Industrial Fault Diagnosis Research Articles

Iterative Deep Graph Learning and Embedding for Industrial Fault Diagnosis With Limited Labeled Data

Iterative Deep Graph Learning and Embedding for Industrial Fault Diagnosis With Limited Labeled Data

Fault Diagnosis of Complex Industrial Systems Based on Multi-Granularity Dictionary Learning and Its Application

Nowadays, the intelligent fault diagnosis problem of modern industrial systems has received increasing attention. However, with the increasing scale of industrial systems, the same category of faulty data often contains multiple working conditions, which leads to the multi-granularity of faulty data and the increasing complexity of data distribution. The possible existence of fine-granularity inter-class similarities in multi-granularity data can interfere with coarse-granularity recognition tasks, which brings challenges to data-driven fault diagnosis tasks. To solve the above issue, a fault diagnosis method based on multi-granularity dictionary learning is proposed in this paper. Specifically, the proposed method integrates prior knowledge into the dictionary learning framework by introducing the concept of “centroid atom”, which embeds the multi-granularity structure in the dictionary and improves the discrimination of dictionary atoms. In order to validate the proposed approach, experiments are conducted on a numerical simulation case and a zinc smelting roasting process. Compared with some state-of-the-art methods, the proposed approach performs better in the classification accuracy which shows that it can make full use of the knowledge provided by the multi-granularity structure while it is difficult to be modeled by the other methods. Therefore, it can be applied to fault diagnosis tasks more precisely and efficiently. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the fact that industrial faults often occur in different modes, and different faults may occur in the same mode, the fault data often has multi-granularity characteristics, this paper proposes a multi-granularity dictionary learning method for complex industrial fault diagnosis. The proposed method can learn the multi-granularity structure in the data, and the obtained model contains both coarse-grained and fine-grained information, which effectively improves the fault diagnosis accuracy. Intensive experimental results show that the proposed method performs better than some state-of-the-art methods, it can make full use of the knowledge provided by the multi-granularity structure, while it is difficult to be modeled by others. Above all, it is verified as suitable for fault diagnosis of real industrial systems.

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis

Domain adaptation has been widely applied in data-driven fault diagnosis tasks to address the domain shift problem between source and target data. However, conventional domain adaptation methods require both domains to be known, which is not always feasible due to privacy concerns and big data transmission. To overcome this limitation, a dedicated method called source-free domain adaptation (SFDA) has been developed to ensure reliable performance without relying on source data during target model adaptation. SFDA can achieve accurate classification tasks under domain shift problems and source data-free scenarios. We propose a generalized source model with manifold Mixup data augmentation and label smoothing techniques to avoid overfitting during the source model training. Based on this model, a novel self-training framework is proposed to implement the domain adaptation task and achieve accurate prediction performance. The experimental results from three real-world datasets demonstrate the effectiveness of the proposed approach.

Hierarchical Graph Convolutional Networks With Latent Structure Learning for Mechanical Fault Diagnosis

Industrial sensor signals are essentially non-Euclidean graph structures due to the interplay between process variables; thus, graph convolutional networks (GCNs) have been widely studied and applied. However, most of the existing GCN-based methods may suffer from two drawbacks: 1) it is difficult to characterize multiple interactions among nodes and 2) the input graph constructed from the original data may contain errors and missing edges, which will degenerate the fault diagnosis performance. To address the abovementioned issues, this article designs a hierarchical GCN with latent structure learning for industrial fault diagnosis, which can organize hierarchical networks to collaboratively improve the quality of latent graph structure, and enhanced diagnostic performance can be guaranteed. First, a high-quality updated graph is formed by incorporating the original graph with the new graph in the graph constructing layer, which can not only eliminate the adverse effects of noise and outliers but also characterize the multiple interactions among nodes. Then, the updated graph is fed into the multilayer GCN layer for better feature learning and enhances the node representation through intra- and inter-layer convolutional operations simultaneously. After that, the produced node embeddings are used to guide the latent structure learning process for optimal graph. Finally, the proposed method is verified in both the simulated and real industrial processes. The experimental results demonstrate that the new approach has better fault diagnosis accuracy and practicability than state-of-the-art methods.

