Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis
MEASUREMENT SCIENCE AND TECHNOLOGY(2024)
摘要
The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.
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关键词
cross-sparse filtering,blind deconvolution,compound fault separation,sigmoid function,fault diagnosis
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