Learning Method For Fault Diagnosis Research Articles

Interpretable domain adaptation transformer: a transfer learning method for fault diagnosis of rotating machinery

Domain adaptation-based transfer learning methods have been widely investigated in fault diagnosis of rotating machinery, but their basic convolution or recurrent structure is subject to poor global feature representation ability, which hinders the learning of domain-irrelevant modulation information. In addition, the “black box” nature of deep learning models limits their applications in high risk-sensitive scenarios. In this paper, an interpretable domain adaptation transformer (IDAT) is proposed for the transferable fault diagnosis of rotating machinery. First, a multi-layer domain adaptation transformer framework is proposed, which can capture the global information that is crucial for learning the modulation information of different domains, and meanwhile reduce the feature distribution discrepancy. Second, an ensemble attention weight is applied to enable the transfer learning framework to be interpretable, which is implemented by averaging the integral values of the multi-head attention maps along the key direction. In addition, the raw vibration signals are embedded as the input of the model, which provides an end-to-end fault diagnosis. The proposed IDAT is tested by various cross-condition and cross-machine bearing fault diagnosis tasks, and results confirm the advantages of the method.

Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift

Recently, federated fault diagnosis has garnered growing attention due to its promising capabilities in information fusion with privacy preservation. However, most of the existing approaches are based on the assumptions of no domain shift between multiple factories and no unseen domains for online applications. In actual industry, these assumptions are generally unsatisfied due to prominent environmental noises, mechanical wear, and changes in working conditions. Federated models that ignore domain shifts would face the negative aggregation problem and are not robust to unseen domains. To solve the domain shift problem, a federated domain generalization method is proposed for privacy-preserving fault diagnosis in this article. The key idea is to construct a sharable reference domain in cloud, which can convert the privacy-risky centralized alignment problem into a privacy-preserving pairwise alignment problem. Based on the recognition that any fault category in a discriminative feature space can be characterized by a particular position and volatility, we design a shareable domain generator to provide a reference for pairwise alignment. Then, the non-deterministic sampling and non-parametric alignment criterion are introduced to realize local domain alignment, which facilitates the domain-invariant feature extraction. Finally, by the alternation of local domain alignment and global reference synchronization, the alignment of multi-source domains is achieved implicitly. We give convergence guarantees for the proposed method and derive the generalization error bound of federated DG, which illustrates the positive effect of the proposed method on improving generalization. Experiments on two cases demonstrate the consistent superior generalization performance of our method without the risk of data leakage.

DeFedTL: A Decentralized Federated Transfer Learning Method for Fault Diagnosis

DeFedTL: A Decentralized Federated Transfer Learning Method for Fault Diagnosis

ECOC-based integrated learning method for fault diagnosis in nuclear power plants

Abstract The fault diagnosis system of nuclear power plants plays an important role in ensuring the safety and economy of nuclear power plant operations. This paper first analyzes typical faults of nuclear power plants and their phenomena, and fault samples are obtained. A comprehensive study of the structure of the nuclear power plant system, its working mode and the association between each subsystem is carried out to analyze the monitoring parameters and fault characteristics and establish the fault data set. Secondly, an IFWA (Improved Fireworks Algorithm - Integrated Learning) algorithm is proposed to assess the severity of faults in the first circuit of a nuclear power plant. Finally, the fault diagnosis module is divided into three units according to the functional logic, i.e., condition monitoring unit, fault identification unit, and fault severity assessment unit. The results show that the diagnostic accuracy of the IFWA algorithm is 94.25% for SGTR in the single-fault diagnosis experiment and 96.25% for SGTR-LOCA in the multiple-fault diagnosis experiment. It shows that the IFWA algorithm proposed in this paper has the optimal performance capability when applied to nuclear power plant fault diagnosis and effectively assists managers in diagnosing faults and giving maintenance recommendations.

