Fault Diagnosis Technology Research Articles

Development of All Solid State Nanosecond Pulse Transformer Fault Detection Device Based on Marx Circuit

Abstract a nanosecond pulse generator with a fast leading edge for transformer fault diagnosis is developed. The device is composed of a full solid-state nanosecond pulse generator based on Marx circuit and with a tail cut switch and a field programmable logic gate array (FPGA) device. Among them, the nanosecond pulse source is mainly composed of DC power supply, control circuit and main circuit. The main circuit is a 9-level modular Marx circuit, using MOSFET as the main switch and tail cut switch; The control circuit generates a synchronous trigger pulse signal, which drives the MOSFET to work synchronously after being isolated through the optical fiber. The output nanosecond pulse voltage parameters are adjustable amplitude 0-8kv, pulse width 100˜1000ns, repetition frequency 1hz˜1000hz, rising edge 30ns and falling edge 40ns. The transformer fault diagnosis platform is built, and the transformer off-line and on-line fault diagnosis is carried out, which paves the way for further exploration of transformer fault diagnosis technology.

Deep conditional adversarial subdomain adaptation network for unsupervised mechanical fault diagnosis

In recent years, intelligent fault diagnosis technology has rapidly advanced, achieving remarkable results. Most of them assume that the source and target domains have similar distributions. However, the actual working conditions of rotating machinery are variable, leading to a significant distribution discrepancy between the source and target domains. The majority of existing domain adaptation methods mainly consider either the marginal distribution or the conditional distribution between the source and target domains, which limits the improvement of diagnostic performance in the target domain. Considering the discrepancy in domain distributions, a deep conditional adversarial subdomain adaptation network (DCASAN) is proposed in this paper for unsupervised mechanical fault diagnosis under unknown working conditions. In DCASAN, a backbone network based on the feature fusion of convolutional neural network (CNN) and graph convolutional network (GCN) is designed for feature extraction. Furthermore, a conditional adversarial subdomain adaptation strategy is proposed, which simultaneously considers both the marginal distribution and the conditional distribution. Under the joint constraints of marginal and conditional distribution alignment, the extracted features exhibit improved inter-class discriminability and intra-class consistency, effectively reducing the distribution discrepancy between different domains. Results from multiple transfer tasks on three rotating machinery datasets demonstrate that the proposed method can learn rich transferable features. The overall average accuracy of the proposed method on these three datasets is 88.07%, 89.17%, and 96.63%, respectively, significantly outperforming other methods in terms of robustness and generalization.

Fault Diagnosis of Hydropower Units Based on Gramian Angular Summation Field and Parallel CNN

To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model’s accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units.

Research on Mechanical Fault Diagnosis and Prediction Technology Based on Deep Learning

Research on Mechanical Fault Diagnosis and Prediction Technology Based on Deep Learning

Research on Online Monitoring and Fault Diagnosis Technology of Intelligent Electrical Equipment

Research on Online Monitoring and Fault Diagnosis Technology of Intelligent Electrical Equipment

MBRB: Micro-belief rule Base model based on cautious conjunctive rule for interpretable fault diagnosis

—In aerospace engineering, implementing interpretable fault diagnosis technology is critical for improving the credibility of diagnostic results and ensuring the reliability of complex electromechanical systems. As a typical interpretable modeling method, the belief rule base (BRB) approach still faces two challenges that need to be addressed for its application in interpretable fault diagnosis: 1) The lengthy rule structures raise the cost of model interpretability, and 2) over-optimization of parameters diminishes the interpretability. To solve these problems, this paper proposes a modified micro-belief rule structure and develops a new micro-belief rule base (MBRB) model based on this structure. In addition, considering the independence of rules and the correlations between multiple BRBs, a cautious conjunctive rule-based reasoning process is established as the inference engine of MBRB. Moreover, an interpretable optimization method based on projection covariance matrix adaptive evolutionary strategy (PCMAES-I) is proposed for the MBRB model to balance model interpretability and diagnostic accuracy. Finally, the availability and effectiveness of the proposed MBRB is verified through an electromagnetic relay experiment.