Pyramid-type zero-shot learning model with multi-granularity hierarchical attributes for industrial fault diagnosis

Pyramid-type zero-shot learning model with multi-granularity hierarchical attributes for industrial fault diagnosis

Trustworthy Artificial Intelligence Based on an Explicable Temporal Feature Network for Industrial Fault Diagnosis

Trustworthy Artificial Intelligence Based on an Explicable Temporal Feature Network for Industrial Fault Diagnosis

Federated knowledge amalgamation with unbiased semantic attributes under cloud–edge collaboration for heterogeneous fault diagnosis

Federated fault diagnosis attracts increasing attention in industrial cloud–edge collaboration scenarios due to the consideration of privacy and security issues. However, statistical heterogeneity caused by non-independent and identically distributed (non-IID) fault data is a ubiquitous problem. Previous methods designed for this problem mainly apply weighted averaging or knowledge distillation to integrate biased local knowledge from clients, ignoring the discrepancy between knowledge extracted from non-IID data solely. This is prone to result in slow and unstable model convergence and global model drift. In this article, a Federated Knowledge Amalgamation (FedKA) framework is proposed to alleviate the knowledge discrepancy caused by statistical heterogeneity. To mitigate the knowledge discrepancy, we first reveal and mine unbiased public knowledge for industrial fault diagnosis, i.e., fault attributes, which describe the fault characterization and mechanism without changing with data distribution. Then, a generative mapping between unbiased fault attributes and fault data is established for clients to incorporate unbiased knowledge into the diagnostic model. Moreover, to mitigate knowledge discrepancy and overcome the global model drift, a knowledge amalgamation algorithm is designed. Through the proposed layer-wise alignment, the multi-level fault semantic knowledge that stems from fault attributes is finely exploited and aggregated. Additionally, unlike the common fixed weighting strategy, we explore an adaptive weighting mechanism through the sample-wise diagnostic confidence to further alleviate the global model drift. Extensive experiments on a real thermal power plant process demonstrate the superiority of the proposed method for federated fault diagnosis.

A Flexible Probabilistic Framework With Concurrent Analysis of Continuous and Categorical Data for Industrial Fault Detection and Diagnosis

Operational data from industrial processes typically consist of continuous and categorical variables. They reveal different aspects of the operational conditions, and both are useful for comprehensive fault detection and diagnosis (FDD). However, due to the multiple production modes, the variables usually do not follow Gaussian or Bernoulli distributions. Furthermore, their correlations can be different across normal and various faulty classes. Thus, the main challenge lies in how to properly fuse the complementary information in the two types of variables and accurately characterize the complicated distribution for each class. This work proposes a flexible probabilistic framework that can concurrently analyze continuous and categorical variables for FDD. Our framework specifies a finite mixture model for each class. Thus, it can handle non-Gaussian and non-Bernoulli variables and capture their correlations under the conditional independence assumption. We then introduce the variational inference for parameter estimation, which makes our framework adaptive to the different distributions of various classes. Furthermore, a unified statistical index is designed for two types of variables, giving our method extra capability to distinguish unknown faults. Finally, the effectiveness of our method is validated on the Tennessee Eastman (TE) process and a practical industrial plant process. The average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> score of our method is improved by 3.4/3.9 percentage points in the single-mode/multimode situation on the TE process compared with traditional mixture discriminant analysis. In the industrial plant process, when unknown faults are added, the average <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F1</i> score of our method is 91.1% and only 1.2 percentage points lower than that on the test set without unknown faults.

Domain Discrepancy-Guided Contrastive Feature Learning for Few-Shot Industrial Fault Diagnosis Under Variable Working Conditions

Recent advances in data-driven methods have significantly promoted intelligent fault diagnostics for varied industrial applications. However, due to the limitations of machine fault data and the varied scenarios in the context of industrial working conditions, existing diagnostic models can hardly achieve satisfactory results. In this study, we propose a domain discrepancy-guided contrastive feature learning framework for few-shot fault diagnosis under varied working conditions. Unlike the conventional contrastive learning paradigm using manually augmented data, a sample pairs construction is implemented based on the differences between domain distributions for data acquired under different working conditions. The similarity contrast learns the domain-invariant features from a small number of sample pairs. The learned fault features can then be used for fault identification without parameter fine-tuning. In two case studies, we validated the performance of the proposed framework with small training samples under varying speeds, loads, and significant noises. Compared with the state-of-the-art methods, the proposed solution achieved higher diagnostic accuracy for the targeted applications.

