Showing papers on "Fault (geology) published in 2022"
01 Jan 2022
TL;DR: In this paper , a review of the research results on intelligent fault diagnosis with small and imbalanced data (S&I-IFD) is presented, which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification.
Abstract: The research on intelligent fault diagnosis has yielded remarkable achievements based on artificial intelligence-related technologies. In engineering scenarios, machines usually work in a normal condition, which means limited fault data can be collected. Intelligent fault diagnosis with small & imbalanced data (S&I-IFD), which refers to build intelligent diagnosis models using limited machine faulty samples to achieve accurate fault identification, has been attracting the attention of researchers. Nowadays, the research on S&I-IFD has achieved fruitful results, but a review of the latest achievements is still lacking, and the future research directions are not clear enough. To address this, we review the research results on S&I-IFD and provides some future perspectives in this paper. The existing research results are divided into three categories: the data augmentation-based, the feature learning-based, and the classifier design-based. Data augmentation-based strategy improves the performance of diagnosis models by augmenting training data. Feature learning-based strategy identifies faults accurately by extracting features from small & imbalanced data. Classifier design-based strategy achieves high diagnosis accuracy by constructing classifiers suitable for small & imbalanced data. Finally, this paper points out the research challenges faced by S&I-IFD and provides some directions that may bring breakthroughs, including meta-learning and zero-shot learning.
113 citations
TL;DR: Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.
108 citations
TL;DR: The results confirm the feasibility of the proposed modified deep autoencoder driven by multi-source parameters in cross-domain fault prognosis of aeroengines, which outperforms the existing methods.
Abstract: The existing fault prognosis techniques of aeroengine mostly focus on a single monitoring parameter under stable condition, and have low adaptability to new prognosis scenes. To boost the fault prognosis capability cross aeroengines, modified deep autoencoder (MDAE) driven by multi-source parameters is proposed in this article. First, the sensitive multi-source parameters are selected and fused using linear local tangent space alignment to define a fused health index (FHI) to characterize performance degradation of aeroengine. Second, MDAE model is constructed with adaptive Morlet wavelet to flexibly establish accurate mapping hidden in the FHI under analysis. Third, parameter transfer learning is used to provide good initial parameters for enabling the constructed MDAE to have cross-domain fault prognosis capability. The proposed method is used to analyze both the simulation multisource performance degradation parameters of aeroengines (system level) and experiment run-to-failure bearing datasets (component level). The results confirm the feasibility of the proposed method in cross-domain fault prognosis of aeroengines, which outperforms the existing methods.
104 citations
TL;DR: A new approach for fault detection and diagnosis in rotating machinery is proposed, namely: unsupervised classification and root cause analysis, and a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI).
94 citations
TL;DR: In this article , a modified deep autoencoder (MDAE) driven by multi-source parameters is proposed to boost the fault prognosis capability cross aeroengines, and the proposed method is used to analyze both the simulation multisource performance degradation parameters of aeroENGines (system level) and experiment run-to-failure bearing datasets (component level).
Abstract: The existing fault prognosis techniques of aeroengine mostly focus on a single monitoring parameter under stable condition, and have low adaptability to new prognosis scenes. To boost the fault prognosis capability cross aeroengines, modified deep autoencoder (MDAE) driven by multi-source parameters is proposed in this article. First, the sensitive multi-source parameters are selected and fused using linear local tangent space alignment to define a fused health index (FHI) to characterize performance degradation of aeroengine. Second, MDAE model is constructed with adaptive Morlet wavelet to flexibly establish accurate mapping hidden in the FHI under analysis. Third, parameter transfer learning is used to provide good initial parameters for enabling the constructed MDAE to have cross-domain fault prognosis capability. The proposed method is used to analyze both the simulation multisource performance degradation parameters of aeroengines (system level) and experiment run-to-failure bearing datasets (component level). The results confirm the feasibility of the proposed method in cross-domain fault prognosis of aeroengines, which outperforms the existing methods.