Vehicle Engine Fault Diagnosis Approach Based on a Decision Tree and Knowledge Base

Vehicle Engine Fault Diagnosis Approach Based on a Decision Tree and Knowledge Base

Dynamic simulation-assisted Gaussian mixture alignment approach for fault diagnosis of rotation machinery under small samples

Abstract Obtaining a substantial number of actual samples for rotating machinery in an industrial setting can be challenging, particularly when faulty samples are acquired under hazardous working conditions. The issue of insufficient samples hinders the effective training of reliable fault diagnosis models, impeding the industrial implementation of advanced intelligent methods. This study proposes an innovative dynamic simulation-assisted Gaussian mixture alignment model (DSGMA) to address the challenge of applying fault diagnosis technologies, with its performance mined by advanced transfer algorithms. Specifically, we establish a fault dynamics model for rotating machinery and acquire a substantial amount of simulated data as the source domain to facilitate the training of the deep neural network model. Subsequently, we propose a Gaussian mixture-guided domain alignment approach that assigns a domain-independent Gaussian distribution to each category as prior knowledge, with the parameters calculated using limited actual samples. Diagnostic knowledge is transferred from the source domain to the target domain by minimizing the Kullback–Leibler divergence between the features of the simulated samples and the Gaussian mixture priors. Furthermore, the DSGMA model incorporates Gaussian clustering loss to augment the clustering capability of samples belonging to the same category from real devices and enhances the computational stability of the parameters in the Gaussian mixture model. The efficacy of the DSGMA method is validated using three publicly available datasets and compared against five widely adopted methods. The experimental findings illustrate that DSGMA exhibits superior diagnostic and robust capabilities, facilitating efficient fault diagnosis under scenarios of small samples.

Research on Virtual Simulation Experiment Teaching of Scraper Conveyor Condition Monitoring and Fault Diagnosis

The scraper conveyor status monitoring and fault diagnosis virtual simulation experiment system is designed and developed based on the operating environment of the scraper conveyor in the fully mechanized mining face. Using augmented virtual reality teaching resources, through immersive display and interactive operation experiments, students are guided on basis of scraper conveyor mechanical cognition, master the virtual teaching experiment of state monitoring and fault diagnosis such as broken chain monitoring, motor fault classification and identification, reducer compound fault separation, etc. The teaching practice shows that the system can overcome the harsh production environment in coal mines and the risk factors caused by equipment failure, improve students' understanding of the mechanical fault diagnosis technology, and mobilize students' enthusiasm for experiments and initiative to enhance students' innovative ability.

Intelligent fault diagnosis of machinery based on hybrid deep learning with multi temporal correlation feature fusion

AbstractWith the advent of intelligent manufacturing era, higher requirements are put forward for the fault diagnosis technology of machinery. The existing data‐driven approaches either rely on specialized empirical knowledge for feature analysis, or adopt single deep neural network topology structure for automatic feature extraction with compromise of certain information loss especially the time‐series information's sacrifice, which both eventually affect the diagnosis accuracy. To address the issue, this paper proposes a novel multi‐temporal correlation feature fusion net (MTCFF‐Net) for intelligent fault diagnosis, which can capture and retain time‐series fault feature information from different dimensions. MTCFF‐Net contains four sub‐networks, which are long and short‐term memory (LSTM) sub‐network, Gramian angular summation field (GASF)‐GhostNet sub‐network and Markov transition field (MTF)‐GhostNet sub‐network and feature fusion sub‐network. Features of different dimensional are extracted through parallel LSTM sub‐network, GASF‐GhostNet sub‐network and MTF‐GhostNet sub‐network, and then fused by feature fusion sub‐network for accurate fault diagnosis. Two fault diagnosis experimental studies on bearings are implemented to validate the effectiveness and generalization of the proposed MTCFF‐Net. Experimental results demonstrate that the proposed model is superior to other comparative approaches.

