Description

This curriculum spans the technical and operational rigor of a multi-workshop program to design and deploy predictive maintenance systems, comparable to an internal capability build for integrating machine learning into fleet operations, from sensor integration through model governance.

Module 1: Defining Predictive Maintenance Objectives and Success Metrics

Selecting failure modes to prioritize based on mean time between failures (MTBF) and operational impact
Defining acceptable false positive and false negative thresholds for maintenance alerts
Aligning model outputs with existing maintenance workflows and technician response capacity
Establishing cost-based KPIs such as reduction in unplanned downtime or spare parts inventory turnover
Mapping sensor availability to failure mechanisms for feasibility assessment
Negotiating data access rights with OEMs for proprietary vehicle subsystems
Determining latency requirements for real-time vs. batch prediction delivery
Documenting regulatory implications of automated maintenance recommendations

Module 2: Vehicle Data Acquisition and Sensor Integration

Choosing between CAN bus, telematics gateways, and aftermarket sensors based on fleet compatibility
Configuring sampling rates for high-frequency signals like vibration and engine knock
Handling missing or intermittent telematics connectivity in mobile fleets
Normalizing data formats across vehicle makes, models, and model years
Implementing edge filtering to reduce bandwidth usage from raw sensor streams
Validating timestamp synchronization across distributed vehicle ECUs
Designing fallback strategies for failed sensor readings during inference
Integrating maintenance logs and work order systems with sensor data pipelines

Module 4: Feature Engineering for Mechanical Degradation Signals

Deriving health indicators from time-series data, such as oil pressure decay rate or brake pad wear trends
Calculating rolling statistical features (e.g., RMS, kurtosis) on vibration signals for bearing analysis
Creating composite indices from multiple sensors to represent subsystem health (e.g., cooling system)
Encoding categorical maintenance events (e.g., oil change, filter replacement) as time-decaying features
Applying domain-specific transformations like FFT for frequency-domain fault detection
Handling non-uniform time intervals in field data due to irregular reporting cycles
Designing lagged features that capture degradation progression over service intervals
Validating feature stability across different operating conditions (e.g., temperature, load)

Module 5: Model Selection and Training for Failure Prediction

Choosing between survival models, binary classifiers, and regression for time-to-failure estimation
Addressing class imbalance in failure events using stratified sampling or cost-sensitive learning
Training models on partial fleet data while ensuring generalizability across vehicle variants
Implementing cross-validation strategies that respect temporal data ordering
Comparing LSTM, XGBoost, and random survival forests on diagnostic accuracy and inference speed
Setting prediction thresholds based on maintenance team capacity and alert fatigue
Designing model calibration procedures to ensure probability outputs reflect true failure likelihood
Managing training compute costs for large-scale time-series datasets

Module 6: Model Deployment and Edge Inference Constraints

Converting trained models to edge-compatible formats (e.g., ONNX, TensorFlow Lite)
Optimizing model size and latency for deployment on vehicle gateways with limited RAM
Implementing model versioning and rollback procedures for over-the-air updates
Designing local caching of predictions when cloud connectivity is unavailable
Monitoring inference drift due to changes in sensor calibration or vehicle configuration
Securing model payloads against tampering in uncontrolled environments
Coordinating model refresh cycles with fleet software update schedules
Logging inference inputs and outputs for audit and retraining traceability

Module 7: Monitoring Model Performance and Concept Drift

Tracking prediction stability across vehicle age, mileage, and environmental conditions
Implementing statistical process control charts for model output distributions
Designing feedback loops from completed work orders to validate prediction accuracy
Measuring operational drift when new vehicle models are introduced into the fleet
Triggering retraining based on degradation in precision-recall curves over time
Correlating model performance with external factors like fuel quality or driving patterns
Establishing thresholds for alert fatigue and adjusting model sensitivity accordingly
Logging and analyzing false positives to refine feature engineering

Module 8: Integration with Maintenance Workflows and CMMS

Mapping model outputs to standard fault codes (e.g., SAE J1939) for technician interpretation
Automating work order creation in CMMS based on prediction severity and confidence
Designing user interfaces that prioritize alerts by operational criticality and repair lead time
Coordinating predictive alerts with scheduled maintenance to minimize downtime
Enforcing approval workflows for high-cost interventions triggered by model outputs
Integrating parts inventory systems to validate spare part availability before alerting
Logging technician override decisions to improve model calibration
Aligning prediction time horizons with maintenance scheduling cycles (daily, weekly)

Module 9: Governance, Auditability, and System Evolution

Documenting model lineage, including training data sources and preprocessing logic
Implementing role-based access controls for model configuration and threshold adjustments
Establishing change management procedures for model updates in production
Designing audit trails for prediction decisions impacting safety-critical components
Conducting periodic bias assessments across vehicle types and operating regions
Archiving deprecated models and associated performance benchmarks
Planning for model sunsetting when vehicle platforms are retired from the fleet
Coordinating with legal teams on liability implications of missed or incorrect predictions