Fault Diagnosis Of Components Research Articles

Boosting efficient attention assisted cyclic adversarial auto-encoder for rotating component fault diagnosis under low label rates

In practical engineering scenarios with limited labeled samples, conventional semi-supervised diagnostic methods face challenges in achieving satisfactory identification outcomes. To address the aforementioned issues, this paper introduces an incremental semi-supervised learning (ISL) approach based on boosting efficient attention (BEA) assisted cyclic adversarial auto-encoder (CAAE), referred to as BEA-CAAE. The CAAE enhances unsupervised feature representation by simultaneously constraining the distribution of encoded features and aligning elements of the reconstructed samples through a cyclic encoding strategy. The BEA improves classical attention weights' activation strength to better capture vital information, thereby boosting the feature extraction capabilities of both CAAE and the classifier. The ISL employs a stepwise pseudo-label propagation strategy to incrementally filter high-confidence samples, enhancing sample and label utilization, and improving diagnostic accuracy under low label rate conditions. Experiments conducted on multiple test rigs with simple structures as well as large-scale rotating components test rigs that mimic real-world engineering conditions have demonstrated that the proposed method exhibits a significant advantage over existing semi-supervised fault diagnosis approaches in terms of fault diagnosis accuracy and generalization capability, especially under low label rate conditions.

Intelligent fault diagnosis methods for hydraulic components based on information fusion: review and prospects

Abstract Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data characteristics, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, including the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are described and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin technology is introduced, and the advantages and research status of intelligent fault diagnosis based on digital twin are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and digital twin to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.

Fault Diagnosis of Hydraulic Components Based on Multi-Sensor Information Fusion Using Improved TSO-CNN-BiLSTM.

In order to realize the accurate and reliable fault diagnosis of hydraulic systems, a diagnostic model based on improved tuna swarm optimization (ITSO), optimized convolutional neural networks (CNNs), and bi-directional long short-term memory (BiLSTM) networks is proposed. Firstly, sensor selection is implemented using the random forest algorithm to select useful signals from six kinds of physical or virtual sensors including pressure, temperature, flow rate, vibration, motor power, and motor efficiency coefficient. After that, fused features are extracted by CNN, and then, BiLSTM is applied to learn the forward and backward information contained in the data. The ITSO algorithm is adopted to adaptively optimize the learning rate, regularization coefficient, and node number to obtain the optimal CNN-BiLSTM network. Improved Chebyshev chaotic mapping and the nonlinear reduction strategy are adopted to improve population initialization and individual position updating, further promoting the optimization effect of TSO. The experimental results show that the proposed method can automatically extract fusion features and effectively utilize multi-sensor information. The diagnostic accuracies of the plunger pump, cooler, throttle valve, and accumulator are 99.07%, 99.4%, 98.81%, and 98.51%, respectively. The diagnostic results of noisy data with 0 dB, 5 dB, and 10 dB signal-to-noise ratios (SNRs) show that the ITSO-CNN-BiLSTM model has good robustness to noise interference.

Denoising graph neural network based hydraulic component fault diagnosis method

Accurately identifying faults in hydraulic components is essential for improving the reliability and safety of hydraulic systems. Since the hydraulic components of heavy-duty equipment usually work in complex and variable environments and are subject to interference from multiple sources of noise. As a result, fault diagnosis of hydraulic components is challenging, leading to the development of various fault diagnosis models. However, current hydraulic component fault diagnosis models have the following limitations: (1) only able to handle Euclidean space data and ignore the topological relationships within the signals that could provide additional useful information for fault diagnosis, (2) only diagnose faults using single-dimensional acceleration data and fail to incorporate more equipment working conditions. This study focuses on the hydraulic directional valve and proposes a fault diagnosis method based on denoising graph neural network. Firstly, computational fluid dynamics(CFD) is used to analyze the flow field of the spool and sleeve of the valve under different wear states and working conditions. It is found that different degrees of wear have similarities under different working conditions, and there is a regular correlation between the acceleration signal of the valve body and the fault mode under the same working condition. Therefore, acceleration and working condition information are selected as the raw information for fault diagnosis. Then, horizontal visibility graph(HVG) was utilized to transform acceleration signal to graph, and the graph nodes, which can incorporate multidimensional information, were set to be the equipment working conditions and acceleration values. However, the noise in the raw signal could seriously affect the quality of the HVG. To solve this problem, an adaptive threshold denoising algorithm was proposed to reduce the interference noise in the acceleration signal. The denoising threshold parameter was learned directly from the raw data. Finally, graph neural network(GNN) is applied to fault diagnosis of hydraulic components from non-Euclidean multidimensional information. Experimental results demonstrate the superior robustness and accuracy of this method in handling strong noise and multiple working conditions compared to several classical fault diagnosis methods. This research provides an effective approach for hydraulic component fault diagnosis.

Fault diagnosis based on federated learning driven by dynamic expansion for model layers of imbalanced client

Federated Learning is a promising tool for fault diagnosis of critical components for electrical driving systems. However, the performance of existing method is limited by negative transferring owing to inter-client inhomogeneity related to the sample size, sample quality, and fault type. How to make balanced client benefit from imbalanced client who involves unique fault information in its poor data is crucial to encourage the balanced clients to take an active part in federation. This study aims to design an imbalanced federated learning framework based on dynamic expansion for model layers of an imbalanced client to make it powerful enough to make it powerful in extracting the unique fault feature involved. The proposed method can ensure that balanced client be benefited from imbalanced client by designing a collaboration mechanism of inter-client layer-by-layer federation and intra-client cross-scale recursive fusion. The effectiveness of the proposed method was validated using both the benchmark dataset and the data set of an actual project of Nio Inc. When there is significant inhomogeneity between clients, the proposed method can achieve an accuracy increase of 13.97% compared to existing methods.

Cross-Component Transferable Transformer Pipeline Obeying Dynamic Seesaw for Rotating Machinery with Imbalanced Data.

Due to the lack of fault data in the daily work of rotating machinery components, existing data-driven fault diagnosis procedures cannot accurately diagnose fault classes and are difficult to apply to most components. At the same time, the complex and variable working conditions of components pose a challenge to the feature extraction capability of the models. Therefore, a transferable pipeline is constructed to solve the fault diagnosis of multiple components in the presence of imbalanced data. Firstly, synchrosqueezed wavelet transforms (SWT) are improved to highlight the time-frequency feature of the signal and reduce the time-frequency differences between different signals. Secondly, we proposed a novel hierarchical window transformer model that obeys a dynamic seesaw (HWT-SS), which compensates for imbalanced samples while fully extracting key features of the samples. Finally, a transfer diagnosis between components provides a new approach to solving fault diagnosis with imbalanced data among multiple components. The comparison with the benchmark models in four datasets proves that the proposed model has the advantages of strong feature extraction capability and low influence from imbalanced data. The transfer tests between datasets and the visual interpretation of the model prove that the transfer diagnosis between components can further improve the diagnostic capability of the model for extremely imbalanced data.

Engine gas path component fault diagnosis based on a sparse deep stacking network

Accurate engine gas path component fault diagnosis methods are key to ensuring the reliability and safety of engine operations. At present, the effectiveness of the data-driven gas path component fault diagnosis methods has been widely verified in engineering applications. The deep stack neural network (DSN), as a common deep learning neural network, has been gaining more attention in gas path fault diagnosis studies. However, various gas path component faults with strong coupling effects could occur simultaneously, resulting the DSN method less effective for engine gas path fault diagnosis. In order to improve the prediction performance of the DSN handling multiple gas path component fault diagnosis, a sparse regularization and representation method was proposed. The sparse regularization term is used to expand the traditional deep stacking neural network in the sparse representation, and the predicted output tag is close to the target output tag through this term. The diagnosis performance of six different neural network methods were compared by various engine gas path component fault diagnosis types. The results show that the proposed sparse regularization method significantly improves the prediction performance of the DSN, with an accuracy rate 99.9% under various gas path component fault conditions, which is higher than other methods. The proposed engine gas path component fault diagnosis method can handle multiple coupling gas path faults, and help engine operators to develop maintenance plans for the purpose of engine health management.

