Class-incremental fault diagnosis requires a model to adapt to new fault classes while retaining previous knowledge. However, limited research exists for imbalanced and long-tailed data. Extracting discriminative features from few-shot fault data is challenging, and adding new fault classes often demands costly model retraining. Moreover, incremental training of existing methods risks catastrophic forgetting, and severe class imbalance can bias the model's decisions toward normal classes. To tackle these issues, we introduce a supervised contrastive knowledge distillation for class incremental fault diagnosis (SCLIFD) framework proposing supervised contrastive knowledge distillation for improved representation learning capability and less forgetting, a novel prioritized exemplar selection method for sample replay to alleviate catastrophic forgetting, and the random forest classifier to address the class imbalance. Extensive experimentation on simulated and real-world industrial datasets across various imbalance ratios demonstrates the superiority of SCLIFD over existing approaches.

Although convolution neural network (CNN) has achieved certain success in fault detection and diagnosis (FDD) tasks in the chemical engineering industry, the performance and credibility of CNN-based FDD methods are greatly limited by two factors. First, CNN relies upon strong temporal/spatial correlation in data, which is very difficult to obtain in generic chemical tabular data. Second, most CNN methods have poor interpretability due to the encapsulation mechanism of feature extraction, and thus there is great difficulty in identifying the root-cause features from the results obtained using these methods. To address these difficulties, we propose an order-invariant and interpretable dilated convolution neural network (OIDLCNN), which is composed of feature clustering, dilated convolution, and a deep Shapley additive explanations (SHAP) explainer. Specifically, the feature clustering technique is adopted to reorder the features thus those with strong correlations are placed to be adjacent to each other. The large receptive field of dilated convolution can capture long-range correlations so then it can further recover the feature correlations and improve the classification performance. Last but not least, the proposed method provides interpretability by including the SHAP values to quantify feature contribution and identify the root-cause feature as the one with the highest contribution. Computational experiments are conducted on the Tennessee Eastman chemical process benchmark dataset. Compared with the other state-of-the-art methods, the proposed OIDLCNN-SHAP method achieves better performance in capturing feature correlations, detecting faults, and identifying the root-cause features.Note to Practitioners—Fault detection and diagnosis (FDD) is significant for reducing maintenance costs and improving safety in chemical processes. In this paper, we investigate the difficulty in detecting faults and identifying the root-cause features in chemical multivariate processes. This work was motivated by the fact that the existing CNN-based FDD algorithms are not designed for generic chemical tabular data and fail to capture vital information about feature correlations. In addition, the commonly used bayesian network-based root cause analysis methods are expensive since they require much prior knowledge and expert rules. We present a novel framework to capture the unique feature correlations and obtain the root-cause features without any expert knowledge. The proposed method provides an automatic and low-cost way for chemical process FDD tasks. It can be further integrated into the condition monitoring system of real chemical processes to analyze potential faults and identify the corresponding root causes in real time.

Fault diagnosis is crucial in monitoring machines within industrial processes. With the increasing complexity of working conditions and demand for safety during production, diverse diagnosis methods are required, and an integrated fault diagnosis system capable of handling multiple tasks is highly desired. However, the diagnosis subtasks are often studied separately, and the current methods still need improvement for such a generalized system. To address this issue, we propose the generalized out-of-distribution fault diagnosis (GOOFD) framework to integrate diagnosis subtasks. Additionally, a unified fault diagnosis method based on internal contrastive learning and Mahalanobis distance is put forward to underpin the proposed generalized framework. The method involves feature extraction through internal contrastive learning and outlier recognition based on the Mahalanobis distance. Our proposed method can be applied to multiple fault diagnosis tasks and achieve better performance than the existing single-task methods. Experiments are conducted on benchmark and practical process datasets, indicating the effectiveness of the proposed framework.

In real industrial processes, fault diagnosis methods are required to learn from limited fault samples since the procedures are mainly under normal conditions and the faults rarely occur. Although attention mechanisms have become increasingly popular for the task of fault diagnosis, the existing attention-based methods are still unsatisfying for the above practical applications. First, pure attention-based architectures like transformers need a substantial quantity of fault samples to offset the lack of inductive biases thus performing poorly under limited fault samples. Moreover, the poor fault classification dilemma further leads to the failure of the existing attention-based methods to identify the root causes. To develop a solution to the aforementioned problems, we innovatively propose a supervised contrastive convolutional attention mechanism (SCCAM) with ante-hoc interpretability, which solves the root cause analysis problem under limited fault samples for the first time. First, accurate classification results are obtained under limited fault samples. More specifically, we integrate the convolutional neural network (CNN) with attention mechanisms to provide strong intrinsic inductive biases of locality and spatial invariance, thereby strengthening the representational power under limited fault samples. In addition, we ulteriorly enhance the classification capability of the SCCAM method under limited fault samples by employing the supervised contrastive learning (SCL) loss. Second, a novel ante-hoc interpretable attention-based architecture is designed to directly obtain the root causes without expert knowledge. The convolutional block attention module (CBAM) is utilized to directly provide feature contributions behind each prediction thus achieving feature-level explanations. The proposed SCCAM method is testified on a continuous stirred tank heater (CSTH) and the Tennessee Eastman (TE) industrial process benchmark. Three common fault diagnosis scenarios are covered, including a balanced scenario for additional verification and two scenarios with limited fault samples (i.e., imbalanced scenario and long-tail scenario). The effectiveness of the presented SCCAM method is evidenced by the comprehensive results that show our method outperforms the state-of-the-art methods in terms of fault classification and root cause analysis.

