针对深度残差收缩网络（Deep residual shrinkage network, DRSN）在降噪过程中引起的信号失真问题，提出了一种改进的深度残差收缩网络（Improved deep residual shrinkage network, IDRSN）并将其应用于滚动轴承的故障诊断中。首先，引入一种改进的半软阈值函数（Improved Semi-Soft Threshold Function, ISSTF）用于解决恒等偏差的问题并消除软阈值函数引起的信号失真。然后，设计了半软阈值模块（Semi-Soft Threshold Block, SSTB）和自适应斜率模块（Adaptive Slope Block, ASB）构建改进的残差收缩单元（Improved residual shrinkage building unit, IRSBU），用于自适应设置最优阈值并进一步修正输出。最后，将所提方法应用于两种不同工况的滚动轴承故障诊断中。研究结果表明，与现有方法相比，所提方法的分类准确率和鲁棒性更高，对于变转速工况下的故障诊断更为有效。
Abstract
Aiming at the signal distortion problem caused by deep residual shrinkage network (DRSN) in the noise reduction process, an improved deep residual shrinkage network (IDRSN) is proposed and applied to the fault diagnosis of rolling bearing. Firstly, an improved semi-soft threshold function (ISSTF) was introduced to solve the problem of identity deviation and eliminate the signal distortion caused by the soft threshold function. Then, a semi-soft threshold block (SSTB) and an adaptive slope block (ASB) were designed to construct an improved residual shrinkage building unit (IRSBU), which was used to adaptively set the optimal threshold value and further correct the output. Finally, the proposed method was applied to fault diagnosis of rolling bearing in two different operating conditions. The results showed that the proposed method had higher classification accuracy and robustness compared with the existing methods, and was more effective for fault diagnosis under variable speed conditions.
关键词
故障诊断 /
滚动轴承 /
深度残差收缩网络 /
半软阈值函数 /
自适应斜率模块
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Key words
fault diagnosis /
rolling bearing /
deep residual shrinkage network /
semi-soft threshold function /
adaptive slope block
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