Deep Variational Autoencoder Classifier for Intelligent Fault Diagnosis Adaptive to Unseen Fault Categories
Deep Variational Autoencoder Classifier for Intelligent Fault Diagnosis Adaptive to Unseen Fault Categories
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DOI：
10.1109/tr.2021.3090310
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发表时间：
2021-12-01
影响因子：
5.9
通讯作者：
Jin, Xiaoning
中科院分区：
文献类型：
--
作者：
He, Anqi;Jin, Xiaoning
With the rapid development of artificial intelligence (AI) in recent years, fault diagnostics for industrial applications have leaped toward partially or fully automatic provided by the capability of analyzing massive condition monitoring data from sensors and actuators.一般来说，当训练数据集和测试数据集中出现的故障类型相同时，基于人工智能的故障诊断可以实现较高的准确率。 These diagnostic methods could be invalidated for applications dealing with unprecedented faults because the pretrained classifier for diagnostics tends to misclassify the novel instances into existing known classes. In order to address these limitations of conventional diagnostic approaches, we propose a unified diagnostics framework that can achieve novel fault detection and known fault classification tasks together. Through jointly training a variational autoencoder and a deep neural networks classifier, we convert the original entangled raw data into latent variables with Gaussian probabilistic distributions in the latent space and utilize the probabilistic latent variables to detect novel samples against known fault classes or classify them into one of the existing fault classes if they are not novel.我们提出的联合训练框架的有效性通过对两个不同轴承数据集的实验研究得到了验证。 Compared with the state-of-the-art methods in the literature, our unified framework is able to not only accurately detect the novel fault classes but also achieve high classification accuracy of known fault classes.
With the rapid development of artificial intelligence (AI) in recent years, fault diagnostics for industrial applications have leaped toward partially or fully automatic provided by the capability of analyzing massive condition monitoring data from sensors and actuators. Generally, AI-based fault diagnostics can achieve high accuracy when failure types appear in training dataset and testing dataset are the same. These diagnostic methods could be invalidated for applications dealing with unprecedented faults because the pretrained classifier for diagnostics tends to misclassify the novel instances into existing known classes. In order to address these limitations of conventional diagnostic approaches, we propose a unified diagnostics framework that can achieve novel fault detection and known fault classification tasks together. Through jointly training a variational autoencoder and a deep neural networks classifier, we convert the original entangled raw data into latent variables with Gaussian probabilistic distributions in the latent space and utilize the probabilistic latent variables to detect novel samples against known fault classes or classify them into one of the existing fault classes if they are not novel. The effectiveness of our proposed joint-training framework is validated through experimental studies on two different bearing datasets. Compared with the state-of-the-art methods in the literature, our unified framework is able to not only accurately detect the novel fault classes but also achieve high classification accuracy of known fault classes.