Intelligent Fault Diagnosis Model Research Articles

End-to-End Intelligent Fault Diagnosis of Transmission Bearings in Electric Vehicles Based on CNN

Environmental noise and transmission components can cause significant interference in vibration signals, rendering the extraction of bearing fault features challenging in service scenarios. Traditional fault diagnosis methods rely heavily on professional domain knowledge, prior models, and signal preprocessing methods. The accuracy of fault diagnosis depends on the quality of the fault-sensitive features extracted by vibration signal preprocessing methods. An improved convolutional neural network (CNN) end-to-end intelligent fault diagnosis model based on raw vibration data (RVDCNN) is proposed. The time-domain vibration signal of the transmission bearing is converted into a continuous two-dimensional numerical matrix, and a two-dimensional CNN model is constructed through network structure optimization. The original time-domain vibration signal numerical matrix of the bearing is trained and tested to extract and learn abstract fault features of different fault types, and then the fault classification of the bearing is achieved. To verify the generalizability of the RVDCNN intelligent fault diagnosis model, it is applied to the recognition of rolling bearings in the two-speed mechanical automatic transmission of electric vehicles, achieving recognition accuracy of 99.11% for seven types of bearings.

A Collaborative Domain Adversarial Network for Unlabeled Bearing Fault Diagnosis

At present, data-driven fault diagnosis has made significant achievements. However, in actual industrial environments, labeled fault data are difficult to obtain, making the industrial application of intelligent fault diagnosis models very challenging. This limitation even prevents intelligent fault diagnosis algorithms from being applicable in real-world industrial settings. In light of this, this paper proposes a Collaborative Domain Adversarial Network (CDAN) method for the fault diagnosis of rolling bearings using unlabeled data. First, two types of feature extractors are employed to extract features from both the source and target domain samples, reducing signal redundancy and avoiding the loss of critical signal features. Second, the multi-kernel clustering algorithm is used to compute the differences in input feature values, create pseudo-labels for the target domain samples, and update the CDAN network parameters through backpropagation, enabling the network to extract domain-invariant features. Finally, to ensure that unlabeled target domain data can participate in network training, a pseudo-label strategy using the maximum probability label as the true label is employed, addressing the issue of unlabeled target domain data not being trainable and enhancing the model’s ability to acquire reliable diagnostic knowledge. This paper validates the CDAN using two publicly available datasets, CWRU and PU. Compared with four other advanced methods, the CDAN method improved the average recognition accuracy by 7.85% and 5.22%, respectively. This indirectly proves the effectiveness and superiority of the CDAN in identifying unlabeled bearing faults.

Automated fault diagnosis of rotating machinery using sub domain greedy Network Architecture search

Network Architecture Search (NAS) automates hyperparameter adjustments in deep learning models, offering a potent solution for building intelligent fault diagnosis models. Despite its potential, NAS faces challenges in this application. The primary issues include the excessive time required when searching across the full data domain and the inefficacy of the common random search strategy in yielding high-performance sub networks. This study addresses these challenges through several key initiatives: It defines a specific library of hyperparameters tailored for intelligent fault diagnosis tasks; constructs sub data domain model clusters to reduce the computational costs associated with NAS; and introduces a hyperparameter search strategy leveraging a greedy algorithm, which enhances the search efficiency for optimal diagnostic sub network and minimizes its prediction errors. When applied to datasets involving gear and bearing faults, the proposed method demonstrates a reduction in both the time cost of searches and the prediction errors of sub networks, compared to traditional Network Architecture Search. Moreover, it outperforms manually tuned deep learning models by simultaneously optimizing multiple sets of hyperparameter and automatically generating higher accuracy diagnostic sub networks.

