About Prompt
- Prompt Type – Dynamic
- Prompt Platform – ChatGPT, Grok, Deepseek, Gemini, Copilot, Midjourney, Meta AI and more
- Niche – Failure Prediction
- Language – English
- Category – Equipment Maintenance
- Prompt Title – AI Prompt for Predictive Maintenance of Manufacturing Equipment
Prompt Details
**Prompt Type:** Dynamic
**Target AI Platforms:** All
**Purpose:** Equipment Maintenance – Failure Prediction
**Prompt Structure:**
This prompt is designed to be adaptable to various data formats and equipment types. Replace the bracketed placeholders with specific information relevant to your equipment and data.
“`
Predict the remaining useful life (RUL) and probability of failure within the next [Time Window – e.g., 24 hours, 7 days, 30 days] for [Equipment Name – e.g., CNC Milling Machine, Pump A, Robot Arm 3] based on the following data:
**Data Input:**
* **Data Format:** [Specify data format – e.g., CSV, JSON, Time Series Database]
* **Data Source:** [Specify data source – e.g., Attached CSV file, API endpoint: [API URL], Database query: [SQL Query]]
* **Data Description:** Provide a detailed description of the provided data, including:
* **Timestamp:** [Format of the timestamp – e.g., YYYY-MM-DD HH:MM:SS, Unix timestamp]
* **Sensor Readings:** List all sensor readings and their units. For example:
* `vibration_x` (mm/s)
* `vibration_y` (mm/s)
* `vibration_z` (mm/s)
* `temperature` (°C)
* `pressure` (bar)
* `current` (A)
* …
* **Operational Data (if applicable):**
* `operating_mode` (Categorical – e.g., Idle, Running, Maintenance)
* `speed` (RPM)
* `load` (%)
* …
* **Historical Maintenance Records (if applicable):**
* `maintenance_date` (YYYY-MM-DD)
* `maintenance_type` (Categorical – e.g., Routine, Repair, Overhaul)
* `component_replaced` (Text)
* …
**Prediction Output:**
* **RUL:** Provide the estimated Remaining Useful Life in [Time Units – e.g., hours, days, cycles]. Express uncertainty using a confidence interval or range (e.g., RUL: 120 ± 20 hours).
* **Probability of Failure:** Provide the probability of failure within the specified [Time Window] as a percentage (e.g., 15%).
* **Failure Mode (if possible):** If possible, predict the most likely failure mode (e.g., Bearing failure, Overheating, Tool wear). Indicate the confidence level of the failure mode prediction.
* **Explanation (Optional but recommended):** Provide a brief explanation of the prediction, highlighting key factors contributing to the predicted RUL and probability of failure. This could include identifying anomalous sensor readings or patterns in the data.
**Assumptions:**
* [Specify any assumptions made about the data or the equipment. For example: “Assume constant operating conditions within the prediction window.”]
**Additional Instructions:**
* If historical maintenance data is provided, consider its impact on the RUL prediction.
* If the data contains missing values, specify how to handle them (e.g., imputation, exclusion).
* Consider external factors that might influence equipment health, such as environmental conditions (temperature, humidity) if data is available.
* If multiple failure modes are possible, prioritize them based on their likelihood and potential impact.
“`
**Example Usage:**
To predict the RUL of a pump within the next 7 days based on sensor data from a CSV file:
“`
Predict the remaining useful life (RUL) and probability of failure within the next 7 days for Pump A based on the following data:
**Data Input:**
* **Data Format:** CSV
* **Data Source:** Attached CSV file: pump_data.csv
* **Data Description:**
* **Timestamp:** YYYY-MM-DD HH:MM:SS
* **Sensor Readings:**
* `vibration` (mm/s)
* `temperature` (°C)
* `pressure` (bar)
* `flow_rate` (L/min)
**Prediction Output:**
* **RUL:** Provide the estimated RUL in days.
* **Probability of Failure:** Provide the probability of failure within the next 7 days.
* **Failure Mode (if possible):** Predict the most likely failure mode.
* **Explanation (Optional but recommended):** Provide a brief explanation of the prediction.
“`
This dynamic prompt allows users to easily customize the input data and desired output, making it suitable for various predictive maintenance scenarios across different AI platforms. The structured format and detailed instructions enhance the clarity and effectiveness of the prompt, resulting in more accurate and insightful predictions.