Introduces new toolbox for designing and testing condition monitoring and predictive maintenance algorithms
NATICK, Mass. — (BUSINESS WIRE) — June 6, 2018 — MathWorks today announced Predictive Maintenance Toolbox, a new MATLAB product that helps engineers design and test condition monitoring and predictive maintenance algorithms. Predictive Maintenance Toolbox offers capabilities and reference examples for engineers who are designing algorithms to organize data, design condition indicators, monitor machine health and estimate remaining useful life (RUL) to prevent equipment failures.
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Predictive Maintenance Toolbox can help train predictive models that can estimate remaining useful life (RUL) and provide confidence intervals associated with the prediction. (Graphic: Business Wire).
With Predictive Maintenance Toolbox, engineers can analyze 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 modeling methods that build on techniques such as spectral analysis and time series analysis let engineers preprocess 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 reused for developing custom predictive maintenance and condition monitoring algorithms.
Now, 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.
“Predictive maintenance is a key application of the industrial Internet of Things. This is critical to reduce unnecessary maintenance costs and eliminate unplanned downtime. Engineers who typically don’t have a background in machine learning or signal processing find designing algorithms for predictive maintenance particularly challenging,” said Paul Pilotte, technical marketing manager, MathWorks. “Now, these teams can quickly ramp up by using Predictive Maintenance Toolbox as a starting point for learning how to design and test these algorithms.”
Predictive Maintenance Toolbox is available worldwide, with more information available at mathworks.com/products/predictive-maintenance.
Learn more about how engineering teams use MATLAB to reduce equipment downtime by forecasting failures, automatically determine the root cause of the failure, and avoid the costs of unnecessary maintenance at mathworks.com/discovery/predictive-maintenance.html.
About MathWorks
MathWorks is the leading developer of
mathematical computing software. MATLAB, the language of technical
computing, is a programming environment for algorithm development, data
analysis, visualization, and numeric computation. Simulink is a
graphical environment for simulation and Model-Based Design for
multidomain dynamic and embedded systems. Engineers and scientists
worldwide rely on these product families to accelerate the pace of
discovery, innovation, and development in automotive, aerospace,
electronics, financial services, biotech-pharmaceutical, and other
industries. MATLAB and Simulink are also fundamental teaching and
research tools in the world's universities and learning institutions.
Founded in 1984, MathWorks employs more than 4000 people in 16
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