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: bit.ly/3Fraa6Y
- How to extract features and train a model: bit.ly/2MA6nvv
- Anomaly detection: bit.ly/3LBFyRJ
Visit this GitHub repo for the anomaly detection example mentioned in the video: bit.ly/3LBFyRJ
Watch our video on Condition-based maintenance vs. Predictive maintenance:
• Condition-Based Mainte...
Predictive Maintenance Toolbox: bit.ly/3ORLHve
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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[@qifangzhou2609]

Another great video! Can you provide the triplex pump dataset? Thank you!

[@meldaulusoy8389]

Hi Qifang, please check out this example to access the dataset: www.mathworks.com/help/predmaint/ug/analyze-and-select-features-for-pump-diagnostics.html

[@abhinawsingh546]

How to arrange matrix data in column like pump data with matrix is given in this video?

[@meldaulusoy8389]

@@abhinawsingh546 Please refer to this example (www.mathworks.com/help/predmaint/ug/analyze-and-select-features-for-pump-diagnostics.html) to access the data set used in the video. You can check out this example (www.mathworks.com/help/predmaint/ug/multi-class-fault-detection-using-simulated-data.html) to see how simulation ensembles are created to store the generated healthy and faulty data.

[@noumanmemon2360]

Can I get the live script code link, please? In the videos, it's not explained data preprocessing.

[@abhinawsingh546]

How to make data file as given in this video..i have data for 8 faults and each fault have 225 set of matrix of size 16000*1