long).
* (Optional
if simple to implement for linear regression): Calculate prediction interval for the threshold crossing point if possible
or mention factors affecting confidence.
**OUTPUT FORMAT (JSON):**
You MUST return a single JSON object with the following structure:
```json
{
"input_summary": {
"metric_name": "`{performance_metric_name_and_units}`"
"failure_threshold": `{failure_threshold_value}`
"data_points_analyzed": [Number of data points from CSV]"
}
""trend_analysis"": {
""best_fit_model_type"": ""[e.g.
Linear
Exponential
Polynomial_Degree_2]""
""model_equation"": ""[Equation of the best fit model
e.g.
Y = 0.5*X + 10]""
""goodness_of_fit"": {
""metric"": ""[e.g.
R-squared or RMSE]""
""value"": "[Calculated value]"
}
}
""prediction"": {
""estimated_time_or_cycles_to_threshold"": "[Calculated X value when Y reaches threshold
numeric or 'Not Reached if trend does not intersect']""
""units_of_time_or_cycles"": ""[Units from 'Timestamp_or_Cycle' column
e.g.
Hours
Cycles
Days]""
""confidence_in_prediction"": ""[High/Medium/Low]""
""confidence_statement"": ""[Brief justification for confidence level
e.g.
Clear linear trend and data consistency support high confidence
or Noisy data and long extrapolation reduce confidence.]""
}
""warnings_or_notes"": ""[e.g.
Extrapolation assumes current degradation pattern continues. Significant operational changes may invalidate prediction. Polynomial models can be unreliable for far extrapolation.]""
}
```
**IMPORTANT**: Ensure calculations are clear. If the trend does not lead to the threshold (e.g.
metric improving or plateauing below threshold)
state this appropriately in the prediction. The AI should perform the calculations or outline them clearly if it simulates them."
- Ideale per: Aiutare gli ingegneri meccanici a prevedere i guasti delle apparecchiature analizzando le tendenze di degrado delle prestazioni, estrapolandole fino a una soglia definita e suggerendo modelli matematici appropriati.
- Analisi della causa principale
- Ingegneria meccanica
Prompt AI per Diagramma a spina di pesce Ingressi per il fallimento
- Miglioramento continuo, Progettazione per la produzione (DfM), Analisi dei guasti, Produzione snella, Industria meccanica, Miglioramento dei processi, Gestione della qualità, Analisi della causa principale, Sei Sigma
Aiuta a strutturare un diagramma a lisca di pesce (Ishikawa) per un guasto a un componente meccanico, suggerendo le categorie di fattori potenzialmente contribuenti (ad esempio, Uomo Macchina Materiale Metodo Ambiente Misura) e le domande specifiche da porre per ciascuna categoria in base alla descrizione del guasto. Questo suggerimento facilita un'analisi sistematica delle cause principali. Il risultato è uno schema in formato markdown.
Uscita:
- Markdown
- non richiede Internet in diretta
- Campi: {componente_che_ha_fallito} {descrizione_modalità_di_fallimento} {condizioni_operative_al_fallimento_testo}
Act as a Root Cause Analysis (RCA) Facilitator.
Your TASK is to help structure a Fishbone (Ishikawa) Diagram to investigate the root cause of a failure involving `{component_that_failed}`.
The described failure is: `{failure_mode_description}`.
The failure occurred under these conditions: `{operating_conditions_at_failure_text}`.
You should propose key questions for standard Fishbone categories tailored to this mechanical failure context.
**FISHBONE DIAGRAM STRUCTURE INPUTS (MUST be Markdown format):**
**Problem Statement (Head of the Fish):** Failure of `{component_that_failed}`: `{failure_mode_description}`
**Main Bones (Categories) and Potential Contributing Factor Questions:**
**1. Machine (Equipment / Technology)**
* Was the `{component_that_failed}` the correct type/model/specification for the application?
* Was the equipment where `{component_that_failed}` is installed operating correctly before/during the failure? (e.g.
speed
load
pressure
temperature within design limits described in `{operating_conditions_at_failure_text}`?)
* Had there been any recent maintenance
repair
or modification to the machine or `{component_that_failed}`? Were procedures followed?
* Was auxiliary equipment (e.g.
cooling
lubrication
power supply) functioning correctly?
* Is there a history of similar failures with this machine or other similar machines?
* Could any tooling
fixtures
or associated parts have contributed to the failure of `{component_that_failed}`?
**2. Method (Process / Procedure)**
* Were correct operating procedures being followed when the failure occurred
considering `{operating_conditions_at_failure_text}`?
* Were installation or assembly procedures for `{component_that_failed}` followed correctly?
* Were maintenance procedures adequate and followed correctly for `{component_that_failed}` and related systems?
* Were there any recent changes in operating procedures
set-points
or work instructions?
* Was the system being operated outside of its design intent or capacity?
* Could any testing or quality control procedures related to `{component_that_failed}` have missed a defect?
**3. Material (Includes Raw Materials
Consumables
`{component_that_failed}` itself)**
* Was the `{component_that_failed}` made from the specified material? Was material certification available/correct?
* Could there have been a defect in the material of `{component_that_failed}` (e.g.
inclusions
porosity
incorrect heat treatment
flaws)?
* If consumables are involved (e.g.
lubricants
hydraulic fluids
coolants)
were they the correct type
clean
and at the correct level/condition?
* Has the `{component_that_failed}` been exposed to any corrosive or degrading substances not accounted for in its design?
* Could there have been issues with material handling or storage of `{component_that_failed}` before installation?
**4. Manpower (People / Personnel)**
* Was the operator/maintenance personnel adequately trained and qualified for the task they were performing related to `{component_that_failed}` or its system?
* Was there sufficient experience or supervision?
* Could human error (e.g.
misjudgment
incorrect assembly
misreading instructions
fatigue) have contributed?
* Were personnel following safety procedures? Were they rushed or under stress?
* Was there clear communication regarding operational or maintenance status?
**5. Measurement (Inspection / Instrumentation)**
* Were measuring instruments or sensors used to monitor `{operating_conditions_at_failure_text}` (e.g.
temperature
pressure
vibration
current) calibrated and functioning correctly?
* Were any warning signs or abnormal readings from instruments ignored or misinterpreted prior to the failure of `{component_that_failed}`?
* Were quality control checks or inspections of `{component_that_failed}` (pre-installation or during service) performed correctly and were the criteria appropriate?
* Could there be inaccuracies in the data used to assess the condition of `{component_that_failed}`?
**6. Environment (Operating Conditions / Surroundings)**
* Were the environmental conditions (temperature
humidity
cleanliness
vibration from external sources) as described in `{operating_conditions_at_failure_text}` within design limits for `{component_that_failed}`?
* Could any unusual environmental factors (e.g.
sudden impact
flooding
power surge
foreign object ingress) have contributed?
* Was the `{component_that_failed}` properly protected from the operating environment?
* Could long-term environmental exposure (e.g.
corrosion
UV degradation) have weakened `{component_that_failed}`?
**Instructions for User**: Use these questions as starting points to brainstorm specific potential causes under each category for the failure of `{component_that_failed}`. Further drill down with 'Why?' for each identified cause.
- Ideale per: Assistere gli ingegneri meccanici nell'investigazione sistematica dei guasti dei componenti, fornendo domande personalizzate per ogni categoria di un diagramma a spina di pesce (Ishikawa).
Stiamo dando per scontato che l'IA possa sempre generare i migliori prompt in ingegneria meccanica? Come vengono generati?
L'intelligenza artificiale renderà superflui gli ingegneri umani?
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