Enspectrumix : Novel adaptive methodology for fault component extraction from vibration mixtures

Machine condition monitoring is important in industry to monitor machine health conditions and avoid unexpected machine breakdowns. Vibration mixtures contain sufficient machine health information, but they have lots of vibration interferences. Therefore, it is crucial to adaptively extract fault components from vibration mixtures. Blind-filter-based methods have been extensively studied to extract fault components and they use impulsiveness and cyclo-stationarity properties as prior knowledge to optimize signal processing parameters. The difficulties of state-of-the-art methods include that: (1) statistical indices (e.g., kurtosis, Gini index, correlated kurtosis, etc.) cannot simultaneously characterize impulsiveness and cyclo-stationarity properties so that blind-filter-based methods are always affected by impulsive noise and low-frequency components, resulting in unreliable performance for fault components extraction; (2) optimization of signal processing parameters is time-consuming; (3) band-in noise cannot be removed; (4) only a frequency band rather than several frequency bands can be adaptively selected, resulting in the incomplete fault components extraction. Instead of using the aforementioned prior knowledge, this paper reports a new perspective that use healthy vibration mixtures as prior knowledge to improve the existing blind-filter-based methods and provide more reliable fault components extraction for practical implementation in industry. Firstly, an ensemble difference spectrum-based new index is proposed to provide a completely new perspective for traditionally existing statistical indices. Subsequently, it is theoretically proved that the new index satisfies two new properties for quickly and better quantifying fault components. Next, a new methodology named Enspectrumix is designed to decompose a raw vibration mixture into sub-signals and use the newly designed index to adaptively select informative sub-signals existing in different frequency bands. The effectiveness and superiorities of the proposed Enspectrumix are validated by two real-world dataset cases. Moreover, the Enspectrumix can be extended to extract concerned difference components in many other domains. © 2024 Elsevier Ltd.