基于NSST4-SVD-DBN的带式输送机托辊轴承故障诊断方法

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关键词：故障诊断；声信号；二重四阶同步压缩变换；奇异值分解；深度置信网络中图分类号：TP133.3DOI：10.3969/j.issn.1004-132X.2026.03.015 开放科学（资源服务）标识码（OSID）：

Abstract：Aiming at the problems of dificulty in extracting feature information generated by belt conveyor roller bearing faults ，as wellas low accuracy and poor robustnessof fault diagnosis and identification， NSST4，SVD and DBN methods were combined to propose a suitable method for belt conveyor roler bearing acoustic signal fault diagnosis. Firstly，sequential variational mode decomposition （SVMD） was used to process the acoustic signals to enhance the recognizability of fault features.Second，the processed one-dimensional signals were converted to a two-dimensional time-frequency matrix by NSST4，which was used as the inputs of the feature matrix. Subsequently，the feature matrix was downsized using SVD technique to extract the key singular value vectors that might characterize the status of the roll bearings. These singular value vectors were then input into DBN，and the DBN core parameters were optimized by the improved sparrow search algorithm （ISSA）to improve the recognition performance of the model. Finally，in order to further validate the efectiveness of the proposed method，it was tested by simulated fault experiments and field experiments. In the simulated fault experiments of the rollr bearings，the accuracy rate of the proposed method reaches 97.91% . Compared with other 5 methods，the accuracy of the proposed method is the highest，and the mean absolute error（MAE） is the lowest. In the field experiments， the recognition accuracy reaches 96.57% ：

Key words：fault diagnosis；acoustic signal；double fourth-order synchronous compression transform （NSST4）；singular value decomposition（SVD）；deep belief network（DBN）

0 引言

带式输送机以其长距离输送、大运输量、连续作业和易于自动化控制等优势，已成为世界上使用范围最广、最为重要的散状物料连续运输设备之一，在煤炭、电力、物流和建材等多个关键经济领域中发挥着重要作用[]。（剩余17676字）