Fault Diagnosis Precision Research Articles

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.

ESG guidance and artificial intelligence support for power systems analytics in the energy industry.

In order to increase the precision and effectiveness of power system analysis and fault diagnosis, this study aims to assess the power systems in the energy sector while utilizing artificial intelligence (AI) and environmental social governance (ESG). First, the ESG framework is presented in this study to fully account for the effects of the power system on the environment, society, and governance. Second, to coordinate the operation of various components and guarantee the balance and security of the power system, the CNN-BiLSTM power load demand forecasting model is built by merging convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM). Lastly, the particle swarm optimization (PSO) algorithm is used to introduce and optimize the deep belief network (DBN), and a power grid fault diagnostic model is implemented using the PSO technique and DBN. The model's performance is assessed through experimentation. The outcomes demonstrate how the CNN-BiLSTM algorithm significantly increases forecasting accuracy while overcoming the drawback of just having one dimension of power load data. The values of 0.054, 0.076, and 0.102, respectively, are the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Effective processing of large-scale nonlinear data is achieved in the area of power grid fault diagnosis, resulting in prediction accuracy of 96.22% and prediction time of only 129.94s. This is clearly better than other algorithms and increases fault prediction efficiency and accuracy. Consequently, the model presented in this study not only produces impressive results in fault diagnosis and load demand forecasting, but also advances the field of power system analysis in the energy industry and offers a significant amount of support for the sustainable and intelligent growth of the energy industry.

Fault diagnosis in high-speed computing systems using big data analytics integrated evolutionary computing on the Internet of Everything platform

The significance of high-speed computing systems is paramount for diverse applications. However, their intricate structure and rigorous operational prerequisites render them susceptible to malfunctions. The conventional fault diagnosis models need help managing the copious data produced by these systems, resulting in diagnostic procedures that are both time-consuming and ineffective. The research proposed an Internet of Everything (IoE) platform and big data analytics (BDA) based Fault Diagnosis System (IBFDS) in conjunction with evolutionary computing techniques. The system integrates a hybrid intelligent algorithm that amalgamates the advantages of evolutionary computing and machine learning to enhance the precision and effectiveness of fault diagnosis. The utilization of big data analysis methodologies facilitates the processing and examination of vast quantities of system data, thereby enabling the comprehensive detection and identification of faults. Furthermore, cloud services based on the IoE allow data storage, administration, and instantaneous cooperation among various parties involved.
 Furthermore, a detection technique utilizing a Reduced Boltzmann Machine (RBoM) improves the precision of fault diagnosis. The efficacy of the proposed IBFDS was demonstrated through experimental evaluation using a high-speed computing system dataset. The results obtained were impressive, with a fault detection rate (94.11%), fault identification accuracy (93.76%), fault localization accuracy (93.71%), false positive rate (1.54%), and false negative rate (0.98%). The findings of this study make a valuable contribution to the advancement of fault diagnosis systems that are resilient and effective for high-speed computing systems. This facilitates proactive maintenance, enhances system reliability, and optimizes system performance.

Power System Fault Diagnosis and Prediction System Based on Graph Neural Network

The stability and reliability of the power system are of utmost significance in upholding the smooth functioning of modern society. Fault diagnosis and prediction represent pivotal factors in the operation and maintenance of the power system. This article presents an approach employing graph neural network (GNN) to enhance the precision and efficiency of power system fault diagnosis and prediction. The system's efficacy lies in its ability to capture the intricate interconnections and dynamic variations within the power system by conceptualizing it as a graph structure and harnessing the capabilities of GNN. In this study, the authors introduce a substitution for the pooling layer with a convolution operation. A central role is played by the global average pooling layer, connecting the convolution layer and the fully connected layer. The fully connected layer carries out nonlinear computations, ultimately providing the classification at the top-level output layer. In experiments and tests, we verified the performance of the system.

IGSA-PNN-based Methods for Power Transformer Fault Diagnosis

To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling into local optimum during the training process. Then, the improved GSA algorithm is used to optimize the parameters of the PNN model itself to improve the prediction accuracy of the model. Finally, the prediction results are compared with the prediction results of other traditional diagnostic models. The results show that IGSA-PNN fault diagnosis model performs better in generalization ability and classification accuracy.

Bearing Fault Diagnosis Based on Graph Formulation and Graph Convolutional Network

Bearing fault diagnosis stands as a critical component in the maintenance of rotating machinery. Many prevalent deep learning techniques are tailored to Euclidean datasets such as audio, image, and video. However, these methods falter when confronting non-Euclidean datasets, notably graph representations. In response, here we introduce an innovative approach harnessing the Graph Convolutional Network (GCN) to analyze graph data derived from vibration signals related to bearing faults. This enhances the precision and reliability of fault diagnosis. Our methodology initiates by deriving a periodogram from the unprocessed vibration signals. Subsequently, this periodogram is mapped into a graph format, upon which the GCN is engaged for classification purposes. We substantiate the efficacy of our approach through rigorous experimental assessments conducted on a collection of ten bearing sets. Within these experiments, an accelerometer chronicles vibration signals across varying load conditions. We probe into the diagnostic accuracy rates across diverse loads and Signal-to-Noise Ratios (SNRs). Furthermore, a comparative evaluation of our method against several established algorithms delineated in this study is undertaken. Empirical observations confirm that our GCN-based strategy registers an elevated diagnostic accuracy quotient.

Research on mechanical fault diagnosis method of high voltage circuit breaker based on new measurement

Aiming at the problem that the detection of a single signal is difficult to accurately and comprehensively reflect the mechanical failure of the circuit breaker operating mechanism, the VS1 (VBM7) -12 high-voltage circuit breaker is selected as the experimental object in this paper. The coil current signal is collected by a sensor based on new measurements, and the fault diagnosis method of high-voltage circuit breaker is studied from the acquisition, preprocessing, and feature extraction of vibration signal and sound signal, combined with artificial intelligence algorithm. The research shows that the application of electro-acoustic joint features can effectively enhance the precision of fault diagnosis of high-voltage circuit breakers, and verify the feasibility and superiority of the mechanical fault diagnosis method of high-voltage circuit breakers based on new measurements.

Power Transformer Fault Diagnosis Using Neural Network Optimization Techniques

Artificial Intelligence (AI) techniques are considered the most advanced approaches for diagnosing faults in power transformers. Dissolved Gas Analysis (DGA) is the conventional approach widely adopted for diagnosing incipient faults in power transformers. The IEC-599 standard Ratio Method is an accurate method that evaluates the DGA. All the classical approaches have limitations because they cannot diagnose all faults accurately. Precisely diagnosing defects in power transformers is a significant challenge due to their extensive quantity and dispersed placement within the power network. To deal with this concern and to improve the reliability and precision of fault diagnosis, different Artificial Intelligence techniques are presented. In this manuscript, an artificial neural network (ANN) is implemented to enhance the accuracy of the Rogers Ratio Method. On the other hand, it should be noted that the complexity of an ANN demands a large amount of storage and computing power. In order to address this issue, an optimization technique is implemented with the objective of maximizing the accuracy and minimizing the architectural complexity of an ANN. All the procedures are simulated using the MATLAB R2023a software. Firstly, the authors choose the most effective classification model by automatically training five classifiers in the Classification Learner app (CLA). After selecting the artificial neural network (ANN) as the sufficient classification model, we trained 30 ANNs with different parameters and determined the 5 models with the best accuracy. We then tested these five ANNs using the Experiment Manager app and ultimately selected the ANN with the best performance. The network structure is determined to consist of three layers, taking into consideration both diagnostic accuracy and computing efficiency. Ultimately, a (100-50-5) layered ANN was selected to optimize its hyperparameters. As a result, following the implementation of the optimization techniques, the suggested ANN exhibited a high level of accuracy, up to 90.7%. The conclusion of the proposed model indicates that the optimization of hyperparameters and the increase in the number of data samples enhance the accuracy while minimizing the complexity of the ANN. The optimized ANN is simulated and tested in MATLAB R2023a—Deep Network Designer, resulting in an accuracy of almost 90%. Moreover, compared to the Rogers Ratio Method, which exhibits an accuracy rate of just 63.3%, this approach successfully addresses the constraints associated with the conventional Rogers Ratio Method. So, the ANN has evolved a supremacy diagnostic method in the realm of power transformer fault diagnosis.

An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer

This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve the precision of fault diagnosis. To begin with, an experimental platform was used to simulate the OLTC contact, enabling the collection of mechanical vibration signals and high-frequency current signals under different operational states. These signals underwent wavelet packet transform for denoising, followed by correlation analysis to investigate their interrelationships across various states. Features were then extracted and analyzed using ensemble empirical mode decomposition and the Hilbert–Huang transform. Subsequently, a support vector machine (SVM) was employed to analyze both the mechanical vibration signal and high-frequency current signal, facilitating the classification of the OLTC contact state. The results demonstrated that the joint analysis of electrical and mechanical signals provided a comprehensive representation of the actual contact state under different conditions. The SVM classification achieved an error below 10% in predicting the values of the two signal types, validating the efficiency and feasibility of the proposed fault diagnosis method for OLTC contacts. The findings presented in this paper offer valuable insights for on-site fault diagnosis of practical OLTCs.