91 citations
TL;DR: In this article , a fault information-guided variational mode decomposition (FIVMD) method is proposed for extracting the weak bearing repetitive transient, and two nested statistical models based on the fault cyclic information, incorporated with the statistical threshold at a specific significance level, are used to approximately determine the mode number.
88 citations
TL;DR: A novel data synthesis method called deep feature enhanced generative adversarial network is proposed to improve the performance of im balanced fault diagnosis and outperforms other intelligent methods and shows great potential in imbalanced fault diagnosis.
80 citations
TL;DR: A novel deep neural network based on bidirectional-convolutional long short-term memory (BiConvLSTM) networks to determine the type, location, and direction of planetary gearbox faults by extracting spatial and temporal features from both vibration and rotational speed measurements automatically and simultaneously.
72 citations
TL;DR: This paper provides a comprehensive review of blind deconvolution methods from history to state-of-the-art methods and finally to research prospects, as well as provides a survey and summarize the current progress of BDMs applied in machinery fault diagnosis.
72 citations
TL;DR: Wang et al. as mentioned in this paper proposed a novel compound fault diagnosis method based on optimized maximum correlation kurtosis deconvolution (MCKD) and sparse representation, namely MDSRCFD, which has better global optimization performance and fast convergence speed.
Abstract: The effective separation of fault characteristic components is the core of compound fault diagnosis of rolling bearings. The intelligent optimization algorithm has better global optimization performance and fast convergence speed. Aiming at the problem of poor diagnosis effect caused by mutual interference between multiple fault responses, a novel compound fault diagnosis method based on optimized maximum correlation kurtosis deconvolution (MCKD) and sparse representation, namely MDSRCFD, is proposed in this article. For the MCKD, because it is very difficult to set reasonable parameter combination values, artificial fish school (AFS) with global search capability and strong robustness is fully utilized to optimize the key parameters of MCKD to achieve the best deconvolution and fault feature separation. Aiming at the problem that orthogonal matching pursuit (OMP) is difficult to be solved in sparse representation, an artificial bee colony (ABC) with global optimization ability and faster convergence speed is employed to solve OMP to obtain the approximate best atom and realize the reconstruction of signal transient components. The envelope demodulation analysis method is applied to realize feature extraction and fault diagnosis. The simulation and practical application results show that the proposed MDSRCFD can effectively separate and extract the compound fault characteristics of rolling bearings, which can realize the accurate compound fault diagnosis.
71 citations
TL;DR: In this article , a new approach for fault detection and diagnosis in rotating machinery is proposed, which consists of three parts: feature extraction, fault detection, and fault diagnosis, and two tools for diagnosis are proposed, namely unsupervised classification and root cause analysis.
TL;DR: A parameter optimization and feature metric-based fault diagnosis method with few samples, called model unknown matching network model, for the problem of sparse fault samples and cross-domain between data sets in real industrial environments, indicating the feasibility of the method.
Abstract: With the rapid development of industrial informatization and deep learning technology, modern data-driven fault diagnosis (MIFD) methods based on deep learning have been receiving attention from the industry. However, most of these methods require sufficient training samples to achieve the desired diagnostic effect, and the scarcity of fault samples in the actual industrial environment leads to the limitation of the development of MIFD methods. In addition, data-driven fault diagnosis methods often need to face cross-load or even cross-domain problems across different devices due to changes in equipment operating conditions and production requirements. In this paper, we design a parameter optimization and feature metric-based fault diagnosis method with few samples, called model unknown matching network model, for the problem of sparse fault samples and cross-domain between data sets in real industrial environments. The method combines both a parametric optimization-based meta-learning network, which extracts optimization information to adapt between different domains, and a metric-based metric learning network, which extracts metric information for similarity discriminations. The experimental results show that the method outperforms the current baseline method for the five-shot fault diagnosis problem of bearings under limited data conditions and achieves an accuracy of up to 94.4 % in cross-device diagnosis experiments from bearings to gas regulators, indicating the feasibility of the method. The features are visualized by T-SNE to show the validity of the model.