A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis

Although current deep learning models for bearing fault diagnosis have achieved excellent accuracy, the lack of constraint-guided learning of the physical mechanisms of real bearing failures and a physically scientific training paradigm leads to low interpretability and unreliability of intelligent fault diagnosis models. In this study, a sound-vibration physical-information fusion constraint-guided (PFCG) deep learning (DL) method is proposed, aiming at weighted fusion of sound and vibration multi-physical information into a deep learning model, to guide the DL model to learn more realistic physical laws of bearing failure. Firstly, a 15-degree-of-freedom nonlinear dynamics model of multi-stage degraded bearing failure mechanism with sound-vibration response is developed, which considers the evolutionary mechanism of bearing failure from healthy state to different stages, and utilizes a particle filtering algorithm for dynamic calibration of hidden parameters. Moreover, a lightweight DL fault diagnosis model is designed to realize the deep interaction between the physical model and the DL model through the weighted fusion of the cross-entropy loss function, physical consistency loss and uncertainty loss. Moreover, the superior diagnostic performance of the proposed sound and vibration PFCG-DL model is verified by comparing the performance fluctuations and parameter attributes of different DL benchmark models before and after being guided by physical information fusion constraints (PFCG). Eventually, the proposed PFCG-Transformer model achieves a diagnostic accuracy of 99.45% while keeping the number of parameters at only 0.62M, which significantly improves the accuracy and reduces the computational complexity by 81.5% compared to the CAME-Transformer model's 3.24 M number of parameters and 95.00% diagnostic accuracy. In addition, the test time of PFCG-Transformer is reduced to 1.02 s, which is 60.2% less than CAME-Transformer, demonstrating higher computational efficiency and real-time performance. Importantly, in terms of interpretability, the engineering interpretability and credibility of the models are further improved by visualizing the feature learning results of the vibration modal and multimodal fusion models and the sensitivity analyses of the sound-vibration response models with internal and external physical hyperparameters. Therefore, this study proposes a physical information-guided deep learning method with strong interpretability and superior performance, which provides an important reference for further research and application in the field of bearing fault diagnosis.

A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis

Existing data-driven Intelligent Fault Diagnosis (IFD) methods often struggle with continuous learning and integrating new diagnostic knowledge. These methods necessitate a complete retraining process when new fault data emerges. Furthermore, collecting sufficient data for model training is impractical in real-world applications. This study proposes a Feature Extension and Reconstruction (FER) method for model incremental updating with limited samples. FER employs scalable and modular feature extractors for continuous representation learning of new fault modes. In addition, a domain mapping algorithm based on optimal transport is devised to leverage old classification nodes in aiding new nodes’ training, enhancing models’ generalization performance under limited samples. A feature reconstruction technique is also developed to compress the expanded multi-backbone model, eliminating redundant parameters and dimensions for adaptability in subsequent incremental tasks. Validated on simulated transmission systems and real planetary gearboxes, FER demonstrates a better stability-plasticity trade-off in incremental tasks with limited training samples, offering a novel solution for adaptive IFD model updates in sample-constrained scenarios.

Concurrent fault diagnosis of small pressurized water reactors based on long-short term memory networks

The control systems of small pressurized water reactors (SPWR) with complex structures, compact layouts, and variable operating environment may be involved in various types of signal and concurrent faults. Concurrent faults can be taken as two or more single faults occurring simultaneously, which usually cause much larger damage to the system and are more difficult to be diagnosed than single faults. However, their fault diagnosis methods are rarely studied because of the numerous fault types and tremendous diagnostic difficulty. This paper explores the concurrent fault diagnosis method for sensors and actuators in SPWR control systems. An intelligent current fault diagnosis model is developed using long short-term memory network with the training and test datasets generated based on a fault simulation platform of the target SPWR. The test results show that both single and concurrent faults of the SPWR can be diagnosed rapidly in an average of 1.06 s after their occurrence with the classification and diagnosis accuracies reaching up to 96.61% and 97.27%, respectively. Moreover, by injecting different noise signals on the faulty dataset for training and validation, it is shown that the proposed LSTM network has strong noise immunity. This demonstrate the excellent diagnosis accuracy and efficiency of the model under both single and concurrent fault conditions. This study provides valuable guidance for the accuracy diagnosis of complex concurrent faults of nuclear power plants.