A unified diagnostic framework via symmetrized data aggregation

In statistical process control of high-dimensional data streams, in addition to online monitoring of abnormal changes, fault diagnosis of responsible components has become increasingly important. Existing diagnostic procedures have been designed for some typical models with distribution assumptions. Moreover, there is a lack of systematic approaches to provide a theoretical guarantee of significance in estimating shifted components. In this article, we introduce a new procedure to control the False Discovery Rate (FDR) of fault diagnosis. The proposed method formulates the fault diagnosis as a variable selection problem and utilizes the symmetrized data aggregation technique via sample splitting, data screening, and information pooling to control the FDR. Under some mild conditions, we show that the proposed method can achieve FDR control asymptotically. Extensive numerical studies and two real-data examples demonstrate satisfactory FDR control and remarkable diagnostic power in comparison to existing methods.

Joint diagnosis of process mean vector and covariance matrix for multivariate statistical process control

The monitoring methodology in statistical process control is very useful and efficient to detect abnormal changes of a process, however diagnosis ability, accurate fault diagnosis of responsible components for the process change, is limited. Currently, most of work has focused on the diagnosis task of mean shifts. Nevertheless, the shifts of a process also likely occur in the variances/covariances. In this article, we develop a Bayesian procedure to simultaneously diagnose shifts in both mean vector and covariance matrix. Moreover, the proposed method can provide the directions of shifts and corresponding probability. These diagnostic information help decision makers to find quickly root causes of abnormal changes. Compared with existing methods, numerical simulations favor the proposed method. Finally, we use a real dataset from bolt production process to demonstrate the implementation of the proposed Bayesian method.

Component Fault Diagnosability of Hierarchical Cubic Networks

The fault diagnosability of a network indicates the self-diagnosis ability of the network, thus it is an important measure of robustness of the network. As a neoteric feature for measuring fault diagnosability, the r -component diagnosability ct r (G) of a network G imposes the restriction that the number of components is at least r in the remaining network of G by deleting faulty set X , which enhances the diagnosability of G . In this article, we establish the r -component diagnosability for n -dimensional hierarchical cubic network HCN n , and we show that, under both PMC model and MM* model, the r -component diagnosability of HCN n is rn -½( r -1) r +1 for n ≥ 2 and 1≤ r≤ n-1 . Moreover, we introduce the concepts of 0-PMC subgraph and 0-MM* subgraph of HCN n . Then, we make use of 0-PMC subgraph and 0-MM* subgraph of HCN n to design two algorithms under PMC model and MM* model, respectively, which are practical and efficient for component fault diagnosis of HCN n . Besides, we compare the r -component diagnosability of HCN n with the extra conditional diagnosability, diagnosability, good-neighbor diagnosability, pessimistic diagnosability, and conditional diagnosability, and we verify that the r -component diagnosability of HCN n is higher than the other types of diagnosability.

A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction

The damage of vulnerable components inside the cylinder of reciprocating compressor, including the valve, piston ring, packing and piston ring, will cause the unexpected shutdown of the compressor unit. The indicator diagram which reflects the thermodynamic process in the cylinder is suitable for fault diagnosis of vulnerable components. However, most of the published fault diagnosis methods based on indicator diagram are aimed at the fault diagnosis of gas valve. In addition, the extracted features lack physical meaning in most fault diagnosis methods using machine learning algorithm, which is not conducive to be widely applied in practical engineering. In this study, features with definite physical meaning, including average suction pressure, average discharge pressure, area of indicator diagram and centroid coordinates of indicator diagram, are extracted from indicator diagram, and the threshold database of features under normal states and various fault states is established according to the contrast experiment. The results of the experiment show that the thresholds of the extracted parameters are obviously different under normal states and various fault states. During fault diagnosis, several groups of indicator diagrams of the compressor to be diagnosed are collected at first. After feature extraction, the extracted features are compared with the thresholds under different compressor states to obtain the average numbers of features within the threshold range under different compressor states to determine the compressor states. The accuracy of the method for judging whether the compressor is faulty or normal could reach 98.3%. Furthermore, the accuracy of identifying individual faulty components and multiple faulty components could reach 86.86%. The reason for the low overall diagnostic accuracy is that certain faults have similar effects on the features extracted from indicator diagram. The proposed method is believed as an excellent fault diagnosis method for the vulnerable components inside the cylinder of reciprocating compressor.

