Traditional Fault Diagnosis Methods Research Articles

An acoustic emission identification model for train axle fatigue cracks based on deep belief network

Railway axles are safety-critical components of the railroad rolling stock and the consequences of possible in-service failures can have a huge impact. Axle fatigue cracks are relatively common defects during train operation, but how to intelligently identify axle fatigue cracks in running trains is still a great challenge. In order to identify axle fatigue cracks more intelligently, the problem that needs to be solved is how to overcome the manual extraction of features by manual experience as well as shallow networks. Therefore, in this paper, an acoustic emission signal identification method based on deep belief networks (DBNs) for axle fatigue cracks is proposed. In this method, a DBN model is constructed. The axle fatigue crack acoustic emission signal data were obtained by our designed acquisition experimental setup, and these data were used to verify the accuracy of the constructed DBN network model identification. The experimental results show that the method of identification of axle fatigue cracks based on DBN, compared with the traditional fault diagnosis method, eliminates the operations of data feature extraction, feature screening, feature fusion, etc and makes complete use of all the information contained in the fault data. The method can not only identify fatigue crack signals but also has a high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

Traditional fault diagnosis methods for mineral oil‐immersed power transformer based on dissolved gas analysis: Past, present and future

AbstractA key factor in ensuring the efficient and safe operation of power transformers is the early and accurate diagnosis of incipient faults. Among the tools available to achieve this goal, dissolved gas analysis (DGA) is widely used by power transformers' maintenance professionals. It is a preventive maintenance tool, used for condition monitoring, fault diagnosis and unplanned outage prevention. With the development of artificial intelligence (AI), many intelligent‐based methods using AI tools have been proposed in the literature for DGA data interpretation. Although these methods achieve high diagnostic accuracies and improve DGA efficiency, they are generally complicated and the research documented in these publications is difficult to replicate. Traditional DGA‐based methods are simple, easy to understand and implement, and widely used by power transformers' maintenance professionals. Many methods proposed in recent years overcome the limitations of the pioneer methods and are increasingly effective. The authors present a detailed and comprehensive literature review of the traditional DGA‐based methods for mineral oil‐immersed power transformer faults diagnosis. This review also addresses ways to improve the efficiency of the available traditional methods. Some pitfalls that need to be taken into account to improve the efficiency of the DGA‐based diagnostic methods are also presented.

A novel subdomain adaptive intelligent fault diagnosis method based on multiscale adaptive residual networks

Traditional fault diagnosis methods may not capture key information during feature extraction due to the large distribution difference under different working conditions, which can result in poor accuracy of the diagnostic model. To address this issue, a subdomain adaptive bearing fault identification method guided by the local maximal mean difference (LMMD) under the multiscale adaptive residual network is proposed in this paper. The bearing vibration signals are preprocessed by wavelet convolution and wide convolution to generate initial features. Then, the multi-scale adaptive residual network is used to adjust the feature weights of different scales and extract richer feature information. To reduce the intra-class distribution difference, the LMMD is employed. Additionally, local interclass maximum mean difference (LIMMD) is used to increase the inter-class difference, preventing misclassification of samples from different classes due to their close proximity and achieving sub-domain distribution alignment. The fault diagnosis performance of the domain distance metric model guided by LMMD and LIMMD under the multiscale adaptive residual network is verified through two different bearing model validation experiments.

Intelligent Mechanical Fault Diagnosis Method for High-Voltage Circuit Breakers Based on Grey Wolf Optimization and Multi-Grained Cascade Forest Algorithms

High-voltage circuit breakers (HVCBs) handle the important tasks of controlling and safeguarding electricity networks. In the case of insufficient data samples, improving the accuracy of the traditional HVCB mechanical fault diagnosis method is difficult, so it poses challenges in meeting performance requirements for mechanical fault diagnosis. In this study, a HVCB fault diagnosis method is introduced. It utilizes a combination of grey wolf optimization (GWO) and multi-grained cascade forest (gcForest) algorithms to resolve these issues and improve the accuracy of HVCB mechanical fault diagnosis. To simplify the original vibration signal, the input feature quantity for the fault diagnosis method is obtained by calculating the energy entropy of the wavelet packet decomposition. The GWO algorithm is employed to optimize the parameters of the gcForest model, leading to identification of the optimum parameter configuration. Subsequently, the diagnostic effect in the case of a small sample size was analyzed through a VS1 vacuum circuit breaker example, and the accuracy reached 95.89%. In the case of unbalanced samples, further analysis and comparison with different methods confirm the feasibility and efficiency of the combination of GWO and gcForest algorithms. This study provides an effective solution for the diagnosis of mechanical faults in HVCBs.

