Abstract
The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.
Highlights
The new era of a smart manufacturing environment is characterized by the rapid development of industrial technology, information systems and components of industrial systems becoming increasingly complex
Only a threshold is used as a metric to identify anomalies. This method has been successfully implemented in different applications. (2) is a method that integrates a deep-autoencoder model without the improved feature space compaction process and the anomaly detection method based on a one-class support-vector machine. (3) is a simplified version of the DAECC-OC-support vector machines (SVMs) method
It can be observed that in the case of true positive rate (TPR), that is, the known cases, the best performance obtained is through the DAE-MSE method, as it is higher in thirteen of the fifteen scenarios (S1, S3–S14)
Summary
The new era of a smart manufacturing environment is characterized by the rapid development of industrial technology, information systems and components of industrial systems becoming increasingly complex. Some of the challenges that DDCMs facing to smart manufacturing environment include: (1) having a high capacity for pattern management, (2) be adaptive to the complexity of the systems, (3) as well as to the different operating conditions and the occurrence of faults on different components In this regard, multiple DDCM approaches have been proposed for fault diagnosis in industrial systems. Rauber, et al [4], proposed a methodology based on feature extraction and dimensionality reduction with principal component analysis applied to bearing fault diagnosis Another relevant studies that address the basic guidelines of DDCM approaches on machine learning are [5,6]. These uncharacterized patterns may be deviations from system due to the evolution in the useful life of the machinery under supervision, the presence of new fault scenarios and/or the capability of increasing the knowledge to assess additional severities of faults that have been already identified
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