Industrial Fault Diagnosis Research Articles

Pre-connected and trainable adjacency matrix-based GCN and neighbor feature approximation for industrial fault diagnosis

Industrial fault diagnosis methods based on graph convolution network (GCN) becomes a hot topic for its great feature extraction ability to multivariate time-series data. However, GCNs ignore inter-sample temporality when constructing the adjacency matrix (AM), leading to low prediction accuracy. A novel fault diagnosis method based on pre-connected and trainable AM-based GCN and neighbor feature approximation (PTGCN-FA) is proposed at the node-level task. Firstly, PTGCN-FA introduces the temporal nearest neighbors into spatial nearest neighbors to pre-connect and construct the AM. Then, the AM is trained only where the samples are connected, which makes the best weights obtained and reduces the time complexity of the model. Finally, after the GCN layers, the trained AM is introduced into the approximation of features, which are neighbors in the original sample space. Two process industry cases are carried out, and the simulation results including diagnosis accuracy, confusion matrix, study to the ratio of labeled data and an ablation experiment verify PTGCN-FA has more efficient and accurate diagnostic performance than related methods. Additionally, the analysis of the temporal neighborhood weight parameter shows that the performance of fault diagnosis can be improved by considering both temporal and spatial information between samples.

An Industrial Fault Diagnosis Method Based on Graph Attention Network

An Industrial Fault Diagnosis Method Based on Graph Attention Network

Solving industrial fault diagnosis problems with quantum computers

In this article, we investigate in how far quantum computers can be leveraged to solve NP-complete fault diagnosis problems within the area of industrial cyber-physical systems. Therefore, two approaches are proposed which exploit quantum computing to solve diagnosis problems: The first method employs Grover’s algorithm, and the second is based on the Quantum Approximate Optimization Algorithm. To show the industrial application, we present an integrated approach to learn the diagnosis model from process data, check whether the model is suitable, and use it for diagnosis. The result is a method for quantum industrial fault diagnosis. For this approach, the diagnostic capabilities and the runtime have been evaluated on an IBM Falcon processor using three publicly available benchmarks from the process industry. Further, the scaling between quantum computers and classical PCs has been analyzed.

A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation

The problem of data imbalance and limited sample poses a prominent challenge to fault diagnosis in industry, especially in rotating machinery fault diagnosis due to the constraints of sampling and labeling. Reinforcement learning is known for its auto-optimized learning ability but it is severely limited by the quality and quantity of data it is able to collect. This paper applies the Equilibrium Deep Q-Network (Equilibrium-DQN) based agent with the Variational Autoencoder with Wasserstein Generative Adversarial Network and Gradient Penalty (VAE-WGAN-GP) for rotating machinery fault diagnosis. The Equilibrium-DQN possesses the property of leveraging multiple target networks to improve training stability and accuracy. The VAE-WGAN-GP excels in resolving data scarcity problems in fault diagnosis, especially in enriching labeled data with more typical samples while keeping the original data features. Herein, a novel framework is presented that integrates both data augmentation techniques with reinforcement learning agent for fault diagnosis. The proposed framework improves the weakness of insufficient samples in fault diagnosis and reveals its potential in complex industrial applications. The experiments, both on the test bench dataset and on the real locomotive dataset, confirm the advantage of the proposed framework.

Generalized zero-sample industrial fault diagnosis with domain bias

Generalized zero-sample fault diagnosis (GZSFD) is a challenging task involving the diagnosis of all samples from both previously seen and unseen faults. However, the scarcity of unseen samples for training causes that existing methods are hindered by domain bias, where unseen faults are more likely to be misclassified as seen faults. In this article, an efficacious solution is proposed by constructing an unseen fault detector for test samples in GZSFD with domain bias, which utilizes the detected unseen-sample knowledge to enhance the diagnosis performance. Specifically, a ResNet-based one-dimensional convolutional neural network is designed for high-quality feature extraction. Also, a Kullback–Leibler divergence-based distribution threshold detector is constructed for the identification of test samples. Afterwards, test samples are detected and distinguished into seen or unseen classes. In detected unseen classes, a zero-sample fault diagnosis (ZSFD) problem is undertaken, while in detected seen classes, a sub-GZSFD problem is addressed. For ZSFD tasks, to leverage the unseen samples in the test set, a clustering-based scheme without a predefined cluster number is used for the detected unseen fault. For sub-GZSFD tasks, combined with classification results in the ZSFD task, two embedding strategies are proposed to further mitigate the domain bias. They learn a shared weight and the optimal weights of semantic attributes from the feature space to the semantic embedding space, respectively. Using the shared fine-grained semantic attribute descriptions as auxiliary information, the final fault category can be determined. Experimental results showcase that the proposed strategies effectively alleviate the domain bias in GZSFD tasks.

