Multiple Fault Diagnosis Research Articles

A Multi-Source Domain Adaptation Method for Bearing Fault Diagnosis with Dynamically Similarity Guidance on Incomplete Data

In actual industrial scenarios, collecting a complete dataset with all fault categories under the same conditions is challenging, leading to a loss in fault category knowledge in single-source domains. Deep learning domain adaptation methods face difficulties in multi-source scenarios due to insufficient labeled data and significant distribution differences, hindering domain-specific knowledge transfer and reducing fault diagnosis efficiency. To address these issues, the Dynamic Similarity-guided Multi-source Domain Adaptation Network (DS-MDAN) is proposed. This method leverages incomplete data from multiple-source domains to address distribution disparities in deep domain adaptation. It enhances diagnostic performance in the target domain by transferring knowledge across diverse domains. DS-MDAN uses convolution kernels of different scales to extract multi-scale feature information and achieves feature fusion through upsampling and operations like addition and concatenation. Adversarial training with domain and fault classifiers optimizes feature extraction for widely applicable representations. The similarity between source and target domain data is calculated based on features extracted by a shared-weight network, dynamically adjusting the contribution of different source domain data to minimize distribution differences. Finally, matched source and target domain samples are mapped to the same feature space for fault diagnosis. Experimental validation on various bearing fault datasets shows that DS-MDAN improves performance in multiple fault diagnosis tasks, increasing accuracy and demonstrating good generalization capabilities.

Multiple fault diagnosis method for regional power grid based on DTS simulation system

Multiple fault diagnosis method for regional power grid based on DTS simulation system

Multiple IGBT Open-Circuit Fault Diagnosis Strategy for MMC Using Feature Reconstruction-Based Recurrent Neural Network

Multiple IGBT Open-Circuit Fault Diagnosis Strategy for MMC Using Feature Reconstruction-Based Recurrent Neural Network

Tri-training algorithm based nuclear power systems semi-supervised fault diagnosis under multiple restricted data conditions

An improved tri-training semi-supervised classification method was proposed as a solution to the restricted data conditions of supervised classification models relying on labeled data, semi-supervised models not considering imbalanced proportion of faults data and the presence of faults data with similar feature or varying severity in existing data-driven nuclear power system fault diagnosis studies. Based on the structure of three identical sub-classifiers in the original tri-training algorithm, data update paths have been added, the updated data selection has been strengthened, and the initial information and permissions of sub-models have been differentiated. The novel method only requires the dataset to meet the Smoothness Assumption, and through multiple strict data update paths, it improves the utilization of unlabeled training data while reducing Pseudo-labeling process errors. The difference in permissions and initial information between sub-classifiers is utilized to improve the training rigor for fault types with smaller sample sizes, in order to alleviate the problem of imbalanced data. Based on a marine nuclear power system secondary circuit model and a widely recognized nuclear power plant dataset, multiple nuclear power system common restricted fault diagnosis datasets faced in ships and power plants were obtained to verify the novel method's advantages and generalization. The conclusion is that under the same conditions, compared with the original tri-training method and other semi-supervised learning methods, the Accuracy, AUC, Precision Rate and Recall Rate of the novel method have improved by about 21 %, 10 %, 20 %, and 21 %, respectively, and can reduce about 25 % of misjudgments between similar feature faults.

Multiple Fault Diagnosis in a Wind Turbine Gearbox with Autoencoder Data Augmentation and KPCA Dimension Reduction

Multiple Fault Diagnosis in a Wind Turbine Gearbox with Autoencoder Data Augmentation and KPCA Dimension Reduction

Multisource partial domain adaptation for bearing fault diagnosis

Abstract Domain adaptation has been widely used in fault diagnosis and dealt with data distribution discrepancies, but the labels in source and target domains are usually assumed to be identical. The complexity of the working conditions, in reality, leads to the fact that the labels in target domains are often a subset of the labels in source domains. This special case is called the partial domain problem. However, most of the existing proposed methods for solving partial domain problems are limited to single-source-domain scenarios and fail to effectively integrate multisource knowledge. Hence, this study proposes a new approach of multisource domain obfuscation-subdomain alignment (MSDO-SA) for partial domain adaptation fault diagnosis in multisource domains. Through domain obfuscation, the multisource domains are converted into a single source domain. The subdomain alignment aims at improving the generalization ability and relevance of the model, and effectively alleviates the domain shift problem. Finally, multiple partial domain fault diagnosis tasks using the CWRU dataset validate the effectiveness, robustness, and superiority of the proposed method.

Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation

Intelligent diagnostic methods for identifying faults in rotating machinery, based on domain adaptation, have garnered significant attention. However, most current domain adaptation approaches are primarily designed for single-source domain and single-target domain (SSST) applications. There is a dearth of domain adaptation approaches tailored for single-source to multi-target domains (SSMT). In contrast to SSST, SSMT takes a more comprehensive approach by considering relationships across multiple target domains. This approach offers increased versatility and a broader range of potential applications. To address this, an end-to-end multi-target adversarial subdomain adaptation method is proposed that leverages attention mechanism data fusion and mixup feature augmentation. Firstly, the attention mechanism is used to fuse data from different sensors in both channel and spatial dimensions. Subsequently, a mixup-based feature augmentation method is proposed for multi-target domain adaptation. The method is combined with subdomain adaptation and domain discrimination to further reduce the distributional differences between the source and various target domains while relieving the overfitting problem during domain adaptation. Finally, with the above approach, a robust and stable model for multiple target domain fault diagnosis can be trained. Our experimental results illustrate that our approach has higher accuracy and robustness compared to several popular domain adaptation methods.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

Multiple model fault diagnosis and fault tolerant control for the launch vehicle’s attitude control system

Abstract For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

Research on engine multiple fault diagnosis method based on cascade model

The engine is the core component of the power system, and the health status of the components of the engine is very important for the normal operation of the power system. Most of the exist-ing fault diagnosis methods diagnose the engine fault type without further analysis of the sever-ity of the fault. Different fault severity requires different maintenance measures. Therefore, this paper proposes a cascading fault diagnosis model based on Gated Recurrent Unit (GRU) to di-agnose the fault type and the severity of the corresponding fault type.Firstly, the effective fea-tures are extracted from the vibration signals, and then the features are input into the GRU for fault type diagnosis to obtain the sub-fault diagnosis model. After training, one fault type diag-nosis model and four fault severity diagnosis models are obtained. Then the obtained model is cascaded to obtain the total fault diagnosis network. Fault type diagnosis is located at the first level, and four fault severity diagnosis is located at the second level. The effectiveness of the proposed method is verified by experimental data

Diagnosis of Multiple Open-Circuit Faults in Three-Phase Induction Machine Drive Systems Based on Bidirectional Long Short-Term Memory Algorithm

Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.

Robust Diagnosis of Multiple Open-Circuit Faults in DFIG Wind Turbines in Both Sub- and Super-Synchronous Operating Modes

Robust Diagnosis of Multiple Open-Circuit Faults in DFIG Wind Turbines in Both Sub- and Super-Synchronous Operating Modes

Voiceprint Fault Diagnosis of Converter Transformer under Load Influence Based on Multi-Strategy Improved Mel-Frequency Spectrum Coefficient and Temporal Convolutional Network

In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a multi-strategy improved Mel-Frequency Spectrum Coefficient (MFCC) for voiceprint signal feature extraction, and combines it with a temporal convolutional network for fault diagnosis. Firstly, it improves the hunter–prey optimizer (HPO) as a parameter optimization algorithm and adopts IHPO combined with variational mode decomposition (VMD) to achieve denoising of voiceprint signals. Secondly, the preprocessed voiceprint signal is combined with Mel filters through the Stockwell transform. To adapt to the stationary characteristics of the voiceprint signal, the processed features undergo further mid-temporal processing, ultimately resulting in the implementation of a multi-strategy improved MFCC for voiceprint signal feature extraction. Simultaneously, load signal segmentation is introduced for the diagnostic intervals, forming a joint feature vector. Finally, by using the Mish activation function to improve the temporal convolutional network, the IHPO-ITCN is proposed to adaptively optimize the size of convolutional kernels and the number of hidden layers and construct a transformer fault diagnosis model. By constructing multiple sets of comparison tests through specific examples and comparing them with the traditional voiceprint diagnostic model, our results show that the model proposed in this paper has a fault recognition accuracy as high as 99%. The recognition accuracy was significantly improved and the training speed also shows superior performance, which can be effectively used in the field of multiple fault diagnosis of converter transformers.

Fault Diagnosis of PEM Fuel Cells: An Assessment of two Bayes’ Filters

In this paper a comparative performance assessment of two Bayes’ Filters, the square root unscented Kalman filter (SRUKF) and the particle filter (PF), is conducted in terms of detecting the failure events of flooding and catalytic degradation. The Bayes’ filters operate within a multiple model fault diagnosis framework, where the models are distinguished by an augmented state vector incorporating a system parameter, namely the mass transfer coefficient of the gas diffusion layer or the exchange current density. To achieve a solid comparison, a multi-scale PEMFC degradation model is derived and used in the simulation study to better emulate the behaviour of the PEMFC in the case of catalytic degradation. Simulation results demonstrate that the early detection of flooding and catalytic degradation can be achieved by both the SRUKF based and the PF based multiple model fault diagnosis system, with a slight performance superiority for the SRUKF.

