Deep Learning-based Fault Diagnosis Research Articles

A model-data combination driven digital twin model for few samples fault diagnosis of rolling bearings

Abstract Deep learning-based fault diagnosis methods for rolling bearings are widely utilized due to their high accuracy. However, they have limitations under conditions with few samples. To address this problem, a model-data combination driven digital twin model (MDCDT) is proposed in this work for fault diagnosis with few samples of rolling bearings. The simulation signals generated by different fault dynamic models of rolling bearings and the measured signals are mixed through MDCDT. The MDCDT generates virtual signals to bridge the gap between the simulated signals and the measured signals by combining their respective advantages. This paper also proposes image coding method based on the Markov transfer matrix (MTMIC) to convert one-dimensional vibration signals into two-dimensional images with both frequency domain information and time domain information, making it easier to extract fault features in neural network training. In the end, the developed MDCDT was evaluated using real rolling bearing data. Experiments show that the MDCDT can generate virtual data for fault diagnosis, and the fault diagnosis accuracy is significantly improved.

Learning to generalize with latent embedding optimization for few- and zero-shot cross domain fault diagnosis

Ensuring the safety and reliability of rotating machinery in modern industrial production and intelligent manufacturing is of paramount importance. While deep learning-based fault diagnosis methods offer promise, the scarcity of fault samples and the variations in distributions between training and test data due to variable working conditions make it difficult for these methods to be applied in industrial scenarios. To surmount these obstacles, we present a novel solution: learning to generalize with latent embedding optimization. Our proposed method, tailored for few-shot and zero-shot cross domain fault diagnosis, shows promise in addressing the industrial fault diagnosis problems under small samples and various working condition. The proposed method builds upon the latent embedding optimization algorithm which capitalizes on the essence of meta-learning, effectively addressing few-shot challenges. Additionally, we harness an efficient pretraining model, enhancing feature extraction and domain adaptation, effectively handling the scarcity of fault data. In tackling the cross domain issue, we introduce an innovative meta-task organization and amplify the episode training strategy within meta-learning. These enhancements empower the model to develop the ability to generalize effectively. The proposed approach is substantiated through comprehensive case studies in bearing and gearbox fault diagnosis. The results demonstrate its exceptional efficacy in both few-shot and zero-shot cross domain fault diagnosis scenarios.

A Deep Learning-Based Fault Diagnosis Approach for Power System Equipment via Infrared Image Sensing

Automatic fault diagnosis for power system equipment has always been an essential concern in this industry. Conventionally, such works are conducted by manual patrol inspection, which consumes much human labor and expert knowledge. Fortunately, infrared images can present diagnosis areas inside the equipment via the thermal sensing function. In such context, this work utilizes deep neural network to construct a specific infrared image processing framework that can realize automatic fault diagnosis. Thus in this paper, a deep learning-based fault diagnosis approach for power system equipment via infrared image sensing is proposed. First, a pulse-coupled neural network structure is employed to enhance feature representation for infrared images of the equipment. Next, a fuzzy [Formula: see text]-means (FCM)-based segmentation method is developed to filter diagnosis areas from the infrared images. Finally, a convolution operation-based fault diagnosis operator is adopted to identify the diagnosis types. After that, some simulation experiments are conducted on real-world infrared images on the power system equipment, in order to make the performance evaluation of the proposed approach. The proposal realizes the end-to-end process of feature extraction and fault detection and identification, and avoids the problems of single feature. It is due to manual extraction of fault features, and the inability to detect and identify faults effectively in specific situations and scenarios.

SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samples

Real industrial scenarios struggle with the issues of a limited number of labeled samples and difficulty in accessing, which results in deep learning-based fault diagnosis models having poor generalization capabilities and decreased diagnostic accuracy. To address this problem, a semi-supervised prototype enhancement network (SSPENet) is proposed for rolling bearing fault diagnosis in this study. Firstly, a dual pooling attention residual network is proposed to be used in the feature extraction module. The goal is to efficiently extract the hidden features within rolling bearings, thus enabling the accurate classification of different sample categories. Subsequently, the Hungarian algorithm is utilized to design a strategic approach to update prototypes with pseudo-labels, which achieves the effect of augmenting prototypes by accurately adjusting the prototype position of each class of limited labeled samples through unlabeled samples, to improve the discriminative ability of the network model for fault classes. Finally, validation and experimental analysis are carried out on two bearing datasets, which achieve the average diagnostic accuracy of the proposed model to be above 90 % for both 1-shot and 2-shot cases, obtaining more satisfactory diagnostic results.

