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Rotating machinery fault diagnosis under nonstationary conditions still mainly relies on manual analysis of the frequency spectrums or the time-frequency representations of vibration signals. However, not only do the results of those methods depend on heavily the expert experience, but for some samples, the intricate interference frequency components caused by the complex modulation characteristics and the operation conditions also make it difficult to identify the frequency content. In this article, a novel intelligent fault diagnosis method for rolling bearings under nonstationary conditions is investigated. A new flexible generalized demodulation method is first proposed. Different from traditional demodulation methods, it can map the interest time-varying frequencies of extensive samples under different speed conditions to the defined base frequency as well as its multiples, and thus overcomes the effects of operation conditions on demodulation spectrums. Based on the demodulation method, a fault feature extraction method is further proposed to capture the useful fault information in the spectrums. Experiments validate that, for some samples, the health conditions cannot be manually identified by the demodulation spectrums due to the interference frequency components, but can be recognized by the proposed method automatically, and because of the definite physical meaning, the method is more adaptive to new operation conditions.
Liu et al. (Wed,) studied this question.