
通过增强人类在设计、分析方面的能力,在线人工智能工具正在迅速改变机械工程、 制造业和维护。与传统方法相比,这些人工智能系统可以更快地处理海量数据、识别复杂模式并生成新的解决方案。例如,人工智能可以帮助您优化性能和可制造性设计,加速复杂的模拟,预测材料特性,并自动执行各种分析任务。
例如,下面提供的提示有助于生成设计、加速模拟(有限元分析/有限差分分析)、预测性维护(人工智能通过分析机械的传感器数据来预测潜在故障,从而实现主动服务并最大限度地减少停机时间)、材料选择等方面的帮助。
- 预测建模
- 机械工程
人工智能提示 材料性能预测模型策略
- 机器学习, 材料, 机械工业, 机械性能, 预测性维护算法, 工艺优化, 质量管理, 统计分析
使用描述的数据集,根据成分和加工参数,概述为特定材料特性开发预测模型的策略。该提示可帮助机械工程师启动数据驱动的材料设计或选择。输出为标记符格式的策略文档。
输出:
- Markdown
- 不需要实时互联网
- 字段:{target_material_property_too_predict} (预测的目标材料属性{available_input_features_csv_description} {dataset_size_and_characteristics_text} 数据集大小和特征文本
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.
- 最适合从初始数据处理到模型评估,指导机械工程师开发数据驱动的材料性能预测模型。
- 预测建模
- 机械工程
人工智能提示 RUL 模型关键输入参数识别
- 腐蚀, 面向制造设计 (DfM), 故障模式和影响分析(FMEA), 机械工业, 预测性维护算法, 工艺优化, 传感器, 振动分析
确定并列出与开发特定类型旋转机械设备剩余使用寿命 (RUL) 预测模型最相关的关键输入参数和传感器数据类型。该提示有助于选择合适的预报数据。输出为 CSV 格式的列表。
输出:
- CSV
- 不需要实时互联网
- 字段:{设备类型和功能} {已知故障模式列表_csv} {可用传感器数据流描述文本}
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.
- 最适合用于协助机械工程师选择最相关的传感器数据和运行参数,为旋转设备开发剩余使用寿命 (RUL) 预测模型。
- 预测建模
- 机械工程
人工智能提示 性能退化趋势外推法
- 故障分析, 机械工业, 性能跟踪, 预测性维护算法, 工艺优化, 质量管理, 统计过程控制 (SPC), 振动分析
分析机械系统的时间序列性能数据,以确定退化趋势并进行推断,从而预测何时可能达到预定的故障阈值。该提示通过为趋势建议合适的数学模型并估算故障发生时间来帮助预报工作。输出是一个 JSON 对象,包含模型类型预测和置信度。
输出:
- JSON
- 不需要实时互联网
- 字段:{performance_metric_name_and_units} 性能指标名称和单位{时间序列数据_csv} {故障阈值_value}{时间序列数据_csv}{故障阈值_value}.
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."
- 最适合用于帮助机械工程师预测设备故障,分析性能下降趋势,将其推断到规定的临界值,并提出适当的数学模型。
- 根本原因分析
- 机械工程
人工智能提示 鱼骨图 失败输入
- 持续改进, 面向制造设计 (DfM), 故障分析, 精益制造, 机械工业, 流程改进, 质量管理, 根本原因分析, 六西格玛
通过建议潜在的促成因素类别(如人机材料方法环境测量)以及根据故障描述针对每个类别提出的具体问题,帮助构建机械部件故障的鱼骨图(石川)。这一提示有助于进行系统的根本原因分析。输出为标记符格式的大纲。
输出:
- Markdown
- 不需要实时互联网
- 字段:{发生故障的组件} {故障模式描述} {故障发生时的操作条件文本}。
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.
