Efficiency Of Fault Diagnosis Research Articles

Intelligent diagnostic method for beam pumping units based on torque indicator cards

An innovative approach is introduced to the intelligent diagnosis of torque indicator card conditions using electric parameters, effectively addressing the high costs associated with acquiring polished rod indicator cards and the convergence difficulties encountered when converting electrical parameters. The proposed method begins by establishing a library of 15 typical operating conditions, followed by an engineering feature analysis. Key to this approach is the use of the AlexNet model for intelligent recognition training, which successfully enhances model convergence and stability, even with varying sample sizes. This allows for automated recognition and classification of operating conditions through model training. The research outcomes demonstrate that the model achieved a fault diagnosis accuracy of over 95% for beam pumping units. This not only improves the accuracy and efficiency of fault diagnosis in oil well pumping units but also presents a novel and effective approach for intelligent diagnostic applications in oil field extraction, offering substantial practical value.

Improving Transmission Line Fault Diagnosis Based on EEMD and Power Spectral Entropy.

The fault diagnosis on a transmission line based on the characteristics of the power spectral entropy is proposed in this article. The data preprocessing for the experimental measurement is also introduced using the EEMD. The EEMD is used to preprocess experimental measurements, which are nonlinear and non-stationary fault signals, to overcome the mode mixing. This study focuses on the fault location detection of transmission lines during faults. The proposed method is adopted for different fault types through simulation under the fault point by collecting current and voltage signals at a distance from the fault point. An analysis and comprehensive evaluation of three-phase measured current and voltage signals at distinct fault locations is conducted. The form and position of the fault are distinguished directly and effectively, thereby significantly improving the transmission line efficiency and accuracy of fault diagnosis.

A novel infrared thermography image analysis for transformer condition monitoring

Electrical systems are deeply ingrained in most industrial facilities, their maintenance is increasingly becoming a critical and significant component of economic policy. Condition monitoring of electrical transformers is essential for improving their dependability and availability, averting costly maintenance and additional significant breakdowns. This research adopts the approach based on infrared thermography techniques (IRT) to keep eye on electrical transformers and detect their defects. Thermal images of the transformer were captured at two distinct operating states with an infrared camera. These images were then compiled into a dataset for further analysis. This method uses infrared imaging (IRT), along with feature analysis and machine learning, to identify issues in electrical transformers in a new way. To find the best performing machine learning model, different techniques are compared here in terms of their accuracy and stability. Two approaches are investigated for identifying features in thermography images. Approach-1 employed five common machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Decision Tree (DT), Logistic Regression (LR), and Least Squares Support Vector Machine (LS-SVM). Approach-2 utilized four deep learning techniques, such as MobileNetV2 (MNV2), InceptionV3 (InV3), DenseNet121(DN121), and our proposed modified VGG-16. Among all evaluated methods, the modified VGG-16 architecture achieved the highest level of dependability, demonstrating exceptional efficiency and accuracy in transformer condition monitoring and fault diagnosis.

Hard sample mining-enabled supervised contrastive feature learning for wind turbine pitch system fault diagnosis

Abstract The presence of multiple failure severities in the wind turbine pitch system due to the long-term wear and tear poses challenges in accurately classifying the system’s health condition, thus increasing maintenance costs or damage risks. This paper proposes a novel method based on hard sample mining (HSM)-enabled supervised contrastive learning to address this problem. The proposed method leverages the powerful feature extraction capabilities of supervised contrastive learning to extract discriminative features from highly imbalanced data. Additionally, the method incorporates a cosine similarity-based HSM framework that constructs hard sample pairs within mini-batches during both the representation learning and classifier training phases, thereby improving the diagnostic performance of the model for hard samples. The proposed method achieves macro G-mean of 0.9991 and 0.9971 on two real datasets containing data on wind turbine pitch system cog belt fractures. These results indicate significantly superior fault diagnosis performance compared to existing methods, highlighting its potential for enhancing the reliability and efficiency of fault diagnosis in wind turbine pitch systems.

