Dandan Zhao

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.

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.

Health state prediction and fault time prediction are two key tasks in the fault prediction field. However, existing fault prediction techniques perform these tasks hierarchically and separately without considering the time-varying dynamics of the system operation process, which reduces the prediction efficiency and accuracy. Therefore, a Gaussian process regression prediction method based on the update strategy is proposed for the dual tasks of aeroengines. In this method, for new samples collected continuously, the predictive distributions are deduced and the model parameters are updated. Specifically, through variable selection and multivariable fusion technology, the most beneficial variables corresponding to the health state are used to construct a shared health index for health state and fault time prediction. The proposed health index can better characterize the health state. Then, by the update strategies including single-point and multipoint update strategies, a unified Gaussian process regression framework with newly collected samples information is obtained. Thereby, the health index and fault time prediction are realized synchronously. Experimental results on the commercial modular aero-propulsion system simulation dataset demonstrate that the proposed method outperforms state-of-the-art ones.

Principal component analysis (PCA) is one of the most successful unsupervised subspace learning methods and has been used in many practical applications. To deal with the outliers in real-world data, robust principal analysis models based on various measure are proposed. However, conventional PCA models can only transform features to unknown subspace for dimensionality reduction and cannot perform features’ selection task. In this article, we propose a novel robust PCA (RPCA) model to mitigate the impact of outliers and conduct feature selection, simultaneously. First, we adopt $\sigma$ -norm as reconstruction error (RE), which plays an important role in robust reconstruction. Second, to conduct feature selection task, we apply $\ell_{2,0}$ -norm constraint to subspace projection. Furthermore, an efficient iterative optimization algorithm is proposed to solve the objective function with nonconvex and nonsmooth constraint. Extensive experiments conducted on several real-world datasets demonstrate the effectiveness and superiority of the proposed feature selection model.

This study investigates the sample value imbalance problem of process monitoring. A fault detection approach based on variable selection and support vector data description (SVDD) is developed for efficient process monitoring. First, Kullback–Leibler divergence serves as the variable selection algorithm, which highlights the most beneficial information about the concerned faults. The attained variables are segmented by block division to avoid faults information being covered in single space monitoring, so that the relevant variables and the most beneficial information are concentrated in the same block. Then, Kernel principal component analysis is applied in each block to address the challenge that variables may still be high-dimensional and nonlinear. After that, the monitoring result is given based on the proposed SVDD with a restructured radius index, which is more sensitive to the fault. As demonstrated from experimental results on the Tennessee Eastman process, this method is effective and outperforms counterparts with higher mean fault detection rate. Note to Practitioners—Recently, multivariate statistical process monitoring (MSPM) has attracted much attention. In general, MSPM incorporates all variables for the large-scale process. However, only a small number of variables are fault-dependent. Namely, the sample value imbalance problem is encountered in application. In this scenario, the monitoring performance degrades and the online computational complexity increases. To this end, a SVDD-based fault detection method, which considers the fault-related variables, is proposed for process monitoring. The proposed method is verified by the Tennessee Eastman process and it is more sensitive to the concerned fault.