Semi-supervised variational bi-directional sampling on multi-class imbalanced electric power data for fault diagnosis

Imbalanced data is one of the inevitable problems in electric power industrial fault diagnosis, which will affect the performance of classification models. Therefore, a semi-supervised variational bi-directional sampling (SVBDS) method is proposed to balance the dataset with data-level and feature-level combined. First, a novel bi-directional sampling framework suitable for the dataset with various imbalance degrees between multi classes is built, which corresponds with the real dataset structure. This framework can separate normal data and balance the rest fault sets. Second, a two-step synthesis sampling method is proposed for minority fault categories based on generative model to obtain homo-distributed and diverse data. This method can quantitatively generate specific categories and ensure generative quality. The experimental results on the actual industrial power transformer dataset and the wind turbine dataset demonstrate the effectiveness and application prospect of the proposed method.

Semi-supervised learning for industrial fault detection and diagnosis: A systemic review

The automation of Fault Detection and Diagnosis (FDD) is a central task for many industries today. A myriad of methods are in use, although the most recent leading contenders are data-driven approaches and especially Machine Learning (ML) methods. ML algorithms fall into two main categories: supervised and unsupervised methods, depending on whether or not the instances are labeled with the expected outputs. However, a new approach called Semi-Supervised Learning (SSL) has recently emerged that uses a few labeled instances together with other unlabeled instances for the training process. This new approach can significantly improve the accuracy of conventional ML models for industrial environments where labeled data are scarce. SSL has been tested as a promising solution over the past few years for several FDD problems, although there have been no systemic reviews of this sort of approach up until the present review. In this study, an attempt to organize the existing literature on SSL for FDD using the taxonomy of van Engelen & Hoos is reported. The most and the least frequently used SSL algorithms are identified and considered in terms of different fault detection tasks and their most common dataset structure. Moreover, a set of best practices are proposed in the conclusions of this work for implementation under real industrial conditions, so as to avoid some of the most common faults.

Finding trustworthy neighbors: Graph aided federated learning for few-shot industrial fault diagnosis with data heterogeneity

Federated fault diagnosis has drawn increased attention recently, which makes use of datasets from different clients with data privacy. However, data distribution varies across clients since facilities often work under different conditions, and thus clients may have the few-shot problem on different fault type. Personalized federated learning (PFL) methods have been proposed to analyze client relationship related with data distribution and establishes multiple models suitable for different clients. Nevertheless, such relationship analysis adopts a single description, which may be disturbed by clients with the few-shot problem and cannot be adapted to new clients with new dataset and unknown client relationships. In this paper, a graph aided federated learning method with a few-shot node inhibition mechanism (GAFL) is proposed to solve the problem by finding trustworthy neighbors for all clients. Collaboration graphs are designed in pair-wise and category-wise levels to describe client relationships so that the varied data distribution under different fault type could be distinguished. After proper initialization, graphs and personalized models are trained alternately with the designed few-shot node inhibition mechanism, where information flow from neighbors with the few-shot problem are controlled and less trusted during the training process. Moreover, an incremental learning strategy is designed to address new clients, in which graphs and model updates are restricted to a small range, so that old models are less interfered. The proposed GAFL is evaluated on the real-world datasets in load bearings, demonstrating the superiority of the proposed method for few-shot fault diagnosis problem with data heterogeneity.