An improved industrial fault diagnosis model by integrating enhanced variational mode decomposition with sparse process monitoring method

With the continuous development of intelligent industrial processes, the sparse principal component analysis (SPCA), as a promising process monitoring method, has been widely used in the field of industrial fault detection. However, due to the inadequacy of data preprocessing and the insufficient detection accuracy for minor faults, the SPCA models exhibit obvious limitations in dealing with the processes with dynamic and temporal features. In this study, a Harris Hawk optimization method enhanced variational mode decomposition (HHO-VMD) coupled with the sliding window optimized adaptive SPCA (SWOASPCA) method is proposed to improve the fault detection performance of the SPCA models. In the HHO-VMD-SWOASPCA method, the process data is first preprocessed by adaptively and iteratively optimizing the number of modes and penalty terms in the VMD method via the Harris Hawk Optimization (HHO) method, and then the original SPCA model is combined with the sliding window method and the weight assignment strategy to enhance the model's adaptive capability and accuracy to detect minor faults. Moreover, an improved reconstruction-based contribution (RBC) method is presented to diagnose the detected faults for determining the fault causes. The effectiveness of the proposed method is verified by its application in the industrial sugar production process.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

Data imbalance bearing fault diagnosis based on fusion attention mechanism and global feature cross GAN network

Abstract As one of the important equipment of motor transmission, bearings play an important role in the production and manufacturing industry, if there are problems in the manufacturing process will bring significant economic losses or even endanger personal safety, so its state prediction and fault diagnosis is of great significance. In bearing fault diagnosis, it is a challenge to eliminate the effect of data imbalance on fault diagnosis. Generative adversarial network (GAN) networks have achieved some success in data imbalance fault diagnosis, but GAN networks suffer from sample generation bias when balancing samples. To solve this problem, fusion attention mechanism and global feature cross GAN networks is proposed. Firstly, the spatial channel fusion attention mechanism is added to the generator, so that the generator selectively amplifies and processes sample features from different regions, which helps the generator learn more representative features from a few categories; secondly, the global feature cross module is added to the discriminator, so that the discriminator efficiently extracts features from different samples, and improves its ability of recognizing the sample discrepancy; at the same time, in order to improve the model’s anti-noise ability, an anti-noise module is added to the discriminator to improve the efficiency of the model’s data imbalance fault diagnosis; finally, this paper’s method is validated by using two public bearing datasets and one self-constructed dataset. The experimental results prove that this method can effectively overcome the effect of data imbalance on GAN networks, and has a high accuracy rate in real industrial fault diagnosis tasks, what’s more, it proves that the method in this paper has a very good anti-noise performance and practical application value.

Advanced Industrial Fault Detection: A Comparative Analysis of Ultrasonic Signal Processing and Ensemble Machine Learning Techniques

Modern condition monitoring and industrial fault prediction have advanced to include intelligent techniques, aiming to improve reliability, productivity, and safety. The integration of ultrasonic signal processing with various machine learning (ML) models can significantly enhance the efficiency of industrial fault diagnosis. In this paper, ultrasonic data are analyzed and applied to ensemble ML algorithms. Four methods for reducing dimensionality are employed to illustrate differences among acoustic faults. Different features in the time domain are extracted, and predictive ensemble models including a gradient boosting classifier (GB), stacking classifier (Stacking), voting classifier (Voting), Adaboost, Logit boost (Logit), and bagging classifier (Bagging) are implemented. To assess the model’s performance on new data during experiments, k-fold cross-validation (CV) was employed. Based on the designed workflow, GB demonstrated the highest performance, with less variation over 5 cross-folds. Finally, the real-time capability of the model was evaluated by deployment on an ARM Cortex-M4F microcontroller (MCU).