TL;DR: Wang et al. as discussed by the authors proposed a joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain to reduce the dependence on the resources of laboratory test rigs.
Abstract: Unsupervised cross-domain fault diagnosis of bearings has practical significance; however, the existing studies still face some problems. For example, transfer diagnosis scenarios are limited to the experimental domain, cross-domain marginal distribution and conditional distribution are difficult to align simultaneously, and each source-domain sample is assigned with equal importance during the domain adaptation process. Aiming at the above-mentioned challenges, this article proposes a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain. The sufficient bearing simulation data containing rich fault label information are used to construct the source domain to reduce the dependence on the resources of laboratory test rigs. An improved loss function embedded with joint maximum mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions across domains in unsupervised scenarios. A weight allocation mechanism for each source-domain sample is developed to suppress negative transfer. Two experimental datasets collected from laboratory test rigs are used as the target domains to validate the effectiveness of the proposed method. The results show that the proposed method is superior to other popular unsupervised cross-domain fault diagnosis methods.
01 Apr 2022
TL;DR: In this paper , a wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolution layer of the standard CNN.
Abstract: Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful filters. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized filter bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental verification using data from laboratory environment are carried out to verify effectiveness of the proposed method for mechanical fault diagnosis. The results show the importance of the designed CWConv layer and the output of CWConv layer is interpretable. Besides, it is found that WKN has fewer parameters, higher fault classification accuracy and faster convergence speed than standard CNN.
TL;DR: Wang et al. as mentioned in this paper proposed a deep feature enhanced generative adversarial network (DFAGAN) to improve the performance of imbalanced fault diagnosis, where a pull-away function is integrated to design a new objective function of the generator.
01 Feb 2022
TL;DR: In this paper , a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery.
Abstract: Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods.
TL;DR: Wang et al. as discussed by the authors explored the fault diagnosis in a probabilistic Bayesian deep learning framework by exploiting an uncertainty-aware model to understand the unknown fault information and identify the inputs from unseen domains.
TL;DR: In this paper , a Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. And the results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.
Abstract: Hydraulic axial piston pump is broadly-used in aerospace, ocean engineering and construction machinery since it is the vital component of fluid power systems. In the light of the undiscoverability of its fault and the potential serious losses, it is valuable and challenging to complete the fault identification of a hydraulic pump accurately and effectively. Owing to the limitations of shallow machine learning methods in the intelligent fault diagnosis, more attention has been paid to deep learning methods. Hyperparameter plays an important role in a deep learning model. Although some manual tuning methods may represent good results in some cases, it is hard to reproduce due to the differences of datasets and other factors. Hence, Bayesian optimization (BO) algorithm is adopted to automatically select the hyperparameters. Firstly, the time–frequency images of vibration signals by continuous wavelet transform are taken as input data. Secondly, by setting some hyperparameters, a preliminary convolutional neural network (CNN) model is established. Thirdly, by identifying the range of each hyperparameter, BO based on Gaussian process is employed to construct an adaptive CNN model named CNN-BO. The performance of CNN-BO is verified by comparing with traditional LeNet 5 and improved LeNet 5 with manual optimization. The results indicate that CNN-BO can accomplish the intelligent fault diagnosis of a hydraulic pump accurately.
TL;DR: In this paper , a novel method called data reconstruction hierarchical recurrent meta-learning (DRHRML) is proposed for bearing fault diagnosis with small samples under different working conditions, which contains data reconstruction and meta learning stages.
TL;DR: In this article , an imbalanced fault diagnosis approach based on improved multi-scale residual generative adversarial network (GAN) and feature enhancement-driven capsule network is proposed to solve it.
01 Jul 2022
TL;DR: In this paper , an adaptive fuzzy fault-tolerant control strategy is introduced to deal with the difficulties associated with the actuator faults and external disturbance, and a modified performance function, which is called the finite-time performance function (FTPF), is presented.
