High-level API design for a Predictive Maintenance service, estimating the health, remaining useful life (RUL), or failure probability of equipment or machinery based on multi-variate sensor data and historical maintenance logs.

1. Overview

• Purpose
  1. Ingest sensor and maintenance data from machinery or equipment.
  2. Train or retrain predictive maintenance models that forecast:
     • Remaining Useful Life (RUL)
     • Probability of failure within a given time window
     • Health or degradation scores
  3. Provide endpoints to obtain predictions for new data (e.g., “Will this component fail in X days?”).
  4. Offer a history of predictions, model details, and recommended maintenance actions.
• Data Flow
  1. Data Ingestion: System collects and stores raw time-series signals (temperature, vibration, pressure, etc.) and relevant metadata (maintenance logs, usage conditions).
  2. Model Training: Train models (e.g., RUL regression, survival analysis, machine learning classification/regression, deep learning-based approaches).
  3. Maintenance Prediction: Predict which components need maintenance or are at high risk of failure in the near future.
  4. Result Retrieval: Retrieve predictions, recommended maintenance schedule, or model performance stats.

2. Common Predictive Maintenance Algorithms

1. Regression-based RUL Models
   • E.g., linear regression, random forest regression, gradient boosted trees that estimate a continuous RUL.
2. Survival Analysis
   • Cox Proportional Hazards, Weibull analysis, or Kaplan-Meier-based methods that estimate the probability of survival (i.e., no failure) up to a specific time.
3. Classification-based Approaches
   • Classify whether a component will fail within a certain time horizon (“fail in next 7 days” vs. “not fail”).
4. Neural Network Approaches
   • LSTM / GRU: Model temporal sequences of sensor data, predict time-to-failure.
   • Autoencoders: Learn normal degradation patterns, track reconstruction error for early signs of failure.
   • Transformers: Capture complex patterns in multi-variate sensor data for more accurate RUL estimation.
5. Physics-informed or Hybrid Models
   • Combine domain-specific knowledge (e.g., vibration analysis for bearings) with data-driven models.

3. API Endpoints

Below is a proposed structure for a Predictive Maintenance API, focusing on sensor data ingestion, model training, prediction, and maintenance logs.

3.1. Data Ingestion

Endpoint:

POST /api/v1/predictive-maintenance/data

Description:

• Ingest new multi-variate sensor data, as well as relevant metadata (e.g., operating conditions, usage hours).