Research on fault diagnosis algorithm of automobile noise based on deep learning

Abstract This study is devoted to improving the accuracy and efficiency of automobile noise fault diagnosis by using deep learning technology. By constructing a convolutional neural network (CNN) model, we deeply analyze and learn the automobile noise data, aiming at realizing the automatic identification of different types of faults. The experimental results show that the deep learning model shows significant improvement in several performance indicators compared with the traditional methods. Different parameter configurations are used to train the deep learning model. The model structure includes a convolution layer, a maximum pooling layer, and a full connection layer. By extracting the hierarchical features of the data, the model can better adapt to the complex characteristics of noise data. Compared with traditional methods, the CNN model has achieved obvious advantages in accuracy, recall, and F1 score. The research results not only verify the effectiveness of deep learning in automobile noise fault diagnosis but also show the robustness and adaptability of the model under more complex parameter configurations. The conclusion of this study provides empirical support for the application of deep learning technology in the field of automobile noise fault diagnosis.

Fault diagnosis of high-speed motorized spindles based on lumped parameter model and enhanced transfer learning

Condition monitoring of rotating components is crucial for ensuring the reliability and safety of mechanical systems, and artificial intelligence (AI) plays a significant role in achieving the goal. However, the high costs and complexity associated with components like high-speed motorized spindles present significant challenges in collecting complete fault samples. Therefore, we propose a new approach to tackle the challenges. The process commences with establishing a lumped parameter dynamic model for the high-speed motorized spindle. Then, the parameters such as stiffness, eccentricity, and damping in the lumped parameter model were optimized using genetic algorithm. Subsequently, simulated fault samples are acquired by introducing excitation to normal simulated signals. Finally, transfer learning techniques are utilized for intelligent fault diagnosis. The training set consists of simulated fault samples, while the testing set comprises experimental fault samples. Our approach aims to enhance the efficiency and accuracy of fault diagnosis for high-speed motorized spindles while also addressing the challenge of high cost.

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features of vibration signals, continuous wavelet transform is employed to convert the original one-dimensional fault signals into wavelet time–frequency images. Second, for the data dimensionality reduction and model simplification, the time–frequency wavelet images are processed in grayscale to generate single-channel grayscale time–frequency images. Then, sample augmentation is performed on grayscale time–frequency images to balance the dataset by using a variational autoencoder generation adversarial network. Finally, the generated images and the original images are fused and trained by using a focus-loss-optimized CNN classifier to achieve fault diagnosis under unbalanced conditions. The experimental results show that the VGAIC-FDM effectively captures the potential spatial distribution of real samples and alleviates the impact caused by the inconsistent difficulty of sample classification. As a result, it enhances the fault diagnosis performance of the model when dealing with unbalanced datasets, leading to higher accuracy and F1-score values.

Advanced Industrial Fault Detection: A Comparative Analysis of Ultrasonic Signal Processing and Ensemble Machine Learning Techniques

Modern condition monitoring and industrial fault prediction have advanced to include intelligent techniques, aiming to improve reliability, productivity, and safety. The integration of ultrasonic signal processing with various machine learning (ML) models can significantly enhance the efficiency of industrial fault diagnosis. In this paper, ultrasonic data are analyzed and applied to ensemble ML algorithms. Four methods for reducing dimensionality are employed to illustrate differences among acoustic faults. Different features in the time domain are extracted, and predictive ensemble models including a gradient boosting classifier (GB), stacking classifier (Stacking), voting classifier (Voting), Adaboost, Logit boost (Logit), and bagging classifier (Bagging) are implemented. To assess the model’s performance on new data during experiments, k-fold cross-validation (CV) was employed. Based on the designed workflow, GB demonstrated the highest performance, with less variation over 5 cross-folds. Finally, the real-time capability of the model was evaluated by deployment on an ARM Cortex-M4F microcontroller (MCU).

An improved ensemble learning model-based strategy for fault diagnosis of lithium battery double roller press equipment

