High-level API design for Sensor Fault Detection, closely mirroring approaches for anomaly detection or predictive maintenance. However, the primary focus here is on detecting whether sensor readings themselves are faulty (e.g., stuck values, calibration drift, sensor breaks) rather than whether the underlying system is failing. The design covers data ingestion, fault labeling, model training, fault detection, and retrieval of detection results.

1. Overview

1. Purpose
   • Detect faulty sensor behavior as soon as possible, which might include:
     • Sudden spikes or unrealistic values
     • Stuck signals (e.g., sensor not updating)
     • Calibration drift over time
     • Noise or dropout issues
   • Prevent inaccurate or misleading data from affecting subsequent models or operations.
2. Data Flow
   1. Data Ingestion: Real-time or batch ingestion of sensor readings (possibly from multiple sensors).
   2. Fault Labeling (optional): Domain experts or automated heuristics can label known sensor faults for supervised approaches.
   3. Model Training: Train or retrain a sensor fault detection model (e.g., rule-based, machine learning, deep learning).
   4. Fault Detection: For new or incoming data, the system flags potential sensor faults.
   5. Result Retrieval: Retrieve detection results, model details, and fault statistics for analysis.

2. Common Sensor Fault Detection Methods

1. Rule-based / Threshold Methods
   • Simple rules (e.g., reading outside acceptable range).
   • Good for quick checks (e.g., “Temperature should never be negative for this sensor.”).
2. Statistical / Signal Analysis
   • Moving averages, standard deviation checks, z-score checks, etc.
   • Detect stuck signals if variance is zero for a prolonged period.
3. Machine Learning Classifiers
   • Trained with labeled data indicating normal vs. faulty sensor states.
   • Could use decision trees, SVM, or random forests.
4. Time-Series Models
   • ARIMA, LSTM, or Transformers used to predict expected sensor readings; large deviations or abnormal patterns might indicate sensor faults.
5. Redundancy / Sensor Fusion
   • Compare a sensor reading with other redundant or correlated sensors. If one sensor deviates strongly from expected correlation, it may be faulty.
6. Autoencoders (Unsupervised)
   • Learn “normal” sensor patterns. Reconstruction error can flag faults.

3. API Endpoints

3.1. Data Ingestion

Endpoint:

POST /api/v1/sensor-fault-detection/data

Description:

• Ingest new sensor data (historical or real-time).