A hybrid semantic attribute-based zero-shot learning model for bearing fault diagnosis under unknown working conditions

Most current intelligent fault diagnosis models, dependent on specific working condition data for training, cannot effectively diagnose faults in unknown working conditions without data. Zero-shot learning (ZSL) can identify samples unseen during the training phase and has now been applied to the field of fault diagnosis. However, effective methods for constructing fault semantics in unknown conditions for zero-shot fault diagnosis are still lacking. This paper proposes a hybrid semantic attribute-based zero-shot learning model (HSAZLM) to address this issue. A novel approach to constructing the semantic space is introduced by combining manually defined semantic attributes (SA) with non-semantic attributes (NSA) to form hybrid semantic attributes (HSA). This addresses the issue of insufficient semantic information due to limitations in expert knowledge when defining SA. In the hybrid semantic attribute construction module, a denoising residual convolutional autoencoder (DRCAE) is employed as a semantic learning model to acquire NSA containing high-dimensional abstract features, enhancing the model's generalization and robustness. To verify the effectiveness and superiority of the proposed HSAZLM, experiments were conducted on both a publicly available bearing dataset and a self-built bearing dataset. The average accuracy rates are 96.82% and 96.42% respectively, exceeding the current state-of-the-art methods.

Intelligent fault diagnosis of bearings driven by double-level data fusion based on multichannel sample fusion and feature fusion under time-varying speed conditions

Bearing fault diagnosis holds paramount importance in guaranteeing the smooth operation of mechanical systems. However, single-channel vibration sensor data poses limitations in providing a comprehensive representation of bearing fault characteristics, and the fault diagnosis model extracts fault features with insufficient representativeness, resulting in low reliability and poor generalization capability of the model in the face of variable operating conditions. Therefore, a novel intelligent fault diagnosis model of bearings driven by double-level data fusion (DLDF) is developed in this paper. Initially, the fusion weights of each channel's data are obtained using the information entropy method. Then, a novel multi-modal image fusion strategy is proposed to integrate the time-frequency representations of vibration data from channels X, Y, and Z at a specific measurement point. Finally, the shallow and deep feature components extracted from the CNN model and the feature components obtained through different mapping methods are fused to extract more representative fault features, achieving a reliable diagnosis of bearing faults under time-varying speed conditions. The efficacy and reliability of the proposed approach are demonstrated through the fault diagnosis outcomes of two sets of bearing experimental data under time-varying speed conditions.

Hydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

Intelligent diagnosis method for machine faults based on federated transfer learning

Intelligent fault diagnosis model based on federated learning can effectively solve the problem of fault data privacy and sharing, and ignores the difference of fault data distribution. Transfer learning can avoid the difference of data distribution. Combining the advantages of transfer learning and federated learning, a fault diagnosis model with data privacy based on federated transfer learning is proposed to achieve cross-domain fault diagnosis without sharing the data. In the constructed model, the local models on the client are firstly established based on deep convolution neural network to extract the feature of the source and target domains. The alignment loss is introduced to minimize the similar feature distribution differences among different source domains and target domain. The parameters of local models are fused and updated to generate a global model, which can not only identify the fault types in the target domain, but also retain the ability to recognize the fault types in the source domain. The two experiments, including different bearings with same feature distribution and label and the bearing and planetary gear in the same transmission system with similar feature distribution, are used to verify the effectiveness of the proposed model. The experiments suggest that the fault diagnosis model based on federated transfer learning can reduce the difference of the newly added fault type data distribution, and can accurately recognize the fault data of the source domain and target domain. Compared with the traditional diagnosis model based on deep learning, transfer learning and federated learning, the proposed model can effective perform the cross-domain fault diagnosis with data privacy.

