Fault Diagnosis Approach Research Articles

Intelligent Fault Diagnosis Across Varying Working Conditions Using Triplex Transfer LSTM for Enhanced Generalization

Fault diagnosis plays a pivotal role in ensuring the reliability and efficiency of industrial machinery. While various machine/deep learning algorithms have been employed extensively for diagnosing faults in bearings and gears, the scarcity of data and the limited availability of labels have become a major bottleneck in developing data-driven diagnosis approaches, restricting the accuracy of deep networks. To overcome the limitations of insufficient labeled data and domain shift problems, an intelligent, data-driven approach based on the Triplex Transfer Long Short-Term Memory (TTLSTM) network is presented, which leverages transfer learning and fine-tuning strategies. Our proposed methodology uses empirical mode decomposition (EMD) to extract pertinent features from raw vibrational signals and utilizes Pearson correlation coefficients (PCC) for feature selection. L2 regularization transfer learning is utilized to mitigate the overfitting problem and to improve the model’s adaptability in diverse working conditions, especially in scenarios with limited labeled data. Compared with traditional transfer learning approaches, such as TCA, BDA, and JDA, which demonstrate accuracies in the range of 40–50%, our proposed model excels in identifying machinery faults with minimal labeled data by achieving 99.09% accuracy. Moreover, it performs significantly better than classical methods like SVM, RF, and CNN-based networks found in the literature, demonstrating the improved performance of our approach in fault diagnosis under varying working conditions and proving its applicability in real-world applications.

Research on Fault Diagnosis Method for Photovoltaic Array Based on XGBoost Algorithm

INTRODUCTION: Photovoltaic (PV) energy sources frequently experience issues, including fragmentation, open-circuit, short-circuiting, and other common and hazardous problems. The current focus of PV research is on fault detection within solar arrays. Traditional models encounter challenges in identifying errors due to uncertainties in panel settings and the complex nature of the actual PV structure.OBJECTIVES: This study aims to introduce a novel Extreme Gradient Boosting (XGBoost) approach for fault diagnosis in PV arrays.METHODS: The XGBoost algorithm is trained using collected PV array defect data samples. Data preprocessing is performed to manage missing values and remove noisy data. Feature extraction is conducted using Linear Discriminant Analysis (LDA) to improve detection accuracy. To further enhance XGBoost’s performance, the World Cup Optimization (WCO) approach is applied to select optimal features from the extracted data. Fault detection is then conducted using the XGBoost algorithm on the processed data. Various indicators are utilized for performance assessment within the Python environment.RESULTS: The comparative analysis demonstrates that this research improves fault detection efficiency in PV arrays compared to existing methodologies.CONCLUSION: The study presents an effective method for enhancing fault detection in PV systems, showcasing the advantages of the XGBoost and WCO-based approach over conventional methods.

Bearing fault diagnosis method for unbalance data based on Gramian angular field

In the application of deep learning-based fault diagnosis, more often than not, the network model could perform better with a balanced dataset input, whereby the number of fault data is equivalent to that of normal data. However, in the context of real-world applications, the number of fault data is generally insufficient compared to the normal data. In this study, a new approach for fault diagnosis in unbalanced data sets is proposed using the Gramian angular field (GAF) method. Firstly, the GAF method is employed to convert one-dimensional data into two-dimensional data, which enhances the feature extraction process. Secondly, to balance the sample distribution, fault data is generated using Generative Adversarial Networks (GANs). Finally, the Residual neural network (ResNet) with an attention mechanism is utilized to improve the accuracy of fault diagnosis. The proposed method is experimentally validated using open-source bearing datasets that are published by Case Western Reserve University and the University of Ottawa. The experimental results show that the proposed method has greatly improved fault diagnosis performance in cases of data distribution imbalance, surpassing that of the compared methods.

