CNN-RNN融合法在旋转机械故障诊断中的应用
Application of CNN-RNN fusion method in fault diagnosis of rotating machinery
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摘要: 针对目前旋转机械故障诊断存在运算时间长、精度不高等问题，将CNN的特征提取能力和RNN时序处理能力相结合，提出了CNN-RNN融合分析法.该方法使用一维CNN网络提取特征数据，剔除受环境噪音等因素影响的无效信息且依然具有时序性，再由处理时序数据精度较高的RNN对该特征数据进行计算处理进而对旋转机械进行故障诊断.在测试集上的验证实验结果表明，该方法不需要手动提取特征数据，运算时间大约减少1/2，故障诊断精度提高约2%，具有可行性.Abstract: Aiming at the problems of current fault diagnosis of rotating machinery with long calculation time and low accuracy, a CNN-RNN fusion analysis method was proposed by combining the feature extraction capability of CNN and the processing capability of RNN timing. A one-dimensional CNN network was used to extract feature data, which removed invalid information affected by environmental noise and other factors and still had timeliness. Then, the RNN with high accuracy of processing time-series data calculated the feature data and then applied to the fault diagnosis of rotating machinery. The experimental results on the test set showed that the method did not require manual extraction of feature data, the computing time was reduced by about 1/2, and the accuracy of fault diagnosis was increased by about 2%.This method had feasibility.
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