A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data

With the rapid development of railroads and the yearly increase in the scale of operation, the safe operation and maintenance of rail trains have become particularly important. Among them, deep learning-based bearing fault diagnosis methods have attracted more and more attention in rail train operation and maintenance. However, rail trains usually operate normally. Collecting complete fault data for deep learning model training is often difficult. Such scenarios with a large difference between the number of normal data and fault data usually affect the performance of fault diagnosis models. Here, an interactive generative feature space oversampling-based autoencoder (IGFSO-AE) is proposed to realize fault sample generation under imbalanced data. First, the original vibration signal is converted into a semantically stable amplitude–frequency signal by fast Fourier transform and input into the autoencoder; second, the order of the hidden layer space features of the autoencoder is randomly exchanged, and the interactive sample generation learning strategy trains the autoencoder; then, interpolation oversampling is used to interpolate samples in the hidden layer space where the Euclidean distance between samples is large, and is input into the decoder, the generated samples are mixed with the original samples to form a new training set, which is used to train the intelligent fault diagnosis model and output the diagnosis results. Finally, the performance of the proposed method is evaluated using the publicly available bearing dataset and the bogie-bearing fault simulation bench in our lab. The experimental results show that IGFSO-AE can generate diverse samples with incremental information and exhibits robustness and superiority in different imbalanced proportions of data.

TSoft-Net: A novel transfer soft thresholding network based on self-attention for intelligent fault diagnosis of rotating machinery

In the industrial scenes, the machinery operates under diverse working conditions and generates varying levels of noise, which can hinder the performance of the intelligent fault diagnosis model that is trained in the laboratory. This is because the different working conditions and environmental noise change the distribution of laboratory vibration signal. To tackle this issue, we proposed a novel transfer soft thresholding network (TSoft-Net) based on self-attention for intelligent fault diagnosis of rotating machinery, which has good anti-noise performance and can be transferred to different working conditions with excellent accuracy. This paper constructs a soft residual block for extracting the fault representation and enhancing the residual learning. In this block, we propose a residual factor to learn and enhance domain-invariant fault representation. Furthermore, a representation soft fusion block is built for extracting and fusing the different scale fault representation. In this block, we propose a scale-attention weight to dynamically fuse the different scale fault representation. The experiments show that the TSoft-Net, compared with six existing methods on eight sub-datasets, has better anti-noise ability and achieves an accuracy of up to 100% on the target domain.

M-Net: a novel unsupervised domain adaptation framework based on multi-kernel maximum mean discrepancy for fault diagnosis of rotating machinery

AbstractThe intelligent fault diagnosis model has made a significant development, whose high-precision results rely on a large amount of labeled data. However, in the actual industrial environment, it is very difficult to obtain a large amount of labeled data. It will make it difficult for the fault diagnosis model to converge with limited labeled industrial data. To address this paradox, we propose a novel unsupervised domain adaptation framework (M-Net) for fault diagnosis of rotating machinery, which only requires unlabeled industrial data. The M-Net will be pretrained using the labeled data, which can be accessed through the labs. In this stage, we propose a multi-scale feature extractor that can extract and fuse multi-scale features. This operation will generalize the features further. Then, we will align the distribution of the labeled data and unlabeled industrial data using the generator model based on multi-kernel maximum mean discrepancy. This will reduce the distribution distance between the labeled data and the unlabeled industrial data. For now, the unsupervised domain adaptation problem has shifted to a semi-supervised domain adaptation problem. The results, obtained through experimental comparison, demonstrate that the M-Net can achieve an accuracy of over 99.99% with labeled data and a maximum transfer accuracy of over 99% with unlabeled industrial data.

