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.
Effective fault diagnosis is essential to ensure the safe and reliable operation of equipment. In recent years, several transfer learning-based methods for diagnosing faults under variable working conditions have been developed. However, these models are designed to completely match the feature distributions between different domains, which is difficult to accomplish because each domain has unique characteristics. To solve this problem, we propose a framework based on the maximum classifier discrepancy with marginal probability distribution adaptation that focuses on task-specific decision boundaries. Specifically, this method captures ambiguous target samples through the predicted discrepancy between two classifiers for the target samples. Furthermore, marginal probability distribution adaptation facilitates the capture of target samples located far from the source domain, and these target samples are brought closer to the source domain through adversarial training. Experimental results indicate that the proposed method demonstrates higher performance and generalization ability than existing fault diagnosis methods.
In recent times, machine learning has shown its efficiency in the field of fault diagnosis. Nevertheless, in many real-world applications, the basic data are often collected under the condition of machine working condition change, thereby leading to large distribution divergences. Thus, we propose the novel general normalized maximum mean discrepancy (GNMMD) feature-learning method to overcome the limitation of unstable conditions. The proposed algorithm can efficiently handle high-dimensional inputs by enforcing three constraints on the matrix of the learned features, and can optimize the objective function-based generalized norm features and MMD. First, this study analyzes the mapping characteristics of the generalized norm. Second, the feature selection approach based on GNMMD is further studied. Third, the current research also discusses the effects of different choices of norm on the diagnosis performance. Lastly, the data sets of the rolling bearing and planetary gear under unstable conditions are used to verify that the proposed method can achieve superior results.
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