This paper proposes ABIGX (Adversarial fault reconstruction-Based Integrated Gradient eXplanation), a unified framework for explainable fault detection and classification (FDC). ABIGX builds on the foundational principles of established fault diagnosis methods, including contribution plots (CP) and reconstruction-based contribution (RBC), while extending their applicability to general FDC models and improving fault explanation results. Central to ABIGX is the Adversarial Fault Reconstruction (AFR) method, which rethinks fault reconstruction from the perspective of adversarial attacks, introducing a novel fault index applicable to both fault detection and classification tasks. In fault detection, we theoretically bridge ABIGX with conventional fault diagnosis methods by proving that CP and RBC are the linear specifications of ABIGX. For fault classification, we address the challenge of fault class smearing, an inherent issue that can obscure accurate explanations. We demonstrate that ABIGX effectively mitigates this issue, outperforming current gradient-based explanation methods. The experiments evaluate the explanations of FDC by quantitative metrics and intuitive illustrations. The results validate the generality and accuracy of AFR, and show that ABIGX provides more comprehensive and precise explanations across various FDC models, offering a significant improvement over existing methods.

The rapid advancement of deep learning and time-frequency analysis techniques have brought about a revolution in fault diagnosis for mechanical systems, offering flexible and efficient solutions. Nevertheless, data imbalance issues continue to pose significant obstacles in fault diagnosis modeling. In this research, we propose the use of a domain adaptation generative adversarial network (DAGAN) that capitalizes on transfer learning to extract valuable information from the majority-class data, while concurrently generating and augmenting minority-class data to expand the training dataset. DAGAN incorporates advanced techniques, including deep domain confusion and parameter forgetting, to enhance knowledge extraction and transfer during the transfer learning process, resulting in more realistic and comprehensive generation outcomes when dealing with small sample training. Furthermore, we have developed an imbalanced fault diagnosis method based on DAGAN, which further incorporates Continuous Wavelet Transform and Deep Residual Networks. Finally, the effectiveness and superiority of proposed method are validated on bearing and gearbox datasets. The experimental results demonstrate the outstanding performance of our method in effectively addressing imbalanced fault diagnosis. Note to Practitioners—In this paper, we present a practical approach to enhance the diagnosis of imbalanced faults. Our approach begins by utilizing Time-frequency images, which offer a comprehensive representation of the temporal and spectral characteristics of mechanical systems’ behavior. These images serve as input features and form the foundation for our fault diagnosis modeling. To address the limitations imposed by imbalanced data, we introduce DAGAN and incorporate advanced techniques such as deep domain confusion and parameter forgetting. These techniques facilitate the extraction and transfer of knowledge during the transfer learning process of DAGAN. Consequently, our approach generates more realistic and comprehensive outcomes, even when confronted with limited training samples. To validate the effectiveness and superiority of our proposed approach, we conducted extensive experiments on bearing and gearbox datasets. The results of these experiments demonstrate that our practical approach, which combines wavelet transform-based time-frequency analysis and the innovative DAGAN framework, offers a reliable and comprehensive solution for overcoming challenges associated with imbalanced fault data.

In the past years, latent variable models have played an important role in various industrial AI systems, among which quality prediction is one of the most representative applications. Inspired by the idea of deep learning, those basic latent variable models have been extended to deep forms, based on which the quality prediction performance has been significantly improved. However, different latent variable models have their own strengths and weaknesses, a model works well under one scenario might not provide satisfactory performance under another. The motivation of this article is based on the viewpoint of information fusion and ensemble learning for heterogeneous latent variable models. Particularly, a collaborative deep learning and model fusion framework is formulated for the purpose of industrial quality prediction. In the first stage of the framework, collaborative layer-by-layer feature extractions are implemented among different latent variable models, through which different patterns of latent variables are identified in different layers of the deep model. Then, in the second stage, an ensemble regression modeling strategy is proposed to fuse the quality prediction results from different latent variable models, which is based on a well-designed data description method. Two real industrial examples are used for performance evaluation of the proposed method, based on which we can observe that information fusions in terms of both collaborative layer-by-layer feature extraction and heterogeneous model ensemble have positive effects in improving prediction accuracy and stability.

Inspired by the idea of deep learning, several latent variable models have been successfully extended to the deep forms for industrial data analytics. Compared to traditional deep neural networks, deep latent variable models are more fitted to the requirement of data-driven modeling and applications in industrial production systems, due to the lightweight model structure and high-efficient data analytics process. The aim of this article is to develop a deep latent variable predictive modeling framework, which is based on a newly designed Bayesian soft attention mechanism. Instead of only using extracted features from the last hidden layer for predictive modeling, different attentions are focused across all hidden layers of the deep model. As a result, different layer-wise models make different contributions in predicting different data patterns, through which the ability of the deep latent variable model will be further explored. Two real industrial examples are provided for performance evaluation and comparative studies among different prediction models, based on which the superiority of the online Bayesian soft attention mechanism has been confirmed.

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance.

