I migliori prompt di intelligenza artificiale per l'ingegneria meccanica

				
					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.