基于和声搜索优化栈式自编码器的柴油发动机故障诊断

Diesel Engine Fault Diagnosis Based on Stack Autoencoder Optimized by Harmony Search

针对机械故障诊断中专业知识的不足会影响手工特征提取效果的问题,提出了应用栈式自编码器(Stacked autoencoder,SAE)直接从复杂的原始信号中逐层提取深度特征。通过逐层预训练、微调等操作来训练栈式自编码器的提取特征能力,并通过在网络中的每一个隐含层前引入Dropout正则化层、批规范层来防止过拟合,加速收敛。针对SAE网络中的超参数取值问题,首先通过一系列对照试验得到各超参数合适的取值范围,然后在该范围内进一步提出了使用和声搜索算法(Harmony search,HS)优化超参数,达到自适应调整网络结构,提高特征提取能力的效果。试验结果表明,当使用包含七种气门健康状态的柴油机振动数据测试时,所提出的HS-SAE方法的故障分类精度优于SAE和多种传统故障诊断算法。

A stack autoencoder(SAE) is proposed to extract deep features hierarchically from complex raw signals, in view of the lack of professional knowledge will weaken the efficiency of handcrafted feature extraction in mechanical fault diagnosis. The SAE can mine deep features via layer-by-layer pre-training, fine-tuning, etc., moreover, the dropout regularization layer and the batch normalization layer are introduced before each hidden layer in the network to prevent over-fitting and accelerate convergence. Aiming at the value of hyperparameters in SAE network, firstly, the appropriate range of values for each hyperparameter is obtained via a series of experiments, then, the harmony search(HS) algorithm is proposed within the range to optimize the hyperparameters to achieve adaptive adjustment of the network structure and improve feature extraction. The experimental results show that the proposed HS-SAE scheme outperforms original SAE and many traditional fault diagnosis algorithms in terms of the fault classification accuracy when testing with the diesel engine vibration data consisting of seven valve health states.