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:
Watch our video on Condition-based maintenance vs. Predictive maintenance:
Chapters:
0:00 Why Condition Monitoring?
0:40 What is Condition Monitoring?
1:07 Condition Monitoring Algorithms
1:38 Anomaly Detection for Condition Monitoring: Abrupt Signal Changes
2:24 Anomaly Detection for Condition Monitoring: Value of Feature Extraction
3:13 Condition Monitoring Algorithm Development Workflow
5:12 Example: Condition Monitoring of a Pump
6:21 Feature Extraction and Ranking with the Diagnostic Feature Designer app
9:37 Generating a MATLAB Function for Feature Extraction
10:20 Training a Condition Monitoring Algorithm with Classification Learner app
11:58 Testing the Condition Monitoring Algorithm on New Data
12:31 Summary of Condition Monitoring
#conditionmonitoring
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