Deep dynamic high-order graph convolutional network for wear fault diagnosis of hydrodynamic mechanical seal

The hydrodynamic mechanical seal (HDMS) in the reactor coolant pump of third-generation nuclear power units is vulnerable to failure due to prolonged operational periods and inevitable wear. However, traditional fault diagnosis methods are not robust to noise and can not leverage both the topological relationships among samples and local features. To resolve these challenges, in this paper, we propose a novel graph convolutional network (GCN) for wear fault diagnosis of HDMS called deep dynamic high-order graph convolutional network (DDHGCN). A dynamic graph learning module is designed to control the connectivity and sparsity of the iterated graph and thus eliminate errors and redundancies caused by noise. A high-order GCN module is proposed to effectively model the correlations between nodes, capturing contextual information and mutual influences among them. A residual convolutional module is applied to extract local features hidden in individual samples to further improve the classification performance. All three modules are jointly optimized for reliable wear fault diagnosis of HDMS. Experimental results demonstrate that our DDHGCN can achieve higher performance when compared with the state-of-the-arts.

Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index

When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance.

High-Resistance Connection Fault Diagnosis in Ship Electric Propulsion System Using Res-CBDNN

The fault detection and diagnosis of a ship’s electric propulsion system is of great significance to the reliability and safety of large modern ships. The traditional fault diagnosis method based on mathematical models and expert knowledge is limited by the difficulty of establishing an accurate model of the complex system, and it is easy to cause false alarms. Data-driven methods, such as deep learning, can automatically learn from the mass of data, extract and analyze fault characteristics, and create a more objective distinction system state. A deep learning fault diagnosis model based on ResNet feature extraction capability and bidirectional long-term memory network timing processing capability is proposed to realize fault diagnosis of high resistance connections in ship electric propulsion systems. The results show that the res-convolutional BiLSTM deep neural network (Res-CBDNN) can fully integrate the advantages of the two networks, efficiently process fault current data, and achieve high-performance fault diagnosis. The accuracy of Res-CBDNN can be kept above 85% in a noisy environment, and it can effectively monitor the high resistance connection fault of ship electric propulsion systems.

FTSDC: A novel federated transfer learning strategy for bearing cross-machine fault diagnosis based on dual-correction training

In recent years, although traditional intelligent fault diagnosis methods have achieved satisfactory development in transfer learning tasks, the sample information that the single client can generally provide is extremely limited in real industrial scenarios. And the private data needs to be guaranteed not to leave the local storage during the application process, which leads to obstacles for fault diagnosis methods to solve cross-device and cross-scenario client transfer tasks. Therefore, a novel federated transfer learning strategy based on dual-correction training (FTSDC) is proposed, which enables fault diagnosis for multi-device tasks without target domain samples to participate in model training. The dual correction training of source client is proposed to enhance the local network’s generality, which involves two links: The multiple-functions correction model hyperparameters link and the transition training correction network attention area link. Multiple sets of optimization functions are introduced into local network training to reduce the covariate drift phenomenon caused by domain discrepancies. And the local model fine-tunes the parameters of the trained network through the transition dataset to correct attention areas. Furthermore, The central server evaluates each source client model according to the contribution of the local model to the transfer strategy, and the local model parameters are cumulatively optimized by referring to the diagnosic experience of the global model. It is worth noting that the trained local model can still demonstrate commendable performance even when faced with more complex scenarios. Finally, the proposed scheme was rigorously assessed against diverse federated learning methods across various task settings during the experimental session. The results confirmed the effectiveness of the proposed scheme in safeguarding the data privacy of each client while concurrently enhancing the diagnostic accuracy of the target task.

