Adaptive Convergent Visibility Graph Network: An interpretable method for intelligent rolling bearing diagnosis

Voltage transformers are an important component of power systems. However, transformers work in complex environments such as high noise and strong magnetic interference for a long time, resulting in problems such as multiple types of faults, difficulty in extracting fault characteristics, and low accuracy in fault diagnosis. Based on this, this article proposes a fault diagnosis method based on adaptive local iterative filtering (ALIF)-fuzzy dispersion entropy (FDE)-adaptive wavelet threshold filtering and improved graph neural networks (GNNs). Firstly, ALIF is used to perform modal decomposition on the original signal, and FDE is employed to eliminate noise-dominant modes and extract optimal voltage sample feature vectors. Secondly, an adaptive wavelet threshold function based on FDE features is proposed to suppress residual noise. By dynamically adjusting parameters, it synchronously suppresses excessive high-frequency smoothing and pseudo-Gibbs phenomenon, achieving noise suppression and feature fidelity of the signal. Finally, a multi-layer bridged high-order GNN integrating dynamic attention mechanisms and contrastive normalization is constructed. This network enhances the ability to capture long-range dependencies in voltage signals through hierarchical feature aggregation. Experimental results show that the fault diagnosis algorithm proposed in this paper exhibits high noise robustness and diagnostic accuracy. Compared to fixed threshold functions and traditional GNNs, it improves the signal-to-noise ratio by 6.4%, reduces the mean squared error by 42.5%, increases the diagnostic accuracy by 2.61%, and enhances the F1 score by 3.60%, achieving high-precision fault diagnosis for voltage transformers.

Nowadays, chemical processes are becoming more complex, and so the safety requirement is getting higher and higher. Intelligent and effective fault detection and diagnosis are becoming more and more important. However, complex industrial systems generate high-dimensional data, which has bad influence on fault detection and diagnosis. Thus, it is necessary to handle industrial data with high dimensionality. Discriminant Locality Preserving Projections (DLPP) has attracted much interest as a dimensionality reduction method. However, there is a small size sample problem when the data dimension is higher than the number of data classes. Under this condition, DLPP cannot achieve acceptable performance in reducing data dimension. In order to solve this problem, this paper proposes a novel DLPP based on improved Synthetic Minority Oversampling Technique (SMOTE-DLPP). The improved SMOTE is used to sample the original data set to generate new data sets so that the number of data classes is basically the same as the number of data dimension. Simulation results on the Tennessee Eastman process (TEP) show that the proposed SMOTE-DLPP can achieve acceptable performance in fault diagnosis.

This paper introduces a novel metaheuristic model-based scheme for fault monitoring in squirrel cage induction motors (SCIMs). This method relies on the combination of the ant lion optimizer (ALO) and the unscented Kalman filter (UKF) to detect and quantify the number of broken bars. Contrary to the UKF-based fault diagnosis, the improved ALO-UKF algorithm tunes optimally and automatically the noise covariance matrices Q and R, which reduces the estimation errors, and then obtains an effective and accurate fault diagnosis. Firstly, a mathematical model of the fault under study has been developed based on rotor parameter value as signature. Secondly, a sixth order ALO-UKF algorithm has been synthesized for simultaneous estimation of rotor resistance and speed. Several broken bar fault conditions have been simulated. Simulation results show the effectiveness and robustness of the proposed ALO-UKF scheme in broken bar detection and identification, and exhibit a more superior performance than the simple-UKF and EKF algorithms in term of stability, accuracy and response time.

Multi-agent based coordinated protection systems for distribution feeder fault diagnosis and reconfiguration

The battery system, as the core energy storage device of new energy vehicles, faces increasing safety issues and threats. An accurate and robust fault diagnosis technique is crucial to guarantee the safe, reliable, and robust operation of lithium-ion batteries. However, in battery systems, various faults are difficult to diagnose and isolate due to their similar features and internal coupling relationships. In this paper, the current research of advanced battery system fault diagnosis technology is reviewed. Firstly, the existing types of battery faults are introduced in detail, where cell faults include progressive and sudden faults, and system faults include a sensor, management system, and connection component faults. Then, the fault mechanisms are described, including overcharge, overdischarge, overheat, overcool, large rate charge and discharge, and inconsistency. The existing fault diagnosis methods are divided into four main types. The current research and development of model-based, data-driven, knowledge-based, and statistical analysis-based methods for fault diagnosis are summarized. Finally, the future development trend of battery fault diagnosis technology is prospected. This paper provides a comprehensive insight into the fault and defect diagnosis of lithium-ion batteries for electric vehicles, aiming to promote the further development of new energy vehicles.