Deep Learning Method for Fault Diagnosis in High Voltage Transmission Lines: A Case of the Vietnam 220kV Transmission Line

Deep Learning Method for Fault Diagnosis in High Voltage Transmission Lines: A Case of the Vietnam 220kV Transmission Line

A Lifelong Learning Method for Gearbox Diagnosis With Incremental Fault Types

Deep learning (DL)-based fault diagnosis models have to collect the most comprehensive data of mechanical fault types to ensure reliability. In real scenarios, due to complex, variable operating conditions, machines often generate unexpected faults that lead to an increment of fault types, causing the diagnosis model to be invalid. Therefore, the data of new fault types are needed to retrain the model. However, DL models suffer from catastrophic forgetting when incrementally learning new classes. To solve the problem of the diagnosis of increasing fault types, a lifelong learning method for fault diagnosis (LLMFD) is proposed in this paper under the lifelong learning paradigm. The key of LLMFD is a proposed dual-branch aggregation networks (DBANets) framework that is combined with reserved exemplars to learn the new fault types without forgetting the old ones. In DBANets, each residual block layer has a dynamic block and a steady block to solve the stability–plasticity dilemma in lifelong learning. The aggregation weights are adopted to balance stability and plasticity. LLMFD is applied to a diagnosis case of incremental fault types. Results verify that LLMFD is superior to other lifelong learning methods and has satisfactory robustness.

A deep transfer learning method for system-level fault diagnosis of nuclear power plants under different power levels

The system-level fault diagnosis technology of nuclear power plants (NPPs) can provide operating status information, and further assist operators in making decisions to support the safe operation of NPPs. In traditional intelligent fault diagnosis research, the required assumption that the training set and test set follow the same distribution limits the application of the model. The fault diagnosis methodology integrating deep learning and transfer learning is proposed in this paper to solve the problem of poor generalization performance caused by inconsistent probability distribution under different power levels. First, the monitoring parameter preprocessing is carried out to suit the input of deep convolutional neural network (DCNN), which can effectively extract the transferable features between source and target power levels. More importantly, a promising transfer learning approach is introduced to sufficiently reduce the feature distribution discrepancy between different power levels, and eventually realize the fault diagnosis of the target power level. Finally, the simulation data test verifies the effectiveness of the transfer learning method for fault diagnosis under different power levels. Based on the feature visualization technology, the data discrepancy reduction effect is intuitively present, consequently demonstrating the potential application value in improving the generalization ability of NPP fault diagnosis.

A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory.

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability

Power transformer faults are considered rare events, so data samples in normal operations are much more readily available than in faulty conditions. Traditionally, power transformer fault diagnoses were enabled through gas-in-oil data, where erroneous diagnoses of faulty conditions as normal could have a more significant effect on power system operations than wrong diagnoses of normal operations as a faulty condition. Therefore, it is imperative to analyze gas-in-oil data characteristics more effectively to improve the performance of diagnostic methods. In this paper, an explainable bi-level machine learning method is proposed for oil-immersed power transformer fault diagnoses, consisting of a binary imbalanced classification model and a multi-classification model. The proposed Extreme Gradient Boosting models are designed with custom functions at each level, and automatic hyperparameters tuning is conducted based on Bayesian optimization. A fault feature selection is developed using the SHapley Additive exPlanations method to explain the diagnosis results, which could mine the impacts of fault features on diagnosis results and find the approach to improve the model performance. The fault diagnosis results are presented with performance analysis and comparative studies, and the feature selection results with importance analysis for each fault type based on SHAP value is provided, which demonstrates the feasibility and effectiveness of the proposed method.

A supervised ensemble learning method for fault diagnosis in photovoltaic strings

This study proposes a fault diagnosis method based on the use of a machine learning (ML) technique called ensemble learning (EL) for photovoltaic (PV) systems. EL methods aim to obtain better generalizability and prediction accuracy than a single ML algorithm by combining the predictions of multiple algorithms. In this context, first the most relevant features are selected by using grid-search with cross-validation. Then each learning algorithm and the EL model that will combine them have been improved in terms of parameter optimization.Results show that, with the appropriate features and optimized parameters for each single learning algorithm and the EL model, the proposed method not only improves the classification performance but also has a strong generalization ability for PV system fault diagnosis.

