Design and test condition monitoring and predictive maintenance algorithms

Predictive Maintenance Toolbox™ provides tools for labeling data, designing condition indicators, and estimating the remaining useful life (RUL) of a machine. You can analyze and label machine data imported from local files, cloud storage, and distributed file systems. You can also label simulated failure data generated from Simulink® models.

Signal processing and dynamic modeling methods that build on techniques such as spectral analysis and time series analysis let you preprocess data and extract features that can be used to monitor the condition of the machine. To estimate a machine's time to failure, you can use survival, similarity, and trend-based models to predict the RUL.

The toolbox includes reference examples for motors, gearboxes, batteries, and other machines that can be reused for developing custom predictive maintenance and condition monitoring algorithms.

Overcoming Four Common Obstacles to Predictive Maintenance


Remaining Useful Life (RUL) Estimation

Use time-series data and lifetime data to forecast RUL and compute confidence intervals.

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Condition Indicator Design

Extract features from sensor data that can be used as inputs to diagnostic and machine learning algorithms.

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Data Organization and Labeling

Access and manage data from files stored locally, on the cloud, or in HDFS.

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Failure Data Generation from Simulink

Create simulation data that is representative of failures and store it automatically in MAT files.

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Reference Examples for Algorithm Development

Develop condition monitoring and predictive maintenance algorithms for batteries, gearboxes, pumps, and other machines.

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Product Resources

Discover more about Predictive Maintenance Toolbox by exploring these resources.


Explore documentation for Predictive Maintenance Toolbox functions and features, including release notes and examples.


Browse the list of available Predictive Maintenance Toolbox functions.

Product Requirements

View product requirements for the latest release of Predictive Maintenance Toolbox.

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Contact Aditya Baru,
Predictive Maintenance Toolbox Technical Expert

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News and Events

In this webinar we will use machine learning techniques to estimate remaining useful life of equipment. We will explore importing, pre-processing, and labeling data, as well as selecting features
In this webinar, we will use physical modeling techniques to detect the onset of failures in equipment. We also generate simulation data by injecting failures in the model and use it to train a predictive algorithm that can predict the equipment’s remaining useful life.