Intelligent Fault Diagnosis Technology Research Articles

Enhancing equipment safeguarding in IIoT: A self-supervised fault diagnosis paradigm based on asymmetric graph autoencoder

Thanks to the sufficient monitoring data provided by Industrial Internet of Things (IIoT), intelligent fault diagnosis technology has demonstrated remarkable performance in safeguarding equipment. However, the effectiveness of existing methods heavily relies on manually labeled data. Unfortunately, data collected from equipment often lacks labels, leading to a scarcity of fault data. Furthermore, an additional significant challenge is the feature domain shift resulting from speed variation. To address this, we propose a self-supervised paradigm based on an asymmetric graph autoencoder for fault diagnosis under domain shift, aiming to mine valuable health information from unlabeled data. Unlike Euclidean-based methods, the proposed method transforms time series samples into graphs and extracts domain invariant features through information interaction between nodes. To efficiently mine unlabeled data and enhance generalization, the self-supervised learning paradigm utilizes an asymmetric graph autoencoder architecture. This architecture includes an encoder that learns self-supervised representations from unlabeled samples and a lightweight decoder that predicts the original input. Specifically, we mask a portion of input samples and predict the original input from learned self-supervised representations. In downstream task, the pre-trained encoder is fine-tuned using limited labeled data for specific fault diagnosis task. The proposed method is evaluated on three mechanical fault simulation experiments, and the results demonstrate the its superiority and potential.

Research on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism

Abstract Utilizing computer technology to realize the application of ferrographic intelligent fault diagnosis technology is a foundational investigation to oversee the operations of mechanical equipment. To continuously improve the accuracy of artificial intelligence recognition, the complexity and computation of the model will be increased. The proposal of the transformer model (the core technology of chatgpt) has fundamentally changed the intelligence level of artificial intelligence, but it has also greatly increased the demand for computer computing power. What's more, it is difficult to equip industrial quality inspection sites with high computing power computers. The channel overlapping technique developed in this paper is a technology to segment the three channels of image information and reserve overlapping areas for an information communication mechanism. With this mechanism, the model location channel overlapping convolutional neural network can obtain high recognition accuracy by using only one-half of the original training computing power. When channel overlapping combines with no position information, information fusion is formed. The model channel overlapping technique fusion convolutional neural network established by the information fusion mechanism will get a higher prediction accuracy through joint training with the original image. However, the computation consumption is nearly one-third of the pure traditional convolutional neural network algorithm.

A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples

Rotating machinery is a critical equipment widely used in nuclear power plants (NPPs). Effective fault diagnosis technology can provide reliable operation and maintenance support for rotating machinery. Data-driven intelligent fault diagnosis technology has attracted much attention in recent years. However, in practical situations, the available fault data is limited, and the imbalanced samples will make the intelligent fault diagnosis model prone to problems such as poor generalization performance and low diagnostic accuracy. Therefore, a fault diagnosis method based on deep learning is proposed to alleviate the impact of imbalanced samples on fault diagnosis of rotating machinery. First, the adaptive synthetic sampling (ADASYN) approach is used to synthesize the multi-channel vibration data to expand the unbalanced samples. Subsequently, ensemble empirical mode decomposition (EEMD) and continuous wavelet transform (CWT) are used to convert the vibration data into time–frequency images to highlight the fault features of the samples. Then, the deep residual neural network is constructed to extract the features of mixed samples and implement fault diagnosis. Fault simulation experiments are carried out based on motors and bearings, and various imbalance degree datasets are formed to provide data support for verifying the effectiveness of the proposed method. The proposed method can achieve good diagnostic performance under various degrees of imbalanced samples. In addition, the comparison results with other methods show that the proposed method has the best comprehensive performance, demonstrating the potential application value in the fault diagnosis of NPPs rotating machinery.

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

Research on intelligent algorithm-based power system fault prediction and diagnosis technology

A novel intelligent identification approach based on modified hierarchical diversity entropy and extension theory for diagnosis of rotating machinery faults

Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance.

Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review

As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.

