Network Fault Diagnosis Research Articles

Application of WCA-RBF Neural Network in Fault Diagnosis of Analog Circuits

With the fast growth of electronic technology today, the hybrid circuit of analog and digital circuits has become a trend in the growth of electronic technology. To address fault diagnosis in analog circuits, Haar wavelet is applied for fault feature extraction. The K-means clustering method and pseudoinverse algorithm were used to optimize the center value and weight value of the radial basis function neural network, respectively. The adaptive step size was improved in the wolf pack algorithm, the parameters of the radial basis function neural network were optimized based on the wolf pack algorithm, and a wolf pack algorithm optimized radial basis function neural network model was constructed. The test results show that this model converges after 40 times of training, with an error value of 10−3 and an average value of the mean squared error of 0.45. Comparing the fault diagnosis rates of the original model, genetic algorithm optimized radial basis function neural network model, and wolf colony algorithm optimized radial basis function neural network model, the last model has the best fault diagnosis rate, reaching 95.52%. The wolf colony algorithm is utilized to optimize the radial basis function neural network model to diagnose the faults in the standard filter circuit, and the fault diagnosis rate reaches 96.17%. The findings express that the radial basis function neural network model optimized by the wolf colony algorithm has a good diagnosis effect for different faults in the analog circuit.

A Novel Multi–Scale One–Dimensional Convolutional Neural Network for Intelligent Fault Diagnosis of Centrifugal Pumps

Centrifugal pumps are susceptible to various faults, particularly under challenging conditions such as high pressure. Swift and accurate fault diagnosis is crucial for enhancing the reliability and safety of mechanical equipment. However, monitoring data under fault conditions in centrifugal pumps are limited. This study employed an experimental approach to gather original monitoring data (vibration signal data) across various fault types. We introduce a multi–scale sensing Convolutional Neural Network (MS–1D–CNN) model for diagnosing faults in centrifugal pumps. The network structure is further optimized by examining the impact of various hyperparameters on its performance. Subsequently, the model’s efficacy in diagnosing centrifugal pump faults has been comprehensively validated using experimental data. The results demonstrate that, under both single and multiple operating conditions, the model not only reduces reliance on manual intervention but also improves the accuracy of fault diagnosis.

CFI-LFENet: Infusing cross-domain fusion image and lightweight feature enhanced network for fault diagnosis

Data-driven fault diagnosis has become a hot topic of research in recent years, due to its wide applicability, high accuracy, and ease of modeling. In data-driven fault diagnosis, feature extraction is usually performed through a combination of one-dimensional time domain or frequency domain analysis methods. However, the constraints of data format and diagnostic methods give rise to three primary issues: inadequate utilization, expression, and mining of fault features, thereby impeding the broader application of data-driven methods. This paper presents a fault diagnosis framework that integrates cross-domain fusion images and a lightweight feature-enhanced network to address these issues. We leverage the temporal and frequency features of raw time series data, transforming them into images. This approach not only offers a greater amount of fault information but also highlights the inherent features more prominently. In terms of diagnosis methods, a novel feature-enhanced network model is proposed. This model takes images generated by a cross-domain image fusion module as inputs and then goes through a customized feature extraction module, a feature fusion module, a feature enhancement module, and a classification module to achieve high-precision and fast fault diagnosis. The effectiveness of this framework is validated on two publicly available datasets. The results show that the average accuracy of this fault diagnosis framework can reach 100%, surpassing the findings of existing research. The experiments on noise resistance further showcase the superiority of the proposed framework. It is expected that the framework presented in this study will provide a new perspective and direction for fault diagnosis.

Guided sampling-based evolutionary deep neural network for intelligent fault diagnosis

The selection of model architecture and hyperparameters has a significant impact on the diagnostic performance of most deep learning models. Because training and evaluating the various architectures of deep learning models is a time-consuming procedure, manual selection of model architecture becomes infeasible. Therefore, we have proposed a novel framework for evolutionary deep neural networks that uses a policy gradient to guide the evolution of the DNN architecture towards maximum diagnostic accuracy. We have formulated a policy gradient-based controller that generates an action to sample the new model architecture at every generation so that optimality is obtained quickly. The fitness of the best model obtained is used as a reward to update the policy parameters. Also, the best model obtained is transferred to the next generation for quick model evaluation in the NSGA-II evolutionary framework. Thus, the algorithm gets the benefits of fast non-dominated sorting as well as quick model evaluation. The effectiveness of the proposed framework has been validated on three datasets: the Air Compressor dataset, the Case Western Reserve University dataset, and the Paderborn University dataset.