A Generalised Intelligent Bearing Fault Diagnosis Model Based on a Two-Stage Approach

This paper introduces a two-stage intelligent fault diagnosis model for rolling element bearings (REBs) aimed at overcoming the challenge of limited real-world vibration training data. In this study, bearing characteristic frequencies (BCFs) extracted from a novel hybrid method combining cepstrum pre-whitening (CPW) and high-pass filtering developed by the authors’ group are used as input features, and a two-stage approach is taken to develop an intelligent REB fault detect and diagnosis model. In the first stage, various machine learning (ML) methods, including support vector machine (SVM), multinomial logistic regressions (MLR), and artificial neural networks (ANN), are evaluated to identify faulty bearings from healthy ones. The best-performing ML model is selected for this stage. In the second stage, a similar evaluation is conducted to find the most suitable ML technique for bearing fault classification. The model is trained and validated using vibration data from an EU Clean Sky2 I2BS project (An EU Clean Sky 2 project ‘Integrated Intelligent Bearing Systems’ collaborated between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and tested on datasets from Case Western Reserve University (CWRU) and the US Society for Machinery Failure Prevention Technology (MFPT). The results show that the two-stage model, using an SVM with a polynomial kernel function in Stage-1 and an ANN with one hidden layer and 0.05 dropout rate in Stage-2, can successfully detect bearing conditions in both test datasets and perform better than the results in literature without the requirement of further training. Compared with a single-stage model, the two-stage model also shows improved performance.

A fault diagnosis method for nuclear power plants rotating machinery based on deep learning under imbalanced samples

Rotating machinery is a critical equipment widely used in nuclear power plants (NPPs). Effective fault diagnosis technology can provide reliable operation and maintenance support for rotating machinery. Data-driven intelligent fault diagnosis technology has attracted much attention in recent years. However, in practical situations, the available fault data is limited, and the imbalanced samples will make the intelligent fault diagnosis model prone to problems such as poor generalization performance and low diagnostic accuracy. Therefore, a fault diagnosis method based on deep learning is proposed to alleviate the impact of imbalanced samples on fault diagnosis of rotating machinery. First, the adaptive synthetic sampling (ADASYN) approach is used to synthesize the multi-channel vibration data to expand the unbalanced samples. Subsequently, ensemble empirical mode decomposition (EEMD) and continuous wavelet transform (CWT) are used to convert the vibration data into time–frequency images to highlight the fault features of the samples. Then, the deep residual neural network is constructed to extract the features of mixed samples and implement fault diagnosis. Fault simulation experiments are carried out based on motors and bearings, and various imbalance degree datasets are formed to provide data support for verifying the effectiveness of the proposed method. The proposed method can achieve good diagnostic performance under various degrees of imbalanced samples. In addition, the comparison results with other methods show that the proposed method has the best comprehensive performance, demonstrating the potential application value in the fault diagnosis of NPPs rotating machinery.

Gearbox fault diagnosis method based on lightweight channel attention mechanism and transfer learning

In practical engineering, the working conditions of gearbox are complex and variable. In varying working conditions, the performance of intelligent fault diagnosis model is degraded because of limited valid samples and large data distribution differences of gearbox signals. Based on these issues, this research proposes a gearbox fault diagnosis method integrated with lightweight channel attention mechanism, and further realizes the cross-component transfer learning. First, time–frequency distribution of original signals is obtained by wavelet transform. It could intuitively reflect local characteristics of signals. Secondly, based on a local cross-channel interaction strategy, a lightweight efficient channel attention mechanism (LECA) is designed. The kernel size of 1D convolution is affected by channel number and coefficients. Multi-scale feature input is used to retain more detailed features of different dimensions. A lightweight convolutional neural network is constructed. Finally, a transfer learning method is applied to freeze lower structures of the network and fine-tune higher structures of the model using small samples. Through experimental verification, the proposed model could effectively utilize samples. The application of transfer learning could realize accurate and fast fault classification of small samples, and achieve good gearbox fault diagnosis effect under varying working conditions and cross-component conditions.

