An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by time-frequency distribution image dimensionality reduction. Since time-frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time-frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.