- 最适合用于通过为鱼骨图(石川)的每个类别提供量身定制的问题,协助机械工程师系统地调查组件故障。
- 根本原因分析
- 机械工程
人工智能提示 流程异常的 5 个为什么协议
- 5个为什么, 持续改进, 故障分析, 精益制造, 机械工业, 解决问题的技巧, 流程改进, 质量管理, 根本原因分析
指导用户通过结构化的 5 个 "为什么 "进行机械工程制造流程异常的根本原因分析。该提示根据最初的问题和流程背景,通过反复询问原因,帮助深入分析根本原因。输出结果是基于文本的结构化提问路径。
输出:
- 文本
- 不需要实时互联网
- 字段:{初始问题陈述文本}。{进程名称和上下文}。
Act as a Quality Engineering Coach facilitating a "5 Whys" Root Cause Analysis.
Your TASK is to guide the user through the 5 Whys methodology to find the potential root cause of the `{initial_problem_statement_text}` within the `{process_name_and_context}`.
You will provide a structured questioning pathway. For each 'Why?'
you will prompt the user for an answer
and then formulate the next 'Why?' based on a hypothetical (but plausible for mechanical engineering) user response. The user would then answer your 'Why?' in a real scenario.
Since this is not interactive
generate a plausible chain of 5 Whys and answers to illustrate the process
and then provide a template for the user to fill.
**ILLUSTRATIVE 5 WHYS EXAMPLE (AI Generated Plausible Chain):**
**Process & Context**: `{process_name_and_context}` (e.g.
CNC Machining of aluminum brackets)
**Initial Problem**: `{initial_problem_statement_text}` (e.g.
High scrap rate (20%) due to oversized holes on Part #XYZ)
**1. Why is there a high scrap rate (20%) due to oversized holes on Part #XYZ?**
* *AI's Assumed Plausible User Answer*: The holes are being drilled too large by the CNC machine.
**2. Why are the holes being drilled too large by the CNC machine?**
* *AI's Assumed Plausible User Answer*: The drill bit is wobbling excessively during operation.
**3. Why is the drill bit wobbling excessively?**
* *AI's Assumed Plausible User Answer*: The drill bit is not securely held in the tool holder collet.
**4. Why is the drill bit not securely held in the tool holder collet?**
* *AI's Assumed Plausible User Answer*: The collet is worn beyond its service limit and has lost its clamping force.
**5. Why is the collet worn beyond its service limit and not replaced?**
* *AI's Assumed Plausible User Answer*: The preventive maintenance schedule for checking/replacing collets based on usage hours was not being consistently followed due to a lack of clear responsibility assigned for this specific task.
* **Potential Root Cause Reached**: Lack of clear responsibility and adherence to preventive maintenance for tooling components.
**TEMPLATE FOR USER TO CONDUCT THEIR OWN 5 WHYS:**
*(Please use this template to conduct your own analysis. For each 'Why?'
record your team's answer
then formulate the next 'Why?' based on that answer.)*
**Process & Context**: `{process_name_and_context}`
**Initial Problem Statement**: `{initial_problem_statement_text}`
**1. Why? (Why is/did [`{initial_problem_statement_text}`] occur?)**
* **Answer 1**: _________________________________________________________
**2. Why? (Why did [Answer 1] occur?)**
* **Answer 2**: _________________________________________________________
**3. Why? (Why did [Answer 2] occur?)**
* **Answer 3**: _________________________________________________________
**4. Why? (Why did [Answer 3] occur?)**
* **Answer 4**: _________________________________________________________
**5. Why? (Why did [Answer 4] occur?)**
* **Answer 5**: _________________________________________________________
* **(Continue if needed - '5' is a guideline
not a strict limit. Stop when you reach an actionable root cause
often related to a process
system
or policy.)**
**Potential Root Cause(s) Identified**: _________________________________________________________
**Recommended Corrective Actions to Address Root Cause(s)**: _________________________________
**IMPORTANT**: The key is to avoid jumping to conclusions and to base each 'Why?' on the factual answer to the previous question. The goal is to find systemic causes
not just to assign blame.