Enhanced process monitoring for industrial coking furnace using a dual‐channel pooling and homologous bilinear model‐based convolutional neural network

AbstractThis paper introduces a process monitoring method using a convolutional neural network (CNN) with dual‐channel pooling and homologous bilinear models (DCP‐HBM). This method aims to enhance the stability of industrial coking furnaces and reduce maintenance costs. It is significantly relevant for addressing challenges such as complexity, high dimensionality, and strong coupling, which are difficult to manage with traditional fault diagnosis methods. In this study, a deep feature extraction module is employed to gather feature information. To effectively reduce the impact of noise and strengthen the correlation between features, DCP is incorporated after deep feature extraction. Concurrently, a HBM is introduced for feature fusion, further refining the characteristics of each state. Faults are then accurately classified through a classification module. The method utilizes DCP to filter and focus input data, thus making the model more attentive to task‐relevant information. This enhances the model's understanding and representation of the input data. The introduction of the HBM further refines the features extracted by the network and increases the precision of feature extraction. This leads to improved recognition of faults with high similarity, thereby enhancing the accuracy of the overall process monitoring. Experimental results demonstrate that this method exhibits strong performance in the monitoring of industrial coking furnaces, indicating its wide‐ranging application prospects.

Fault diagnosis of reciprocating compressor using Teager-Kaiser energy operator and envelope spectral feature extraction

This paper proposed and implemented the Teager-Kaiser energy operator (TKEO) and envelope spectral analysis techniques for the fault detection of discharge valves of a reciprocating compressor. Based on the extraction of fault features, the instantaneous frequency and amplitude of the signals due to the discharged valve based on energy identification can be effectively characterized by the TKEO that was used to identify the characteristic fault signals accurately. The synthesized signal is processed by envelope spectral analysis and TKEO, which can extract the characteristic signal and eliminate the noise. The experimental design is verified experimentally through different reciprocating compressor gas valve conditions. The simulation results verify the feasibility of the proposed method. The experimental verification is carried out through the measurement signals of the six-cylinder reciprocating compressor under different valve operating conditions. TKEO can remove background noise to obtain reciprocating compressor fault feature signals. Feature extraction is based on TKEO and envelope spectra for fault detection of reciprocating compressors. It is expected to reduce the errors produced by traditional manual fault diagnosis methods and improve the accuracy and efficiency of fault diagnosis. The research results of vibration fault feature extraction using TKEO can be used as the basis for fault diagnosis of the reciprocating compressor system.

Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network

In response to limitations in traditional intelligent fault diagnosis methods, such as accuracy, robustness, generality, and susceptibility to noise, this article proposes Multiple Wavelet Coefficient Dimensionality Reduction and Improved Residual Network (MWCDR-IResNet). This novel approach represents the first combination of multi-wavelet transform for extracting wavelet coefficients and Principal Component Analysis (PCA) for dimensionality reduction. It incorporates a Residual Network enhanced by the Squeeze and Excitation (SE) module to focus on relevant channels and capture critical fault-related features, followed by fault classification. Validation is performed using rolling bearing datasets from Paderborn University(PBU) and Southeast University (SEU). Experimental results consistently demonstrate MWCDR-IResNet superior performance in terms of robustness, generality, and average accuracy when compared to existing methods.

A zero-shot fault attribute transfer learning method for compound fault diagnosis of power circuit breakers

Diagnosis of compound mechanical faults for power circuit breakers (CBs) is a challenging task. In traditional fault diagnosis methods, however, all fault types need to be collected in advance for the training of diagnosis model. Such processes have poor generalization capabilities for industrial scenarios with no or few data when faced with new faults. In this study, we propose a novel zero-shot learning method named DSR-AL to address this problem. An unsupervised neural network, namely, depthwise separable residual convolutional neural network, is designed to directly learn features from 3D time-frequency images of CB vibration signals. Then we build fault attribute learners (ALs), for transferring fault knowledge to the target faults. Finally, the ALs are used to predict the attribute vector of the target faults, thus realizing the recognition of previously unseen faults. The orthogonal experiments are designed and conducted on real industrial switchgear to validate the effectiveness of the proposed diagnosis framework. Results show that it is feasible to diagnose target faults without using their samples for training, which greatly saves the costs of collecting fault samples. This will help to accurately identify the various faults that may occur during CB’s life cycle, and facilitate the application of intelligent fault diagnosis system.

