Intelligent Fault Diagnosis Approach Research Articles

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.

A time-saving fault diagnosis using simplified fast GAN and triple-type data transfer learning

Existing intelligent fault diagnosis approaches demand substantial data for training diagnostic models. However, factors such as the inherent characteristics of bearings, operating conditions, and privacy security make collecting comprehensive fault-bearing data very difficult. Although generating synthetic data through generative adversarial networks (GANs) is feasible, the data generation of GANs is a time-consuming process. To address these challenges, a fault diagnosis framework based on GAN and deep transfer learning (DTL) is proposed, termed the simplified fast GAN triple-type data transfer learning (SFGAN-TDTL) method. Initially, an SFGAN is proposed as a replacement for traditional GANs. The time-frequency image data generated by SFGAN serve to augment the training dataset, offering faster and higher-quality data generation compared to traditional GANs. To further reduce time consumption for GAN-based methods, the TDTL method is proposed. Differing from DTL, which utilizes synthetic data to construct a pre-trained model and conducts targeted fine-tuning with real data, TDTL employs open-source data, synthetic data, and real data to fill the weights of the task-insensitive layer, task-sensitive layer, and fully connected layer, respectively. Numerical results demonstrate that SFGAN-TDTL maintains higher diagnostic accuracy while significantly reducing time consumption.

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning.

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.

Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator

The gas turbine engine is a predominant prime mover in the transport and energy sectors, and ensuring its reliable operation holds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advancements within the gas turbine FD landscape, many existing methods operate under the assumption of identical health states during both data collection and the FD process. Moreover, most previous studies have overlooked the diagnosis of both sensors and actuators. Another critical challenge lies in isolating simultaneous and multiple faults and providing compromising FD performance, especially in the face of continued system performance degradation. Aiming at these problems, this study develops a novel unsupervised data-driven FD strategy based on leveraging the potential of transfer learning and the Koopman operator. A deep neural network-based transfer learning framework is proposed for realizing a precise adaptive linear model called the deep transfer linear (DTL) model enabling reliable prediction of the system’s behavior in various situations and designing structured fault residuals. To this end, a deep neural network framework is used to obtain a precise Koopman model realization using the richly collected data in the source domain. Subsequently, the realized model is fine-tuned for the target domains associated with the degraded system, mitigating the adverse effects of domain shift and addressing the rich data scarcity problem in the target domain. In addition, the dedicated and generalized residual sets are designed and generated employing the geometric approach for fault isolation and a decision-making analysis is developed to diagnose simultaneous faults. The reliability of the proposed strategy is demonstrated through various experiments in the presence of noise and performance degradation, and a comparative performance analysis is conducted between the proposed strategy and another data-driven method showcasing the superiority of the proposed approach.

Research on fault diagnosis of electric submersible pump based on improved convolutional neural network with Bayesian optimization

A conventional intelligent fault diagnosis approach for electric submersible pumps (ESPs) is heavily dependent on manual expertise for feature extraction. Meanwhile, a conventional convolutional neural network (CNN) exhibits an excess of parameters and requires a substantial volume of training samples. In this paper, a fault diagnosis algorithm of ESPs based on a Bayesian optimization-one-dimensional convolutional neural network-support vector machine (BO-1DCNN-SVM) is proposed by combining a 1DCNN with a SVM and using the algorithm BO to tune the improved 1DCNN. First, the method uses the self-extracting feature capability of the 1DCNN to solve the problem that traditional diagnosis methods over-rely on manual experience extraction. Meanwhile, the last layer of the convolutional neural network Softmax layer is replaced by the SVM to effectively process a few sample data. The accuracy and generalization ability of the fault classification of the electric submersible pump are improved. Then, the Bayesian optimization algorithm is used to find the optimal combination of hyperparameters for the improved 1DCNN-SVM model to further improve the prediction performance of the fault diagnosis model. Finally, the experimental results achieved a classification accuracy of 96.64%, which is 5% higher than existing CNN approaches with data samples of similar scale in this paper. The proposed method also proved to be highly accurate and robust in fault diagnosis.