Intelligent protection scheme using combined Stockwell-Transform and deep learning-based fault diagnosis for the active distribution system

Intelligent protection scheme using combined Stockwell-Transform and deep learning-based fault diagnosis for the active distribution system

A new feature boosting based continual learning method for bearing fault diagnosis with incremental fault types

Rotating machinery can unexpectedly generate many new fault types under changing operating conditions. The capability of fault diagnostic models to adapt and acquire knowledge regarding new fault types is increasingly vital. However, traditional deep learning-based fault diagnosis models often encounter the challenge of catastrophic forgetting when facing new fault types. Numerous attempts were trapped with the stability–plasticity dilemma when tackling this phenomenon. In this study, we draw inspiration from the gradient boosting algorithm and propose a feature boosting based continual learning method. This method allows the diagnostic model to continuously and adaptively acquire knowledge of new fault types. Initially, the concept of gradient boosting is employed to construct an initial fault diagnostic model. Then, new modules are continuously extended dynamically for the initial diagnostic model to fit the residuals between the actual label and the output of the initial diagnostic model. Finally, to maintain the backbone of the fault diagnostic model as a single one, redundant parameters and feature dimensions are removed using an effective distillation strategy. Experimental results demonstrate that the feature boosting based continual learning method effectively mitigates catastrophic forgetting and enhances the plasticity of the fault diagnosis model, outperforming other existing methods.

Fault Diagnosis of the Rolling Bearing by a Multi-Task Deep Learning Method Based on a Classifier Generative Adversarial Network.

Accurate fault diagnosis is essential for the safe operation of rotating machinery. Recently, traditional deep learning-based fault diagnosis have achieved promising results. However, most of these methods focus only on supervised learning and tend to use small convolution kernels non-effectively to extract features that are not controllable and have poor interpretability. To this end, this study proposes an innovative semi-supervised learning method for bearing fault diagnosis. Firstly, multi-scale dilated convolution squeeze-and-excitation residual blocks are designed to exact local and global features. Secondly, a classifier generative adversarial network is employed to achieve multi-task learning. Both unsupervised and supervised learning are performed simultaneously to improve the generalization ability. Finally, supervised learning is applied to fine-tune the final model, which can extract multi-scale features and be further improved by implicit data augmentation. Experiments on two datasets were carried out, and the results verified the superiority of the proposed method.

Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.

A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network

A Unified Label Noise-Tolerant Framework of Deep Learning-Based Fault Diagnosis via a Bounded Neural Network

An Adversarial Single-Domain Generalization Network for Fault Diagnosis of Wind Turbine Gearboxes

In deep learning-based fault diagnosis of the wind turbine gearbox, a commonly faced challenge is the domain shift caused by differing operational conditions. Traditional domain adaptation methods aim to learn transferable features from the source domain and apply them to the target data. However, such methods still require access to target domain data during the training process, which limits their applicability in real-time fault diagnosis. To address this issue, we introduce an adversarial single-domain generalization network (ASDGN). It relies solely on data from a single length of data acquisition in wind turbine fault diagnosis. This novel approach introduces a more flexible and efficient solution to the field of real-time fault diagnosis for wind turbines.

Data augmentation on fault diagnosis of wind turbine gearboxes with an enhanced flow-based generative model

Deep learning-based fault diagnosis has been rapidly applied in recent years. However, datasets under the normal condition can be easily obtained rather than the faulty datasets. Consequently, such an imbalanced state will be caused between normal and faulty conditions that will lead to poor fault diagnosis. To this end, data augmentation based on an enhanced flow-based generative model (EFBG) is proposed to mitigate the data imbalance problem. Firstly, the Wavelet packet transform is performed on the time series to enhance the feature extraction. Secondly, a flow-based generative model named Glow (Generative flow with invertible 1 × 1 convolutions) is used to augment the faulty datasets. Finally, a random forest is adopted to perform the fault classification. The proposed method was validated in the experiment of the wind turbine gearboxes. Results show that this proposal can increase the fault diagnosis accuracy from 83.26 % to 88.01 %, which demonstrates that this proposal can be set as a data augmentation tool for the fault diagnosis of wind turbine gearboxes.