Purpose In practical engineering applications, the working load of the rotor system keeps changing, and its working environment is severely polluted by noise, leading to a decline in the performance of traditional fault diagnosis methods. To better extract fault features in a noisy environment and improve the diagnostic accuracy, a fault diagnosis model based on the Visibility Graph algorithm and Graph Transformer is proposed. Design/methodology/approach This paper proposes a fault diagnosis model based on Visibility Graph algorithm and Graph Transformer. Firstly, the spectrum of the original vibration signal is constructed into a Visibility Graph, with the spectrum mean value as the node value and the visibility relationship between nodes defined. Subsequently, the graph transformer is used to extract the global node features and topological structure information in the graph, and the long-range dependencies are captured through the self-attention mechanism. Finally, the fully connected layer and the Softmax classifier are combined to achieve end-to-end fault diagnosis. Findings Experimental results on the MAFAULDA dataset demonstrate that the proposed model achieves a diagnostic accuracy of 99.91% under noise-free conditions, surpassing comparative methods by 1.12–14.53%. Under −10 dB Gaussian white noise interference, the model maintains an accuracy of 74.36%, outperforming baseline models by 1.42–48.49%. Further cross-scenario verification on the CWRU and XJTU-SY datasets shows that this method has strong robustness against compound faults and extreme noise. The results show that this method effectively enhances the ability to extract fault features in noisy environments, providing an efficient and reliable solution for intelligent monitoring of rotor systems. Originality/value In order to effectively realize end-to-end intelligent fault diagnosis, enhance the robustness and anti-noise performance of the model, a new fault diagnosis method of the rotor system based on the Visibility Graph algorithm and Graph Transformer is proposed in this paper. By constructing the vibration signal spectrum as a viewable data and then effectively capturing the topological relationship of the graph in the global range through the self-attention mechanism, the fault diagnosis is realized in the form of node classification. Finally, the fault analysis and classification of the vibration signals of the rotor system under the background of no noise and noise are carried out.

Buildings are subject to faults in their heating, ventilation and air-conditioning (HVAC) systems that can lead to excessive energy wastage, poor indoor climate, equipment failures and high maintenance costs. Field studies have shown that employing fault detection, diagnosis and prognosis (FDDP) tools followed up with equipment services and corrections can help achieve up to 40% of energy savings within the HVAC system and improve indoor climate, increase equipment lifecycle and reduce maintenance costs. The increasing adoption of building automation systems (BAS), Internet of Things (IoT) and other smart technologies in recent years have allowed large amounts of data to be continuously collected from building systems. This data-rich environment, along with the surge in data analytics and machine learning tools, has made cost-effective data-driven FDDP strategies possible. Compared to purely physics-based methods, data-driven methods require less explicit knowledge of the underlying physical system, thus are often easier to develop, and can learn certain intricate relationships that exist among data. Within the reported data-driven FDDP methods, there exists a few research gaps: 1) data imputation methods that leverage mutual information from correlated measurements to defy poor data quality from BAS have not been utilized efficiently; 2) there lacks a systematic and scalable fault diagnosis framework that incorporates probabilistic temporal relationships to track fault evolution; 3) existing fault diagnosis strategies typically focus on traditional rule-based control strategies and their scalability for advanced control strategies such as Guideline 36 have not been explored yet; 4) active threats information such as cyber-attacks, are typically not incorporated in an FDDP framework; 5) fault prognosis strategies to preemptively identify gradual faults for predictive maintenance have rarely been studied. This research attempts to address the above-mentioned research gaps through the following: Data Imputation: reported data imputation methods that are suitable for handling and repairing multi-source BAS data are evaluated. Data collected from a medium-sized, mixed-use institution building situated in Stockholm, Sweden and a small commercial building simulated in a laboratory setup is used to evaluate five different data imputation methods. Results demonstrate that incorporating time-lagged cross-correlations within the k-Nearest Neighbor (kNN) method helps to significantly improve the imputation accuracy and minimize the impact of repaired data on data-driven algorithms. Dynamic Bayesian Network (DBN)-based Framework for Cyber-Physical Fault Diagnosis: a DBN framework with discretized conditional probabilities parameters to represent the temporal relationships among building measurements is developed. Both domain knowledge and machine learning methods are used to develop the DBN structure and parameter model. The developed framework is evaluated for both traditional rule-based and Guideline 36 controls using datasets from a real building, a laboratory building, and a virtual testbed. Results show that the developed DBN framework is effective in diagnosing and isolating faults in systems even with different control strategies. The framework also successfully distinguishes whether system abnormalities originate from cyber-attacks or naturally occurring physical faults. Potential future direction to improve fault isolation using modified DBN topological structure is also reported in this study. DBN-based Framework for Fault Prognosis: an extension of the DBN framework in conjunction with Robust Multivariate Temporal (RMT) variate selection is proposed for fault prognosis. The RMT variate selection is used to extract localized temporal features from high dimensional datasets to determine the best inputs for training forecasting models. The expected fault-free behavior of multiple target variates, selected using domain knowledge, is forecasted using incoming data. The prediction errors generated from the forecasting phase are used as evidences in the DBN inference to estimate future fault probabilities. Gradual faults simulated in the virtual testbed are used to evaluate the prognosis framework. Results show that the developed framework is effective in prognosing gradual faults by leveraging the trending growth on the prediction errors. The research presented in this thesis contributes to the overall objective of developing a robust and cost-effective DBN-based framework for fault diagnosis and prognosis of building HVAC systems. Potential solutions to other existing challenges of implementing data-driven FDDP strategies, such as obtaining high-quality datasets, handling and repairing missing data, establishing a baseline model for detecting abnormalities despite other disturbances such as weather and internal conditions changes, and extracting temporal features from timeseries data are also examined.

Rotating machinery fault diagnostics has received a lot of attention in recent years. As a result, there has been an increase in research interest in rotating machine intelligent fault detection, especially when using measurement from multi-sensors. However, an accurate fault diagnosis is still challenged based on nonlinear and non-stationary vibration signals. On the other hand, not enough research has been done on structural information fusion from multi-sensor measurement due to the complexity of spatial–temporal correlation. This paper explores the use of vibration signals in multi-sensor measurement fusion for rotating machinery fault diagnostics, and a method using a cross-attention-based dual-branch graph neural network (CA-GNN) is proposed. First, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose vibration signals. Variational mode decomposition is then used to further deconstruct the highest-frequency subsequence after its parameters have been optimized using the whale optimization algorithm. Next, we build the CA-GNN, which contains two space branches for high- and low-frequency signals after initially creating the graph using multiple sensor data sets. Information across both branches for high- and low-frequency components can then be efficiently fused. Lastly, two experimental scenarios are used to illustrate the suggested technique and assess its viability and accuracy for rotating machinery fault diagnosis. Results indicate that the proposed method can diagnose rotating machinery health issues with an average accuracy of up to 99%, indicating that the method’s performance can fulfill real-world requirements.

The problem of the wind turbine gearbox fault diagnosis was investigated by employing the support vector machine (SVM), which is optimized by the improved fruit fly intelligent algorithm with decreasing steps. First of all, the fault characteristic value extracted by Hilbert transform envelope is presented. Secondly, the general SVM solution to the wind turbine gearbox fault diagnosis problem is presented, where the improved fruit fly intelligent algorithm is adopted to optimize the performance of the model of the fault detection and diagnosis. Thirdly, the effectiveness of three fault diagnosis models are compared, including the fault detection and diagnosis model by the traditional SVM, its improved model optimized by the partical swarm optimization algorithm, and improved model by the SVM optimized by the proposed approach. By using the proposed optimization algorithm, the accuracy of gearbox fault diagnosis is much better than other two models, which are validated by the extensive simulation results based on practical historical operation data.

Accurate aircraft engine fault detection and diagnosis is vitally important reducing operating costs and improving safety. Various data and knowledge could be collected from manufacture, test bed measurement systems, on-board measurement systems, maintenance history and experts' experience. Integrating and fusing these data and information to provide intelligent fault diagnosis and maintenance schedules are essentially to both civil and military engines. Information fusion strategies and architectures have been developed over the last several years for improving upon the accuracy, robustness and overall effectiveness of diagnostic and prognostic technologies. Fusion of relevant sensor data, maintenance database information, and various diagnostic and prognostic technologies has been proven effective in reducing false alarm rates, increasing confidence levels in early fault detection, and predicting time to failure or degraded condition requiring maintenance action. In this paper, we researched on the architectures of fusion systems. A four-level model was presented to fit the usage in aircraft engines fault detection and diagnosis. To generate the deviations for gas path fault detection, baseline values should be chosen firstly. We analyzed choosing baseline values and generating deviations in detail. Then, the fuzzy set was introduced to descript the degree of symptoms belonging to fault patterns. A method based on fuzzy set to isolate fault and quantify the deterioration of performance of components was presented. We also demonstrated the method with an example. For this method, it is not necessary to have more measurements than fault patterns. The fault diagnosis system based on it is very easy to construct and extend too

In order to realize a stable and effective gearbox fault diagnosis system, an uncertain information fusion method based on BP neural network and modified DS evidence theory is proposed. The common gearbox fault diagnosis system consists of two dominating parts: signal feature extraction and fusion decision. Actually, the main task of most papers is to extract the characteristics of gearbox fault with various algorithms, whereas the fusion decision part is still absent. To manage the integration of gearbox fault diagnosis in the fusion decision part, this paper puts forward a novel fusion method by the introduction of two-layer fusion. In this paper, BP neural network is used to establish the local-layer fusion, while an improved DS evidence theory is taken to build decision-layer fusion. It can be seen from the definition and derivation of the two-layer fusion, the original method settles the uncertainty of multi-sensor information fusion in gearbox fault diagnosis system. According to the experiment results and analyses, the innovative algorithm improves the accuracy of fault identification and diagnosis, in addition, reduces the uncertainty of fault diagnosis system.

Prosumers refer to the integration of production and consumption. Due to a large number of access to distributed power sources, electric vehicles, etc., which have a certain impact on power transformers, and increasing potential failures, transformers need to be monitored. In recent years, image recognition technology based on deep learning has been widely used in intelligent inspection image analysis. Aiming at the problem that the accuracy of appearance fault diagnosis in intelligent inspection images is limited by image quantity and quality, an image data set optimization method based on a seamless cloning algorithm and image cleaning is proposed. First, a sample generation method based on the seamless fusion algorithm is proposed, which seamlessly fuses the corroded texture of other power equipment into the transformer image to generate the rust transformer image. On this basis, an image quality evaluation and screening method based on the XGBoost algorithm is proposed to evaluate the image quality of the data set and clean the low-quality images. In addition, aiming at the limitation of a single diagnosis algorithm, an appearance diagnosis method based on heterogeneous model ensemble learning is proposed. By constructing multiple learning models and using a weighted voting strategy to fuse model outputs as final outputs, the accuracy of fault diagnosis is improved.

This paper proposes a new method for fault diagnosis of new energy transmission system based on combined BiLSTM-1DCNN model. The EV dataset is preprocessed and converted into a format acceptable to the model. The BiLSTM neural network is used to extract temporal features, and the 1DCNN network is used to capture the spatial features of the data. The combination of the two networks can effectively improve the accuracy and efficiency of fault diagnosis. Comparing the proposed method with traditional machine learning methods, it is found that the proposed method has higher accuracy and faster convergence speed. Sensitivity analysis is used to study the impact of different hyperparameters on model performance. The results show that the performance of the model is affected by the number of layers, learning rate and window size in the 1DCNN network. In addition, the experiments and datasets of this study are from ADVISOR of the US Department of Energy (DOE). The proposed combined BiLSTM-1DCNN model provides a promising method for fault diagnosis of new energy drive systems, which can improve the efficiency and accuracy of fault diagnosis.

Fault identification and diagnosis of steam turbine generator unit is very important for safely and economically operation of a power plant. Currently, on-line monitoring is already widely utilized for alarming and recording in steam turbine. However, large amount of on-line monitoring data is not fully utilized to realize on-line fault diagnosis and identification, especially multi-concurrent fault. In the present study, model-based method was used for on-line vibrational fault identification and diagnosis based on rotordynamics. A 660MW supercritical steam turbine rotor system was modeled using FEM. Single faults, such as mass unbalance, local shaft bow and transverse crack, and multi-concurrent faults were simulated. Fault-induced changes of equivalent loads were analyzed to figure out fault type, location and severity. For model-based method, fault diagnosis accuracy is influenced by the model accuracy and signal noise. Model sensitivity was studied in this research by comparing the influence of different model error and SNR (Signal to Noise Ratio). It was used to evaluate the degree of confidence of the diagnosis result. The smaller was the model sensitivity, the higher was the degree of confidence. Based on this research, model-based method was utilized to analyze real vibration signals extracted from the historical data of this unit. Fault identification result was an effective basis for conditioned based maintenance.

In an intelligent manufacturing context, the smooth operations of mechanical equipment in the production process of enterprises and timely fault diagnosis during operations have become increasingly important. However, the effect of traditional fault diagnosis depends on the feature extraction quality and experts’ empirical knowledge, which is inefficient and costly, and cannot match the needs of mechanical equipment fault diagnosis in intelligent manufacturing. The TSK fuzzy system has a strong approximation capability and the ability to interpret expert knowledge. The broad learning system (BLS) has strong feature extraction and fast computation capabilities. In this paper, we present a new model—the TSK fuzzy broad learning system (TSK-BLS). The model integrates the advantages of the BLS and the fuzzy system at the same time, which can be calculated quickly and accurately by pseudo-inverse and symmetry methods. On the other hand, the model is an embedded model-building mechanism, which extends the application scope of BLS theory. The model was tested on a bearing fault data set, provided by Case Western Reserve University, and the model’s fault diagnosis accuracy was as high as 0.9967. The results were compared with those of the convolutional neural network (CNN) and the BLS models, whose fault diagnosis accuracies are 0.6833 and 0.9133, respectively. Comparison showed that the proposed fault diagnosis model—TSK-BLS, achieved significant improvements.