Main Page > MATLAB for Industry > Predictive Maintenance

Every day we rely on a wide range of machines, but every machine eventually breaks down unless it’s being maintained. Predictive maintenance lets you estimate when machine failure will occur. 

This way, you can plan maintenance in advance, better manage inventory, eliminate unplanned downtime, and maximize equipment lifetime.

Maintenance Strategies

Reactive Maintenance

With reactive maintenance, the machine is used to its limit and repairs are performed only after the machine fails. 

Preventive Maintenance

Many organizations try to prevent failure before it occurs by performing regular checks on their equipment. One big challenge with preventive maintenance is determining when to do maintenance, since it is hard to tell when failure is likely to occur.

Predictive Maintenance

Predictive maintenance lets you estimate time-to-failure of a machine. Knowing the predicted failure time helps you find the optimum time to schedule maintenance for your equipment. Predictive maintenance not only predicts a future failure, but also pinpoints problems in your complex machinery and helps you identify what parts need to be fixed.

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MATLAB and Simulink for Predictive Maintenance

Engineers use Predictive Maintenance Toolbox to develop and deploy condition monitoring and predictive maintenance software to enterprise IT and OT systems.

  • Access streaming and archived data using built-in interfaces to cloud storage, relational and nonrelational databases, and protocols such as REST, MQTT, and OPC UA.
  • Preprocess data and extract features to monitor equipment health using apps for signal processing and statistical techniques.
  • Develop machine learning models to isolate root cause of failures and predict time-to-failure and remaining useful life (RUL).
  • Deploy algorithms and models to your choice of in-operation systems such as embedded systemsedge devices, and the cloud by automatically generating C/C++, Python, HDL, PLC, GPU , .NET, or Java based software components.

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Detect and Predict Faults Using Machine Learning

Identify root cause of failures and predict time-to-failure using classification, regression, and time-series modeling techniques.

  • Interactively explore and select the most important variables for estimating RUL or classifying failure modes.
  • Train, compare and validate multiple predictive models with built-in functions.
  • Calculate and visualize confidence intervals to quantify uncertainty in predictions.

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About Statistics and Machine Learning Toolbox

Already using predictive maintenance solutions?

Whether your team is just starting out, or you’re experienced MATLAB and Simulink users, the expert team of Gamax Laboratory Solutions and our network of consultants work with you to develop a customized project plan.

Together we can:

  • bring your ideas to life by transferring knowledge and best practices to you to build in-house competency through customized, project-based coaching sessions.
  • make sure that your project is successful by releasing your revised procedures into production to reduce your maintenance and operating costs.
  • raise the bar at your modeling and simulation by determining appropriate data preprocessing, feature selection, and predictive modeling techniques, and apply these to your data.

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