Research on fault diagnosis method of linear vibration screen based on fused RP-improved CNN

A fault diagnosis method based on fused RP (recurrence plot) and improved CNN (convolutional neural networks) is proposed for the traditional fault diagnosis method of linear vibrating screen manually designed and optimized features with feature quality uncertainty. A deep convolutional neural network combination model (MDCNN) with high-level feature fusion is designed. The collected multi-source vibration signals were converted into black and white recurrence plot, and fusion into three-channel RGB recurrence plot. Fault diagnosis was classified by the combination model of deep convolution neural network with high level feature fusion. The results show that the recurrence plot of homologous multi-sensor signals contains more information about the characteristics of the vibrating screen than the original colorful signal map or the Gram Point Field, which contributes to the feature learning and classification of MDCNN, with an average recognition accuracy of 97.59% under strong vibrating sift-5db noise. Compared with manifold classical neural network models, the MDCNN model is proved to be effective, stable and small space occupying.

Fault Classification and Diagnosis Approach Using FFT-CNN for FPGA-Based CORDIC Processor

Within the realm of digital signal processing and communication systems, FPGA-based CORDIC (Coordinate Rotation Digital Computer) processors play pivotal roles, applied in trigonometric calculations and vector operations. However, soft errors have become one of the major threats in high-reliability FPGA-based applications, potentially degrading performance and causing system failures. This paper proposes a fault classification and diagnosis method for FPGA-based CORDIC processors, leveraging Fast Fourier Transform (FFT) and Convolutional Neural Networks (CNNs). The approach involves constructing fault classification datasets, optimizing features extraction through FFT to shorten the time of diagnosis and improve the diagnostic accuracy, and employing CNNs for training and testing of faults diagnosis. Different CNN architectures are tested to explore and construct the optimal fault classifier. Experimental results encompassing simulation and implementation demonstrate the improved accuracy and efficiency in fault classification and diagnosis. The proposed method provides fault prediction with an accuracy of more than 98.6% and holds the potential to enhance the reliability and performance of FPGA-based CORDIC circuit systems, surpassing traditional fault diagnosis methods such as Sum of Square (SoS).

Fault Diagnosis of Airborne Electronic Equipment Based on Dynamic Bayesian Networks

With the rapid development of the aerospace industry, the structure of airborne electronic equipment has become more complex, which to some extent increases the difficulty of fault detection and maintenance of airborne electronic equipment. Traditional manual fault diagnosis methods can no longer fully meet the diagnostic needs of airborne electronic equipment. Therefore, this chapter uses dynamic Bayesian network to diagnose the faults of airborne electronic equipment. The basic idea of using a dynamic Bayesian network-based fault diagnosis method for airborne electronic devices is to mine data based on historical fault data of airborne electronic devices, and obtain fault symptoms and training data of airborne electronic devices. For non-essential fault symptoms, rough set theory was introduced to reduce their attributes and obtain the simplest attribute set, thereby simplifying the network model.

Deep learning based on rolling bearing fault diagnosis method

With the continuous development of industrial automation, rolling bearings play a crucial role in many fields as key mechanical components, and the fault diagnosis of rolling bearings have great significance. This paper discusses a deep learning based rolling bearing fault diagnosis method, aiming to improve the accuracy and efficiency of fault detection. Firstly, the vibration signals of rolling bearings are pre-processed to extract the feature information that helps fault diagnosis. Then, the features were automatically learned and classified by using BP neural network. Finally, the effectiveness and robustness of the method were verified through experiments. Compared with the traditional fault diagnosis method, the deep learning-based rolling bearing fault diagnosis method has higher accuracy and practicality, which provides strong support for the fault detection and preventive maintenance of rolling bearings.