Coupling mechanical model and failure of aeroengine ceramic matrix composite based on genetic algorithm

In order to understand the coupling mechanical model of aeroengine ceramic matrix composite, the author proposed a coupling mechanical model and failure study of aeroengine ceramic matrix composite based on genetic algorithm. The author first analyzed that the real-time model of an aircraft engine and the matching accuracy of the engine directly affect the accuracy of aircraft engine fault diagnosis, a least squares support vector regression (AGA LSSVR) method based on adaptive genetic algorithm was proposed to modify the real-time model of an aircraft engine, effectively improving the matching accuracy of the model. Secondly, the impact of parameter selection in least squares support vector machines on model correction was analyzed, and an adaptive genetic algorithm was used to search for the optimal parameters in the parameter selection space. Finally, compare the correction effects of methods such as (BP) neural network, support vector regression machine, AGA LSSVR in airborne models. The results indicate that: The proposed AGA LSSVR has good correction accuracy, which verifies the effectiveness of the correction model.

DCSN: Focusing on hard samples mining in small-sample fault diagnosis of marine engine

Fault samples of marine engine are extremely scarce, and there are unavoidably some hard samples with small inter-class differences, which pose a serious challenge to fault diagnosis of marine engines. This paper proposes a deep metric learning method, namely deep concentric Siamese network (DCSN), to apply strong forces to hard samples towards their corresponding correct distribution areas under small-sample conditions. First, DCSN is committed to learn discriminative information from limited fault samples through a carefully designed metric learning strategy. Then, DCSN distinguishes hard samples using inner and outer boundaries, and applies strong forces to them, making the deep model more focusing on the correct classification of hard samples. Third, DCSN shrinks the distribution area of intra-class samples, which improves intra-class compactness and inter-class separability. Finally, the experimental results on the marine engine fault dataset show that the proposed DCSN yields higher diagnostic performance compared to the considered competitive methods.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

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 rotor fault diagnosis technology of three-phase asynchronous motor based on NA-MEMD mutual information and SVM

Aiming at the problem of mode aliasing in the adaptive decomposition of nonlinear and non-stationary current signals generated by three-phase asynchronous motor faults, and the fault features contained in signals collected by a single sensor can not be accurately and comprehensively extracted and characterized when early rotor bar breakage and air gap eccentricity faults occur, A fault diagnosis method for three-phase asynchronous motor based on noise assisted multivariate empirical mode decomposition (NA-MEMD) and mutual information is proposed. Firstly, the NA-MEMD algorithm is used to decompose the three-phase stator current signal of the asynchronous motor to obtain multi-scale intrinsic mode functions (IMFs). Then, the correlation algorithm is used to screen the IMFs containing useful information. Then, the filtered IMF components are reconstructed into new signals and their features are extracted, Finally, support vector machines (SVM) are used to identify the rotor broken bars and air gap eccentric faults of the three-phase asynchronous motor. The experimental results show that the NA-MEMD method has a higher recognition rate than the traditional empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD) methods.

DA-VICReg: a data augmentation-free self-supervised learning approach for diesel engine fault diagnosis

Self-supervised learning (SSL) aims to extract useful representations from unlabeled data by maximizing the agreement between positive pairs. However, traditional SSL relies on carefully designed data augmentation methods to generate positive pairs. When dealing with 1D vibration signals, data augmentation prone to potentially compromise the fault information in the original signals. Therefore, this paper proposes a data augmentation-free SSL framework for diesel engine fault diagnosis called Domain Adaptation Variance Invariance Covariance Regularization (DA-VICReg). The DA-VICReg uses cyclic angular vibrations collected within the same time period as positive pairs and extracts useful features from unlabeled data using a loss function composed of three terms: Variance, Invariance, and Covariance. We found that when positive pairs originate from different operating conditions, such as varying speeds and torques, the model can develop feature extraction capabilities that remain unaffected by changes in operating conditions. In addition, a spatial pyramid pooling layer and a trilinear attention module are used to extract vibration features at different scales and focus on critical spatial locations and channels. Finally, the proposed approach was validated through experiments on two types of diesel engines, and a comparison with prominent SSL methods confirms the superiority of the proposed approach. In engineering practice, this method can utilize a large amount of signals stored in different time periods for self-supervised training and learn useful features for downstream fault diagnosis tasks.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

