基于小波时频分析和Inception-BiGRU模型的盾构滚刀偏磨故障诊断

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振动与冲击 ›› 2023, Vol. 42 ›› Issue (15) : 232-240.

论文

樊翔翔1,2，项载毓1,2，孙瑞雪1,2，张敏1，莫继良1,2

作者信息 +

Fault diagnosis of TBM hob eccentric wear based on wavelet time-frequency analysis and Inception-BiGRU model

FAN Xiangxiang1,2, XIANG Zaiyu1,2, SUN Ruixue1,2, ZHANG Min1, MO Jiliang1,2

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文章历史 +

摘要

盾构机（tunnel boring machine，TBM）滚刀在重载、冲击和地质复杂的环境中服役，极易发生偏磨等失效故障，因此，掌握滚刀的磨损状态、实现基于数据驱动的滚刀偏磨故障诊断并指导滚刀的运维尤为重要。本文提出了一种基于小波时频分析和Inception-BiGRU模型的诊断模型以提高滚刀偏磨故障诊断效率。以滚刀为研究对象，在多功能缩比滚刀试验台上进行直线破岩试验，采集滚刀破岩时产生的振动加速度信号。采用连续小波变换获取反映振动信号时频域特征的小波时频图，进而以Inception模块的不同大小卷积核自适应地提取时频图中的多尺度空间信息，并通过添加双向门控循环单元（bidirectional gated recurrent units，BiGRU）使模型可更为准确地学习到时频图中丰富的时序依赖性关系，模型的超参数由贝叶斯优化算法确定。四种不同偏磨程度滚刀的诊断实验表明所提模型能够有效提取时频图中滚刀的偏磨特征并完成滚刀偏磨状态识别，实现端到端的盾构滚刀偏磨故障诊断。模型平均诊断准确率可达到98.5%，其诊断准确度和稳定性均优于其他常用算法，证明了所提方法的可行性。

Abstract

Cutters of shield boring machine (TBM) generally work in a complex geological environment with heavy load and impact, which can easily result in failure due to the partial wear of the cutter. Therefore, it is of particular importance to monitor the wear situation of the cutter to diagnose the cutter partial wear fault and guide the operations of the equipment based on data-driven techniques. In this study, aiming at the cutter partial wear problem, a novel diagnostic model based on wavelet time-frequency analysis and Inception-BiGRU is proposed, which can improve the fault diagnosis efficiency compared with existing models. The disc cutter is selected to perform linear rock breaking experiments on a multi-functional scaled rock-breaking test machine, during the process the vibration signals are collected. After that continuous wavelet transform (CWT) is used to obtain the wavelet time-frequency diagram that reflects the time-frequency domain characteristics of the vibration signals, and then the multi-scale spatial information is adaptively extracted using the convolutional kernels of different sizes of the Inception model. Furthermore, bidirectional gated recurrent units (BiGRU) are added to enable the model to learn the rich time-dependent relationships in the time-frequency diagram more accurately, and the hyperparameters of the model are determined by Bayesian optimization algorithm. The four diagnostic experiments of different cutter wear degree indicate that the proposed model can effectively extract the partial wear characteristics from the time-frequency diagram and diagnose the partial wear state of the cutting tools, which can realize end-to-end fault diagnosis of the TBM cutter's partial wear. The proposed model shows an average diagnostic accuracy up to 98.5%, whose diagnostic accuracy and stability are both indicated to outperform other algorithms, which proves the superiority of the proposed method.

导出引用

樊翔翔1,2，项载毓1,2，孙瑞雪1,2，张敏1，莫继良1,2. 基于小波时频分析和Inception-BiGRU模型的盾构滚刀偏磨故障诊断[J]. 振动与冲击, 2023, 42(15): 232-240

FAN Xiangxiang1,2, XIANG Zaiyu1,2, SUN Ruixue1,2, ZHANG Min1, MO Jiliang1,2. Fault diagnosis of TBM hob eccentric wear based on wavelet time-frequency analysis and Inception-BiGRU model[J]. Journal of Vibration and Shock, 2023, 42(15): 232-240

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