MathWorks Predictive Maintenance Toolbox for MATLAB

MathWorks has announced Predictive Maintenance Toolbox, a MATLAB product that helps engineers design and test condition monitoring and predictive maintenance algorithms. The product offers capabilities and reference examples for engineers who are designing algorithms to organise data, design condition indicators, monitor machine health and estimate remaining useful life (RUL) to prevent equipment failures.

The toolbox enables engineers to analyse and label sensor data imported from files that are stored locally or on cloud storage. They can also label simulated failure data generated from Simulink models to represent equipment failures. Signal processing and dynamic modelling methods that build on techniques such as spectral analysis and time series analysis let engineers pre-process data and extract features that can be used to monitor the condition of the machine.

Using survival, similarity and trend-based models to predict the RUL helps engineers estimate a machine’s time to failure. The toolbox includes reference examples for motors, gearboxes, batteries and other machines that can be re-used for developing custom predictive maintenance and condition monitoring algorithms.

Engineers can develop and validate the algorithms needed to predict when an equipment failure might occur or to detect any underlying anomalies by monitoring sensor data. These algorithms are developed by accessing historical data that is stored in local files, on cloud storage systems such as Amazon S3 and Windows Azure Blob Storage, or on a Hadoop Distributed File System.

Another source of data is simulation data from physical models of the equipment that incorporate failure dynamics. Engineers can extract and select the most suitable features from this data, and then use interactive apps to train machine learning models with these features to predict or detect equipment failures.