KEYWORDS: Data modeling, Wavelets, Data acquisition, Signal processing, Performance modeling, Statistical modeling, Machine learning, Data processing, Feature selection, Reliability
In order to monitor the working condition of vacuum pump in real time and find the fault equipment, implemented a vacuum pump fault monitoring system. The system includes data acquisition, fault diagnosis, information display and other functions. It uses multi-thread and producer-consumer model to realize thread-safe data flow, and has a graphical interface. The system collects data, calculates its wavelet packet energy, selects the feature by mean decrease impurity method, and then uses the improved random forest algorithm for diagnosis after optimizing the weight of decision tree by out of bag estimation. Through testing, the accuracy of the improved model is more than 95%, and the system can realize real-time judgment, which has achieved good effect in practical application.
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