Difference Histograms: A new tool for time series analysis applied to bearing fault diagnosis

A powerful tool for bearing time series feature extraction and classification is introduced that is computationally inexpensive, easy to implement and suitable for real-time applications. In this paper the proposed technique is applied to two rolling element bearing time series classification problems and shown that in some cases no data pre-processing, artificial neural network or nearest neighbour approaches are required. From the results obtained it is clear that for the specific applications considered, the proposed method performed as well as or better than alternative approaches based on conventional feature extraction.

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