As a special ring signature, a traceable ring signature (TRS) provides limited anonymity and traceability, and has been shown useful in many practical applications such as anonymous voting. On the other hand, DualRing, a novel generic construction of ring signature introduced in CRYPTO 2021, can reduce computation and communication costs. Recently, building upon DualRing, Ye et al. proposed a lattice-based TRS that introduced TripleRing construction to ensure traceability. However, the proposed TRS encounters issues with certificate management and linear signature size. To address these concerns, we introduce a certificateless TRS with logarithmic size based on SM2-DualRing (SDR-CTRS) in the discrete logarithm setting. The SDR-CTRS leverages the TripleRing construction and non-interactive sum argument. Security analysis indicates that our SDR-CTRS satisfies tag-linkability, anonymity, and exculpability under the discrete logarithm hypothesis. Theoretical analysis shows that the SDR-CTRS features logarithmic communication cost and acceptable computation cost. Additionally, the SDR-CTRS does not require a fully trusted party during the tracking phase. Finally, to illustrate the practicality of the ring signature, we present a blockchain-based data sharing system with conditional privacy protection based on it.

In recent years, deep neural networks (DNNs) have been widely applied in fault classification tasks. Their adversarial security has received attention, but little consideration has been given to the robustness of adversarial attacks against imperfect DNNs. Owing to the data scarcity and quality deficiencies prevalent in industrial data, the performance of DNNs may be severely constrained. In addition, black-box attacks against industrial fault classification models have difficulty in obtaining sufficient and comprehensive data for constructing surrogate models with perfect decision boundaries. To address this gap, this article analyzes the outcomes of adversarial attacks on imperfect DNNs and categorizes their decision scenarios. Subsequently, building on this analysis, we propose a robust adversarial attack strategy that transforms traditional adversarial attacks into an iterative targeted attack (ITA). The ITA framework begins with an evaluation of DNNs, during which a classification confidence score (CCS) is designed. Using the CCS and the prediction probability of the data, the labels and sequences for targeted attacks are defined. The adversarial attacks are then carried out by iteratively selecting attack targets and using gradient optimization. Experimental results on both a benchmark dataset and an industrial case demonstrate the superiority of the proposed method.

Nowadays, data-driven models have been prevalent in predicting hard-to-measure key quality indicators of industrial processes in order to improve product quality and process safety. Such models are called soft sensors as they can serve the same role as physical sensors, but they do not require extra physical devices. Despite their success, soft sensors suffer from poor reliability. Reliability is the ability of soft sensors to give accurate predictions not only at the time they are trained, but also in the long term, despite potential drifts of the process. This is important when soft sensors are to be applied in critical industrial processes. In order to alleviate this problem, in this article, we propose a graph-constrained soft-sensor (GCSS) model that uses graph convolutions based on the a priori undirected graph of the process variables. Based on the modern control theory, we also propose an approach to extracting an undirected graph from process diagrams of the target process. This approach can identify relationships between process variables, which is easy to use and can be applied to a majority of industrial processes. The extracted graph structure serves as a constraint, and pushes the data-driven GCSS model into the direction of the true inner structure of the target process. With the aid of a priori graph knowledge, the GCSS model enjoys better generalizability and reliability. This has been validated in a simulation example and a real-world high-low transformer process. Compared to other soft sensors, the test performance of the GCSS model is improved by 6.5%. In the high-low transformer process, the GCSS model has the best test performance and the gap between training and test performance is reduced by 54%.

Nonlinear and multimode characteristics commonly appear in modern industrial process data with increasing complexity and dynamics, which have brought challenges to soft sensor modeling. To solve these issues, in this article, a dynamic mixture variational autoencoder regression model is first proposed to handle the multimode industrial process modeling with dynamic features. Furthermore, to deal with the partially labeled process data with rare quality values and large-scale unlabeled samples, a semi-supervised mixture variational autoencoder regression model is proposed, where a corresponding semi-supervised data sequence division scheme is introduced to make full use of the information in both labeled and unlabeled data. Finally, to verify the feasibility and effectiveness of the proposed methods, the models are applied to a numerical case and a methanation furnace case. The results show that the proposed methods have superior soft sensing performance, compared with the state-of-the-art methods.

With the advent of the big data era and the timeliness requirements of data processing, a large amount of streaming industrial big data is continuously obtained in real time. Facing this kind of flowing and time-varying knowledge information form, incremental learning is necessary. Lifelong learning (LL), as a typical incremental learning method, can continuously retain and accumulate old knowledge while learning new knowledge, which is very suitable for streaming industrial big data scenarios. At the same time, Bayesian nonparametric (BNP) models can adjust the complexity of the model based on observation data. Motivated by ideas of BNP and LL, a lifelong Bayesian learning machines framework is proposed in this article, which includes model expansion and model optimization. In general, this framework not only learns new effective knowledge and accumulates knowledge through incremental variational Bayesian under model expansion but also uses optimization steps to avoid model degradation caused by unnecessary component information. As an example, Dirichlet processes Gaussian mixture regression (DPGMR) is utilized for process modeling under this framework. To evaluate the feasibility and efficiency of the developed method, a synthetic and a real industrial case are demonstrated.