Federated Zero-Shot Industrial Fault Diagnosis With Cloud-Shared Semantic Knowledge Base

Recently, a considerable literature has grown up around the few-sample fault diagnosis task, in which few samples of fault data are available for model training. The lack of fault samples usually causes significant degradation of diagnosis performance, which is referred as few-sample problem. Many federated learning-based fault diagnosis methods are proposed as a popular strategy to solve this problem. However, the vast majority of these algorithms assume that few-sample categories of the local client are well-sampled in some clients. In this sense, they are unable to cope with faults that have rare or even no sample in all clients, namely global unseen faults. To diagnosis global unseen fault categories, a federated zero-shot fault diagnosis framework is proposed in this article. First, we propose a general approach for building a semantic knowledge base, which provides auxiliary discriminant descriptions of different faults. Second, a bidirectional alignment network is designed using two coupled variational autoencoders, enabling the fusion of data and attributes and allowing attribute descriptions to assist in fault diagnosis tasks. Third, a cloud-edge collaboration model aggregation strategy is developed, which utilizes generative replay mechanism and integrates the knowledge of each client, thus enhancing the generalization and generation ability of the global model. Experiments conducted on the thermal power plant group illustrate the feasibility and effectiveness of the proposed framework to categorize local and global unseen classes.

Intelligent Fault Diagnosis With Noisy Labels via Semisupervised Learning on Industrial Time Series

Deep neural networks (DNNs) excel at industrial fault diagnosis. Their performance heavily relies on the quality of human-annotated labels. Due to perception limitations of annotators, industrial time series samples (such as vibration and voltage signals) are frequently mislabeled in several conditions, such as samples with frequency domain feature differences and samples on class borders. Hence, an annotated industrial dataset will inevitably contain noisy labels at a certain level, leading to over-fitting and poor generalization of DNNs. In this work, we introduce an Industrial Noisy Label Semi-Supervised Learning (INL-SSL) fault diagnosis approach, addressing the problem that a certain number of samples in an industrial dataset are mislabeled. The proposed INL-SSL architecture simultaneously trains two DNNs, which cross-train on each other to filter noisy label errors. In particular, a fitted Gaussian mixture model divides time series samples of each DNN flow into an unlabeled set with samples likely to be noisy and a labeled set with samples likely to be clean. Given the labeled and unlabeled data, we proposed a time series MixMatch semi-supervised learning strategy to train the diagnostic model. Ablation study verifies the benefit of the proposed time series augmentation techniques for semi-supervised training. Extensive experiments on a benchmark industrial dataset of rolling element bearings (REB) reveal that the INL-SSL outperforms state-of-the-art approaches. On another self-collected REB dataset, the proposed approach also exceeds other comparison methods under noise ratios from 20% to 90%, validating the model's generalizability.

Fault Diagnosis in Industrial Control Networks Using Transferability-Measured Adversarial Adaptation Network

In recent years, the increasing number of industrial infrastructure security incidents around the world has drawn public attention to industrial control networks (ICNs) security issues. Fault diagnosis of industrial devices is an indispensable part of the security system in ICNs. The mainstream fault diagnosis models rely on long-term training and massive fault data, which results in the inability to update the model effectively and timely when the environment changes. Thus, some researchers focus on developing cross-domain industrial fault diagnosis methods. However, they usually presume that the samples of the target and source domains share the same fault mode sets, and existing prior knowledge concerning the label spaces of these two domains. These are difficult to satisfy in actual ICNs. To respond to these challenges, we develop a transferability-measured adversarial adaptation network (TAAN) to identify unknown classes without prior knowledge. It embeds the hybrid transferability estimation into an adversarial domain adaptive network to weigh the contribution of each sample. In this way, TAAN can properly classify samples in a public label space by selectively aligning source and target samples with high transferability. The experimental results obtained using two diagnosis datasets prove that the developed TAAN can achieve satisfactory diagnostic accuracy by effectively bridging the distribution discrepancy under various working conditions.

Semantic-Consistent Embedding for Zero-Shot Fault Diagnosis

In the traditional fault diagnosis task, it is difficult to collect training samples to exhaust all fault classes. There are massive target faults that can not be collected in advance, which may restrict the performance of fault diagnosis methods. In this paper, a novel method named semantic-consistent embedding (SCE) is proposed for zero-shot industrial fault diagnosis. SCE tries to classify unseen class faults only by using seen class faults for training. The fault samples and their human-specified attribute vectors are embedded into a semantic-consistent space and then reconstructed from that space. A specific <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Barlow matrix</i> is designed to measure the consistency between the embedding of fault samples and the embedding of attribute vectors. The diagonal elements and the off-diagonal elements of the Barlow matrix encode the within-dimension consistency and between-dimension consistency of the cross-modal embeddings, respectively. Through optimizing the Barlow matrix to an identity matrix, SCE learns a significant space where the cross-modal embeddings have consistent representation while reducing the redundant components. Extensive experiments show that SCE gets significant superiority on the three-phase transmission system (26.9% gains) and the Tennessee Eastman process (15.5% gains). Moreover, SCE even gets competitive results with supervised learning methods. The code of SCE can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/htz-ecust/Semantic-consistent-Embedding</uri> .