Dynamic model-assisted transferable network for liquid rocket engine fault diagnosis using limited fault samples

The accurate detection and diagnosis of faults in Liquid Rocket Engines (LREs) are critical for ensuring space mission safety. However, the limited availability of actual fault samples and the diversification of potential faults present significant challenges in achieving precise diagnosis. To overcome these obstacles, we propose a dynamic model-assisted transfer learning approach. In this study, we first modularize the LRE and establish a dynamic model based on the mathematical principles of each module. Subsequently, we artificially established a fault module model, injected faults into the normal model, and simulated various fault modes to expand the fault sample library. Leveraging this augmented dataset in combination with a limited number of actual fault samples, we employ transfer learning to fine-tune a Convolutional Neural Network (CNN). Compared with other classic methods, the migrated CNN effectively adapts to the distribution of real data and significantly improves the accuracy of LRE fault diagnosis.

Attention mechanism‐based multisensor data fusion neural network for fault diagnosis of autonomous underwater vehicles

AbstractThe autonomous underwater vehicle (AUV) frequently operates in harsh underwater environments, and timely fault diagnosis of the AUV can prevent mission failure and equipment loss. Data‐driven methods based on a single data source have been widely utilized for fault diagnosis of the AUV because they do not require the construction of complex mechanism models and have high fault diagnosis accuracy. However, these methods face challenges in accomplishing complex fault diagnosis tasks because the single data source provides very restricted fault features. To address this issue, an attention mechanism‐based multisensor data fusion neural network (MDFNN) for AUV fault diagnosis is proposed in this work. First, a feature extraction layer based on the two‐dimensional (2D) convolutional method with a 1D kernel is introduced to extract features from each sensor data separately, significantly optimizing the model architecture. Second, an efficient channel attention mechanism‐based feature fusion layer is proposed to reassign weights to the features of each sensor data, enabling the model to focus more on crucial features. Finally, the fused features are input to the fully connected layers and softmax layer to realize the fault diagnosis of multisensor data. In the end, the diagnostic performance of the proposed MDFNN is evaluated utilizing real AUV experimental data. The experiment shows that the proposed MDFNN has a very fast convergence speed and 98.37% fault diagnosis accuracy, demonstrating its excellent fault diagnosis performance. The proposed MDFNN provides a generalized and simply structured fault diagnosis framework for the AUV with multiple types of sensor data, providing significant engineering value.

Contrastive learning and dynamics embedding neural network for label-free interpretable machine fault diagnosis

Industrial machinery often produces vibration signals that can serve as indicators of underlying faults. However, these signals often need to be labeled, presenting a challenge for accurate and interpretable fault diagnosis. While supervised learning methods, such as deep neural networks, have been applied for fault diagnosis, they need help in effectively distinguishing between different vibration-related faults. In response to this issue, our study introduces an innovative approach for automatic fault diagnosis through the application of the Bootstrap Your Own Latent and Dynamical Systems Model Discovery algorithm (BYOLDIS). This method not only addresses the challenge of unlabelled signals but also provides readily interpretable results. The proposed methodology consists of three fundamental steps. First, we derive a matrix of differential equations to capture the dynamic behavior of faulty bearings. Second, we employ a contrastive learning network alongside a time-delay embedding matrix to reconstruct the coordinates of the fault-dynamical system. Lastly, we construct a library of fault machine dynamic polynomial equations, incorporating prior constraints based on physical models. To assess the effectiveness and robustness of our proposed method, we conducted both simulations and experiments. The results of these case studies affirm that BYOLDIS can accurately diagnose bearing faults and offer dynamic explanations for the diagnostic outcomes. This suggests that BYOLDIS holds substantial promise as a diagnostic tool for processing unlabelled vibrational data.

