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Fault monitoring and classification of rotating machine based on PCA and KNN

Due to the complicated structure of most rotating machines, and the high dimension and strong correlation of process variables, this paper presents a multiple fault classification method of rotating machine based on Principal Component Analysis (PCA) and K Nearest Neighbor (KNN). Firstly, the dimensionality reduction model is established by using the normal historical data, which ensures a small memory footprint and computational consumption in fault classification. After combining different fault types data of rotating machine, PCA model is used to project them to low-dimension space. The KNN function is adopted to classify the training data after dimensionality reduction. The alternative data is utilized for online monitoring simulation by using SPE (Square Prediction Error) and T2 statistic, and then KNN training model is used to identify fault types from the abnormal data. The experiment result shows that the proposed method is accurate and effective in multiple faults identification.