- 最适合指导机械工程师通过 5 个为什么的方法,系统地发现制造过程异常或故障背后的根本原因。
- 根本原因分析
- 机械工程
人工智能提示 故障树分析 顶部事件设置
- 面向制造设计 (DfM), 设计验证, 故障分析, 故障树分析 (FTA), 机械工业, 质量管理, 风险分析, 风险管理, 安全
帮助启动故障树分析 (FTA),方法是定义最不希望发生的事件,并针对所描述的机械系统提出直接导致子系统故障或基本事件的建议。该提示为详细的定量或定性风险评估提供了起点。输出为标记符格式的树形结构大纲。
输出:
- Markdown
- 不需要实时互联网
- 字段:{系统描述文本}{undesired_top_event_failure_description} {key_subsystems_or_components_list_csv} 子系统或组件列表说明。
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."
- 最适合用于帮助机械工程师开始故障树分析 (FTA),定义最重要的事件,并提出导致子系统故障的初始分支和逻辑门。
- 根本原因分析
- 机械工程
人工智能提示 重复性故障的 RCA 比较
- 持续改进, 纠正行动, 面向制造设计 (DfM), 故障分析, 精益制造, 机械工业, 流程改进, 质量管理, 根本原因分析
分析多份事故报告中有关机械系统重复故障的文字描述。该提示旨在找出共同模式、潜在的共同根本原因以及有助于解决长期问题的不同事件中的任何区别因素。输出为标记符格式的比较分析。
输出:
- Markdown
- 不需要实时互联网
- 字段:{系统或组件名称} {常见故障描述} {多重故障事件报告文本}
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.
- 最适合用于通过比较分析多个事故报告,找出共同模式和潜在的共同根本原因,从而协助机械工程师诊断反复出现的系统故障。
- 优化实验设计
- 机械工程
人工智能提示 因式实验的 DOE 计划评论
- 机械工业, 工艺优化, 质量控制, 质量管理, 统计分析, 统计测试
对所提出的因子实验设计(DOE)计划提出批评,建议改进因子选择水平的适当性混杂性和统计功率。该提示可帮助机械工程师优化实验设计,提高稳健性和效率。输出为标记符格式的评论。
输出:
- Markdown
- 不需要实时互联网
- 字段:{experimental_objective_text} 实验目标文本{factors_and_levels_json} (因素和水平_json{proposed_experimental_runs_table_csv} {response_variables_list_csv} (建议的实验运行表_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.
- 最适合用于批判机械工程中因子实验的实验设计 (DOE) 计划,以提高因子选择水平的适当性和统计有效性。
- 优化实验设计
- 机械工程
人工智能提示 材料测试对照组建议
- 材料, 机械工业, 机械性能, 性能跟踪, 质量保证, 质量控制, 统计分析, 表面处理, 测试方法
为机械应用中新材料或表面处理的实验研究建议适当的对照组和基线测量,以确保有效的比较和可靠的结论。该提示可帮助工程师设计更稳健的材料测试协议。输出结果是基于文本的建议。
输出:
- 文本
- 不需要实时互联网
- 字段:{测试材料或治疗方法描述} {实验条件文本}{性能指标_待测量_列表_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.
- 最适合用于指导机械工程师为材料测试选择适当的对照组和基线测量,确保实验的有效性和结果解释的可靠性。
- 优化实验设计
- 机械工程
人工智能提示 用于振动测试的传感器位置
- 机械工业, 质量保证, 质量控制, 传感器, 信号处理, 结构工程, 测试方法, 振动分析
根据结构描述和测试目标,为机械结构振动测试推荐最佳传感器类型和放置策略,以捕捉相关模态并确保数据质量。该提示有助于规划有效的试验模态分析或振动监测。输出为标记符格式的建议。
输出:
- Markdown
- 不需要实时互联网
- 字段:{结构描述和材料}{振动测试目标文本}.{振动测试目标文本}{感兴趣的频率范围文本}.{感兴趣的频率范围_文本}{可用传感器类型列表_csv}.{可用传感器类型列表_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.
- 最适合指导机械工程师选择和放置用于振动测试的传感器,确保为模态分析或健康监测获取最佳数据。
我们是否假设人工智能总能生成机械工程方面的最佳提示?这些提示是如何生成的?
人工智能会让人类工程师变得多余吗?
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