Abstract The production process of lithium batteries is intricate, involving the coordination of various types of equipment.The sta-bility and precision of double roller press equipment directly affect product performance. With the increasing global de-mand for green energy, the application of lithium batteries in electric vehicles and energy storage systems is expanding, which imposes higher requirements on the stability and quality of lithium battery production. It is an important topic to address the challenges brought about by the gradual intelligentization of double roller presses, such as the complexifica-tion of control systems and the diversification of fault reasons. This paper proposes an enhanced ensemble learning model-based fault diagnosis strategy for lithium battery double roller press equipment. Firstly, the K-nearest neighbors (KNN) algorithm is employed to handle missing data, combined with normalization and standardization methods to improve fea-ture processing, thereby enhancing data quality. Secondly, the Maximum Information Coefficient (MIC) algorithm is utilized to select features highly correlated with fault labels, combined with the Recursive Feature Elimination with Cross-Validation (RFECV) to further optimize feature selection, creating an optimal feature subset. Finally, a RXS-XGBoost model is constructed through the Stacking ensemble learning method, selecting Random Forest (RF), XGBoost, and Sup-port Vector Machines (SVM) as base learners, with XGBoost as the meta-learner. This ensemble approach aims to lever-age the complementary advantages of different algorithms, enhancing the accuracy and robustness of fault diagnosis. The experimental results demonstrate that this improved ensemble learning diagnostic strategy achieves an accuracy rate of up to 99.05%, which is significantly better than other fault diagnosis strategies. It not only effectively reduces the model's training complexity and the risk of overfitting but also significantly enhances the efficiency and precision of fault diagno-sis for lithium battery double roller press equipment.

Research on Big Data Intelligent System for Fault Diagnosis of Computer Aided Ship Power System

This paper aims to build a new fault diagnosis system based on big data and artificial intelligence technology. This study first expounds the application value and prospect of big data technology and intelligent algorithm in the fault diagnosis of ship power system, and realizes the effective integration and mining of massive, multi-source and heterogeneous ship power system operation data through in-depth analysis of the characteristics of ship power system operation data. Secondly, in view of the nonlinear and time-varying characteristics of ship power system faults, this study designs and implements a fault diagnosis model that integrates intelligent algorithms such as machine learning and deep learning. The model can automatically extract key fault features, realize early warning and accurate identification of potential faults, and improve the accuracy and response speed of fault diagnosis through continuous learning and optimization. Finally, this study applies the big data intelligent system for fault diagnosis of computer-aided ship power system to a practical case to verify its effectiveness and practicability in improving fault diagnosis efficiency, reducing maintenance costs, and ensuring navigation safety. The research results have important theoretical significance and practical value for improving the level of ship operation and maintenance management and ensuring the stable operation of ship power system.

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

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.

MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis

AbstractAs industrial production escalates in scale and complexity, the rapid localization and diagnosis of equipment failures have become a core technical challenge. In response to the demand for intelligent fault diagnosis in large‐scale industrial equipment, this study presents “MultiCogniGraph”—a multi‐hop reasoning diagnostic method that integrates multimodal data fusion, knowledge graphs, and graph convolutional networks (GCN). This method leverages internet of things (IoT) sensor data, small‐sample imagery, and expert knowledge to comprehensively characterize the equipment state and accurately detect subtle distinctions in fault patterns. Utilizing a knowledge graph to synthesize data from multiple sources and deep reasoning with GCN, “MultiCogniGraph” achieves swift and effective fault localization and diagnosis. The integration of these techniques not only enhances the efficiency and accuracy of fault diagnosis but also its interpretability, marking a new direction in the field of intelligent fault diagnostics.

Fault Diagnosis Method for Railway Signal Equipment Based on Data Enhancement and an Improved Attention Mechanism

Railway signals’ fault text data contain a substantial amount of expert maintenance experience. Extracting valuable information from these fault text data can enhance the efficiency of fault diagnosis for signal equipment, thereby contributing to the advancement of intelligent railway operations and maintenance technology. Considering that the characteristics of different signal equipment in actual operation can easily lead to a lack of fault data, a fault diagnosis method for railway signal equipment based on data augmentation and an improved attention mechanism (DEIAM) is proposed in this paper. Firstly, the original fault dataset is preprocessed based on data augmentation technology and retained noun and verb operations. Then, the neural network is constructed by integrating a bidirectional long short-term memory (BiLSTM) model with an attention mechanism and a convolutional neural network (CNN) model enhanced with a channel attention mechanism. The DEIAM method can more effectively capture the important text features and sequence features in fault text data, thereby facilitating the diagnosis and classification of such data. Consequently, it enhances onsite fault maintenance experience by providing more precise insights. An empirical study was conducted on a 10-year fault dataset of signal equipment produced by a railway bureau. The experimental results demonstrate that in comparison with the benchmark model, the DEIAM model exhibits enhanced performance in terms of accuracy, precision, recall, and F1.

Research on transformer fault diagnosis based on active learning with imbalanced data of dissolved gas in oil.