A hydraulic motor fault diagnosis method based on weighted multi-channel information fusion

Abstract In response to the limitations of the existing single-sensor hydraulic motor fault diagnosis model, which includes significant fluctuations in fault identification accuracy, low data utilization, poor reliability, and insufficient generalization ability under variable working conditions, a novel hydraulic motor fault diagnosis method based on weighted fusion of multi-channel data and migration learning is proposed. Firstly, in order to fully extract the fault information in the multi-channel data set of the hydraulic motor, a multi-channel fusion method based on information entropy weighting is proposed. The information entropy method is employed to calculate the fusion weight of each channel of data, and the sampled data of each channel is weighted and fused. Subsequently, the fusion data from the source domain is employed to pre-train the deep transfer model, with the model parameters obtained from this pre-training serving as the initialization parameters for the target domain model. Further, the parameters of the target domain model’s feature extractor are fixed, and the parameters of its classifier are fine-tuned using the target domain’s fusion data. The distance between the source and target domains is reduced by incorporating an attention mechanism and constructing a loss function. The migration from the source domain to the target domain is achieved, which enables the classifier to adapt to the novel target sample recognition task. Ultimately, the experimental results of hydraulic motor migration diagnosis under variable operating conditions demonstrate that the proposed method is efficacious for hydraulic motor fault diagnosis. In comparison to conventional models such as CNN, LSTM and ResNet, the proposed method exhibits superior migration diagnosis accuracy and strong generalization and robustness under variable operating conditions.

Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions

Abstract Bearings, as the core component for power transmission, are crucial in ensuring the safe and reliable operation of equipment. However, the fault information contained in a single-channel vibration signal is inherently limited. Additionally, under time-varying speed conditions, features are prone to drift, and the cross-domain diagnostic performance of most traditional domain adaptation (DA) models may drop dramatically. To solve the above problems and enhance the ability of DA models in extracting domain invariant features, this paper introduces a Multi-channel data fusion and Attention-guided Multi-feature Fusion-driven Center-aligned Network (MAMC). Initially, a multi-channel time-frequency information fusion strategy based on wavelet transform is constructed to achieve a comprehensive fusion of multi-channel data, thereby obtaining richer fault feature representations. Subsequently, a multi-branch feature fusion network, integrated with an attention mechanism, is devised to capture significant features across various dimensions and scales, resulting in more comprehensive and representative fault features. Finally, a novel Center-Aligned Domain Adaptation method (CADA) is proposed based on domain adversarial methods and center loss. By minimizing the distance between deep domain invariant features and trainable common class centers, the issue of domain shift between data is effectively alleviated, and the cross-domain diagnostic performance of DA models under the time-varying speed conditions is improved. The experimental results indicate that the MAMC method exhibits superior performance on both bearing datasets and is a promising approach for cross-domain intelligent fault diagnosis.

Handling Domain Drift and Unknown Fault Detection in Rotating Machinery Using Few‐Shot Learning With Data Scaling

ABSTRACTThis article presents a novel approach for fault diagnosis in rotating machinery using the few‐shot learning‐based unknown recognition and classification (FSLB‐UR&C) model. The core contribution of this research lies in addressing the challenges of domain drift, unknown fault detection, and uneven sampling intervals. The model effectively incorporates data scaling using Min–Max scaling to manage vibration data drift without altering the source data distribution, significantly extending the classification range. Principal component analysis visualizations substantiate the superior performance of Min–Max scaling over standard methods in processing drifted data, thus enhancing model accuracy. We validated the model on an in‐house bearing simulator and a coal conveyor motor, demonstrating its enhanced ability to classify drifted data and identify previously unknown faults. Additionally, time interpolation was applied to handle irregular sampling intervals, further optimizing the framework's robustness. Compared to other deep learning techniques, the FSLB‐UR&C model with data scaling exhibits markedly improved performance.

