Get Started with Predictive Maintenance Toolbox
Predictive Maintenance Toolbox™ provides functions and apps for designing condition monitoring and predictive maintenance algorithms for motors, gearboxes, bearings, batteries, and other applications. The toolbox lets you design condition indicators, detect faults and anomalies, and estimate remaining useful life (RUL).
With the Diagnostic Feature Designer app, you can interactively extract time, frequency, time-frequency, and physics-based features. You can rank and export the features to develop application-specific algorithms for fault and anomaly detection. To estimate RUL, you can use survival, similarity, and trend-based models.
The toolbox helps you organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. You can generate simulated failure data from Simulink® and Simscape™ models.
To operationalize your algorithms, you can generate C/C++ code for edge deployment or create production applications for cloud deployment. The toolbox includes application-specific reference examples that you can reuse for developing and deploying custom predictive maintenance algorithms.
This three-part tutorial shows you how to work with ensemble data and extract and rank features in Diagnostic Feature Designer.
About Condition Monitoring and Predictive Maintenance
- What is Predictive Maintenance?
Predictive maintenance is an approach to detecting and anticipating system anomalies and failures before they significantly degrade system performance.
- Designing Algorithms for Condition Monitoring and Predictive Maintenance
Predictive Maintenance Toolbox helps you identify condition indicators in our data and design algorithms for monitoring system condition and predicting remaining useful life.
Predictive Maintenance Part 1: Introduction
Learn about different maintenance strategies and predictive maintenance workflow. Predictive maintenance lets you find the optimum time to schedule maintenance by estimating time to failure.
Predictive Maintenance Part 4: How to Use Diagnostic Feature Designer
for Feature Extraction
Learn how you can extract time-domain and spectral features using Diagnostic Feature Designer for developing your predictive maintenance algorithm.
Condition Monitoring with MATLAB
Learn how you can develop condition monitoring algorithms with MATLAB®. Develop condition monitoring algorithms for the early detection of faults and anomalies to reduce downtime and costs due to unplanned failures and unnecessary maintenance