The power transformer is the core equipment of the power system, a sudden failure of which will seriously endanger the safety of the power system. In recent years, artificial intelligence techniques have been applied to the dissolved gas analysis evaluation of power transformers to improve the accuracy and efficiency of power transformer fault diagnosis. However, most of the artificial intelligence techniques are data-driven algorithms whose performance decreases when the data are limited or significantly imbalanced. In this paper, we propose an active learning framework for power transformer dissolved gas analysis, in which the model can be dynamically trained based on the characteristics of the data and the training process. In addition, this paper also improves the original active learning spatial search strategy and uses the product of sample feature differences instead of the original sum of differences as a measure of sample difference. Compared to passive learning algorithms, the novel approach could significantly reduce the data labeling effort while improving prediction accuracy.

On the enhancement of semi-supervised deep learning-based railway defect detection using pseudo-labels

In recent years, the use of deep learning methods in railway defect detection has expanded rapidly due to the technology’s ability to improve accuracy and efficiency in fault diagnosis. However, modeling deep learning applications requires a large amount of data, and the labeling process for such datasets demands significant manpower. To address this gap this paper introduces a semi-supervised learning approach using a student–teacher model with YOLOv4 for automatically labeling images, aiming to reduce the manual effort required for image classification. We also present a novel dataset generated from images provided by the TCDD (The State Railways of the Republic of Turkey), which includes five distinct railway defects. Results of the experiments on the same test dataset show that the proposed student–teacher model not only improves YOLOv4’s detection performance according to several decision metrics but also extends the training set with high-confidence pseudo-labeled images.

Fault diagnosis of industrial process using attention mechanism with 3DCNN-LSTM

Although architectures such as recurrent neural networks (RNN) perform well in time series prediction, there is still challenges for existing deep learning methods in extracting comprehensive spatio-temporal features. This paper proposes a fault diagnosis method (3D-ALCNN) using a three-dimensional convolutional neural network (3DCNN), a long-short-term memory network (LSTM) and an attentional mechanism to enhance the correlation perception among important features. The 3DCNN and LSTM are firstly used to extract temporal features and a data stacking method to adapt the one-dimensional data to the input requirements of 3DCNN is adopted. Then, the correlation between different features is identified through the attention mechanism and the neural network is guided to focus on more important features. Eventually, the model output is fed into a classifier for fault classification. Simulation experiments on the industrial coke furnace show that the 3D-ALCNN model outperforms existing methods in terms of fault diagnosis accuracy and efficiency, especially when dealing with chemical data with complex time dependencies.

Fault diagnosis of accelerometer servo circuit output saturation based on feature electrical parameters extraction

The servo circuit of quartz flexible accelerometer is an extremely vital microelectronic device in inertial system. The paper introduces the traditional analysis process about output saturation fault of accelerometer servo circuit. For the shortcomings of traditional analysis methods, a fault diagnosis method based on feature electrical parameters extraction is proposed. According to the fault phenomenon, combined with the modular characteristics of hybrid integrated circuits, the feature electrical parameters of fault modes are extracted, and the fault modes' judgment basis is set. By the judgment results of the feature electrical parameters of the fault circuit, the circuit with output saturation fault caused by the electrical damage to the differential capacitance detector and other faults can be located quickly and accurately, and it is verified by the analysis of faulty circuit samples. Targeted for these faults, the improvement measures are put forward. This analysis method improves the fault diagnosis efficiency and reliability about the accelerometer servo circuit, and has guiding significance for enhancing the success rate of the failure analysis in the hybrid integrated circuits.

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.

Fault diagnosis of building electrical system based on compressed perception theory

Building electrical systems in modern buildings are increasingly playing a pivotal role in bringing people the convenience of life, at the same time, the building electrical system will inevitably fail. And the research of intelligent fault diagnosis algorithms for this field is still in its infancy. At this stage, the accuracy and reliability of fault monitoring and diagnosis of most building electrical systems are yet to be improved. Aiming at the current lack of effective diagnosis of faults in the building electrical system, this paper takes the fault situation of the electrical system in the building as the main object of research, and simulates the common building electrical faults through the comprehensive experimental platform of the building electrical, taking into account the problem of low diagnostic efficiency of the building electrical system. This paper puts forward a fault diagnostic algorithm based on the combination of compressed perception and the K-nearest neighbor algorithm, which is aimed at improving the diagnostic efficiency of building electrical system faults. The results show that the proposed fault diagnosis algorithm can not only improve the accuracy of fault classification but also shorten the time of fault classification, which greatly improves the fault diagnosis efficiency.