文献类型：期刊文献

英文题名：Novel Rotating Machinery Structural Faults Signal Adaptive Multiband Filtering and Automatic Diagnosis

作者：Song, Xuewei[1];Liao, Zhiqiang[1,2];Wang, Hongfeng[3];Song, Weiwei[3];Chen, Peng[1]

机构：[1]Mie Univ, Grad Sch Bioresources, Tsu, Mie, Japan;[2]Guangdong Ocean Univ, Maritime Coll, Zhanjiang, Peoples R China;[3]Huangshan Univ, Sch Mech Elect & Informat Engn, Huangshan, Peoples R China

年份：2021

卷号：2021

外文期刊名：MATHEMATICAL PROBLEMS IN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000774901000003)、、EI(收录号：20220111427099)、Scopus(收录号：2-s2.0-85122057795)、WOS

基金：This work was supported by program for scientific research start-up funds of Guangdong Ocean University and Key Research and Development Project from Anhui Province of China (Grant Nos. 202004a05020025 and 202104b11020011).

语种：英文

外文关键词：Fault detection - Adaptive filtering - Adaptive filters

外文摘要：To realize an automatic diagnosis of rotating machinery structure faults, this paper presents a novel fault diagnosis model based on adaptive multiband filter and stacked autoencoders (SAEs). First, to solve the problem where the actual rotating frequency and its harmonics cannot be accurately extracted in engineering applications, an improved adaptive multiband filtering method is designed. This method takes the theoretical rotating frequency as the search center, extracts the maximum within the positive and negative deviation as the actual rotating frequency, and sets a threshold according to the actual value to realize multiband filtering. This method can effectively remove background noise and accurately extract the actual rotating frequency and its harmonics. Second, an unsupervised SAE multiclassification model is established to realize an automatic diagnosis of fault types. This model can automatically extract the in-depth features of the filtered signal and improve the fault classification accuracy. Third, engineering and comparative experiments were carried out to verify the effectiveness and superiority of this model. Results show that the proposed automatic diagnosis model can extract the characteristic components abundantly and accurately recognize rotating machinery structural faults.