Research on a method of fault identification of rolling bearings based on time series shapelets

Recent research on utilizing artificial neural network (ANN) for fault identification of bearings ignores the features of vibration signals as time series, resulting in low interpretability and cannot classify signals of bearing vibration on different dimensions. Time series shapelets is the most identifiable subsequences of time series. Time series can be effectively classified through computing similarities between shapelets and each time series. This paper identifies faulty bearings with a machine learning-based shapelets algorithm, and through modeling, calculation and verification based on CWRU fault bearing data set, some szimportant conclusions are obtained. The results show that this meaningful new method is more accurate and can reduce time consumption compared with traditional time-laboring shapelets, enhancing interpretability of diagnosis in the meantime. Compared with other classification methods, shapelets transforms the classification problem of vibration signal time series into a similarity measurement problem between time series and subsequence shapelets, fully reflecting the essence of bearing vibration signals as a time series. Meanwhile, by using MINDIST distance instead of Euclidean distance in shapelets can effectively reduce the calculation time while ensuring accuracy, which could be a significant method of distance measurement in clustering tasks.