IC points weight learning-based GCN and improving feature distribution for industrial fault diagnosis

Industrial fault diagnosis (FD) is crucial to detect the causes of faults in a timely manner and solve the problems to maintain stable production. Adequately considering the spatial structure of the samples and their features is an urgent problem for better FD. This paper focused on the points located in the within-class boundaries and between-class intersection region (IC points) and proposed IC point weight learning-based graph convolution network (GCN) and improving feature distribution (ICWGCN-FD) method. Firstly, ICWGCN-FD assigns appropriate weights to IC points specially allocated by graph learning layer. Secondly, a cosine distance loss term is particularly designed to make the IC points closer to the center of their respective classes and further away from their nearest neighbor points belongs to other classes. Finally, the spatial feature distribution of the overall samples is improved under the influence of the message passing function possessed by GCN, thus presenting a clearer distribution. Two cases from industrial simulation are carried out to validate the practical feasibility and superiority of ICWGCN-FD, compared with other classic and recent graph neural networks. In addition, the 3D visualization results correspond to the principles of the method, and an ablation experiment successfully verified the validity of each component.

Unsupervised transfer learning for fault diagnosis across similar chemical processes

Fault diagnosis plays a crucial role in chemical processes to prevent major accidents. Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities significantly. However, the success of deep learning diagnosis models primarily relies on access to the extensive labeled dataset of faults. Another limitation is the variation of ambient temperature and raw material components contributing to diverse operating conditions. These challenges lead to the current fault diagnosis techniques being unsuitable for universal application across different devices. In this paper, a novel unsupervised transfer learning method called multiple source domain adaptation network (MSDAN) is proposed for industrial fault diagnosis across similar chemical processes. Owing to the limited availability of fault data in industrial manufacturing, fault samples for model training are expanded via an efficient generative adversarial network variant. Categorical features of various source domains are precisely extracted through distinct channels by an integration model of Transformer and multiscale convolutional neural network. Multi-channel Domain adaptation based on polynomial kernel-induced maximum mean discrepancy aligns the joint distributions of multiple domains and facilitates subsequent classification tasks. The effectiveness and robustness of the proposed method are demonstrated through experiments conducted on the multimode Tennessee Eastman process and the real-world fluid catalytic cracking process across different units.

Dual-weight attention-based multi-source multi-stage alignment domain adaptation for industrial fault diagnosis

Abstract Cross-domain fault diagnosis is crucial for industrial applications with various and unknown operating conditions. However, due to the significant differences in the distribution of features in multiple source domains, it may lead to mutual interference of features between different domains and reduce the accuracy of diagnosis, which is a problem not considered by most current researches. In addition, most of the existing methods focus only on the extraction of low-frequency global information and cannot adequately deal with high-frequency local information. Consequently, this paper provides a multi-stage processing integrated dual-weight attention-based multi-source multi-stage aligned domain adaptation (DAMMADA) method. Global fault features that are shared by various subdomains are extracted by three domain-specific feature extractors from various domains. In a local feature extractor, the dual-weight attention module not only uses shared weights to aggregate local information, but it also uses contextual weights to improve local features. In terms of loss handling, multiple pseudo-labels are used to reduce the loss of the local maximum mean discrepancy in order to learn the domain-invariant characteristics after improving the high-frequency and low-frequency information extraction. To modify the classification boundaries, the pseudo-labels’ mean square errors are combined. Comprehensive experiments were carried out on two platforms for fault diagnosis of SCARA robots and bearings respectively, and the results demonstrated that DAMMADA is superior to other methods in terms of accuracy and its ability to suppress negative transfer for cross-domain tasks.

