Condition Monitoring with MATLAB

Learn how you can develop condition monitoring algorithms with MATLAB®. Develop condition monitoring algorithms for the early detection of faults and anomalies to reduce downtime and costs due to unplanned failures and unnecessary maintenance.

Condition monitoring is the process of collecting and analyzing sensor data from equipment to evaluate its health state during operation. This video walks you through the workflow for developing a condition monitoring algorithm for fault classification of a triplex pump. Learn how to interactively extract features from sensor data using Diagnostic Feature Designer. Use the extracted features to determine the health state of your machine. Deploy condition monitoring algorithms as production applications to the cloud or on-prem server using MATLAB Compiler™ and MATLAB Production Server™. Generate C/C++ code from your algorithms to run them directly on Edge devices, such as PLCs.

Check out the following examples:

• How to generate fault and healthy data from Simulink (Multi-Class Fault Detection Using Simulated Data)
• How to extract features and train a model (Analyze and Select Features for Pump Diagnostics)
• Anomaly detection