Single Fault Diagnosis Research Articles

Fault diagnosis method of rolling bearing of BN ball mill based on AESL-GA

Aiming at the shortcomings of incomplete and inaccurate knowledge-based Bayesian network (BN) construction methods, a BN structure construction method based on knowledge guidance and data mining is proposed. Aiming at the problem of inaccurate single signal fault diagnosis results and the uncertainty problem in fault information, the current signal and the vibration signal are fused to establish the feature nodes of the BN network, and the fault feature parameters of the two signals are extracted respectively. The feature basket is selected using the discrimination index method and used as the node of the BN structure feature layer. The initial BN structure constructed by expert knowledge is combined with the adaptive elite structure genetic algorithm (AESL-GA) for structural optimization. By adaptively restricting the search space in the evolution process, reducing the number of free parameters, improving its global search ability, and obtaining the optimal BN structure. The method is verified by the measured data of the ball mill rolling bearing of Jinchuan Company and the Paderborn University data set, which proves the effectiveness of the proposed method.

Multi-Source Information-Based Bearing Fault Diagnosis Using Multi-Branch Selective Fusion Deep Residual Network.

Rolling bearing is the core component of industrial machines, but it is difficult for common single signal source-based fault diagnosis methods to ensure reliable results since sensor signals are vulnerable to the pollution of background noises and the attenuation of transmitted information. Recently, multi-source information-based fault diagnosis methods have become popular, but the information redundancy between multiple signals is a tough problem that will negatively impact the representational capacity of deep learning algorithms and the precision of fault diagnosis methods. Besides that, the characteristics of various signals are actually different, but this problem was usually omitted by researchers, and it has potential to further improve the diagnosing performance by adaptively adjusting the feature extraction process for every input signal source. Aimed at solving the above problems, a novel model for bearing fault diagnosis called multi-branch selective fusion deep residual network is proposed in this paper. The model adopts a multi-branch structure design to enable every input signal source to have a unique feature processing channel, avoiding the information of multiple signal sources blindly coupled by convolution kernels. And in each branch, different convolution kernel sizes are assigned according to the characteristics of every input signal, fully digging the precious fault components on respective information sources. Lastly, the dropout technique is used to randomly throw out some activated neurons, alleviating the redundancy and enhancing the quality of the multiscale features extracted from different signals. The proposed method was experimentally compared with other intelligent methods on two authoritative public bearing datasets, and the experimental results prove the feasibility and superiority of the proposed model.

Cyclostationarity blind deconvolution via eigenvector screening and its applications to the condition monitoring of rotating machinery

Maximum second-order cyclostationarity blind deconvolution (CYCBD) is accomplished by maximizing the second-order cyclostationarity of signals through the indicator of second-order cyclostationarity (ICS2). It is a significant method for extracting weak periodic pulses related to bearing faults. However, since the interference spectral lines generated by the interference signals in squared envelope spectrum do not be considered by ICS2, the method can be invalid for certain applications. In addition, when the detected signal contains compound faults, only one fault can be enhanced, and misdiagnosis is easily caused. This article proposes cyclostationarity blind deconvolution via eigenvector screening, abbreviated as ESCYCBD, for fault feature enhancement and extraction of rotating machinery in order to solve these problems. Different from existing deconvolution methods which use the maximum value of the index as a criterion to select the filter coefficients, this method proposes the concept of eigenvector subspace, which contains the optimal eigenvector as filter coefficients that are ignored by CYCBD. Subsequently, the Fourier coefficients related to a series of cyclic frequencies are extended for compound fault signals. Besides, by using the harmonic characteristics of fault features in the envelope spectrum, fault feature recognition and optimal selection are carried out in eigenvector subspaces. At the same time, the concept of signals set is created, by which means different feature modes of different fault can be extracted at one time. The analysis results of simulation signals and experimental data show that the proposed ESCYCBD has robustness and accuracy in extracting fault feature modes, whether it is single fault diagnosis or compound fault diagnosis.

