Li Cai

Data imbalance poses a prevalent and urgent challenge in the safety management of mechanical systems. High-speed trains (HSTs), as a class of large-scale high-end equipments, are particularly vulnerable to the lack of fault data for health management. In this paper, a zero-shot learning Transformer (ZSL-Transformer) localization model is proposed for the identification of bogie faults with specific locations, where the fault data is not involved in the model training process. More detailedly, the data acquisition system is first optimized to comprehensively monitor the operational status of target dampers that require attention, and meanwhile an adaptive residual noise reduction module (ARNRM) is incorporated to mitigate the noise interference in the measured track spectrum. Then, the semantic interpretation of fault location is utilized to construct an attribute description matrix, which serves as auxiliary information for ZSL fault positioning. Subsequently, attribute classifiers are constructed by leveraging the Transformer's capability of modeling global information relationships, thereby establishing the fundamental framework of ZSL. Finally, through comparison with other advanced positioning algorithms, the superior performance of the proposed method is demonstrated, as it attains an average accuracy exceeding 95%.

In modern industrial processes, class imbalance occurs when there is a significant disparity in the number of instances between different classes. Current approaches for handling this problem cannot work effectively due to the invalid instance replenishment strategy for rare categories and even exacerbate class imbalance issues. Therefore, this work presents a novel semi-supervised fault diagnosis (FD) method to address imbalances in FD data by leveraging extensive unlabeled samples. Inspired by adversarial discriminative domain adaptation learning, the proposed approach includes a distribution alignment model for extracting domain-invariant fault features from unlabeled data. Additionally, a soft threshold selection strategy is introduced to strategically select unlabeled fault samples, ensuring an abundance of samples for rare categories and enriching their distribution. Extensive experiments on the two industrial process datasets, including a real-world hot rolling of steel process and a well-established public Tennessee Eastman process, demonstrate the effectiveness of the proposed method in alleviating imbalances and utilizing unlabeled samples, establishing its superiority over existing methods. The code is publicly available on https://github.com/Ticuby/SFDM.

Federated learning (FL) and zero-shot learning have been becoming increasingly popular due to the data-privacy protection and the diagnosis of unseen faults in the industrial fault diagnosis. However, most existing diagnosis methods have the consistency assumption of distributions across different clients under multisource domain scenarios and cannot effectively diagnose both seen and unseen faults. Therefore, to diagnose both seen and unseen faults without data sharing and with distribution discrepancies across different clients, a federated generalized zero-sample fault diagnosis (GZSFD) paradigm is proposed in this article. In the client side, a stacked autoencoder (AE)-based feature extractor is introduced in each client for low-level features. In the cloud server, a feature-level distribution alignment scheme is developed to alleviate discrepancies for more discriminative high-level features. Moreover, a bidirectional AE (BAE) with reconstruction and cross-reconstruction streams is designed to enhance the feature-semantic consistency. Finally, a gating model based on BAE is proposed to identify online samples and mitigates the misclassification of unseen samples. Results on two practical industrial cases show that the proposed method achieves the improvement in federated GZSFD and effectively handles distribution discrepancies across different clients.

Zero-sample fault diagnosis (ZSFD) achieves remarkable success and has attracted considerable attention. However, existing methods suffer from the limitations in fault attribute labeling and feature extraction, leading to poor generalization and robustness. To be specific, fault attribute labeling that depends on expert knowledge is time-consuming and laborious. Fault feature extraction is carried out in one projecting space, and the useful knowledge of target data is ignored, which results in nonoptimal ZSFD performance. To tackle the above problems, a novel ZSFD based on transfer learning is proposed. First, a shared knowledge dictionary that automatically learns from source data with labels is transferred into the target data, which reduces the dependence of ZSFD on fault description. Second, a novel multiclass space projection model is designed to obtain the discriminative fault features. Finally, the pseudolabel mechanism is introduced to excavate the interclass and intraclass information in the target domain. The experiment results on the Tennessee–Eastman process and a real hot roll of steel process show the effectiveness of our method as well as its superiority.

The scarcity of fault samples is a common scenario in the field of fault diagnosis. In the context of mechanical fault diagnosis, the emergence of new working conditions and fault modes renders the availability of samples of target (unseen) faults for model training unfeasible, thus limiting the performance of data-driven methods. Consequently, zero-sample learning and diagnosis of mechanical faults is a challenging task. In this regard, this article proposes a multiattribute learning model, inspired by the zero-shot learning paradigm, for zero-sample mechanical fault diagnosis. The key lies in the shared multiclass attribute classifiers. During the attribute learning process, a convolutional neural network is developed to construct multiclass attribute classifiers, which serve as a mapping between visual features and semantic features. These classifiers are transferred from readily available faults to enhance the capability of diagnosing unseen faults. By minimizing the difference among the fault attributes, the diagnosis of unseen faults is achieved, which includes fault location, size, working load, etc. Experiments on two real datasets verify the efficacy and the superiority of the proposed method.

Detection and classification are two significant tasks for industrial fault diagnosis. However, conventional methods typically treat these tasks as separate and independent problems, and necessitate a retraining process when new fault samples or classes are collected. Therefore, an incremental support vector data description scheme using Gaussian kernel function is pro-posed for industrial process fault diagnosis in a unified frame-work. In this framework, the decision boundary is updated incrementally only based on the specific original support vectors and newly collected samples. An adaptive threshold and a restructured radius are proposed to promote accuracy in the fault detection. In the classification procedure, the hyperspheres for all known classes are constructed by decision tree. The new sample that does not belong to any known class is identified as an unknown class. Without a time-consuming retraining process, the proposed diagnosis method with the incremental learning capability can synchronously achieve the fault detection and classification task. Experimental results demonstrate the effectiveness and superiority in terms of diagnosis performance. Note to Practitioners —In practical industrial processes, new fault samples are collected and new fault classes emerge continually. Under such scenarios, fault diagnosis with incremental learning capability is becoming increasingly important. This work proposes a unified incremental framework for fault diagnosis based on support vector data description, which is able to achieve fault detection and classification synchronously. For fault detection, an adaptive threshold and a restructured radius are developed. For fault classification, hypersphere-shaped boundaries of all fault classes are given via the decision tree-based strategy. The proposed method is updated to include new fault samples and fault classes without dimensionality reduction or any distributional assumptions.