A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
A Low-Delay Lightweight Recurrent Neural Network (LLRNN) for Rotating Machinery Fault Diagnosis
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DOI：
10.3390/s19143109
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发表时间：
2019-07
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影响因子：
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通讯作者：
Wenkai Liu;Ping Guo;Lian Ye
中科院分区：
文献类型：
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作者：
Wenkai Liu;Ping Guo;Lian Ye
故障诊断是保证旋转机械系统安全可靠运行的关键。长短时记忆网络(LSTM)在这一领域受到了极大的关注。大多数基于LSTM的故障诊断方法参数过多、计算量过大，导致内存占用大、计算延迟大。为此，基于一种特殊的带遗忘门的LSTM单元结构，提出了一种用于机械故障诊断的低延迟轻量级递归神经网络模型。为了缩短时间序列的长度，将输入的振动信号分割成几个较短的子信号。然后，这些子信号被直接送入网络，并在没有任何人工参与的情况下转换为最终的诊断结果。实验结果表明，与已有方法相比，该方法在保持相同准确率的前提下，具有较小的存储空间占用和较低的计算延迟。
Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy.