Fault Diagnosis Approach Research Articles

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry’s need for real-time, user-friendly solutions.

Lightweight Network Bearing Intelligent Fault Diagnosis Based on VMD-FK-ShuffleNetV2

With the increasing complexity of mechanical equipment and diversification of deep learning models, vibration signals collected from such equipment are susceptible to noise interference. Moreover, traditional neural network models struggle to be effectively deployed in production environments with limited computational resources, severely impacting the accurate extraction and effective diagnosis of FK fault characteristics. In response to this challenge, this study proposes a fault diagnosis method for rolling bearings, integrating a lightweight ShuffleNetV2 network with variational mode decomposition (VMD) and the fast kurtogram (FK) algorithm. Initially, this paper introduces an enhanced FK method where the VMD algorithm is employed for data denoising, extracting FK post-denoising. These feature maps not only preserve critical signal information but also simplify data complexity. Subsequently, these feature maps are utilized to train and test the ShuffleNetV2 model, facilitating effective fault identification and classification. Ultimately, by conducting experimental comparisons with several mainstream lightweight network models, such as MobileNet and SqueezeNet, as well as traditional convolutional neural network models, this study validates the effectiveness of the proposed method in extracting fault characteristics from vibration signals, demonstrating superior diagnostic accuracy and computational efficiency. This provides a novel technical approach for health monitoring and fault diagnosis of industrial bearings and offers theoretical and experimental support for the deployment of lightweight networks in industrial applications.

A transformer winding and core mechanical fault diagnosis approach based on VMD-MFE and GWO-MKELM

A transformer winding and core mechanical fault diagnosis approach based on VMD-MFE and GWO-MKELM

Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems: Advances, Challenges, and Prospects

Progress on the Fault Diagnosis Approach for Lithium-ion Battery Systems: Advances, Challenges, and Prospects

A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine

The existing chiller fault diagnosis approaches often ignore the problem of data imbalance of chiller, which leads to low accuracy in diagnosing minority class fault samples. To conquer this issue, this paper proposes an improved generative adversarial network (IGAN) with an enhanced deep extreme learning machine (EDELM) method. Firstly, to better learn the latent structure of chiller fault data, the multi-head attention (MHA) mechanism is integrated into the traditional generative adversarial network (GAN) method to generate new samples that are more in line with the distribution of minority class fault samples for the purpose of obtaining a rebalanced dataset. Secondly, to fully handle the nonlinear features hidden in the massive chiller data, the deep extreme learning machine (DELM) basic classifier is trained on the rebalanced dataset. To enhance more attention to the misclassified samples, the adaptive boosting (AdaBoost) ensemble strategy is employed to train multiple DELM basic classifiers by updating the sample weights following the classification results through the iterative rounds. The voting weight of the current DELM basic classifier is given according to its fault diagnosis accuracy. Finally, multiple DELM basic classifiers are ensembled according to their voting weights to obtain the final ensemble classifier. The pattern of the snapshot sample is determined through the weighted voting strategy. Detailed experimental results based on the research project 1043 (RP-1043) conducted by the American society of heating, refrigeration, and air conditioning engineers (ASHRAE) confirm the effectiveness of the proposed IGAN-EDELM approach under imbalanced data environments.

A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks

In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.

A novel zero-shot learning approach for cross-domain fault diagnosis in high-voltage circuit breakers

High-voltage circuit breakers (HVCBs) are crucial for power system reliability, yet diagnosing their mechanical failures is a significant challenge due to their complex design and unique operating conditions, especially when facing new, unseen fault types. To address this challenge, we propose a novel zero-shot HVCB fault diagnosis method, named X-SAM, leveraging the Xception network and self-attention mechanism (SAM). X-SAM aims to identify new, unseen fault classes solely using known class fault samples during the training phase. X-SAM innovatively combines adaptive feature extraction from multi-source signals and a novel attribute learning system that maps these features to a universal set of fault attributes. This enables the diagnosis of new fault types by analyzing the similarity between predicted attribute vectors and a predefined attribute matrix. Demonstrated through zero-shot diagnosis tasks on multiple HVCBs, X-SAM not only successfully identifies unseen faults using limited data but also paves the way for cross-domain applications, offering a significant advancement in HVCB and electrical equipment fault diagnosis.

Transformer-based deep learning networks for fault detection, classification, and location prediction in transmission lines

ABSTRACT Fault detection, classification, and location prediction are crucial for maintaining the stability and reliability of modern power systems, reducing economic losses, and enhancing system protection sensitivity. This paper presents a novel Hierarchical Deep Learning Approach (HDLA) for accurate and efficient fault diagnosis in transmission lines. HDLA leverages two-stage transformer-based classification and regression models to perform Fault Detection (FD), Fault Type Classification (FTC), and Fault Location Prediction (FLP) directly from synchronized raw three-phase current and voltage samples. By bypassing the need for feature extraction, HDLA significantly reduces computational complexity while achieving superior performance compared to existing deep learning methods. The efficacy of HDLA is validated on a comprehensive dataset encompassing various fault scenarios with diverse types, locations, resistances, inception angles, and noise levels. The results demonstrate significant improvements in accuracy, recall, precision, and F1-score metrics for classification, and Mean Absolute Errors (MAEs) and Root Mean Square Errors (RMSEs) for prediction, showcasing the effectiveness of HDLA for real-time fault diagnosis in power systems.

A fault diagnosis approach for flange stabilizer based on multi-signal fusion

Abstract As an essential means of energy transportation, pipelines have been widely used in various fields. However, many external factors such as vibration and corrosion can cause damage at the flange part, which seriously affects the safety of pipeline transportation. Quite a number of methods for troubleshooting at pipeline flanges have been continuously proposed, yet there is little research on diagnostic methods for the stabilizer at the flange. Therefore, in this paper, we focus on the stabilizer of the flange and a method that combines traditional detection and machine learning with each other to detect stabilizer faults is proposed. At first, we can obtain a stable and reliable diagnostic data by combining the advantages of the preload of the bolt and the acoustic signal. Subsequently, the optimized N-Beats model is trained based on the measured bolt preload data to predict the service state of the stabilizer. Finally, the data measured by the sensors as well as the predicted data are analyzed by a simplified classification algorithm to determine whether a fault has occurred and to classify the fault. The fault detection method used in this paper not only improves the accuracy of detection and shortens the fault detection time, but also improves the automation level of pipeline inspection. Hence, the work done in this paper has far-reaching practical significance for ensuring the safe and stable operation of pipelines.

Automatic sucker rod pump fault diagnostics by transfer learning using googlenet integrated machine learning classifiers

Oil and gas extraction is vital for meeting the energy needs of a growing global population. Artificial lift (AL) systems play a crucial role in oilfields, especially when reservoir pressure is low. Among these systems, sucker rod pumps (SRPs) are extensively employed for onshore hydrocarbon recovery operations. However, SRPs are susceptible to mechanical failures and operational challenges arising from evolving reservoir conditions. This study proposes an approach for fault diagnosis in SRPs using real-time data from sensors and machine learning (ML) algorithms to detect anomalies and patterns associated with potential problems. The proposed method involves deep learning (DL) for automated feature extraction from dynamometer cards and error-correcting output codes (ECOC) model-based supervised machine learners for efficiently assessing the operational state of the SRP. By forecasting potential failures, preemptive measures can be taken to minimize downtime and reduce maintenance costs. The application of ML in the analysis of SRP provides a potential tool for diagnosing and predicting failures, improving productivity efficiency, and reducing downtime. Also, the accuracy of Convolutional Neural Networks (CNN) models was compared with that of CNN-ECOC model-based learners in this study, and the results demonstrated that integrating algorithms such as Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) with the network caused significant improvements in the accuracy; however, Decision Tree (DT) and Naive Bayes (NB) did not perform well in the pattern classification task. Specifically, the GoogLeNet model achieved improved accuracy of 96.40 % and 99.40 % when SVM and k-NN were integrated, respectively. Conversely, with DT and NB, the accuracies dropped to 66.30 % and 75.10 %, respectively.

Research on a Bearing Fault Diagnosis Method Based on an Improved Wasserstein Generative Adversarial Network

In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.

A General and Efficient Fault Diagnosis Approach Using Hyperdimensional Computing Enhanced by Local Feature Extraction

Abstract Ensuring the safe and reliable operation of industrial equipment is of great importance, and accordingly, machine fault diagnosis has attracted considerable attention. Recently, various deep learning-based fault diagnosis models have been widely developed due to the powerful ability of feature learning and nonlinear modeling of deep learning models, and these methods heavily rely on large amounts of data and high memory and resources. However, it is difficult to obtain enough data in practical applications, thus leading to the unsatisfactory performance of deep learning-based models and high cost. In this paper, we propose a new and efficient fault diagnosis approach based on Hyperdimensional computing (HDC), which can learn high-dimensional, random, and holographic distributed representations of input features. It has the advantages of single training, low training cost, and does not require a large amount of data. Therefore, we apply HDC to the field of few-shot fault diagnosis for the first time, and propose a simple and efficient approach for few-shot fault diagnosis based on hyperdimensional computing with local feature extraction(L-HDC). Instead of using original data, the model firstly extracts the local features of original data by hand-designed features, and then carries out HDC-encoding, training, and test classification. Through the comparison of various models and the analysis of various data, the effectiveness of our proposed model is verified. In the case of few-shot learning, the recognition accuracy is as high as 90\% and the training time of ordinary HDC model is 5-10 times that of L-HDC model, which is superior to other models.

A novel approach of fault diagnosis for gearbox based on VMD optimized by SSA and improved RCMDE

A novel approach of fault diagnosis for gearbox based on VMD optimized by SSA and improved RCMDE

Research on a self-powered rolling bearing fault diagnosis method with a piezoelectric generator for self-sensing

The self-powered fault diagnosis methods provide a new idea to realize fault diagnosis without using wired sensors or battery-supported sensors. However, current self-powered fault diagnosis methods mainly depend on self-powered sensors, most of which may destroy the original structure of mechanical parts or contain connected energy harvesters and sensors, resulting in reduced reliability and stability. In this paper, a self-powered rolling bearing fault diagnosis method with a piezoelectric generator (PEG) for self-sensing is proposed. The PEG can be simply installed without destroying the bearing structure, and its electrical outputs excited by the mechanical vibrations can be further used for fault diagnosis. The energy conversion mechanism of a conventional monostable PEG and the basic knowledge of the convolutional neural network (CNN) model are first introduced, based on which the self-powered fault diagnosis approach is proposed. Experimental work is conducted to identify the parameters of the PEG's governing equation and verify its effectiveness. The responses of a PEG installed onto a rolling bearing experimental table are further measured and analyzed. Finally, the CNN model is trained based on the electrical outputs of the PEG under different installation positions, different rotation speeds and different healthy statuses of the bearings. The research results show that the average classification accuracies of this method for rotational speeds of 1200 rpm, 3000 rpm and 4800 rpm can achieve 88.389%, 98.99% and 98.431%, respectively, which are quite feasible in practical applications. This work shows a totally new idea of adopting a vibration energy harvester as a self-sensing device to achieve self-powered fault diagnosis, which has wide application potential in the future.

Partial Transfer Learning Method Based on Inter-Class Feature Transfer for Rolling Bearing Fault Diagnosis.

Rolling bearing fault diagnosis methods based on transfer learning always assume that the sample classes in the target domain are consistent with those in the source domain during the training phase. However, it is difficult to collect all fault classes in the early stage of mechanical application. The more likely situation is that the training data in the target domain only contain a subset of the entire health state, which will lead to the problem of label imbalance compared with the source domain. The outlier classes in the source domain that do not have corresponding target domain samples for feature alignment will interfere with the feature transfer of other classes. To address this specific challenge, this study introduces an innovative inter-class feature transfer fault diagnosis approach. By leveraging label information, the method distinctively computes the distribution discrepancies among shared classes, thereby circumventing the deleterious influence of outlier classes on the transfer procedure. Empirical evaluations on two rolling bearing datasets, encompassing multiple partial transfer tasks, substantiate that the proposed method surpasses other approaches, offering a novel and efficacious solution for the realm of intelligent bearing fault diagnosis.

DACA: A domain adaptive fault diagnosis approach with class-aware based on cross-domain extreme imbalance data

Existing research on unsupervised domain adaptation (UDA) has primarily centered on mitigating differences in feature distribution across various domains. However, in real-world scenarios, natural data often exhibits not only a disparity in instance counts across different classes within the same domain but also across different domains, leading to imbalances at both the class and domain levels. To address this issue, this study introduces a novel scenario, termed extreme imbalance domain adaptation (EIDA), which encompasses both covariate shifts and labeling shifts. We propose a Domain Adaptation with Class-aware (DACA) framework for bearing fault diagnosis that aims to tackle inter-domain feature shifts as well as intra- and inter-domain category imbalances. Specifically, DACA enhances the recognition accuracy of minority categories without degrading the performance concerning the majority category, reduces the adverse effects of data imbalance during domain transitions, and strengthens the robustness of decision boundaries for improved spatial matching. This is achieved through four sub-modules: adversarial clustering learning, cross-domain category alignment, category-level reweighting, and category-level re-margining. The efficacy of DACA is demonstrated through simulations of three imbalance protocols in the EIDA scenario across two bearing datasets and one gearbox dataset, showcasing significant advancements in cross-domain diagnostic tasks.

Histogram-Based Gradient Boosting Tree: A Federated Learning Approach for Collaborative Fault Diagnosis

Histogram-Based Gradient Boosting Tree: A Federated Learning Approach for Collaborative Fault Diagnosis

Research on diagnosis method of motor vibration signal based on MSCNN-LSTM

Abstract Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. In addition, one-dimensional convolution networks effectively solve the problem of excessively long timing of original signals, and LSTM captures timing dependence for diagnosing motor faults. The experimental results indicate that MSCNN-LSTM has high accuracy in motor fault diagnosis based on vibration signals.

A unified sensor and actuator fault diagnosis in digital twins for remote operations

This paper explores the development of a unified hybrid approach for sensor and actuator fault diagnosis in digital twins for remote operations. Central to this approach is the implementation of a robust adaptive Kalman filter algorithm, which forms the backbone of the proposed unified algorithm. The essence of this unified algorithm lies in its capability to effectively filter the sensor measurements. The algorithm is enriched with tuning parameters, offering flexibility in adjusting the convergence rate to suit operational requirements. Noteworthy for its robustness, our approach excels in handling uncertainties and diverse types of faults, including drift, bias, noise, and freeze fault scenarios. Through comprehensive simulation and experimental evaluations conducted on a small surface vessel, the method demonstrates remarkable proficiency in accurately identifying sensor and actuator faults. This precision enables early detection and prompt mitigation of anomalies, contributing to heightened operational resilience.

A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors

A Cost-Effective Fault Diagnosis and Localization Approach for Utility-Scale PV Systems Using Limited Number of Sensors