Abstract: In this article, finite-time-prescribed performance-based adaptive fuzzy control is considered for a class of strict-feedback systems in the presence of actuator faults and dynamic disturbances. To deal with the difficulties associated with the actuator faults and external disturbance, an adaptive fuzzy fault-tolerant control strategy is introduced. Different from the existing controller design methods, a modified performance function, which is called the finite-time performance function (FTPF), is presented. It is proved that the presented controller can ensure all the signals of the closed-loop system are bounded and the tracking error converges to a predetermined region in finite time. The effectiveness of the presented control scheme is verified through the simulation results.
TL;DR: An online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults.
Abstract: Various faults in the lithium-ion battery system pose a threat to the performance and safety of the battery. However, early faults are difficult to detect, and false alarms occasionally occur due to similar features of the faults. In this article, an online multifault diagnosis strategy based on the fusion of model-based and entropy methods is proposed to detect and isolate multiple types of faults, including current, voltage, and temperature sensor faults, short-circuit faults, and connection faults. An interleaved voltage measurement topology is adopted to distinguish voltage sensor faults from battery short-circuit or connection faults. Based on the established comprehensive battery model, structural analysis is performed to develop diagnostic tests that are sensitive to different faults. Residual generation based on the extended Kalman filter and residual evaluation based on the statistical inference are conducted to detect and isolate sensor faults. Sample entropy is used to further distinguish between the short-circuit faults and connection faults. The effectiveness of the proposed diagnostic method is verified by multiple fault tests with different fault types and sizes. The results also show that the proposed method has good robustness to noise and inconsistencies in the state of charge and temperature.
TL;DR: In this article , a trustworthy analysis with uncertainty-aware deep ensembles is conducted to detect the out-of-distribution (OOD) samples and issue the warnings for the potential untrustworthy diagnosis.
TL;DR: A theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture is provided, and current challenges and potential research directions in nowadays intelligent manufacturing are discussed.
TL;DR: Results show that the proposed method is effective in diagnosing unseen compound faults of machines, and is able to recognize mechanical compound faults when only the data of single faults are accessible for training.
TL;DR: In this article , a weak fault diagnosis method for train axle box bearing based on parameter optimization Variational Mode Decomposition (VMD) and improved Deep Belief Network (DBN) is proposed.
TL;DR: In this paper , a deep neural network based on bidirectional-convolutional long short-term memory (BiConvLSTM) networks was proposed to determine the type, location, and direction of planetary gearbox faults by extracting spatial and temporal features from both vibration and rotational speed measurements automatically and simultaneously.
TL;DR: The established method can extract the fault features as effective as VMD, but the proposed RVME is significantly better than VMD in terms of computational efficiency and compared with other classical fault feature extraction approaches.
TL;DR: This study proposes a novel multiclass wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multisource signals fusion and shows that the proposed strategy can increase wind turbines bearing fault diagnostic accuracy in complex scenarios.
Abstract: Low fault diagnosis accuracy in case of insufficient and imbalanced samples is a major problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large difference in the number of samples of different categories or the lack of a certain fault sample, which requires good learning of the characteristics of a small number of samples. Sample generation in the deep learning generation model can effectively solve this problem. In this study, we proposed a novel multiclass wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multisource signals fusion. This strategy converts multisource 1-D vibration signals into 2-D signals, and the multisource 2-D signals were fused by using wavelet transform. The CVAE-GAN model was developed by merging the variational autoencoder (VAE) with the generative adversarial network (GAN). The VAE encoder was introduced as the front end of the GAN generator. The sample label was introduced as the model input to improve the model’s training efficiency. Finally, the sample set was used to train encoder, generator, and discriminator in the CVAE-GAN model to supplement the number of the fault samples. In the classifier, the sample set is used to do experimental analysis under various sample circumstances. The results show that the proposed strategy can increase wind turbine bearing fault diagnostic accuracy in complex scenarios.
TL;DR: A comprehensive review of blind deconvolution methods for machinery fault detection can be found in this article , where the authors provide a comprehensive review from history to state-of-the-art methods.