Predictive Maintenance Toolbox
Design and test condition monitoring and predictive maintenance algorithms
RUL Estimation Models
Estimate the RUL of a machine to help you predict it’s time to failure and optimize maintenance schedules. The type of RUL estimation algorithm used depends on the condition indicators extracted from the data, as well as how much data is available.
Fault Diagnosis Using Classification Models
Isolate the root cause of a failure by training classification and clustering models using support vector machines, k-means clustering, and other machine learning techniques.
Fault and Anomaly Detection
Track changes in your system to determine the presence of anomalies and faults using changepoint detection, Kalman filters, and control charts.
Diagnostic Feature Designer App
Extract, visualize, and rank features to design condition indicators for monitoring machine health. Generate MATLAB code from the app to automate the entire process.
Signal-Based Condition Indicators
Extract features from raw or preprocessed sensor data using rainflow counting, spectral peak detection, spectral kurtosis, and other time, frequency, and time-frequency domain techniques. Use Live Editor Tasks to interactively perform phase space reconstruction and extract nonlinear signal features.
Model-Based Condition Indicators
Fit linear and nonlinear time-series models, state-space models, and transfer function models to sensor data. Use the properties and characteristics of these fitted models as condition indicators.
Bearings and Gearboxes
Develop algorithms for classifying inner and outer race faults, detecting gear tooth faults, and estimating RUL.
Pumps, Motors, and Batteries
Develop algorithms for detecting leaks and clogs in pumps, tracking changes in motor friction, and estimating battery degradation over time.
Data Import and Organization
Import data from local files, Amazon S3™, Windows Azure® Blob Storage, and Hadoop® Distributed File System.
Failure Data Generation from Simulink and Simscape
Simulate and label failure data using Simulink and Simscape™ models of your machine. Modify parameter values, inject faults, and change model dynamics.
Use MATLAB Compiler™ and MATLAB Compiler SDK™ to deploy predictive maintenance algorithms as C/C++ shared libraries, web apps, Docker containers, Microsoft® .NET assemblies, Java® classes, and Python® packages. Deploy generated libraries to MATLAB Production Server™ on Microsoft® Azure®, AWS®, or dedicated on-prem servers without recoding or creating custom infrastructure.