Research on Three Common Fault Diagnosis Methods for AC Asynchronous Motors Based on Deep Learning

<p>In response to the problem that traditional fault diagnosis methods mainly rely on manual search, this paper proposes an improved convolutional neural network based three item asynchronous motor fault diagnosis method. Taking the motor rotor bar fault as the research object, in the early stage of the fault, the characteristic signal is easily mixed with the motor fundamental frequency signal. Therefore, first, the current characteristics of the motor rotor bar fault are analyzed, and then the motor vibration signal is converted into a time-frequency map using wavelet analysis method. Then, based on the superpixel segmentation method, the image is generated into a superpixel block. Finally, the image information is input into an improved neural network, The improved neural network can adaptively extract fault features. The experimental results show that the method described in this article can improve the diagnostic ability for rotor bar breaking faults, and has a higher fault recognition accuracy compared to traditional methods.</p> <p> </p>

Stator-rotor fault diagnosis of induction motor based on time-frequency domain feature extraction

Since the induction motor operates in a complex environment, making the stator and rotor of the motor susceptible to damage, which would have significant impact on the whole system, efficient diagnostic methods are necessary to minimize the risk of failure. However, traditional fault diagnosis methods have limited applicability and accuracy in diagnosing various types of stator and rotor faults. To address this issue, this paper proposes a stator-rotor fault diagnosis model based on time-frequency domain feature extraction and Extreme Learning Machine (ELM) optimized with Golden Jackal Optimization (GJO) to achieve highprecision diagnosis of motor faults. The proposed method first establishes a platform for acquiring induction motor stator-rotor fault data. Next, wavelet threshold denoising is used to pre-process the fault current signal data, followed by feature extraction to perform time-frequency domain eigenvalue analysis. By comparison, the impulse factor is finally adopted as the feature vector of the diagnostic model. Finally, an induction motor fault diagnosis model is constructed by using the GJO to optimize the ELM. The resulting simulations are carried out by comparing with neural networks, and the results show that the proposed GJO-ELM model has the highest diagnostic accuracy of 94.5%. This finding indicates that the proposed method outperforms traditional methods in feature learning and classification of induction motor fault diagnosis, and has certain engineering application value.

Fault Diagnosis for PV Modules Based on AlexNet and Symmetrized Dot Pattern

Faults in solar photovoltaic (PV) modules often result from component damage, leading to voltage fluctuations and decreased stability in the power system. In this study, the original voltage signals of different PV modules show little variation. Therefore, a solution that combines symmetrized dot pattern (SDP) and AlexNet for fault detection in PV modules was proposed. This solution investigates three common faults: poor welding, cracking, and bypass diode failure, which can be applied to fault-free modules. First, a high-frequency signal was input into the PV module, and the raw signal was captured using an NI PXI-5105 high-speed data acquisition card. Next, we used SDP to process the signal and create images with specific snowflake-like features. These images were used as a basis for fault diagnosis. Finally, deep-learning algorithms were used to perform status detection on the PV module. This research also used 3200 training samples and 800 test samples (200 for each type) to evaluate a new method for diagnosing faults in PV modules. The results show that the accuracy of the new method reached 99.8%, surpassing traditional convolutional neural networks (CNN) and extension neural networks (ENN), whose accuracies were 99.5% and 91.75%, respectively. Furthermore, this study compares the proposed method with more traditional numerical fault diagnosis methods. SDP effectively extracts fault signals and presents them as images. With AlexNet used for fault identification, the method excels in accuracy, training time, and testing time, thereby enhancing the stability and reliability of future energy systems.

Chemical Process Fault Diagnosis Method Based on Deep Learning

To address the problem that the traditional fault diagnosis method for chemical processes under big data relies too much on expert experience and fault features are difficult to distinguish, a deep learning-based fault diagnosis method is proposed, which combines convolutional neural network (CNN), long and short-term memory (LSTM) and attention mechanism (AM). In this method, the spatial sequence features of the input signal are extracted by the CNN adaptively, while the LSTM extracts the time-series features of the signal. Finally, the model performance is enhanced by introducing the attention mechanism and using the SoftMax layer as a classifier for fault diagnosis, so that the model can notice the important features of the faults with the interference of noise. Simulation validation of the method in this paper is performed using the TE chemical process data set, and it is demonstrated that the method can be used for chemical process fault diagnosis studies. Finally, compared with other fault diagnosis methods, the method is more accurate and has certain superiority.