CNN Hardware Accelerator for Real-Time Bearing Fault Diagnosis

This paper introduces a one-dimensional convolutional neural network (CNN) hardware accelerator. It is crafted to conduct real-time assessments of bearing conditions using economical hardware components, implemented on a field-programmable gate array evaluation platform, negating the necessity to transfer data to a cloud-based server. The adoption of the down-sampling technique augments the visible time span of the signal in an image, thereby enhancing the accuracy of the bearing condition diagnosis. Furthermore, the proposed method of quaternary quantization enhances precision and shrinks the memory demand for the neural network model by an impressive 89%. Provided that the current signal data sampling rate stands at 64 K samples/s, the proposed design can accomplish real-time fault diagnosis at a clock frequency of 100 MHz. Impressively, the response duration of the proposed CNN hardware system is a mere 0.28 s, with the fault diagnosis precision reaching a remarkable 96.37%.

Long Short-Term Memory Network-Based HVDC Systems Fault Diagnosis under Knowledge Graph

To enhance the precision of fault diagnosis for high-voltage direct-current (HVDC) systems by effectively extracting various types of fault characteristics, a fault diagnosis method based on the long short-term memory network (LSTM) is proposed in this paper. The method relies on a knowledge graph platform and is developed using measured data from four fault types in an HVDC substation located in southwest China. Firstly, a knowledge graph for the HVDC systems is constructed, then the fault waveform data is preprocessed and divided into a training set and a test set. Various optimizers are employed to train and test the LSTM. The proposed strategy’s accuracy is calculated and compared with recurrent neural network (RNN), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), Naive Bayes classifier, probabilistic neural networks (PNN), and classification learner (CL), which are commonly used in fault diagnosis. Results indicate that the proposed method achieves an accuracy of over 95%, which is 30% higher than RNN, 8% higher than XGBoost, 4% higher than SVM, 7% higher than Naive Bayes, 40% higher than PNN, and 42% higher than classification learner (CL), respectively; the method also has the minimum time cost, fully demonstrating its superiority and effectiveness compared to other methods.

Research on mechanical fault diagnosis based on MADS evidence fusion theory

In machine intelligence fault diagnostic and health status decision-making systems, rich, complex, and fuzzy feature information cannot facilitate fault decision-making merely on a single data source. This requires utilizing the heterogeneity of information gathered from multiple sources to diminish the system’s uncertainty and improve the accuracy of decision-making. In this work, a novel neural network-based multi-source fusion classification model is proposed to diagnose the pump mechanical faults. The multi-head attention Dempster–Shafer (D–S) evidence fusion (MADS) system extends the model’s ability to focus on rich features. Furthermore, the uncertain values throwing mechanism can effectively eliminate samples from uncertain categories and increase the model’s ability to distinguish diagnostic results with low confidence. Compared with a single sensor, our multi-sensor joint decision based on seven sensors considerably improved the fault diagnostic accuracy of MADS system, which has increased by at least 12.34%. Experimental validation demonstrates that utilizing the improved combination rules provided for multi-source evidence fusion fault diagnosis can significantly improve the efficacy of conventional D–S fusion and reduce the probability of misjudgment; combining the multi-head attention mechanism can dramatically increase the precision of model fault diagnosis. The proposed method has the potential to substantially accelerate research in the field of multi-source sensor joint fault diagnosis.

Research on Transformer Fault Diagnosis Model Based on Improved GOA-SVM

A transformer fault diagnosis model based on the improved grasshopper optimization algorithm-optimized support vector machine (SVM) is proposed to improve the precision of transformer fault diagnosis and avoid the issues that the traditional grasshopper optimization algorithm (GOA) is prone to falling into local optimal solution and slow convergence. Firstly, the grasshopper population is initialized using the elite backward learning strategy to improve the initial population quality and search efficiency. Then, the Sigmoid function is introduced to improve the linear weight decreasing of the grasshopper algorithm into nonlinear weight decreasing to strike a balance between the algorithm’s capacity for both local and global exploration. Finally, the kernel function parameters and penalty coefficients of the SVM are optimized with the improved grasshopper optimization algorithm (IGOA) to establish a model based on dissolved gas analysis (DGA) in oil-based IGOA algorithm-optimized SVM for transformer fault diagnosis model and verify the effectiveness and superiority of IGOA-SVM to identify transformer fault states by comparing with PSO-SVM and GOA-SVM.

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

A Rolling Bearing Fault Diagnosis Method Based on Enhanced Integrated Filter Network

Aiming at the difficulty of rolling bearing fault diagnosis in a strong noise environment, this paper proposes an enhanced integrated filter network. In the method, we firstly design an enhanced integrated filter, which includes the filter enhancement module and the expression enhancement module. The filter enhancement module can not only filter the high-frequency noise to extract useful features of medium and low-frequency signals but also maintain frequency and time resolution to some extent. On this basis, the expression enhancement module analyzes fault features intercepted by the upper network at multiple scales to get deep features. Then we introduce vector neurons to integrate scalar features into vector space, which mine the correlation between features. The feature vectors are transmitted by dynamic routing to establish the relationship between low-level capsules and high-level capsules. In order to verify the diagnostic performance of the model, CWRU and IMS bearing datasets are used for experimental verification. In the strong noise environment of SNR = −4 dB, the fault diagnosis precisions of the method on CWRU and IMS reach 94.85% and 92.45%, respectively. Compared with typical bearing fault diagnosis methods, the method has higher fault diagnosis precision and better generalization ability in a strong noise environment.

Fault Diagnosis of Axial Piston Pump Based on Extreme-Point Symmetric Mode Decomposition and Random Forests

Aiming at fault diagnosis of axial piston pumps, a new fusion method based on the extreme-point symmetric mode decomposition method (ESMD) and random forests (RFs) was proposed. Firstly, the vibration signal of the axial piston pump was decomposed by ESMD to get several intrinsic mode functions (IMFs) and an adaptive global mean curve (AGMC) on the local side. Secondly, the total energy was selected as the data of feature extraction by analyzing the whole oscillation intensity of the signal. Thirdly, the data were preprocessed and the labels were set, and then, they were adopted as the training and testing set of machine learning samples. Lastly, the RFs model was created based on machine learning service (MLS) to diagnose the faults of the axial piston pump on the cloud. Using the test and verifying the data set for comparative testing, the fault diagnosis precision rates of the model are above 90.6%, the recall rates are more than 90.9%, the F1 score is higher than 90.7%, and the accuracy rate of this model reached 97.14%. A benchmark data simulation of mechanical transmission systems and an experimental data investigation of an axial piston pump are performed to manifest the superiority of the present method by comparing with classification and regression trees (CART) and support vector machine (SVM).

A Novel Deep Learning Model for Mechanical Rotating Parts Fault Diagnosis Based on Optimal Transport and Generative Adversarial Networks

To solve the poor real-time performance of the existing fault diagnosis algorithms on transmission system rotating components, this paper proposes a novel high-dimensional OT-Caps (Optimal Transport–Capsule Network) model. Based on the traditional capsule network algorithm, an auxiliary loss is introduced during the offline training process to improve the network architecture. Simultaneously, an optimal transport theory and a generative adversarial network are introduced into the auxiliary loss, which accurately depicts the error distribution of the fault characteristic. The proposed model solves the low real-time performance of the capsule network algorithm due to complex architecture, long calculation time, and oversized hardware resource consumption. Meanwhile, it ensures the high precision, early prediction, and transfer aptitude of fault diagnosis. Finally, the model’s effectiveness is verified by the public data sets and the actual faults data of the transmission system, which provide technical support for the application.

Application of an information fusion scheme for rolling element bearing fault diagnosis

This study addresses the high level of misdiagnoses and low reliability of individual rolling element bearing fault diagnosis methods by proposing a fault diagnosis scheme with enhanced diagnosis accuracy that combines the results of two individual diagnosis methods based on an improved information fusion method. The proposed scheme applies variational mode decomposition in conjunction with a support vector machine for conducting fault diagnosis in the frequency domain, which achieves high fault diagnosis precision for learned fault conditions. Meanwhile, good generalization ability is achieved for identifying the operational conditions of bearings in the time domain by integrated mathematical morphology and correlation analysis. Subsequently, conflicts arising between evidences derived from the individual detection results are measured comprehensively using a novel strategy, and the evidences are then combined based on the Dempster–Shafer theory (DST) to enhance the information fusion effect. The effectiveness of the proposed information fusion method is verified by means of a numerical example in comparison with other fusion methods based on DST. The experimental application of the proposed information fusion fault diagnosis scheme demonstrates the complementary advantages of the two individual methods for significantly improving the diagnosis accuracy relative to the accuracies of the individual methods alone.

Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images

Vibration failure is a common problem in most rotating machinery, and vibration fault diagnosis is an important means of ensuring stable equipment operation. The present work proposes a rotor vibration fault diagnosis approach that transforms multiple vibration signals into symmetrized dot pattern (SDP) images, and then identifies the SDP graphical feature characteristic of different vibration states using a convolutional neural network (CNN). SDP images reveal different vibration states in a simple and intuitive manner. In addition, a CNN can reliably and accurately identify vibration faults by extracting the feature information of SDP images adaptively through deep learning. The proposed approach is tested experimentally using a rotor vibration test bed, and the results obtained are compared to those obtained with an equivalent CNN-based image recognition approach using orbit plot images. The rotor fault diagnosis precision is improved from 92% to 96.5%.

Alpha‐Stable Distribution and Multifractal Detrended Fluctuation Analysis‐Based Fault Diagnosis Method Application for Axle Box Bearings

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off‐line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band‐pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha‐stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.