Fault Diagnosis in a Four-Arm Delta Robot Based on Wavelet Scattering Networks and Artificial Intelligence Techniques

This paper presents a comprehensive fault diagnosis approach for a delta robot utilizing advanced feature extraction and classification techniques. A four-arm delta robot prototype is designed in SolidWorks for realistic fault analysis. Two case studies investigate faults through control effort and vibration signals, with control effort detecting motor and encoder faults, while vibration signals identify bearing faults. This study compares time-domain signal features and wavelet scattering networks, applied by classification algorithms including wide neural networks (WNNs), efficient linear support vector machine (ELSVM), efficient logistic regression (ELR), and kernel naive Bayes (KNB). Results indicate that a WNN, using wavelet scattering features ranked by one-way anova, is optimal due to its consistency and reliability, while these features enhance computational efficiency by reducing classifier size. Sensitivity analysis demonstrates the classifier’s capacity to detect untrained faults, highlighting the importance of effective feature extraction and classification methods for fault diagnosis in complex robotic systems. This research significantly contributes to fault diagnosis in delta robots and lays the groundwork for future studies on fault tolerance control and predictive maintenance planning. Future work will focus on the physical implementation of the delta robot in laboratory settings, aiming to improve operational efficiency and reliability in industrial applications.

Conditional Variational Autoencoder and generative adversarial network-based approach for long-tailed fault diagnosis for the motor system

This study addresses the challenge of diagnosing motor faults in long-tailed data distributions, characterized by dominant healthy states and rare fault types. We propose the LT-CVAE-GAN model, which integrates a Conditional Variational Autoencoder (CVAE) with a Conditional Generative Adversarial Network (CGAN) to enhance long-tailed fault diagnosis. Initially, we train the CVAE-GAN model using traditional CVAE and CGAN losses such as Kullback–Leibler (KL) divergence loss, reconstruction loss, and adversarial loss. Additionally, we introduce mean feature matching loss and pairwise feature matching loss to mitigate mode collapse and improve model stability, thereby enhancing the generation ability of less frequent fault samples under long-tail conditions. Subsequently, the pre-trained Generator is used to produce infrequent fault mode data to rebalance the dataset. Classifier parameters are fine-tuned in this step to improve fault diagnosis accuracy. Experimental results demonstrate that our LT-CVAE-GAN surpasses state-of-the-art models in diverse long-tailed conditions.

Topology-aware fault diagnosis for microgrid clusters with diverse scenarios generated by digital twins

Microgrid clusters, characterized by their dynamically changing topologies and flexible operational modes ranging from grid-tied to autonomous functioning, present significant challenges for conventional protective measures. To address these complexities, this paper proposes a novel topology-aware fault diagnosis approach that integrates Message Passing Neural Networks (MPNNs) with a Graph-Lasso-based topology identification method. Our framework is designed to enhance the precision and adaptability of fault diagnosis in microgrid clusters, which are crucial for maintaining system stability. The proposed model demonstrates over 99% accuracy in fault localization and over 97% in fault type recognition, even under varying topological conditions and data loss scenarios. Through the application of digital twin technology, the framework not only improves the diagnostic capabilities but also supports the intelligent operation and maintenance of microgrid clusters, thereby contributing to the overall reliability and quality of their electrical systems.

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

A Novel Approach for Fault Detection and Diagnosis in Multi-mode Processes Based on PCA, Random Forest, and K-means Clustering

Acoustic fault diagnosis of three-phase induction motors using smartphone and deep learning

Faults in induction motors can halt production lines in factories, leading to downtime and resulting in production and economic losses. Therefore, it is crucial to ensure that motors operate reliably. This paper describes an approach for the acoustic fault diagnosis of rotor bars in three-phase induction motors (IM). The authors analyzed the following conditions: a healthy IM, an IM with one broken rotor bar, an IM with two broken rotor bars, and an IM with three broken rotor bars. The FFT method was used to compute the FFT spectrum of the acoustic signals. An original feature extraction method DWV (Differences of Word Vectors) was proposed to compute the acoustic features. DenseNet-201, ResNet-18, ResNet-50, and EfficientNet-b0 were used to classify these acoustic features. The computed recognition efficiency is 100 %. The proposed method was also verified using a low-pass filter of 1–1225 Hz and word coding.