Research on Intelligent Fault Diagnosis Method of Automation Equipment Based on EMD-ISVM

Bearing plays an important role in automation equipment in many fields, such as industry, transportation, and national defense. An intelligent fault diagnosis method for rotating machinery based on EMD-ISVM is proposed to improve the diagnostic accuracy of typical bearing faults. Firstly, the vibration signal is decomposed into multiple IMF components through the adaptability of empirical mode decomposition. Then the IMF components containing effective state information are selected to reconstruct the signal to reduce noise. Then, the fault feature set is formed by extracting multiple features of the reconstructed signal that can represent the machine’s running state. Finally, the global optimization ability of FOA is used to optimize the parameters of SVM and to improve the ability of SVM to identify fault modes. The bearing experiment results verify the effectiveness of the proposed method, which has a certain reference significance for fault diagnosis of other key components of automation equipment.

Pseudo-Label Guided Sparse Deep Belief Network Learning Method for Fault Diagnosis of Radar Critical Components

Effective fault diagnosis of critical components is essential to ensure the safe and reliable operation of the entire system. This paper deals with the fault diagnosis of transmitter/receiver module, which is a critical component in the phased array radar system, by proposing a novel deep belief network learning method. A sparse deep belief network based on Gaussian function is first constructed to automatically learn the relationship between monitoring data and component health conditions. With the trained sparse deep belief network, the pseudo-labels are produced for unlabeled samples, while the information entropy is employed to calculate the confidence levels reflecting their certainty to reduce the effect of pseudo-label noise. The pseudo-labeled samples with high confidence levels are added to the training set to retrain the network. Optimal model configuration parameters are obtained through a chaos game optimization algorithm. The effectiveness of the proposed method is verified on a real-world dataset from a certain type of phased array radar. The experiments show that the mean identification rate of this method can reach 96.33%, which not only exceeds some deep belief network-based modeling methods, but also exceeds other intelligent methods.

A Research on Fault Diagnosis of Wind Turbine CMS Based on Bayesian-GAN-LSTM Neural Network

Fault diagnosis of large components of wind turbines is of great significance in improving the reliability of wind turbines. In the actual fault diagnosis project, insufficient data labels and low recognition accuracy are two major problems. In order to make up for these two deficiencies, this paper proposes to combine the generative adversarial neural (GAN) network and the LSTM model and uses the Bayesian distribution to optimize the GAN and LSTM, respectively. GAN uses the generator to solve the problem of insufficient data labels, and the Bayesian optimized LSTM prediction accuracy is better. This paper uses the actual wind turbine bearing data to test the algorithm, and the accuracy of the test results reaches 97.6%, which shows the algorithm is accurate and robust, and the upgraded algorithm can be applied to the actual fault diagnosis of large components of wind turbines.

Adsorption and gas sensing properties of Co modified InN to SF6-N2 mixture decomposition components

• It is the first time that Co-InN is used as the adsorbent to remove the decomposition components of SF 6 -N 2. • Co-InN shows good adsorption properties to NO 2 , SOF 4 , SOF 2 , SO 2 F 2 by chemisorption. • The theoretical calculation results are important to guide the development of resistive gas sensor based on the Co-InN. This paper presents a detection method of partial discharge decomposition components of SF 6 -N 2 mixture by Co-doped InN. The severity of partial discharge in gas insulated switchgear (GIS) can be judged by the gas components when SF 6 -N 2 decomposes. Exploring sensors with excellent gas-sensing detection capabilities can provide method to partial discharge decomposition component detection and fault diagnosis of GIS. Undoped InN is a wide bandgap semiconductor with good sensing properties. In this paper, the adsorption of SF 6 -N 2 decomposition components on Co-doped InN monolayer, as well as the adsorption mechanism and gas sensitivity characteristics were investigated by density functional theory (DFT), including adsorption energy, charge transfer, the density of state theory and molecular orbital theory. Our calculations found that Co-doped InN has the optimal structure at the T N site and has good adsorption performance for NO 2 , SOF 4 , SOF 2 , and SO 2 F 2 gas molecule. All adsorption reactions belong to chemisorption. These results could direct the development of gas sensor based on the Co-doped InN monolayer, and provides methods to online monitoring of GIS discharge failures.

