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design and test condition monitoring and predictive maintenance algorithms

predictive maintenance toolbox™ lets you manage sensor data, design condition indicators, and estimate the remaining useful life (rul) of a machine.

the toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis. you can monitor the health of batteries, motors, gearboxes, and other machines by extracting features from sensor data. to estimate a machine's time to failure, you can use survival, similarity, and trend-based models to predict the rul.

you can organize and analyze sensor data imported from local files, cloud storage, and distributed file systems. you can label simulated failure data generated from simulink® models. the toolbox includes reference examples for motors, gearboxes, batteries, pumps, bearings, and other machines that can be reused for developing custom predictive maintenance and condition monitoring algorithms.

to operationalize your algorithms, you can generate c/c code for deployment to the edge or create a production application for deployment to the cloud.

tutorials

    this three-part tutorial shows you how to work with ensemble data and extract and rank features in diagnostic feature designer.

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about 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.

videos


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.


learn how to extract condition indicators from your data. condition indicators help you distinguish between healthy and faulty states of a machine.


predictive maintenance lets you estimate the remaining useful life (rul) of your machine. explore three common models to estimate rul: similarity, survival, and degradation


learn how you can extract time-domain and spectral features using diagnostic feature designer for developing your predictive maintenance algorithm.

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