Adaptive fusion graph convolutional network based interpretable fault diagnosis method for HVAC systems enhanced by unlabeled data

Fault diagnosis is critical in maintaining the operational stability and reliability of Heating, Ventilation, and Air Conditioning (HVAC) systems, which are crucial for ensuring indoor environmental quality and energy efficiency in buildings. However, traditional fault diagnosis methodologies face substantial challenges in accurately capturing the dynamic interactions within these systems and effectively utilizing unlabeled data. To overcome these limitations, an innovative fault diagnosis approach utilizing the Adaptive Fusion Graph Convolution Network (AFGCN) is proposed in this paper. This method significantly enhances the model's learning and inference abilities, particularly in scenarios with limited labeled data, by adaptively integrating the associative graph features of both unlabeled and labeled data. Furthermore, to augment the transparency and trustworthiness of the diagnostic outcomes, this paper introduces an interpretability analysis module. This module is designed to quantify the contribution of each sensor node in the fault diagnosis process. Experimental evaluations using the ASHRAE RP-1043, actual building operating chillers datasets, and LBNL FDD Data Sets_SDAHU indicate that this method offers substantial performance improvements in diagnosing faults in HVAC systems.

A class-center fine-tuning prototypical network for few-shot fault diagnosis of turnout switch machine driven by multi-source signals

The turnout switch machine is mainly responsible for controlling the switch converting and locking, which is a crucial component of the rail transit signal system, and its reliability directly impacts the safety of rail transit transportation. However, many existing methods for diagnosing faults in turnout switch machines suffer from low accuracy and poor generalization capabilities. The primary issue is that the fault data of turnout switch machines are scarce, and the single-source data cannot fully reflect the fault characteristics. To address these issues, this paper introduces a fault diagnosis approach for the turnout switch machine based on a class-center fine-tuning prototype network (CFPN). Firstly, current signals of three phases are fused with different weights through an adaptive data fusion strategy, using the fused signal as input data. Secondly, a multiple weighted feature fusion network (MWFFN) based on the activation function Meta-ACON that can learn whether to activate neurons is proposed, which can effectively extract and fuse features at different levels in the fusion signal. In addition, a fine-tuning center balance strategy is proposed to effectively expand the distance between prototypes and enhance intra-class aggregation. Finally, a few-shot sample experiment is conducted to verify the performance of CFPN, using fused current data collected from’ a switch machine. In the comparative experiment, CFPN shows good fault diagnosis performance, the fault diagnosis accuracy reaches up to 99.08%. At the same time, fused vibration data are used to conduct a cross-domain experiment on CFPN, and the results indicate that CFPN still shows good classification effect and has good generalization performance, demonstrating its high practical application value.

New approach to multivariate statistical process control and its application to wastewater treatment process monitoring

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate StatisticalProcess Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. Firstly,a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand userinterface and then a new yet simplified reconfigurable diagnostic model is introduced. The user interface that has beendeveloped is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trendgraphs. The proposed diagnostic model is constructed by aggregating small models with either one or two inputs, whichenhances the tractability of the diagnostic model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment.plant.

KAN-HyperMP: An Enhanced Fault Diagnosis Model for Rolling Bearings in Noisy Environments

Rolling bearings often produce non-stationary signals that are easily obscured by noise, particularly in high-noise environments, making fault detection a challenging task. To address this challenge, a novel fault diagnosis approach based on the Kolmogorov–Arnold Network-based Hypergraph Message Passing (KAN-HyperMP) model is proposed. The KAN-HyperMP model is composed of three key components: a neighbor feature aggregation block, a feature fusion block, and a KANLinear block. Firstly, the neighbor feature aggregation block leverages hypergraph theory to integrate information from more distant neighbors, aiding in the reduction of noise impact, even when nearby neighbors are severely affected. Subsequently, the feature fusion block combines the features of these higher-order neighbors with the target node’s own features, enabling the model to capture the complete structure of the hypergraph. Finally, the smoothness properties of B-spline functions within the Kolmogorov–Arnold Network (KAN) are employed to extract critical diagnostic features from noisy signals. The proposed model is trained and evaluated on the Southeast University (SEU) and Jiangnan University (JNU) Datasets, achieving accuracy rates of 99.70% and 99.10%, respectively, demonstrating its effectiveness in fault diagnosis under both noise-free and noisy conditions.