Data-driven fault diagnosis of PEMFC water management with segmented cell and deep learning technologies

Timely and effective fault diagnosis is crucial to guarantee the reliability and durability of proton exchange membrane fuel cell (PEMFC). Segmented cell technology has been proven to be a powerful tool for exploring the internal characteristics of fuel cells, yet it is seldom applied for fault diagnosis. This paper proposes a data-driven fault diagnosis method that combines segmented cell and deep learning technology for PEMFC water management fault diagnosis. The dynamic characteristics of the current distribution of the fuel cell under different levels of faults are explored with segmented cell technology. A dual-input convolutional neural network is applied to combine current distribution data and sensor data for a more comprehensive system status for fault diagnosis. The results show that the model can efficiently identify flooding and drying faults with more than 98.5% accuracy, faster and more accurately than other algorithms. This work highlights the significance of segmented cell technology for the comprehension of water management inside PEMFC and provides a new concept for PEMFC fault diagnosis.

A Multiple Attention Convolutional Neural Networks for Diesel Engine Fault Diagnosis.

Fault diagnosis can improve the safety and reliability of diesel engines. An end-to-end method based on a multi-attention convolutional neural network (MACNN) is proposed for accurate and efficient diesel engine fault diagnosis. By optimizing the arrangement and kernel size of the channel and spatial attention modules, the feature extraction capability is improved, and an improved convolutional block attention module (ICBAM) is obtained. Vibration signal features are acquired using a feature extraction model alternating between the convolutional neural network (CNN) and ICBAM. The feature map is recombined to reconstruct the sequence order information. Next, the self-attention mechanism (SAM) is applied to learn the recombined sequence features directly. A Swish activation function is introduced to solve "Dead ReLU" and improve the accuracy. A dynamic learning rate curve is designed to improve the convergence ability of the model. The diesel engine fault simulation experiment is carried out to simulate three kinds of fault types (abnormal valve clearance, abnormal rail pressure, and insufficient fuel supply), and each kind of fault varies in different degrees. The comparison results show that the accuracy of MACNN on the eight-class fault dataset at different speeds is more than 97%. The testing time of the MACNN is much less than the machine running time (for one work cycle). Therefore, the proposed end-to-end fault diagnosis method has a good application prospect.

Compound fault diagnosis of diesel engines by combining generative adversarial networks and transfer learning

In order to solve the problem of compound fault diagnosis of diesel engine fuel injection system under the condition of few samples, a comprehensive diagnosis method based on generative adversarial networks and transfer learning based multi label classification models is proposed in this paper, which is based on convolutional attention networks. The generative adversarial networks are used to enhance the original data, and the enhanced data is used as the source data for transfer learning to ensure its effectiveness. Design multi-dimensional labels for compound fault diagnosis to enable the model to diagnose compound faults and their individual faults. Pre train the convolutional attention network with enhanced data generated from adversarial networks. Subsequently, the original data is used to integrate the feature information in the network and perform secondary training on the classification module. Finally, a compound fault diagnosis is implemented based on the model after secondary training. The effectiveness of the comprehensive diagnostic method proposed in the article is verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. The effectiveness of the comprehensive diagnostic method proposed in the article was verified using real fault data of the fuel injection system. The diagnostic accuracy on test data reached 80%. Compared to the method of directly training convolutional attention networks using raw data and the method of directly training convolutional attention networks without transfer learning using GAN enhanced datasets, the accuracy is improved by 16% and 8%, respectively.