Federated Transfer Learning for Intelligent Fault Diagnostics Using Deep Adversarial Networks With Data Privacy

Intelligent data-driven machinery fault diagnosis methods have been popularly developed in the past years. While fairly high diagnosis accuracies have been obtained, large amounts of labeled training data are mostly required, which are difficult to collect in practice. The promising collaborative model training solution with multiple users poses high demands on data privacy due to conflict of interests. Furthermore, in the real industries, the data from different users can be usually collected from different machine operating conditions. The domain shift phenomenon and data privacy concern make the joint model training scheme quite challenging. To address this issue, a federated transfer learning method for fault diagnosis is proposed in this article. Different models can be used by different users to enhance data privacy. A federal initialization stage is introduced to keep similar data structures in distributed feature extractions, and a federated communication stage is further implemented using deep adversarial learning. A prediction consistency scheme is also adopted to increase model robustness. Experiments on two real-world datasets suggest the proposed federated transfer learning method is promising for real industrial applications.

Research on federated learning method for fault diagnosis in multiple working conditions

Research on federated learning method for fault diagnosis in multiple working conditions

The Research of Fault Diagnosis of Nuclear Power Plant Based on ELM-AdaBoost.SAMME

A fault diagnosis framework based on extreme learning machine (ELM) and AdaBoost.SAMME is proposed in a nuclear power plant (NPP) in this paper. After briefly describing the principles of ELM and AdaBoost.SAMME algorithm, the fault diagnosis framework sets ELM algorithm as the weak classifier and then integrates several weak classifiers into a strong one using the AdaBoost.SAMME algorithm. Furthermore, some experiments are put forward for the setting of two algorithms. The results of simulation experiments on the HPR1000 simulator show that the combined method has higher precision and faster speed by improving the performance of weak classifiers compared to the BP neural network and verify the feasibility and validity of the ensemble learning method for fault diagnosis. Meanwhile, the results also indicate that the proposed method can meet the requirements of a real-time diagnosis of the nuclear power plant.

A New Interpretable Learning Method for Fault Diagnosis of Rolling Bearings

In modern manufacturing processes, requirements for automatic fault diagnosis have been growing increasingly as it plays a vitally important role in the reliability and safety of industrial facilities. Rolling bearing systems represent a critical part in most of the industrial applications. In view of the strong environmental noise in the working environment of rolling bearing, its vibration signals have nonstationary and nonlinear characteristics, and those features are difficult to be extracted. In this article, we proposed a new intelligent fault diagnosis method for rolling bearing with unlabeled data by using the convolutional neural network (CNN) and fuzzy $C$ -means (FCM) clustering algorithm. CNN is first utilized to automatically extract features from rolling bearing vibration signals. Then, the principal component analysis (PCA) technique is used to reduce the dimension of the extracted features, and the first two principal components are selected as the fault feature vectors. Finally, the FCM algorithm is introduced to cluster those rolling bearing data in the derived feature space and identify the different fault types of rolling bearing. The results indicate that the newly proposed fault diagnosis method can achieve higher accuracy than other existing results in the literature.

A deformable CNN-DLSTM based transfer learning method for fault diagnosis of rolling bearing under multiple working conditions

Machine learning methods are widely used for rolling bearing fault diagnosis. Most of them are based on a basic assumption that training and testing data are adequate and follow the same distribution. However, for bearings working under multiple working conditions, dynamic changes are inevitable and labelled vibration data are usually insufficient. To deal with the issues, a new fault diagnosis method using deformable convolutional neural network (CNN), deep long short-term memory (DLSTM) and transfer learning strategies is designed. Specifically, a model is constructed by integrating deformable CNN, DLSTM and dense layers. Among them, deformable CNN enhances the ability of standard CNNs for local feature extraction using fixed geometric structures. DLSTM further encodes the sequential information contained in the output of deformable CNN. Dense layers are applied to capture high-level features then classify the data samples as each fault type. The model is firstly pre-trained using data samples under one working condition. Then, transfer learning strategies are implemented to fine-tune the pre-trained model utilising very few samples of another working condition, enabling it to identify fault types of bearing under new condition. Experiments are conducted and results show that the presented model yields higher than comparative performance compared with state-of-the-art methods.