Fault-tolerant learning control of air-breathing hypersonic vehicles with uncertain parameters and actuator faults

This paper investigates fault-tolerant learning control for air-breathing hypersonic vehicles (AHVs) subject to parametric uncertainties, external disturbances, and actuator faults. By treating the flexible dynamics as equivalent disturbance, the AHV model can be decomposed into velocity subsystem and altitude subsystem. An intelligent fault diagnosis approach is deployed to estimate the actuator fault information for each subsystem sequentially. Furthermore, combined with a fixed-time neural disturbance observer, composite learning control is deployed to construct control commands using the historical stack to improve the learning accuracy, while compensating for lumped disturbances, including fault diagnosis errors and external disturbances. The designed controller addresses the fault-tolerant tracking problem by combining offline and online learning strategies, which is a significant advantage over other existing AHV controllers. Simulation results validate the effectiveness of the fault-tolerant learning control.

Inverter Fault Diagnosis for a Three-Phase Permanent-Magnet Synchronous Motor Drive System Based on SDAE-GAN-LSTM

In this study, a novel intelligent inverter fault diagnosis approach based on a stacked denoising autoencoder–generative adversarial network–long short-term memory (SDAE-GAN-LSTM) under an imbalanced sample is proposed for a three-phase permanent-magnet synchronous motor (PMSM) drive system. The proposed method can address the problem of unbalanced fault data samples and improve the accuracy of fault classification. Concretely speaking, firstly, the stacked denoising autoencoder (SDAE) is pre-trained to obtain the optimum decoder network. Afterward, a new generator of generative adversarial networks (GANs) is designed to generate high-quality samples by migrating the pre-trained optimal decoder network to the hidden layer and output layer of the generator of GANs. Additionally, a new model of long short-term memory (LSTM) based on the second discriminator of the GANs is presented for fault diagnosis. The generator of GANs is cross-trained using the reconstruction error gained by SDAE and the fault diagnosis error obtained by LSTM, resulting in the generation of high-quality samples for fault discrimination. Simulation and experimental results demonstrate the effectiveness of the proposed fault diagnosis approach, and the average fault identification accuracy reaches 98.63%.

A robust intelligent fault diagnosis method for rotating machinery under noisy labels

Despite achieving considerable success, the fault diagnosis methods will still be disturbed by noisy labels, this causes the model’s degradation and reduced diagnostic precision. Focused on solving the above issues, a robust intelligent fault diagnosis approach for rotating machinery under noisy labels is proposed. Firstly, we maintain two deep neural networks (DNNs) and alternatively execute parameters updating and models optimization by referring to the Co-teaching strategy, which can maximize filtering different error types and implement pre-training of DNNs. Secondly, adopting a two-component Gaussian mixture model (GMM) to fit training dataset’s cross-entropy (CE) loss and realize the clean and noisy labels division according to the threshold. Then, a data augmentation method called Mixup operation is employed in semi-supervised learning (SSL) to increase noise robustness and avoid error accumulation, subsequently, performing fine-tuning and correction for clean and noisy samples. Challenging experiments on a transmission gearbox dataset under different noisy labels levels show that the proposed method has robustness to noise and significantly surpasses other approaches, which provides an important reference value for accurate fault diagnosis for rotating equipment parts with noisy labels.

Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning

Offshore wind turbines play a vital role in transferring wind energy to electricity, which could help relieve the energy crisis and improve the global climate. In general, offshore wind turbines are installed open sea to avoid the potential interruption of people’s daily life. In such kind of harsh operating environment, the wind turbine transmission system is prone to failure, especially for the rolling bearings. Therefore, it is crucial to conduct condition monitoring of rolling bearings to ensure the safe and efficient operation of offshore wind turbines. Intelligent fault diagnosis is a research hotspot for condition monitoring of rolling bearings. However, the existing intelligent fault diagnosis techniques have some limitations. For example, most of the existing techniques were developed based on single sensory data, which can lead to inaccurate and unstable diagnostic results. Moreover, most existing techniques implicitly assume that there are sufficient labeled samples for classifier training. This may not be the case for offshore wind turbines where the labeled samples are limited. To address the aforementioned issues, an intelligent fault diagnosis technique by integrating an information stream fusion and a semi-supervised learning approach is proposed in this study. In the proposed method, a coupled convolutional residual network is proposed to realize the information streams fusion, in which the vibration signal and acoustic emission signal are served as the inputs of the proposed network, and then a concatenation operation is used to fuse the features obtained from two information streams. Meanwhile, a semi-supervised learning approach is also proposed, which can utilize the labeled samples, the correctly predicted samples, and the unlabeled samples to improve diagnostic accuracy. The diagnostic result on the experimental offshore wind turbine bearing dataset demonstrates that the proposed method achieves the highest diagnostic accuracy compared to existing comparative methods.

