An enhanced sparse filtering method for transfer fault diagnosis using maximum classifier discrepancy

Abstract

The speed of rotating parts is often affected by different working conditions in mechanical equipment, which results in more complications in the feature mapping relationship. However, existing methods that address the large fluctuation problem in rotational speed have been formulated merely to improve test accuracy and do not consider the effects of irregular fluctuation frequency on the fault samples located at the class boundary. Thus, to distinguish the health conditions under frequent or irregular fluctuation speeds, this paper explores an enhanced sparse filtering (SF) algorithm based on maximum classifier discrepancy to diagnose the fault conditions caused by speed fluctuation. It considers the superiority of the task-specific decision boundary and adversarial training for the fault diagnosis network. Unlike traditional SF methods, the proposed framework introduces the Wasserstein distance to reduce the domain discrepancy between the source domain and the target domain and then uses the probability output discrepancy of the classifier to locate the fuzzy fault samples on the class boundary. This paper conducts theoretical analysis and experimental comparison and verifies the performance advantages of the framework through bearing and gear experiments under large speed fluctuation conditions. The proposed model also shows an excellent performance even when the speed fluctuates frequently.

Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction

“…Most research in this area has focused on the transfer between discrete operating conditions. Classic domain adaptation methods, such as distribution alignment [39,40] and adversarial alignment [41,42], have been widely used in this context and have been found to be beneficial [8]. Proposed approaches include discrepancy-based domain adaptation, using various criteria such as Maximum Classification Discrepancy (MCD) [42] and Maximum Mean Discrepancy (MMD) [43,44,45].…”

“…Classic domain adaptation methods, such as distribution alignment [39,40] and adversarial alignment [41,42], have been widely used in this context and have been found to be beneficial [8]. Proposed approaches include discrepancy-based domain adaptation, using various criteria such as Maximum Classification Discrepancy (MCD) [42] and Maximum Mean Discrepancy (MMD) [43,44,45]. Deep domain adversarial frameworks such as DANN [46,47] have been developed and applied for fault diagnosis of machines under varying working conditions.…”

An attention mechanism-guided domain adversarial network for gearbox fault diagnosis under different operating conditions

“…Schwendemann et al (2021) exploited the unique characteristics of the intermediate domain to improve the maximum mean difference, which is called layered MMD. An enhanced sparse filtering algorithm based on the maximum classifier difference was proposed by Bao et al (2021) to minimize the overall gap between different data sets. Xie et al (2016) used the transfer component analysis (TCA) with Gaussian kernel to effectively extract and fuse interdomain features to realize the cross-domain fault diagnosis of gearboxes.…”

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

“…Classical machine learning methods, such as autoregressive integrated moving average (ARIMA) [25], support vector regression (SVR) [26], ANN [27], etc., are widely used in related works. With the development of machine learning, recurrent neural networks (RNNs) [28] have obtained excellent results in prediction tasks. Park et al [29] used long short-term memory (LSTM) to predict the remaining useful life of a battery.…”