Rolling Bearing Fault Diagnosis Based on Attention LSTM

2023 38th Youth Academic Annual Conference of Chinese Association of Automation (YAC)（2023）

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Abstract

Long Short-Term Memory (LSTM) is well-suited for handling time-series data due to its ability to recognize dependencies between consecutive time points. In recent years, diagnosing rolling bearing faults using LSTM has garnered considerable interest. However, the memory bottleneck issue arises when LSTM is applied to long sequences, leading to the propagation of irrelevant information within the network. In this study, we propose an Attention LSTM fault diagnosis method. By incorporating the attention mechanism, this method concentrates on varying amounts of information in different LSTM gates. This approach aims to enhance the network's feature extraction capability, minimize the spread of unnecessary information, and address LSTM's memory bottleneck problem. The proposed method's effectiveness is confirmed through experiments on benchmark data.

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Key words

Fault diagnosis,LSTM,Attention,Rolling bearings

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【要点】：本文提出了一种基于注意力门控循环单元（Attention LSTM）的滚动轴承故障诊断方法，通过引入注意力机制优化LSTM在处理长序列时的记忆瓶颈问题。

【方法】：该方法采用注意力机制调整不同LSTM门控单元关注的信息量，以增强网络特征提取能力并减轻不必要信息的传播。

【实验】：研究通过在基准数据集上的实验验证了所提方法的有效性，实验结果表明该方法能有效提升滚动轴承故障诊断的准确性和效率。