Density-Based Affinity Propagation Tensor Clustering for Intelligent Fault Diagnosis of Train Bogie Bearing

Health monitor of bogie-bearing on the train can ensure constant operation of the rail transit system. Since the metro or other rail transit have high safety requirements, it is hard to acquire numerous fault samples. Besides, diagnosing train bogie-bearings under variable working conditions is challenging due to wheel-rail coupling, speed variation, and load fluctuation. An intelligent approach for bogie-bearing fault diagnosis is proposed to deal with the above problems. A third-order tensor model is established to be suitable for variable working conditions. Furthermore, a density-based affinity propagation tensor (DAP-Tensor) clustering algorithm is presented to identify different failures with unlabeled. Train bogie and public data sets were employed to simulate three probable conditions of train operation: high-frequency impact, speed variation, and load change. Compared with existing clustering methods in three cases, the proposed DAP-Tensor performs better in identifying bearing faults under variable working conditions. Moreover, The DAP-tensor has a comparable recognition rate to some deep learning methods, which unsupervised characteristics show it has potential for applications on rail transit trains.

Unsupervised Continual Source-Free Network for Fault Diagnosis of Machines Under Multiple Diagnostic Domains

Data-driven based intelligent fault diagnosis (IFD) approaches have been broadly developed. In actual industry, not all data of mechanical equipment is accessible, especially in an era of increasing attention to data privacy protection. In addition, as the machine consistently operates, new data of various working conditions is continuously collected. The existence of these problems puts forward more stringent requirements for the application scenarios of algorithms, that is, how to train an intelligent model without source samples, and how to preserve the diagnostic knowledge learned from the previous task as the new data continually is collected. To exploit the application range, a novel unsupervised continual source-free network (UCSN) is proposed for IFD of rotating machinery. A one-dimension convolutional neural network is adopted as the feature extractor to extract invariant features. Based on the extracted features, a local structure clustering is used to process the target samples, which can cluster the same fault samples and separate the different fault samples simultaneously. Meanwhile, the sparse domain attention is utilized to preserve the learned knowledge and to improve the generalization of the well-trained model. In this way, the application scenarios of the IFD can be significantly expanded. Extensive experiments on a popular public bearing dataset and an actual industry dataset are carried out to verify the effectiveness of the proposed UCSN. Comparisons with ablation methods on the same experimental circumstance validate the superiority of key techniques and the proposed UCSN. The experimental results indicate that the proposed UCSN provides a promising tool for IFD of rotating machinery.

An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features

As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-leveled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice.

Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis

Intelligent fault diagnosis based on deep learning (DL) has been widely used in various engineering practices. However, when confronting massive unlabeled industrial data, traditional data-driven intelligent fault diagnosis approaches cannot fully mine the correlation geometric structure information between data, and therefore cannot obtain good fault diagnosis results. To overcome this difficulty, an efficient fault diagnosis method based on deep hypergraph autoencoder embedding (DHAEE) is presented in this study. First, unlabeled vibration signals are converted into hypergraphs by applying the designed hypergraph construction method. Second, a hypergraph convolutional extreme learning machine autoencoder (HCELM-AE) is designed, which can mine the higher-order structural information and subspace structural information of the original unlabeled data by designing hypergraph convolutional and self-representation layers. Furthermore, by stacking multiple HCELM-AE modules in a DL framework, the DHAEE and its corresponding fault diagnosis method is constructed, which not only has the advantage of high computational efficiency of ELM-AE, but also has strong representational learning ability of DL methods. Finally, the effectiveness of the DHAEE based fault diagnosis method is verified by rolling bearing fault data and rotor fault data. Experimental results show that the presented fault diagnosis method has higher accuracy and lower computational complexity than other comparison methods, thus proving that DHAEE is an efficient intelligent massive unlabeled data processing approach for rotating machinery fault diagnosis.

Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis

Recent progress on intelligent fault diagnosis is mainly attributed to the explosive development of convolutional neural networks (CNNs). Many existing CNN-based fault diagnosis models can extract abundant features from the measured vibration signals but cannot explore enough discriminative features under strong noise conditions. This poses a challenge for industrial applications. To address this problem, we develop a new deep CNN model, called a multireceptive field denoising residual convolutional network (MF-DRCN). The major contributions are: a multireceptive field denoising (MFD) block is designed to enhance the deep features extracted by the CNN model and filter out the interference feature information; an adaptive feature integration (AFI) module is embedded in the CNN model to adaptively integrate features, so as to make better use of the extracted information; and an end-to-end CNN model called MF-DRCN is developed based on MFD and AFI. The experimental results demonstrate that the MF-DRCN has better feature extraction and antiinterference capabilities than the other seven competitive methods. Specifically, under strong noise conditions with SNR = −6 dB, the MF-DRCN achieves 84.51% and 86.45% diagnostic accuracy, respectively, on the planetary gearbox dataset and the industrial pump dataset, which suggests the MF-DRCN is a promising intelligent fault diagnosis approach.

Intelligent fault diagnosis of bevel gearboxes using semi-supervised probability support matrix machine and infrared imaging

Fault diagnosis is of great significance to ensure the reliability and safety of complex bevel gearbox systems. Most existing intelligent fault diagnosis approaches of bevel gearboxes are designed with vibration monitoring. However, the collected vibration data are vulnerable to noise pollution and machinery operating conditions. Besides, traditional fault diagnosis models highly rely on numerous labeled samples, and neglect the high cost of label annotation in real-world applications. Therefore, a novel fault diagnosis approach based on semi-supervised probability support matrix machine (SPSMM) and infrared imaging is proposed for bevel gearboxes in this paper, which has the following properties. Firstly, SPSMM classifies 2D matrix data directly without vectorization, thus fully utilizing the spatial information in infrared images. Secondly, a probability output strategy is designed for SPSMM to calculate the posterior class probability estimation of matrix inputs, and consequently enhance the diagnostic accuracy and robustness of the model. Thirdly, a semi-supervised learning (SSL) framework is proposed for SPSMM to carry out sample transfer from the unlabeled sample pool to the labeled sample pool, which can effectively alleviate the problem of insufficient labeled samples. The superiority of the proposed diagnosis approach is demonstrated with an infrared imaging dataset of a bevel gearbox.

Intelligent Fault Diagnosis for Inertial Measurement Unit through Deep Residual Convolutional Neural Network and Short-Time Fourier Transform

An Inertial Measurement Unit (IMU) is a significant component of a spacecraft, and its fault diagnosis results directly affect the spacecraft’s stability and reliability. In recent years, deep learning-based fault diagnosis methods have made great achievements; however, some problems such as how to extract effective fault features and how to promote the training process of deep networks are still to be solved. Therefore, in this study, a novel intelligent fault diagnosis approach combining a deep residual convolutional neural network (CNN) and a data preprocessing algorithm is proposed. Firstly, the short-time Fourier transform (STFT) is adopted to transform the raw time domain data into time–frequency images so the useful information and features can be extracted. Then, the Z-score normalization and data augmentation strategies are both explored and exploited to facilitate the training of the subsequent deep model. Furthermore, a modified CNN-based deep diagnosis model, which utilizes the Parameter Rectified Linear Unit (PReLU) as activation functions and residual blocks, automatically learns fault features and classifies fault types. Finally, the experiment’s results indicate that the proposed method has good fault features’ extraction ability and performs better than other baseline models in terms of classification accuracy.