Fault Diagnosis of Marine Diesel Engines under Partial Set and Cross Working Conditions Based on Transfer Learning

The development of intelligent ships has an urgent demand for intelligent fault diagnosis technology. The working conditions and fault modes of high-power marine diesel engines gradually tend to be diversified and complicated, and the problems of reliability and safety are becoming more and more prominent. There are a lot of working condition data that lack fault labels, and the fault modes are asymmetric among different working conditions, so it is urgent to study effective fault diagnosis methods. Taking a marine diesel engine as the case validation object, we set up cross-condition and partial set fault diagnosis scenarios, proposed transferring knowledge from the source condition to the target condition for the problem of the lack of fault labels in the target condition, and designed a multi-scale and multi-view domain adversarial network (MMDAN) method for experimental validation using 6S50MC-C7 marine diesel engine system operation data. According to the experimental results, the average diagnostic accuracy of this method reached 96.58%, with a short processing time. Furthermore, it exhibits superior diagnostic performance compared to other transfer learning models in the cross-condition partial set transfer task. Additionally, the method proposed in this paper also offers a new approach and reference for the intelligent diagnosis of other equipment in ships.

BesNet: binocular ferrographic image recognition model based on deep learning technology

PurposeUsing computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.Design/methodology/approachThis paper proposes a binocular image classification model to solve ferrographic image classification problems.FindingsThis paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.Originality/valueThe work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

Research on Network Equipment Fault Detection Based on Fault Tree Analysis

Intelligent fault diagnosis technology has strong robustness and fault tolerance, and has good adaptability to the dynamic changes of the system. Network device fault detection ensures proper running of network devices and ensures network security and reliability. Fault tree analysis method comes from the field of digital electronic technology. Based on fault tree analysis, this paper studies the fault detection of network equipment. Firstly, the fault tree modeling is studied, the mathematical model of the fault tree is constructed, and the fault tree analysis process is designed. Then, the qualitative analysis of the fault tree is studied to find out all the possible factors that cause the fault, and to predict the possible fault of the equipment. Finally, the quantitative analysis of fault tree is studied to obtain the importance of fault tree. According to the importance of different minimum cut sets, it provides a basis for fault detection when network equipment fails.

Research Status and Development Trend of Underground Intelligent Load-Haul-Dump Vehicle—A Comprehensive Review

The underground intelligent load-haul-dump vehicle (LHD) is a product of the deep integration of traditional LHD with information network technology, automatic controlling and artificial intelligence technology. It gathers the functions of environmental perception, autonomous driving and fault diagnosis in one machine and exhibits higher safety and greater efficiency than traditional LHD. Hence, it is a particularly important piece of underground mining equipment for building green, safe and smart mines. Taking the studies about intelligent LHD collected by CNKI and WOS databases from 1980 to 2022 as a sample data source, employing Citespace visual analysis software for key feature extraction from the documents, statistical analysis was conducted to clarify the current research progress and the frontier topics of the intelligent LHD academia in the past 40 years, in relation to the future development trends. The development history and application status of underground intelligent LHD was expounded in this article, summarizing the research status at home and abroad from four aspects: ore heap perception and modeling technology, trajectory planning method of bucket shoveling, autonomous navigation technology, real-time monitoring and intelligent fault diagnosis technology. The demerits and merits of the technologies were reviewed as well, with future developing and researching trends of the underground intelligent LHD concluded.

A novel unsupervised directed hierarchical graph network with clustering representation for intelligent fault diagnosis of machines

Intelligent fault diagnosis technology, as a promising approach, is gradually playing an irreplaceable role in ensuring the safety, reliability, and efficiency of mechanical equipment. However, in real-world industrial scenarios, obtaining adequate high-quality label information is typically challenging and unrealistic, resulting in the performance degradation of most existing supervised learning-based diagnosis models, and necessitating the development of unsupervised intelligent diagnostic models. In addition, the sample independence hypothesis is widely used in existing studies, which significantly ignores the further mining of relevant auxiliary information between samples and its positive effect on performance improvement. To overcome these challenges, a novel intelligent fault diagnosis framework, called the convolutional capsule auto-encoder-based unsupervised directed hierarchical graph network with clustering representation (CCAE-UDHGN-CR), is established and employed in unlabeled scenarios. First, a novel convolutional capsule auto-encoder (CCAE), which combines reconstruction loss and semantic clustering loss, is constructed and used to extract deep coding features that contain attribute information of the sample itself. Then, with the assistance of cosine similarity measurement strategy, the internal correlation between samples is fully mined, and on this basis, the conversion of deep coding features to the graph sample set is realized, which serves as the input of the subsequent unsupervised directed hierarchical graph network (UDHGN). Finally, the deep representation features extracted by the UDHGN are further fed into the density-based spatial clustering of applications with noise (DBSCAN) model to complete the determination of category information. A total of three cases based on key functional components and manipulator are employed for performance verification. The comprehensive diagnosis results all show that the proposed CCAE-UDHGN-CR model can effectively alleviate the dependence on label information while maintaining excellent diagnosis performance.

Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles

Recent intelligent fault diagnosis technologies can effectively identify the machinery health condition, while they are learnt based on a closed-world assumption, i.e., the training and testing data follow independently identically distribution (IID). However, in real-world diagnosis, the monitored samples are often from unknown distributions, such as unseen machine faults, leading to an out-of-distribution (OOD) problem. This is a challenging issue that may induce the model to produce unreliable and unsafe decision for unforeseen machine data. To tackle this problem, a novel OOD detection-assisted trustworthy machinery fault diagnosis approach is developed to enhance the reliability and safety of intelligent models. First, multiple deep neural networks are integrated to establish an ensemble diagnosis system, called deep ensembles. Then, the trustworthy analysis with uncertainty-aware deep ensembles is conducted to detect the OOD samples and issue the warnings for the potential untrustworthy diagnosis. A selection criterion of uncertainty threshold is given. Finally, the trustworthy decisions are achieved by comprehensively considering the deep ensembles’ prediction and uncertainty. The proposed trustworthy fault diagnosis approach is validated in two case studies, exhibiting significant advantages for diagnosing OOD samples.

Research on controllable deep learning of multi-channel image coding technology in Ferrographic Image fault classification

Utilizing computer technology to realize ferrographic analysis and intelligent fault diagnosis technology is fundamental research to ensure the normal operation of precision, complex and severe marine mechanical equipment. This work is focused on the issues that affect the feature extraction and accuracy of the computer model, such as fuzzy edge and complex surface texture of wear particle image in ferrographic image. This research creatively establishes a new multi-channel image encoder, which artificially encodes the information of the original ferrographic image. The new encoding image includes the original channel information, artificial edge enhancement information, and artificial surface enhancement information, to improve the edge and surface features of the image. The encoder can not only improve the visible edge and surface characteristics of the picture but also form the MCECNN model after connecting to the convolutional neural network. The model can successfully improve the accuracy, convergence speed, generalization, and controllability of the model, to solve the problem of online intelligent recognition in the field of ferrography to the greatest extent.

Optimized Fault Diagnosis Algorithm under GAN and CNN Hybrid Model

The network fault diagnosis algorithm is one of the most important algorithms to solve network transmission problems. In traditional network fault diagnosis, it mainly analyzes and troubleshoots the fault manually by comparing the alarm information of the network performance index with the expert experience database. Diagnosis methods based on manpower analysis will take up much material resources and manpower and increase maintenance costs. Therefore, there is an urgent need for a more efficient and intelligent fault diagnosis technology. In addition, as the current network is becoming more and more complex, and based on this consideration, a semi-supervised fault diagnosis algorithm is studied in this paper. It is a combination of the GAN and CNN models. Meanwhile, the method of combining relief and mutual information is applied to reducing the dimensionality of network feature parameters, and the optimal feature combination is selected. The fluctuations in the convergence of the generated confrontation network model are stabilized. Moreover, the simulation software is used to build the heterogeneous wireless network scenario studied. Meanwhile, an improved fault diagnosis model is constructed to verify that in the case of both GAN and CNN models, the accuracy of fault diagnosis algorithm can reach 98.6%, which is significantly higher than other comparative analysis methods. It has contributed to ensuring the user’s service experience and reducing the cost of network maintenance and operation.

A Lightweight Model for Bearing Fault Diagnosis Based on Gramian Angular Field and Coordinate Attention

The key to ensuring rotating machinery’s safe and reliable operation is efficient and accurate faults diagnosis. Intelligent fault diagnosis technology based on deep learning (DL) has gained increasing attention. A critical challenge is how to embed the characteristics of time series into DL to obtain stable features that correlate with equipment conditions. This study proposes a lightweight rolling bearing fault diagnosis method based on Gramian angular field (GAF) and coordinated attention (CA) to improve rolling bearing recognition performance and diagnosis efficiency. Firstly, the time domain signal is encoded into GAF images after downsampling and segmentation. This method retains the temporal relation of the time series and provides valuable features for DL. Secondly, a lightweight convolution neural network (CNN) model is constructed through depthwise separable convolution, inverse residual block, and linear bottleneck layer to learn advanced features. After that, CA is employed to capture the long-range dependencies and identify the precise position information of the GAF images with nearly no additional computational overhead. The proposed method is tested and evaluated by CWRU bearing dataset and experimental dataset. The results demonstrate that the CNN based on GAF and CA (GAF-CA-CNN) model can effectively reduce the calculation overhead of the model and achieve high diagnostic accuracy.