A zero-shot learning method for fault diagnosis under unknown working loads

Data-based fault diagnosis is an important technology in modern manufacturing systems. However, most of these diagnosis methods assume that all the data should be identically distributed. In diagnosis tasks, this assumption means that these methods can only handle faults from the same working load. In real-world applications, the working load of the equipment varies for the different productions; if an unknown working load with no prior data available is given, then these traditional methods may be invalid. Zero-shot learning, using known data to diagnose the fault under unknown working loads, provides a transfer approach to solve this problem. In this paper, a zero-shot learning method based on contractive stacked autoencoders is proposed. The proposed method is only trained by the data from the known working load and can diagnose the fault from unknown but related working loads without prior data. The experimental results on the Case Western Reserve University dataset indicate that the proposed method performs better than the traditional methods under unknown working loads and has an accuracy of 97.82%. In addition, the analysis of the singular value and feature space also suggests that the proposed method is more robust and the feature representation is more contractive.

A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM.

Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods.

A negative correlation ensemble transfer learning method for fault diagnosis based on convolutional neural network.

With the development of the smart manufacturing, data-driven fault diagnosis has receiving more and more attentions from both academic and engineering fields. As one of the most important data-driven fault diagnosis method, deep learning (DL) has achieved remarkable applications. However, the DL based fault diagnosis methods still have the following two drawbacks: 1) One of the most major branch of deep learning is to construct the deeper structures, however the deep learning models in fault diagnosis is very shadow. 2) As stated by the no-free-lunch theorem, no single model can perform best on every dataset, and the individual deep learning model still suffers from the generalization ability. In this research, a new negative correlation ensemble transfer learning method (NCTE) is proposed. Firstly, the transfer learning based ResNet-50 is proposed to construct a deep learning structure that has 50 layers. Secondly, several fully-connected layers and softmax classifiers are trained cooperatively using negative correlation learning (NCL). Thirdly, the hyper-parameters of the proposed NCTE are determined by cross validation. The proposed NCTE is conducted on the KAT Bearing Dataset, and the prediction accuracy of NCTE is as high as 98.73%. This results show that NCTE has achieved a good results compared with other machine learning and deep learning method.

Planetary gearbox fault feature learning using conditional variational neural networks under noise environment

The features signals of early fault collected from planetary gearbox are usually weak. It is difficult to extract effective fault features from the collected vibration signals under noise environment. In this paper, a new feature learning method for fault diagnosis of planetary gearbox based on deep conditional variational neural networks (CVNN) is proposed. First, the new method utilizes multi-layer perceptron (MLP) to model the normal distribution features of frequency spectra from noisy vibration signals. Second, the new features are obtained by resampling normal distribution features in order to eliminate the effect of noise. Then the denoised features are compressed and reduced dimensionally by MLP. Third, the effective denoised features are input to classifier. Finally, the trained CVNN is applied for intelligent fault diagnosis of planetary gearbox. The experimental results confirm that CVNN method can extract effective fault features from noisy vibration signals, and it has higher accuracy of fault diagnosis than other methods in the case of low signal to noise ratio (SNR) values.

A Jointed Signal Analysis and Convolutional Neural Network Method for Fault Diagnosis

Fault diagnosis plays a vital role in the modern industry. In this research, a joint vibration signal analysis and deep learning method for fault diagnosis is proposed. The vibration signal analysis is a well-established technique for condition monitoring, and deep learning has shown its potential in fault diagnosis. In the proposed method, the time-frequency technique, named as S transform, is applied to transfer the vibration signals to images, and then an improved convolutional neural network (CNN) is applied to classify these images. The results show the proposed method has achieved the significant improvement.