An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization

The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particle swarm optimization (PSO) is proposed. The main idea behind the use of the PSO algorithm is to remove irrelevant features and extract only the most significant ones from raw data in order to improve the classification task using a neural networks classifier. Then, to overcome the problem of premature convergence and local sub-optimal areas when using the classical PSO optimization algorithm, an improved extension of the PSO algorithm is proposed. The basic idea behind this proposal is to use the Euclidean distance as a dissimilarity metric between observations in which a single observation is kept in case of redundancies. In addition, the proposed reduced PSO-NN (RPSO-NN) technique not only enhances the results in terms of accuracy but also provides a significant reduction in computation time and storage cost by reducing the size of the training dataset and removing irrelevant and redundant samples. The experimental results showed the robustness and high performance of the proposed diagnosis paradigms.

Broad auto-encoder for machinery intelligent fault diagnosis with incremental fault samples and fault modes

Intelligent fault diagnosis (IFD) has been a widely concerned topic in the field of prognostics and health management. Existing machinery IFD approaches are generally developed based on the one-time learning manner. Therefore, they are powerless to deal with the data stream issue in which new fault samples and fault modes will be progressively collected for model training. To overcome this drawback, this paper proposes a broad auto-encoder (BAE) with incremental learning capabilities for on-line IFD of machinery. The BAE is constructed by stacking a series of auto-encoders in the width direction. Then, the output weight matrix of the BAE is calculated by the ridge regression algorithm. After that, the capabilities of sample-incremental learning and class-incremental learning are developed, so that the BAE can easily update itself to accommodate the new fault samples and fault modes without model retraining. With the two incremental learning capabilities, the BAE can be first trained using limited historical fault samples, and then incrementally learn new diagnosis knowledge from the newly coming fault samples and fault modes. In this way, the BAE will be more and more powerful over time. Finally, the proposed BAE is applied to diagnose faults for high-speed train wheelset bearings and disc components. The results show that the proposed BAE offers an efficient solution for machinery IFD to deal with the continuous data stream issue.

An intelligent bearing fault diagnosis based on hybrid signal processing and Henry gas solubility optimization

Bearing is regarded as one of the core elements in rotating machines and its fault diagnosis is essential for better reliability and availability of the rotating machines. This paper puts forward an intelligent vibration signal-based fault diagnosis approach for bearing faults identification at an early stage, irrespective of speed conditions. The proposed methodology comprises of a frequency shift-based hybrid signal processing technique that involves a combination of Hilbert Transform (HT) and Discrete Wavelet Transform (DWT) followed by sliding window-based feature extraction. Thereafter, a newly developed Henry Gas Solubility Optimization (HGSO) is implemented to select the relevant features. At last, the optimal attributes are used to train the Artificial Neural Network (ANN) model for the classification of the different bearing faults. To test the effectiveness of the speed independent model, experimental validation was done with constant and varying speed conditions. The results demonstrate that the proposed methodology has a tremendous potential to eliminate unplanned failures caused by bearing in rotating machinery.

New intelligent fault diagnosis approach of rolling bearing based on improved vibration gray texture image and vision transformer

Rolling bearing is a key component of rotating machines, its working state directly affects the performance and safety of the whole equipment. Deep learning based on big data is a mainstream means of intelligent mechanical fault diagnosis. The key lies in enhancing fault feature and improving diagnosis accuracy. Different from the Convolution Neural Network (CNN) which relies on the convolution layer to extract the image features, the Vision Transformer (VIT) uses the multi-head attention mechanism to establish the relationship among the pixels in an image. In order to improve the accuracy of rolling bearing fault diagnosis, a new fault diagnosis method based on VIT is proposed. The vibration gray texture images to be input are divided into the patches according to the predetermined size and linearly mapped into input sequences, and the global image information is integrated through the self-attention mechanism to realize fault diagnosis. In order to enhance the expressiveness and generalization ability, the pooling layer is introduced into VIT. The tested results show that the fault diagnosis accuracy of VIT on the test set reaches 94.6%, and the corresponding classification indexes top-1 is 84.2% and top-5 is 95.0%. The accuracy of the new Pooling Vision Transformer (PIT) is 3.3% higher than that of the original VIT, which proves that the introduction to pooling layer can improve the image identification performance of VIT.