Rolling bearing intelligent fault diagnosis towards variable speed and imbalanced samples using multiscale dynamic supervised contrast learning

Deep learning-based fault diagnosis methods have already attained remarkable achievements in this field. However, rolling bearing frequently operates under variable speed conditions, and the number of healthy samples collected is often significantly larger than that of failure samples. In this paper, a multiscale dynamic supervised contrast learning (MDSupCon) framework is proposed. First, a multiscale adaptive feature extraction network is designed as the backbone, which utilizes multiple convolutional kernels to enhance feature extraction capabilities under variable speed conditions, and the branch attention mechanism is incorporated to adaptively adjust the weights of various scale branches. Second, the joint channel-space attention mechanism is constructed to enhance the importance of critical features while reducing redundant information, thereby improving fault identification accuracy and interpretability. Third, the dynamic supervised contrast loss function is designed to assign dynamic compensation factors to samples of various categories according to the training results, which reduces the impact of easily classified samples and enhances the contribution of hard-to-classify samples in imbalanced scenarios. Additionally, a dynamic cross-entropy loss is designed to train the backbone and the classifiers. The MDSupCon has achieved superior results of 89.49% and 92.15% on two bearing datasets with an imbalance ratio of 20:1 and variable speeds.

Deep Learning-Based Positioning Error Fault Diagnosis of Wire Bonding Equipment and an Empirical Study for IC Packaging

Little research has been done for artificial intelligence applications of semiconductor backend. This study aims to develop a deep learning based fault diagnosis framework as prognostics and health management (PHM) solutions with a comprehensive analytics process. A linear encoder sensor is employed to measure position, while data preparation is conducted to convert position information into a signal. Then, signal processing is used to extract the features, in which high pass filter is applied to normalize the signal and emphasize the features. High-dimensional features including time domain statistical features, time-frequency domain features obtained by continuous wavelet transform (CWT), and frequency domain features converted by fast Fourier transform (FFT) are extracted to prepare the dataset. An ensemble neural network model that integrates deep neural network (DNN) and convolutional neural network (CNN) is employed to recognize the pattern and diagnose the positioning errors. The accuracy and false alarm rate are considered to support maintenance decisions. An empirical study was conducted in a world leading IC assembly and testing company for validation. The results have shown that the proposed approach can effectively detect inappropriate installation of the bond head in wire bonding equipment for predictive maintenance and cost reduction.

Chemical Process Fault Diagnosis Method Based on Deep Learning

To address the problem that the traditional fault diagnosis method for chemical processes under big data relies too much on expert experience and fault features are difficult to distinguish, a deep learning-based fault diagnosis method is proposed, which combines convolutional neural network (CNN), long and short-term memory (LSTM) and attention mechanism (AM). In this method, the spatial sequence features of the input signal are extracted by the CNN adaptively, while the LSTM extracts the time-series features of the signal. Finally, the model performance is enhanced by introducing the attention mechanism and using the SoftMax layer as a classifier for fault diagnosis, so that the model can notice the important features of the faults with the interference of noise. Simulation validation of the method in this paper is performed using the TE chemical process data set, and it is demonstrated that the method can be used for chemical process fault diagnosis studies. Finally, compared with other fault diagnosis methods, the method is more accurate and has certain superiority.

Structural discrepancy and domain adversarial fusion network for cross-domain fault diagnosis

The current deep learning-based fault diagnosis methods have achieved excellent classification performance under supervised training, but the existence of domain discrepancy under different working conditions will greatly affect data relationship mining and fault diagnosis accuracy. Graph convolutional network (GCN) with topological structured relationships is excellent in solving these issues. In this paper, structural discrepancy and domain adversarial fusion network (SDDAFN) is proposed to learn deep high-dimensional features and eliminate distribution discrepancy across different domains. In data structure construction, the high-dimensional mapping features are first extracted accurately from the input raw signals by a feature encoder, then the output of encoder is converted into an instance graph through graph construction layer (GCL) for the convenient modeling by multi-channel kernel GCN (MCK-GCN). In network optimization, the classification loss function is introduced to optimize the discrimination boundaries of different fault types, and the integrated loss function based on structural discrepancy and domain adversarial are jointly adopted to minimize the domain distribution discrepancy. Experimental cases show the proposed method outperforms the state-of-the-art methods.

