Predictive Maintenance Toolbox
Predictive Maintenance Toolbox provides tools and apps to design condition monitoring and predictive maintenance algorithms for systems like motors, gearboxes, bearings, and batteries.
Use the Diagnostic Feature Designer to extract and rank time, frequency, time-frequency, and physics-based features for fault and anomaly detection. Estimate Remaining Useful Life (RUL) with survival, similarity, or trend-based models.
Import sensor data from local or cloud sources, or generate failure data using Simulink and Simscape. Deploy your algorithms by generating C/C++ code for edge devices or creating cloud-ready applications. Start quickly with built-in reference examples.
Feature Engineering
Use the Diagnostic Feature Designer app or code to extract and rank features from sensor data using signal- and model-based methods for AI-driven fault detection and prediction.
Fault and Anomaly Detection
Use AI, statistical, and dynamic models for condition monitoring to track system changes, detect anomalies, and identify faults.
RUL Estimation
Train RUL estimators on historical data to predict time-to-failure and optimize maintenance planning.
Rotating Machinery
Extract physics-based features for rotating machinery to detect bearing faults, leaks, motor degradation, gearbox issues, and more. Start quickly with built-in reference examples.
Data Management and Preprocessing
Access local or remote sensor data and prepare it by removing outliers, filtering, and applying time, frequency, or time-frequency preprocessing.
Failure Data Generation
Simulate rare faults and degradations with physics-based models in Simulink and Simscape. Adjust parameters, inject faults, and alter dynamics to build digital twins for monitoring and prediction.
Edge Deployment
Use MATLAB Coder to generate C/C++ code from feature computation, condition monitoring, and predictive algorithms for real-time edge deployment.
Cloud Deployment
Use MATLAB Compiler and Compiler SDK to scale algorithms as libraries, web apps, or Docker containers. Deploy to MATLAB Production Server on Azure® or AWS® without recoding.