Medical zero-shot relation triplet extraction, referred to as Med-ZeroRTE, requires the model to extract triplets comprising entities and relations from medical sentences. Importantly, the sentences include relations that were unseen during the model’s training phase. While Med-ZeroRTE had not been formally explored before this work, the limited availability of medical datasets, influenced by privacy concerns and annotation costs, emphasizes the necessity of exploring Med-ZeroRTE. This exploration faces two main challenges: Firstly, there is a gap of work specifically focused on triplet extraction from medical text in a zero-shot setting. Secondly, while a few approaches tackle the general zero-shot problems by employing generative models to produce synthetic data for unseen classes, the quality of some synthetic data remains suboptimal. Therefore, we propose a novel Enhanced Generator - Discriminator - Extractor framework (EGDE), which consists of three core modules, a prompt-tuned generator for generating synthetic samples given unseen relations, a fine-tuned discriminator for filtering qualified synthetic samples, a prompt-tuned extractor for extracting predicted medical triplets, to resolve Med-ZeroRTE and mitigate issues related to poor synthetic samples. The proposed framework is shown to be effective and superior compared to several robust baselines in experiments conducted on two distinct dataset settings.

Compared with traditional multivariate statistical techniques, deep neural networks have been frequently used for single-mode fault diagnosis and have shown promising results. However, in the real world, a complex industrial process may have several modes and fewer fault samples than normal. Although several multivariate statistical techniques focus on multimode fault diagnosis, those methods usually identify modes and then locally diagnose faults while ignoring relative information across modes and data imbalance problems. In this paper, a deep learning-based method combining Multi-gate Mixture-of-Experts (MMOE) and Synthetic Minority Over-sampling Technique (SMOTE) is proposed to address multimode imbalance fault diagnosis. Specifically, MMOE can identify modes and diagnose faults in each mode simultaneously. Furthermore, it investigates the common information of multiple modes by sharing network parameters. Moreover, SMOTE can address the fault imbalance problem by over-sampling minority fault samples. The experiment results show the performance improvements of multimode imbalance fault diagnosis using MMOE-SMOTE on the Tennessee Eastman (TE) Chemical benchmark process.

Robotic Process Automation (RPA) has been widely used in business and enterprises to automate the processing of digital documents and collect information and acquire knowledge. Table structure reconstruction in scanned documents has been extensively studied as an essential application of RPA. However, the detection of table borders often ignores broken borders, which makes it unsuitable for natural scenes. To address this, our paper heavily employs a data augmentation approach to synthesize fake scanned documents to train our table-border semantic segmentation model. We propose a novel segmentation model for table borders based on semantic segmentation. We compare traditional morphology-based line detection algorithms with existing semantic segmentation-based approaches. The results indicate that our proposed algorithm can solve the frame line detection problem effectively, even for low-quality scanned images. Actual cases show that we can reconstruct the table’s structure and obtain the knowledge in the table.

Fault diagnosis in an open world refers to the diagnosis tasks that need to cope with previously unknown faults in the online stage. It faces a great challenge yet to be addressed—that is, the online data of unknown faults may be classified as normal samples with a high probability. In this article, we develop an effective solution for this challenge by using supervised contrastive learning to learn a discriminative and compact embedding for the known normal situation and fault situations. Specifically, in addition to contrasting a given sample with other instances as is the case in conventional contrastive learning methods, our training scheme contrasts the normal samples with negative augmentations of themselves. The negative out-of-distribution data is generated by the Soft Brownian Offset sampling method to simulate the previously unknown faults. Computational experiments are conducted on the Tennessee Eastman Process benchmark dataset and a practical plasma etching process dataset. The proposed method achieves significant improvement compared with four existing methods under three open-set fault diagnosis circumstances, i.e., balanced open-set fault diagnosis, imbalanced fault diagnosis, and few-shot fault diagnosis. This demonstrates its great potentials in real world fault diagnosis applications.

Intelligent fault diagnosis is essential to guarantee the safe operation of industrial processes. And an important issue is how to develop a method to tackle the dilemma where we can only collect limited fault samples. In this paper, we propose a two-stage method based on supervised contrastive learning for imbalanced fault diagnosis tasks. We utilize the supervised contrastive learning technique as it has shown a powerful representation learning ability in previous works. The computational experiments on the Tennessee Eastman dataset show that our proposed two-stage method can achieve improved performance when compared to existing methods.