Explainable Artificial Intelligence for Fault Diagnosis of Industrial Processes

Process monitoring is important for ensuring operational reliability and preventing occupational accidents. In recent years, data-driven methods such as machine learning and deep learning have been preferred for fault detection and diagnosis. In particular, unsupervised learning algorithms, such as auto-encoders, exhibit good detection performance, even for unlabeled data from complex processes. However, decisions generated from deep-neural-network-based models are difficult to interpret and cannot provide explanatory insight to users. We address this issue by proposing a new fault diagnosis method using explainable artificial intelligence to break the traditional trade-off between the accuracy and interpretability of deep learning model. First, an adversarial auto-encoder model for fault detection is built and then interpreted through the integration of Shapley additive explanations (SHAP) with a combined monitoring index. Using SHAP values, a diagnosis is conducted by allocating credit for detected faults, deviations from a normal state, among its input variables. The proposed diagnosis method can consider not only reconstruction space but also latent space unlike conventional method, which evaluate only reconstruction error. The proposed method was applied to two chemical process systems and compared with conventional diagnosis methods. The results highlight that the proposed method achieves the exact fault diagnosis for single and multiple faults and, also, distinguishes the global pattern of various fault types.

Multiple Fault Diagnosis of PMSM Based on Stator Tooth Flux and Parallel Residual Convolutional Neural Network

Multiple Fault Diagnosis of PMSM Based on Stator Tooth Flux and Parallel Residual Convolutional Neural Network

Real-time online resistance-alteration-based multiple-fault diagnosis framework and implementation for mine ventilation systems

Resistance-alteration-based multiple-fault diagnosis of mine ventilation systems is essential to ensuring the safety of mine production. The basic assumptions, definitions, framework, theory, algorithms and experiments regarding the resistance-alteration-based multiple-fault diagnosis of mine ventilation systems are systematically studied here. First, the problems of the single-fault assumption in the conventional ventilation system fault diagnosis framework are analyzed, and a real-time online multiple-fault diagnosis framework is proposed. Then, based on the theory of resistance observability, the sensor layout scheme is optimized, the properties of the ventilation subnetwork are studied, and the interpretable resistance-alteration-based multiple-fault diagnosis (RMFD) algorithm is designed. Finally, a diagnosis experiment with multiple faults was carried out for a real coal mine. The experimental results show that the RMFD algorithm can achieve 100% accuracy of identification and positioning at the ventilation subnetwork level and can perform quantitative resistance-alteration analysis for k − 1 roadways within a k-order star subnetwork, which verifies the effectiveness of the real-time online multiple-fault diagnosis framework and the RMFD algorithm. This study achieves real-time online multiple-fault diagnosis of mine ventilation systems and provides a theoretical reference and technical support for the intelligentization of mine ventilation systems and similar fluid networks.

Fault diagnosis for multiple current sensors in grid‐connected inverter based on average modulation voltage

AbstractGrid‐connected inverters are the core equipment in the renewable power system. There are multiple current sensors which may affect the driving module of the switch in inverter. In multiple current sensor fault diagnosis, the coupling between fault components makes fault diagnosis difficult. This paper presents an offset fault diagnosis method of multiple current sensors based on the average modulation voltage model. Based on the influence of current sensor offset fault in the system, the average modulation voltage model is established in three‐phase stationary coordinates. The difference between the measured value of the current and the actual value is estimated through the model when the offset fault occurs. And then the fault is located. Experimental results show that the fault can be located accurately and fault tolerant control can be performed by this method when there are offset faults in multiple sensors simultaneously.

Transfer learning-based multiple digital twin-assisted intelligent mechanical fault diagnosis

With the advancement of complex system diagnosis, prediction, and health management technologies, digital twin technology has become a prominent research area in the fields of intelligent manufacturing and system operation and maintenance. However, due to the high complexity of practical systems, the difficulty of data acquisition, and the low accuracy of modeling techniques, current digital twin modeling suffers from low accuracy, and the generalization ability of models is poor when applied in model transfer. To address this issue, a novel fault diagnosis method is proposed, which integrates a digital twin model based on transfer learning. The framework introduces an innovative approach to construct multiple digital twin models using both mechanistic and data-driven models. The mechanism twin constructs a universal simulation model based on physical equipment and updates it with system response measurement data. The data twin consists of a high-dimensional fully connected-generative adversarial network twin for extracting deep features from data and an long- and short-term memory twin for extracting time series features. Subsequently, transfer learning is introduced to achieve deep fusion in the multiple digital twins system. The mechanism twin is used to obtain source domain samples to construct a diagnostic network, and the data twin is used to extract target domain features to correct the diagnostic network, thereby improving the accuracy and reliability of fault diagnosis. Finally, the proposed framework is applied to the fault diagnosis of triplex pump equipment. The accuracy of diagnosis continuously improves as the system is updated and ultimately reaches 89.28%, demonstrating the effectiveness of the algorithm and providing a novel solution for the generalization limitations of current digital twin models.

Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms.

Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.