Comparing torsional and lateral vibration data for deep learning-based drive train gear diagnosis

Deep learning-based fault diagnosis models have been demonstrated to recognise machine health conditions from vibration data. However, most related studies have focused on lateral vibration data, and mostly neglected torsional vibration data. Yet, torsional vibration data can provide an advantage in diagnosing gear faults. Torsional vibration is typically less noisy than lateral vibration data as it can be measured directly from the rotating components. To this end, this study presents a large gear fault dataset with artificial faults of varying severity acquired from a downscaled thruster test rig operated at a vast range of rotating speeds. The test rig was equipped with multiple torque transducers, rotary encoders and piezoelectric accelerometers. The lateral and torsional vibration data acquired with these sensors were evaluated with three popular convolutional neural networks in extensive ablation studies. An interpretability analysis was conducted based on amplitude spectra and Grad-CAM visualisations. The results demonstrate that torsional vibration can be an effective source of data for gear fault diagnosis. For example, the models diagnose the most difficult gear conditions using only one torque transducer more accurately than using three accelerometers mounted on the gear box. Furthermore, the highest accuracy in each ablation study related to experiments with combined lateral and torsional vibration data. In addition, the interpretability analysis showed that the lower frequencies had relatively higher amplitudes in torsional vibration than in lateral vibration. The interpretability analysis also indicates that the models reached higher classification accuracies with torsional vibration data due to the lower dominating frequencies. Overall, this study highlights the potential benefits of using torsional vibration data for deep learning-based fault diagnosis of gears.

Sequential Domain Adaptation for Fault Diagnosis in Rotating Machinery

Fault diagnosis of the machinery system is essential to minimize the damage to the industrial field. Recently, with the development of computer and IoT technology, deep learning-based fault diagnosis has been widely researched. However, due to the domain shift, which changes the distribution of data under different operating conditions of the machinery system, the performance of the deep learning-based fault diagnosis algorithm decreases. This paper proposes a sequential domain adaptation to alleviate different distributions between different operating conditions. The proposed method has been validated in open-source datasets and shows a high performance compared to the other models.

A Novel Unsupervised Clustering and Domain Adaptation Framework for Rotating Machinery Fault Diagnosis

Deep learning-based fault diagnosis methods require a large number of labeled datasets. However, considering the changing operating conditions, it is impractical to obtain labeled datasets for all cases. Therefore, this study proposes a new unsupervised clustering and domain adaptation framework to circumvent data deficiency and domain issues. The proposed framework comprises two steps: unsupervised clustering and domain adaptation. In the unsupervised clustering, an expectation-maximization adversarial autoencoder, which combines an expectation-maximization algorithm with an adversarial autoencoder, is used for feature extraction and subspace mapping. Subsequently, the mapped features are clustered using a Gaussian mixture model. In the domain adaptation, a domain synchronization that is based on the symmetric Kullback-Leibler divergence metric is used to infer the relationship between the source and target domain clusters. The experiments on two rolling-element-bearing datasets validate the effectiveness of our method. Specifically, our method performs domain adaptation without retraining, which is promising for real industrial applications.

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Abstract With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.

A novel enhanced deep learning-based fault diagnosis approach for cascaded multilevel inverter

With the widespread use of power electronic converters like Multilevel Inverters (MLI) in various applications, several challenges arise due to increased switch count, capacitor surge current, and reverse current from inductive loads. These factors lead to the onset of faults, which negatively affect the reliability of MLI. Hence, an efficient fault detection and fault tolerant technique are necessary to ensure the reliability of the inverter. The deep learning-based fault detection approach is proposed for a 15-level Cascaded H-Bridge (CHB) MLI to address this issue. The proposed technique involves pre-processing with an Adaptive Hilbert-Huang filter, segmentation using Partial Differential Equation (PDE), feature extraction using Scale-Invariant Feature Transform (SIFT), and classification of fault signals with a Crow Swarm Algorithm (CSA) optimized Convolution Neural Network (CNN) classifier. To evaluate the effectiveness of the proposed approach in fault classification, four complex fault types were considered using MATLAB. The results show that CSA-optimized CNN offers a remarkable classification accuracy of 99.84%.