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To address these engineering pain points, this study integrates the advantages of continuous wavelet convolution and discrete wavelet transform to construct an interpretable wavelet convolution intelligent diagnosis network (WCIDN). First, an adaptive condition convolutional layer (ACC layer) is proposed, which achieves dynamic weight configuration under varying operating conditions by setting frequency kernel functions, channel kernel functions, and filter kernel functions. Subsequently, a discrete wavelet transform layer is employed to convert time-domain signals into the wavelet domain, and an adaptive wavelet attention mechanism (AWAM) is designed. This mechanism can adaptively match fault frequencies, effectively filter noise, and retain fault signals. Finally, the visualization of kernel functions under varying operating conditions and the spectral analysis of the attention mechanism contribute to explaining the decision-making mechanism of the network. The potential engineering practical value of this method was demonstrated using spindle bearing fault data from Jilin University and motor bearing data from the Polytechnic University of Turin.<\/jats:p>","DOI":"10.1093\/jcde\/qwaf059","type":"journal-article","created":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T07:48:35Z","timestamp":1751701715000},"page":"113-128","source":"Crossref","is-referenced-by-count":3,"title":["A frequency-adaptive and interpretable neural network for intelligent fault diagnosis of high-speed motor bearings"],"prefix":"10.1093","volume":"12","author":[{"given":"Sun","family":"Fenghao","sequence":"first","affiliation":[{"name":"Key Laboratory of CNC Equipment Reliability, Ministry of Education, School of Mechanical and Aerospace Engineering, Jilin University , 130022, Changchun 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