A rotor fault diagnosis method based on BP-Adaboost weighted by non-fuzzy solution coefficients

To ensure the long-term safe and reliable operation of mechanical equipment, it is necessary to carry out fault diagnosis of key components in the mechanical system. A rotor fault diagnosis method based on non-fuzzy solution weighted BP-AdaBoost (NFSW-BP-AdaBoost) is proposed in this paper. First, the rotor vibration signal is transformed into a high-dimensional feature set. Then, the VC value is used to obtain the high-dimensional feature sequence. Finally, the NFSW-BP-AdaBoost method is used to diagnose the type and degree of rotor fault. The core of this article is to analyze the fuzzy solution of the combined binary classifier applied to multi-classification problems in decision-making, and put forward the non-fuzzy solution coefficient to weigh the conclusion of the combined multi-classifier, highlighting the sensitivity to the non-fuzzy solutions of each multi-classifier. The experimental analysis results show that the recognition rate of the classification method for fault types can reach more than 97%.

Feature selection algorithm based on density and distance for fault diagnosis applied to a roll-to-roll manufacturing system

Abstract Roll-to-roll systems that include rotary components such as driven rolls and idle rollers have significant potential for application in fabrication of flexible functional devices. They are inexpensive, mass producible, and environmentally friendly; however, even minor defects in their component bearings can render them susceptible to severe damage, which necessitates accurate diagnoses of bearing quality. The main steps in machine learning for fault diagnosis include feature extraction and selection. In the case of high-dimensional feature data, critical study is required to identify the best feature combination for proper diagnosis. Thus, this study aims to develop a method that extracts fault characteristics of a bearing from the measured signal and qualify the bearing according to the Mahalanobis distances and differences in density between normal and faulty data groups. Features extracted from vibration data collected from industry-scale roll-to-roll systems and CWRU data were trained with principal component analysis, other modern feature selection techniques, and the proposed algorithm-based eight classifiers. Compared with the existing algorithm, the accuracy increased by up to 9.24%, the training time decreased by up to 34.46%, and the number of features to obtain the maximum accuracy decreased by up to 59.92%. Thus, the proposed algorithm provides an effective and time-efficient approach to improve the accuracy of fault diagnosis of rotary components.

Rolling Bearing Fault Diagnosis Based on PCA-ResNet

Aiming at the situation that there are many interference factors in the collected signals of rolling bearings working in harsh and complex environment. Propose A method of noise reduction based on principal component analysis (PCA) and fault diagnosis and classification based on residual neural network (ResNet). Experimental results show that this method can effectively complete the task of fault identification and classification, and is superior to LeNet-5, SVM, DNN and other common deep learning methods.

Fault Analysis and Diagnosis of Key Components of Shearer

Fault Analysis and Diagnosis of Key Components of Shearer

Component Fault Diagnosis and Fault Tolerance of Alternating Group Graphs

AbstractReliability of a multiprocessor system becomes an important issue for parallel computing. Component diagnosability and component connectivity of a graph play crucial roles in assessing the vulnerability of an interconnection network, which are two significant indicators for the reliability and fault tolerance of a multiprocessor system. Until now, only a little knowledge of results have been known on $r$-component diagnosability and $r$-component connectivity. In this paper, we first propose the $r$-component diagnosability of $n$-dimensional alternating group graph $AG_{n}$ under PMC model. And then we promote our research on $AG_{n}$ by a fairly good construction for general $r$-component connectivity of $AG_{n}$, where $6\leq r\leq n-1$. The theoretical analysis and simulation show that the general $r$-component connectivity of $AG_{n}$ is larger than those of $Q_{n}$, $D_n$ and $FQ_{n}$.