Gao Shuzhi, Xu Lintao, Zhang Yimin, Pei Zhiming
Equipment Reliability Institute, Shenyang University of Chemical Technology, Shenyang 110142, China.
College of Information Engineering, Shenyang University of Chemical Technology, Shenyang 110142, China.
ISA Trans. 2022 Sep;128(Pt B):485-502. doi: 10.1016/j.isatra.2021.11.024. Epub 2021 Dec 10.
Due to the structure of rolling bearings and the complexity of the operating environment, collected vibration signals tend to show strong non-stationary and time-varying characteristics. Extracting useful fault feature information from actual bearing vibration signals and identifying bearing faults is challenging. In this paper, an innovative optimized adaptive deep belief network (SADBN) is proposed to address the problem of rolling bearing fault identification. The DBN is pre-trained by the minimum batch stochastic gradient descent. Then, a back propagation neural network and conjugate gradient descent are used to supervise and fine-tune the entire DBN model, which effectively improve the classification accuracy of the DBN. The salp swarm algorithm, an intelligent optimization method, is used to optimize the DBN. Then, the experience of deep learning network structure is summarized. Finally, a series of simulations based on the experimental data verify the effectiveness of the proposed method.
由于滚动轴承的结构以及运行环境的复杂性，采集到的振动信号往往呈现出强烈的非平稳和时变特性。从实际的轴承振动信号中提取有用的故障特征信息并识别轴承故障具有挑战性。本文提出一种创新的优化自适应深度信念网络（SADBN）来解决滚动轴承故障识别问题。深度信念网络通过最小批量随机梯度下降进行预训练。然后，使用反向传播神经网络和共轭梯度下降对整个深度信念网络模型进行监督和微调，有效提高了深度信念网络的分类准确率。采用一种智能优化方法——樽海鞘群算法对深度信念网络进行优化。接着，总结深度学习网络结构的经验。最后，基于实验数据进行的一系列仿真验证了所提方法的有效性。
