Abstract
With the booming development of intelligent manufacturing in modern industry, intelligent fault diagnosis systems have become a necessity to equipment and machine, which have attracted many researchers’ attention. However, due to the requirements of enough labeled data for most of the current approaches, it is difficult to implement them in real industrial scenarios. In this paper, an unsupervised intelligent fault diagnosis system based on feature transfer is constructed to extract the historical labeled data of the source domain, using feature transfer to facilitate the fault diagnosis of the target domain. The original feature set is acquired by EEMD time-frequency analysis. Then, the transfer component analysis algorithm is adopted to minimize the distance between the marginal distributions of the source and target domains which reduces the discrepancy of features between the different domains. Finally, SVM is used in multiclassification to identify different categories of faults. The performance of the fault diagnosis system under different loads is tested on the CWRU bearing data set, and the experiments show that the proposed system could effectively improve the recognition ability of unsupervised fault diagnosis.
Highlights
Rotating machinery is a crucial part of the mechanical system in industrial manufacturing
The original vibration signals are decomposed by the EEMD algorithm at first
81 statistical features are calculated to be the initial feature set, which are transferred by transfer component analysis (TCA) to further obtain the sharable features between the different distributions
Summary
Rotating machinery is a crucial part of the mechanical system in industrial manufacturing. Unlike the high cost of feature learning in deep neural networks, we utilize EEMD to decompose the original signals and further extract the statistical features, which is used to learn the common feature space between the source and target domains by reducing the marginal distribution discrepancy. In this way, the proposed intelligent fault diagnosis system can uncover the hidden information in the signals and focus on learning the transferable mapping of the statistical features.
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