Development of an IOT Based Industrial Fault Detection and Diagnosis System

This paper has proposed an IoT based industrial fault detection and diagnosis system. The IoT (Internet of things) based industry protection system employing Arduino is intended to safeguard industries from losses brought on by accidents and monitoring the faults. Industrial accidents such as gas leakage causes fire resulting in significant industrial setbacks. Due to furnace explosions, electrical short circuits, or other circumstances, quick fire detection is also required. The proposed system uses flame sensor along with gas sensor to detect fire as well as gas leakage to avoid any industrial mishaps and prevent economic damage. The system consists of temperature detector to detect the high/low temperature, the phase fault detector (either single or three phases), voltage controller (high/low), and frequency monitoring. Flame and Gas sensors are also interfaced with arduino and LCD screen. The sensor data is constantly scanned to record values and check for fire, gas leakage and then this data is transmitted to online. The wifi module is used to achieve internet functionality. GSM module is sent message to specific numbers in case human operators aren’t present in online always. IP camera monitor the whole system from anywhere in the world.

Deep Transfer Learning Models for Industrial Fault Diagnosis Using Vibration and Acoustic Sensors Data: A Review

In order to evaluate final quality, nondestructive testing techniques for finding bearing flaws have grown in favor. The precision of image processing-based vision-based technology has greatly improved for defect identification, inspection, and classification. Deep Transfer Learning (DTL), a kind of machine learning, combines the superiority of Transfer Learning (TL) for knowledge transfer with the benefits of Deep Learning (DL) for feature representation. As a result, the discipline of Intelligent Fault Diagnosis has extensively developed and researched DTL approaches. They can improve the robustness, reliability, and usefulness of DL-based fault diagnosis techniques (IFD). IFD has been the subject of several thorough and excellent studies, although most of them have appraised important research from an algorithmic standpoint, neglecting real-world applications. DTL-based IFD strategies have also not yet undergone a full evaluation. It is necessary and imperative to go through the relevant DTL-based IFD publications in light of this. Readers will be able to grasp the most cutting-edge concepts and develop practical solutions to any IFD challenges they may encounter by doing this. The theory behind DTL is briefly discussed before describing how transfer learning algorithms may be included into deep learning models. This research study looks at a number of vision-based methods for defect detection and identification utilizing vibration acoustic sensor data. The goal of this review is to assess where vision inspection system research is right now. In this respect, image processing as well as deep learning, machine learning, transfer learning, few-shot learning, and light-weight approach and its selection were explored. This review addresses the creation of defect classifiers and vision-based fault detection systems.

HSELL-Net: A Heterogeneous Sample Enhancement Network With Lifelong Learning Under Industrial Small Samples.

Small sample size leads to low accuracy and poor generalization of industrial fault diagnosis modeling. Domain adaptation (DA) attempts to enhance small samples by transferring samples in other similar domains, but it has limited application in industrial fault diagnosis, since the differences in working conditions lead to large variations of fault samples. To address the above issues, this article proposes a heterogeneous sample enhancement network with lifelong learning (HSELL-Net). First, a heterogeneous DA subnet (HDA-subnet) is presented, in which the designed heterogeneous supporting domain ensures dimension alignment and the designed distribution jointly matching improves the performance of distribution matching; thus, fault samples from other working conditions can be employed to reliably enhance small samples. Second, a lifelong learning subnet (LL-subnet) is designed, in which the proposed Admixup and shared knowledge repository enable incremental samples to further enhance small samples without retraining the network. The two subnets are mutually embedded and reinforced to enhance the number and types of small samples; thus, the accuracy and generalization of fault diagnosis under industrial small samples are improved. Finally, benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed method. Experimental results show the HSELL-Net outperforms the existing works under industrial small samples.