Imbalanced domain generalization via Semantic-Discriminative augmentation for intelligent fault diagnosis

Domain generalization-based fault diagnosis (DGFD) has garnered significant attention due to its ability to generalize prior diagnostic knowledge to unseen working conditions or machines. However, existing DGFD methods are generally implemented under the premise of class balance, which may not accurately reflect real-world diagnosis scenarios since fault data collected in practical engineering often exhibits severe class imbalance. To address this challenge, this paper proposes a semantic-discriminative augmentation-driven network for imbalanced domain generalization fault diagnosis. A semantic regularization-based mixup strategy is devised to synthesize sufficient reliable samples to compensate for minority classes. Subsequently, discriminative representations are acquired by minimizing the triplet loss, thereby enhancing the model's generalization capabilities. Extensive evaluations, including cross-working condition and cross-machine tasks, demonstrate the effectiveness and superiority of the proposed method.

Novel adaptive loss weighted transfer network for partial domain fault diagnosis

Mechanical fault transfer diagnosis has been confirmed as a feasible approach for tackling intelligent diagnosis with incomplete fault information and scarce labeled data on the basis of big data through the transfer of diagnostic knowledge from one or more conditions to any other condition. However, existing research has developed a hypothesis, i.e., the target domain shares an identical label space with the source domain, making it unfeasible to address the practical issue that the target domain label space is a subset of the source domain label space, resulting in low transfer diagnosis accuracy. To address this issue, a novel unsupervised intelligent diagnosis approach named double classifiers-dependent transfer diagnosis network is developed. In this approach, the label distribution weights are generated through the probability output of the classifier of source domain label space to target domain samples, by which small weights are assigned to irrelevant source samples to avoid negative transfer in the global-local maximum mean discrepancies (GL-MMD). In addition, classifiers of the source domain label space and the shared label space are built separately to improve the reliability of label distribution weights and GL-MMD. By training the network in the shared label space, diagnostic knowledge in partial domain issues is effectively transferred. Two cases are implemented to verify the effectiveness of the developed approach. Compared with other transfer diagnosis approaches, the developed approach achieved better diagnostic performance.

A novel multi-scale competitive network for fault diagnosis in rotating machinery

Bearing fault diagnosis plays a vital role in ensuring the safe and reliable operation of rotating machinery. The diagnostic process is more difficult when the fault is in its early stages, as fewer fault components are contained in the vibration signal, making the diagnosis process more difficult. To improve the accuracy and efficiency of fault type identification, a novel multi-scale competitive network for fault diagnosis is proposed in this paper. First, to obtain multi-scale features and fully utilize the features in the intermediate layer, ensuring the completeness of fault information, a novel improved multi-scale feature fusion residual network (IMSFFRN) is proposed to exploit deep features for vibration signals. Specifically, multi-scale features are obtained by convolution with different dilation rates, and features from adjacent intermediate layers are selected for efficient fusion. Second, different features have varying importance in fault detection tasks. To make neurons more sensitive to specific faults, we propose a multiple-winning consciousness self-organizing map (MCSOM) competition layer, in which each neuron learns specific faults through competition, and the neuron that wins the competition updates its weights. This distinguishes the sensitivity of neurons to different faults. Finally, the generalizability of the network is improved by using support vector machines (SVMs) to classify fault classes. To affirm the efficacy of the proposed approach, a comprehensive evaluation is conducted on the CWRU, PU bearing dataset and SEU gear dataset. The results indicate that the accuracies achieved by the method proposed in this paper are 100%, 99.56% and 100%, respectively. It is superior to other methods proposed in this paper. Furthermore, it is observed that the proposed method exhibits high robustness in noisy environments.

Three-dimensional hybrid fusion networks for current-based bearing fault diagnosis

Abstract Intelligent fault diagnosis (IFD) techniques commonly use vibration-based measurements to perform health monitoring of critical rotating components in industrial systems. However, these vibration-based approaches may be limited in cost-sensitive applications, because the installation of vibration sensors is inconvenient and vibration sensors are expensive. Considering the difficulties of IFD using only current-related information from the motor current signal (MCS), this paper proposes a three-dimensional hybrid-fusion neural network (3D-HFN) that can automatically perform both data- and feature-level fusion of multi-phase current signals for MCS-based IFD of the rolling bearing. The 3D-HFN consists of the multivariate variational mode decomposition (MVMD) and an improved three-dimensional convolution neural network (3D-CNN). Firstly, MVMD is proposed to process multi-phase current signals, which adaptively acquire several intrinsic mode functions with mode-alignment properties. Subsequently, signal-to-image conversion and 3D stacking methods are used to construct 3D-like data in the current-phase dimension, which can fully preserve the interaction relationship between different phases using data-level fusion. Finally, an improved 3D-CNN with multiscale feature fusion and the smooth maximum unit is proposed to learn the 3D-like data and identify different health conditions for the rolling bearing. An open-source dataset with composite bearing faults is used to validate the merits of the proposed method. Experimental results show that the proposed approach has achieved more reliable diagnosis performance than other hand-crafted or 2D/3D-CNN-based algorithms in MCS-based IFD of the rolling bearing.