Computer supported cooperative work (CSCW) systems have been widely used to support teamwork in various fields such as design, education, research projects, etc. However, there is a great deal of knowledge generated directly or indirectly in the process of collaboration which is not well utilized and usually ignored instead of being reused and shared to help improve work efficiency. To bridge the gap between knowledge generation and utilization through CSCW, in this paper, we propose a knowledge mining based collaborative framework and first apply it to manufacturing value chains to achieve better work efficiency and reduce repetitive work in collaboration. Overall, this paper introduces the following novel insights and innovations: (1) we argue that there is a gap between the generated knowledge and the utilization of it during cooperative work; (2) a novel collaborative framework with knowledge mining approaches is proposed to bridge the gap; (3) a prototype system is further built and first applied to the manufacturing value chains. To the best of our knowledge, we are the first one to deal with knowledge reusing in CSCW of manufacturing value chains.

Open Knowledge Graph (OpenKG) link prediction is important for using OpenKGs in applications such as question answering and text comprehension. The noun phrases (NPs) and relation phrases in OpenKGs are not canonicalized, making OpenKG link prediction highly challenging. Existing methods addressing this problem infuse canonicalization information into knowledge graph embedding models. However, they still fail to fully exploit the semantics of NPs. First, two different NPs, even referring to the same entity, can carry different versions of information, which has been ignored by previous methods. Second, neighborhood information of NPs in OpenKGs has not been utilized, which contains abundant information for link prediction. Based on these observations, we propose the OpenKG Segmented Embedding (OKGSE) method. Specifically, to fully capture the dissimilarity of NPs belonging to the same cluster, we learn separate parts of embedding for both the NP cluster and NP. Meanwhile, we exploit neighborhood information by integrating graph context into the semantic matching score function. Extensive experiments across four benchmarks show that OKGSE can achieve state-of-the-art performance as well as effectively capture the unique semantics of each NP.

Imbalanced fault diagnosis becomes increasingly im-portant as the number of fault samples is relatively small in practical situations. Sparse auto-encoder(SAE) has been well addressed in fault diagnosis while it is not suitable for imbalanced fault diagnosis. Cost sensitive learning can be utilized to extend the sparse auto-encoder to cost sensitive sparse auto-encoder(CS-SAE). However, the class weights assigned to different classes are usually unknown in practice. So we propose to use particle swarm optimization(PSO) to optimize the class weights for cost sensitive sparse auto-encoder(PSO-CSSAE). The experiments have shown that the proposed approach consistently outperforms the state of the art on Tennesse Eastman(TE) dataset.

Classification with an imbalanced dataset has been a hard task for a long time. As generative models become more and more attractive, many researchers try to use these methods to address the imbalance problem. In this paper, we employ Bidirectional Generative Adversarial Networks (BIGAN) with improved training architecture as an oversampling method for minority class. We introduce an additional feedback by making use of the encoder$E$and further enhance the training of generator$G$. We conduct the qualitative and quantitative assessment on generated samples, and it shows that under the premise of ensuring diversity, the generated data tend to have less noise. Using it to perform data augmentation in training process, our method has been proved to get a better performance than original GAN-based method on multi-class imbalanced classification task.

Real-time fault detection and fault diagnosis is a key part of building smart factories. Principal component analysis (PCA) and kernel principal component analysis (KPCA) are the two most commonly used methods for process monitoring. Nevertheless, PCA cannot deal with nonlinear data and KPCA cannot be applied for large data sets. To tackle the above two problems, a novel fault detection and fault diagnosis method based on Gaussian-Bernoulli restricted Boltzmann machine (GBRBM) is proposed in this paper. The key idea of our approach is using GBRBM as a nonlinear dimensionality reduction technique and applying the reconstruction error to establish monitoring SPE statistic. The upper limit of the SPE is estimated by kernel density estimation (KDE). If the SPE statistic of an online sample exceeds the control limit, then the fault is detected and the contribution plot is utilized to diagnose the fault. Tennessee Eastman (TE) process is applied to evaluate the fault detection and diagnosis performance of the proposed method. The effectiveness of the proposed GBRBM monitoring method is improved by saving significant memory requirement while obtaining the highest average false detection rate and comparable fault diagnosis reliability, relative to PCA and KPCA techniques.

In Industry 4.0, anomaly detection plays an important role in the process of product quality inspection, where the product data with high dimensions and highly imbalanced distribution give rise to some challenges. To handle these challenges, a novel anomaly detection method based on Gaussian Restricted Boltzmann Machine (GRBM) is proposed. To make it more tractable for training the model, the method performs distinct gradient compensations through integrating the free-energy function into the objective function in two stages of product quality inspection. Extensive experimental studies are respectively carried out on two real-world cases, i.e. wine quality and cigarette product testing. Three state-of art anomaly detection methods and two conventional GRBM methods are used for comparison analysis, and the results demonstrate that our proposed method provides effectiveness and superiority in product quality inspection.