Intelligent Fault Diagnosis of Photoelectric Pod Bearing Based on Multi-information Fusion

Intelligent fault diagnosis technology has become the focus of research in various fields. Its realization depends on the acquisition of equipment state by sensors. Because the fault information provided by a single sensor has limitations and cannot fully reflect the fault state of the tested object, we need to use multiple sensors to collect and fuse the fault information of rolling bearings to ensure the accuracy and accuracy of intelligent fault diagnosis. Based on this, this paper analyzes the application of fuzzy rules of multi-sensor information fusion technology in the fault diagnosis of bearings in the optoelectronic pod, so as to provide a reference for the realization of intelligent fault diagnosis of each structure in the optoelectronic pod.

Research on Intelligent Fault Diagnosis Technology of Power Electronic Circuit

With the rapid development of economy, power electronics are widely used in many fields. The use of these power electronic products, the development of many industries and fields in China have played a great role in promoting, but also effectively improve the quality of people's living standards. It is undeniable that the use of power electronic products improves the production efficiency of various industries to a certain extent, and promotes a series of industrial chain reform. But we also need to realize that there are still a series of problems in the use of power electronics, which greatly affect the daily production. In many problems, the problems caused by the fault of nonlinear circuit and tolerance circuit are focused on and studied by us. Because the impact of these two failures is greater, and generally difficult to solve. Only by properly solving these faults can we play a better role in power electronic circuits and ensure the smooth use of products. This paper takes intelligent fault diagnosis technology as the basic argument, analyzes its application in power electronics, and puts forward some suggestions, hoping to provide some help to relevant personnel.

Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

Rotating machinery fault diagnosis is very important for industrial production. Many intelligent fault diagnosis technologies are successfully applied and achieved good results. Due to the fact that machine damages usually happen under different working conditions, and manual scale labeled data are too expensive, domain adaptation has been developed for fault diagnosis. However, the current methods mostly focus on global domain adaptation, the application of subdomain adaptation for fault diagnosis is still limited. A deep transfer learning method is proposed for rotating machinery fault diagnosis in this study, where subdomain adaptation and adversarial learning are introduced to align local feature distribution and global feature distribution separately. Experiments are performed on two rotating machinery datasets to verify the effectiveness of this method. The results reveal that this method has outstanding mutual migration ability and can improve the diagnostic performance.

Intelligent Fault Diagnosis of Machines Based on Adaptive Transfer Density Peaks Search Clustering

Intelligent fault diagnosis technology of the rotating machinery is an important way to guarantee the safety of industrial production. To enhance the accuracy of autonomous diagnosis using unlabelled mechanical faults data, a novel intelligent diagnosis algorithm has been developed for rotating machinery based on adaptive transfer density peak search clustering. Combined with the wavelet packet energy feature extraction algorithm, the proposed algorithm can enhance the computational accuracy and reduce the computational time consumption. The proposed adaptive transfer density peak search clustering algorithm can adaptively adjust the classification parameters and mark the categories of unlabelled experimental data. Results of bearing experimental analysis demonstrated that the proposed technique is suitable for machinery fault diagnosis using unlabelled data, compared with other traditional algorithms.

Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG-CNN) for Bearing Fault Diagnosis

In recent years, intelligent fault diagnosis technology with deep learning algorithms has been widely used in industry, and they have achieved gratifying results. Most of these methods require large amount of training data. However, in actual industrial systems, it is difficult to obtain enough and balanced sample data, which pose challenges in fault identification and classification. In order to solve the problems, this paper proposes a data generation strategy based on Wasserstein generative adversarial network and convolutional neural network (WG-CNN), which uses generator and discriminator to conduct confrontation training, expands a small sample set into a high-quality dataset, and uses one-dimensional convolutional neural network (1D-CNN) to learn sample characteristics and classify different fault types. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that the proposed method has obvious and satisfactory fault diagnosis effect with 100% classification accuracy for few-shot learning. In different noise environments, this method also has excellent performance.