A two-level classification diagnosis method for AC arc faults based on data random fusion and MC-MGCNN network

The series arc faults of the electric lines in the low-voltage distribution network can lead to devastating fires, posing a significant threat to the lives and safety of residents. Aiming at the problems that arc faults are challenging to identify due to the variety of load types in the lines, a new diagnosis method of two-level classification for arc faults based on data random fusion and MC-MGCNN network is proposed. Firstly, the first level of load classification is carried out. Extreme weighted fuzzy entropy is calculated to separate the dimmer load that could easily lead to misjudgment from the total load pool, and the concepts of simple load pool and complex load pool are established. Secondly, the corresponding classifiers are designed for the two different load pools for the second level of diagnosis classification. Specifically, the time–frequency domain features are extracted for the load waveforms in a simple load pool, and extreme gradient boosting (XGBoost) is used for rapid diagnosis. On the contrary, for the nonlinear load waveforms and multi-load combination waveforms in the complex load pool, the random fusion mechanism of data is applied for feature enhancement, and a new one-dimensional data set is constructed. Finally, the latest data is input into the multi-channel convolution neural network combining multi-head attention and gated recurrent unit (MC-MGCNN) network for arc fault diagnosis. The experimental results show that the first level of load classification can effectively reduce the number of misjudgment samples in the complex load pool and improve the overall binary classification accuracy by 7.54 %. The new data set gains different degrees of improvement in the identification accuracy of other comparative networks. In addition, the new network has good feature learning ability and diagnostic accuracy of arc faults. The accuracy of the multi-classification is up to 99.5 %, and the binary classification accuracy is up to 99.94 %. The test is carried out in the Raspberry PI 4b environment, and the detection time is 52.3 ms, which verified the feasibility of hardware deployment.

Signal Feature Extract Based on Dual-Channel Wavelet Convolutional Network Mixed With Hypergraph Convolutional Network for Fault Diagnosis

Signal Feature Extract Based on Dual-Channel Wavelet Convolutional Network Mixed With Hypergraph Convolutional Network for Fault Diagnosis

Deep feature interactive network for machinery fault diagnosis using multi-source heterogeneous data

Infrared thermal images have been applied for monitoring health condition of machines due to the noncontact and nonintrusive manner. While fault diagnosis performance of those deep neural networks (DNNs) that use infrared thermal images is restricted by the information learned from single sensor. In this study, multi-source heterogeneous data (i.e., infrared thermal images and vibration signals) are used for machinery fault diagnosis. A new DNN, i.e., deep feature interactive network (DFINet) is proposed for machinery fault diagnosis, where a novel interactive feature extraction module is developed for adaptive feature fusion on multi-source heterogeneous data. Firstly, the private and public features of multi-source heterogeneous data are extracted separately by measuring the distribution discrepancy between heterogeneous features in the feature interactive module. The feature splicing is implemented to interactively fuse common fault features of heterogeneous data and to preserve private unique features. A global feature fusion module is further proposed for adaptive fusion of superficial local features and deep abstract features learned by different feature interactive modules. The experimental results on a rotor test-bed and gearbox test-bed indicate that DFINet is promising for fusion and feature extraction on multi-source heterogeneous data in machinery fault diagnosis.