An Integrated Approach for Current Balancing and Open-Circuit Fault Diagnosis for Interleaved Boost Converter

An Integrated Approach for Current Balancing and Open-Circuit Fault Diagnosis for Interleaved Boost Converter

Low-Power Chemiresistive Gas Sensors for Transformer Fault Diagnosis.

Dissolved gas analysis (DGA) is considered to be the most convenient and effective approach for transformer fault diagnosis. Due to their excellent performance and development potential, chemiresistive gas sensors are anticipated to supersede the traditional gas chromatography analysis in the dissolved gas analysis of transformers. However, their high operating temperature and high power consumption restrict their deployment in battery-powered devices. This review examines the underlying principles of chemiresistive gas sensors. It comprehensively summarizes recent advances in low-power gas sensors for the detection of dissolved fault characteristic gases (H2, C2H2, CH4, C2H6, C2H4, CO, and CO2). Emphasis is placed on the synthesis methods of sensitive materials and their properties. The investigations have yielded substantial experimental data, indicating that adjusting the particle size and morphology structure of the sensitive materials and combining them with noble metal doping are the principal methods for enhancing the sensitivity performance and reducing the power consumption of chemiresistive gas sensors. Additionally, strategies to overcome the significant challenge of cross-sensitivity encountered in applications are provided. Finally, the future development direction of chemiresistive gas sensors for DGA is envisioned, offering guidance for developing and applying novel gas-sensitive sensors in transformer fault diagnosis.

An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural/machinery health monitoring

An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural/machinery health monitoring

An integrated approach for mechanical fault diagnosis using maximum mean square discrepancy representation and CNN-based mixed information fusion

Bearings are an essential component of modern industry and cross-domain diagnosis of bearings holds significant importance. However, in practical applications, issues such as insufficient training data and differences between equipment present challenges. Transfer learning has become one of the effective methods to address these problems. This article proposes a fault diagnosis method that combines maximum mean square discrepancy (MMSD) measurement with the convolutional neural network (CNN) with mixed information (MIXCNN). MIXCNN enhances spatial position discrimination through deep convolution and achieves cross-channel information interaction using traditional convolution. The introduction of residual connections reduces information loss, while increasing network depth focuses on highly distinguishable features. MMSD constructs a metric that comprehensively reflects the mean and variance information of data samples in the reproducing kernel Hilbert space, thereby enhancing domain confusion. Experimental results show that this method achieves high diagnostic accuracy in various transfer tasks, with a maximum accuracy of 99.29%, providing reliable support for bearing fault diagnosis.

Intelligent fault diagnosis of hoisting systems under complex working conditions using deep graph convolutional generative adversarial networks with limited data

Owing to the difficulty of collecting adequate fault data from hoisting systems under complex working conditions, data-driven diagnostic methods suffer from decreased accuracy due to inadequate fault information. To deal with this problem, we introduce an intelligent few-shot fault diagnosis approach using a deep graph convolutional generative adversarial networks (DGCGANs) algorithm. The goal of the proposed method is to acquire a more comprehensive few-shot fault feature information, thus facilitating the generation and augmentation of scarce data. Results of two cases including laboratory and real-world industrial datasets demonstrate the viability and efficacy of our approach for diagnosing few-shot faults. Specifically, the proposed DGCGAN method outperforms existing advanced diagnostics, thereby offering superior accuracy in identifying hoisting system faults with limited data. Finally, the practicality and adaptability of the proposed DGCGAN-based fault diagnosis method are further substantiated through comprehensive ablation studies.