Meta-learning Based Domain Generalization Framework for Fault Diagnosis With Gradient Aligning and Semantic Matching

Intelligent fault diagnosis models have demonstrated a superior performance in industrial prognostics health management scenarios. However, these models may struggle to generalize in complicated industrial environments, when encountering new working conditions and handling low-resource and heterogeneous data. To cope with the aforementioned issues, we focus on constructing a universal training framework with domain generalization technique that will encourage fault diagnosis model to generalize well in unseen working conditions. Firstly, a model-agnostic meta-learning based training framework called Meta-GENE is proposed for homogeneous and heterogeneous domain generalization. Secondly, a gradient aligning algorithm is introduced in meta-learning framework to learn domain-invariant strategy for robust prediction in unseen working conditions. Thirdly, a semantic matching technique is proposed for utilizing heterogeneous data to alleviate low-resource problem. Our method has yielded excellent performance on the PHM09 fault diagnosis dataset and achieved superior results on a set of generalization tasks across various working conditions.

Deep Mixed Domain Generalization Network for Intelligent Fault Diagnosis Under Unseen Conditions

Emerging intelligent fault diagnosis models based on domain adaptation can resolve domain shift problems produced by different working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address this challenge, a deep mixed domain generalization network (DMDGN) is proposed for intelligent fault diagnosis. In this novel model, data augmentation is applied to both class and domain spaces, adversarial learning is employed to introduce adversarial perturbations, and a domain-based discrepancy metric is used to balance intra-domain and inter-domain distances. The model can effectively learn more domain-invariant and discriminative features from multiple source domains to perform different generalization tasks for different working loads and machines. The feasibility of the DMDGN model is verified on two public datasets and one private dataset collected from practical production processes. Empirical results show that the DMDGN model outperforms several state-of-the-art models.

Contrast-Assisted Domain-Specificity-Removal Network for Semi-Supervised Generalization Fault Diagnosis.

Unknown domain shift caused by the unavailability of target domain during training phase degrades the performance of intelligent fault diagnosis models in practical applications. Domain generalization (DG)-based methods have recently emerged to alleviate the influence of domain shift and improve the generalization ability of models toward invisible working conditions. However, most existing studies are conducted on multiple fully labeled source domains. Meanwhile, domain-specific information related to the variations of working conditions is often neglected during model training. Therefore, in order to realize reliable generalization fault diagnosis based on partially labeled source domains, this article proposes a contrast-assisted domain-specificity-removal network (CDSRN) to extract transferable features from domain-specificity-removal perspective. Concretely, a domain-specific feature removal branch is designed to disentangle domain-invariant features and domain-specific features, thus excavating generalized information only in domain-invariance dimension. Simultaneously, proxy-contrastive representation enhancement module is embedded to facilitate the fault class-discriminative and domain-discriminative feature learning, thereby assisting the model in further improvement of generalization capability. Experimental studies confirm the effectiveness and competitiveness of the proposed CDSRN in semi-supervised generalization fault diagnosis.

Multi-sensor fusion rolling bearing intelligent fault diagnosis based on VMD and ultra-lightweight GoogLeNet in industrial environments

As artificial intelligence and sensor technology develop rapidly, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. However, in practical industrial applications, the complex noise environment affects the performance of the diagnostic model, and the huge model parameters cannot meet the requirements of low cost and high performance in industrial production. To address the above problems, this paper proposes a lightweight intelligent fault diagnosis model using multi-sensor data fusion that not only meets the lightweight requirements of "small, light, and fast", but also realizes high accuracy diagnosis in noisy environments. Firstly, the vibration signals from different sensors of rolling bearings are processed using the variational mode decomposition (VMD) to design a unique method of constructing grayscale feature maps based on each intrinsic modal function (IMF) component. Then, the ultra-lightweight GoogLeNet model (UL-GoogLeNet) is constructed to adjust the traditional GoogLeNet structure, while the Ultra-lightweight subspace attention module (ULSAM) is introduced to reduce the model parameters and enhance the feature extraction capability. UL-GoogLeNet is trained and tested by dividing the grayscale feature maps into training and testing sets to realize the intelligent recognition of different fault types in rolling bearings. Experiments are conducted on two datasets and compared with multiple methods, and the final experimental results prove the effectiveness and superiority of the proposed method in this paper.