Learning to generalize with latent embedding optimization for few- and zero-shot cross domain fault diagnosis

Ensuring the safety and reliability of rotating machinery in modern industrial production and intelligent manufacturing is of paramount importance. While deep learning-based fault diagnosis methods offer promise, the scarcity of fault samples and the variations in distributions between training and test data due to variable working conditions make it difficult for these methods to be applied in industrial scenarios. To surmount these obstacles, we present a novel solution: learning to generalize with latent embedding optimization. Our proposed method, tailored for few-shot and zero-shot cross domain fault diagnosis, shows promise in addressing the industrial fault diagnosis problems under small samples and various working condition. The proposed method builds upon the latent embedding optimization algorithm which capitalizes on the essence of meta-learning, effectively addressing few-shot challenges. Additionally, we harness an efficient pretraining model, enhancing feature extraction and domain adaptation, effectively handling the scarcity of fault data. In tackling the cross domain issue, we introduce an innovative meta-task organization and amplify the episode training strategy within meta-learning. These enhancements empower the model to develop the ability to generalize effectively. The proposed approach is substantiated through comprehensive case studies in bearing and gearbox fault diagnosis. The results demonstrate its exceptional efficacy in both few-shot and zero-shot cross domain fault diagnosis scenarios.

A Multi-Input Convolutional Neural Network Model for Electric Motor Mechanical Fault Classification Using Multiple Image Transformation and Merging Methods

This paper addresses the critical issue of fault detection and prediction in electric motor machinery, a prevalent challenge in industrial applications. Faults in these machines, stemming from mechanical or electrical issues, often lead to performance degradation or malfunctions, manifesting as abnormal signals in vibrations or currents. Our research focuses on enhancing the accuracy of fault classification in electric motor facilities, employing innovative image transformation methods—recurrence plots (RPs), the Gramian angular summation field (GASF), and the Gramian angular difference field (GADF)—in conjunction with a multi-input convolutional neural network (CNN) model. We conducted comprehensive experiments using datasets encompassing four types of machinery components: bearings, belts, shafts, and rotors. The results reveal that our multi-input CNN model exhibits exceptional performance in fault classification across all machinery types, significantly outperforming traditional single-input models. This study not only demonstrates the efficacy of advanced image transformation techniques in fault detection but also underscores the potential of multi-input CNN models in industrial fault diagnosis, paving the way for more reliable and efficient monitoring of electric motor machinery.

STAGED: A Spatial-Temporal Aware Graph Encoder–Decoder for Fault Diagnosis in Industrial Processes

Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating it with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder-decoder (STAGED) model for industrial fault diagnosis. Firstly, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multi-series. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.

Interactive spatiotemporal LSTM approach for enhanced industrial fault diagnosis

PurposeRotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.Design/methodology/approachThe authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.FindingsThrough these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.Originality/valueThe authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

A cross-domain intelligent fault diagnosis method based on multi-source domain feature adaptation and selection

Although fault diagnosis methods integrating transfer learning are research hotspots, their ability to handle industrial fault diagnosis problems with large domain differences still needs to be improved. A multi-source domain feature adaptation and selection method is presented to address the issues of domain mismatch and domain negative transfer. The method integrates the top-level network parameter transfer strategy with the 2D convolutional neural network backbone network to acquire the target domain feature extractor quickly. Multiple feature adaptive extractors (FAEs) are constructed using a multi-branch structure to align the source and target domain’s feature distributions, respectively. The inter-domain distance computed by multi-kernel maximum mean discrepancy is embedded in the FAEs loss function to improve the inter-domain matching degree. Based on the information gain of the adaptively integrated features, the ensemble adaptive selection is performed on the extracted feature matrices to exclude the negative transfer feature. Finally, the effective feature matrix is input into the diagnosis classifier for classification. Cross-domain fault diagnosis experiments are developed based on the data set gathered from several types of rotating machinery operated under varied working conditions. The experimental results show that the proposed method outperforms the existing intelligent fault diagnosis methods in terms of fault detection accuracy, generalization, and stability.

Embedding-enhanced Graph Attention Networks for Imbalanced Industrial Fault Diagnosis

Embedding-enhanced Graph Attention Networks for Imbalanced Industrial Fault Diagnosis

Federated Generalized Zero-Sample Industrial Fault Diagnosis Across Multisource Domains

Federated Generalized Zero-Sample Industrial Fault Diagnosis Across Multisource Domains

Enhanced Security of Industrial Fault Diagnosis Systems With Synergy of Covert Attacks and Low-Overhead Defenses

Enhanced Security of Industrial Fault Diagnosis Systems With Synergy of Covert Attacks and Low-Overhead Defenses