Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.
Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network.
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使用深度置信网络的齿轮传动链无监督故障诊断
DOI：
10.3390/s17071564
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发表时间：
2017-07-04
期刊：
影响因子：
--
通讯作者：
Gan C
中科院分区：
文献类型：
--
作者：
He J;Yang S;Gan C
人工智能技术能够有效分析大量故障数据并自动提供准确的诊断结果，已广泛应用于旋转机械的故障诊断。传统的人工智能方法是使用由人类操作员选择的特征来应用的，这些特征是基于诊断技术和现场专业知识手动提取的。然而，为每个诊断目的开发健壮的特征通常是劳动密集型和耗时的，并且为一个特定任务提取的特征可能不适合其他任务。本文提出了一种基于深度信念网络（DBN）的齿轮传动链无监督故障诊断的人工智能方法，并利用遗传算法对网络结构参数进行优化。与传统的人工智能方法相比，该方法可以通过无监督特征学习自适应地挖掘与故障相关的鲁棒特征，从而减少了对信号处理技术和诊断专业知识的先验知识要求。此外，它在建模复杂的结构化数据方面更强大。利用滚动轴承和齿轮箱的数据集验证了该方法的有效性。为了证明该方法的优越性，将其性能与两种著名的分类器，即反向传播神经网络（BPNN）和支持向量机（SVM）进行了比较。使用该方法时，滚动轴承的故障分类准确率为99.26%，齿轮箱的故障分类准确率为100%，远高于其他两种方法。
Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.
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