GLDAN: Global and Local Domain Adaptation Network for Cross-Wind Turbine Fault Diagnosis

Abstract Operating under harsh conditions and exposed to fluctuating loads for extended periods, wind turbines experience a heightened vulnerability in their key components. Early fault detection is crucial to enhance the reliability of wind turbines, minimize downtime, and optimize power generation efficiency. Although deep learning techniques have been widely applied to fault diagnosis tasks, yielding remarkable performance, practical implementations frequently confront the obstacle of acquiring a substantial quantity of labeled data to train an effective deep learning model. Consequently, this paper introduces an unsupervised global and local domain adaptation network (GLDAN) for fault diagnosis across wind turbines, enabling the model to efficiently transfer acquired knowledge to the target domain in the absence of labeled data. This feature renders it an appropriate solution for situations with limited labeled data availability. Employing adversarial training, GLDAN aligns global domain distributions, diminishing the overall discrepancy between source and target domains, and local domain distributions within a single fault category for both domains, capturing more intricate and specific fault features. The proposed approach is corroborated using actual wind farm data, and comprehensive experimental results demonstrate that GLDAN surpasses deep global domain adaptation methods in cross-wind turbine fault diagnosis, underlining its practical value in the field.

Multi-Stage Approach Using Convolutional Triplet Network and Ensemble Model for Fault Diagnosis in Oil Plant Rotary Machines

Ensuring the operational safety and reliability of rotary machinery systems, especially in oil plants, has become a focal point in both academic and industry arenas. Specifically, in terms of key rotary machinery components such as shafts, the diagnosis of these systems is paramount for achieving enhanced generalization capabilities in fault diagnosis, encompassing multiple sensor-derived variables with their respective fault patterns. This study introduces a multi-stage approach to generalize capabilities for fault diagnosis that considers multiple sensor-derived variables and their fault patterns. This method combines the Convolutional Triplet Network for feature extraction with an ensemble model for fault classification. Initially, vibration signals are processed to yield the most representative temporal and spatial features. Then, an ensemble approach is used to maximize both diversity and accuracy by balancing the contributions of the individual classifiers. The approach can detect three representative types of shaft faults more accurately than traditional single-stage machine learning models. Comprehensive experiments, detailed within, showcase the method’s efficacy in diagnosing rotary machine faults across diverse operational scenarios.

Rolling bearing fault diagnosis method by using feature extraction of convolutional time-frequency image

The vibration signal of the bearing is mostly used in the fault diagnosis research of rolling bearings, and the vibration signal has the characteristics of strong non-smoothness and is easy to be disturbed by noise. This characteristic of the vibration signal has a strong adverse effect on the fault diagnosis of bearings. To improve the accuracy of bearing fault diagnosis, a rolling bearing fault diagnosis method based on the combination of a short-time Fourier transform and a convolutional neural network is proposed. Firstly, a short-time Fourier transform is performed on the original vibration signal of the bearing to obtain the time-frequency image of the vibration signal. Then, the dual model of 2D convolutional neural network which can obtain abundant fault information is built. Secondly, the obtained time-frequency image is image compressed to fit the proposed model. Finally, the images are input into the network for fault diagnosis, and the fault classification accuracy reaches 100%. The proposed model is tested by two methods: adding Gaussian noise to the time-frequency image and testing the model using bearing data from other working conditions, and the results show that the proposed method has good noise immunity and generalization, which can provide an effective diagnostic scheme for bearing 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

Gradient aligned domain generalization with a mutual teaching teacher-student network for intelligent fault diagnosis

Gradient aligned domain generalization with a mutual teaching teacher-student network for intelligent fault diagnosis

Fault diagnosis of wind turbine pitch system based on LSTM with multi-channel attention mechanism

Pitch system can adjust the blade’s pitch angle, control the aerodynamic torque and capture the wind turbine’s aerodynamic power. It is critical guarantee for the turbine’s safety and stability. However, the turbine’s operating environment is harsh and the long-term component abrasion may cause the system failure. Only by the monitoring signals’ instantaneous value, it is difficult to discovery the system fault. Therefore, this paper fuses multiple time sequence signals and uses the multi-channel attention mechanism to improve the long short-time memory networks (MCA-LSTM) for pitch system fault diagnosis. Firstly, the data of SCADA is preprocessed to construct the time sequence ratio vectors and input into MCA-LSTM. Through the attention mechanism, the time sequences ratio vectors are integrated from different channels in MCA-LSTM. Then the MCA-LSTM establishes the logical relationship between the signals and the fault features. Finally, root-mean-square error and the residual fluctuation threshold are used to identify the abnormal states. The experiments utilized the SCADA data of wind farms. The results show that MCA-LSTM can detect the pitch bearing anomalies and hub faults, which are about ten hours earlier than the wind farm fault recording. So, MCA-LSTM can effectively predict pitch system faults and provides an important basis for the WT fault diagnosis.