An Efficient Double Deep Q Learning Network-Based Soft Faults Detection and Localization in Analog Circuits

To identify whether the circuit under test is fault-free or faulty, fault diagnosis is conducted in an analog circuit. However, in the case of fault detection (FD) techniques, the size of FD is the main challenge in testing, especially for complex analog circuits. Hence to reduce the size of FD, specific test nodes are chosen to perform fault diagnosis from the available accessible test nodes. Specific test nodes are selected based on the faults classification efficiency of fault diagnosis. Therefore, this paper proposes an approach for single and multiple soft fault diagnosis using minimum test nodes in analog electronic circuits. The proposed double deep Q-learning-based hybrid Simulated annealing–Tabu search (DDQN-Hybrid SATS) technique determines minimum test nodes with the utilization of distance metric for fault classification. The DDQN-Hybrid SATS technique is used to identify minimum nodes for testing. In this, the double deep Q learning network (DDQN) ensures better reliability and faster convergence in learning but suffers from catastrophic forgetting issues. To prevent such issues, the DDQN approach is optimized using a hybrid SATS algorithm. The hybrid optimization of simulated annealing (SA) and Tabu search (TS) coalesce the advantages of individual optimization procedures to provide the optimum solution in a fast and effective way. The results for a fourth-order low pass filter are presented (i.e., first CUT) and an eight-bit digital to analog converter (i.e., second CUT). Moreover, the simulation experiment reveals that the proposed DDQN-Hybrid SATS technique achieves greater overall fault classification accuracy of about 96.25% than other compared techniques with minimum computation time.

A concurrent fault diagnosis method for electric isolation valves in nuclear power plants based on rule-based reasoning and data-driven methods

In order to improve the fault diagnosis ability of electric isolation valves in nuclear power plants, especially for the diagnosis of concurrent faults, a novel concurrent fault diagnosis method based on rule reasoning and data-driven methods is proposed in this study. The concurrent fault diagnosis method is divided into two layers: the rule-based reasoning layer and data-driven layer. In order to realize the diagnosis of most concurrent faults, the rule-based reasoning layer is established by a rule inference model according to the failure mode and effects analysis (FMEA) of the electric isolation valve. In order to diagnose concurrent faults with mutual interference of fault features that cannot be diagnosed by rule-based reasoning layer, the data-driven layer is established by variational mode decomposition (VMD) and fast independent component analysis (Fast-ICA) method to separate concurrent fault signals and gate recurrent unit (GRU) method to identify fault patterns. After testing on the electric valve test bench and simulation data, multiple concurrent faults of the electric isolation valve are diagnosed accurately by the proposed concurrent fault diagnosis framework, including complex concurrent faults such as internal leakage and external leakage, and the framework can also work in a single fault diagnosis scenario without changing models. Compared with the concurrent fault diagnosis based on multi-label support vector machine, the accuracy of concurrent fault diagnosis is improved by about 25.3%. The research can provide a new idea for the concurrent fault diagnosis of electric isolation valves and other equipment.

Unmanned aerial vehicle fault diagnosis based on ensemble deep learning model

To address the problems of external interference during unmanned aerial vehicle (UAV) flight and the low accuracy and weak generalization ability of the current single fault diagnosis model, this work proposes a weighted ensemble deep learning UAV fault diagnosis method. First, considering the differences in training methods and fault feature recognition principles of deep networks with different structures, three hybrid fault diagnosis models are constructed. These models are constructed by combining convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM) and bidirectional gate recurrent unit (BiGRU). CNN is used to extract the features of the UAV flight data and the obtained feature information is fed into BiLSTM and BiGRU to explore the fault information inherent in the time series data. Then, the three hybrid fault diagnosis models are used as the individual model of the ensemble learning algorithm, and the weights of the three individual models are optimized using a random grid search algorithm to construct a UAV fault diagnosis model based on hybrid deep learning weighted ensemble, which further improves the fault diagnosis performance. Finally, it is demonstrated experimentally that the proposed hybrid deep learning weighted ensemble fault diagnosis model can effectively identify the fault of UAV with an accuracy of 99.22% and 99.62% on binary and multivariate classification, respectively, and reflects better generalization performance in the metrics of precision, recall, and F1 score.

Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis

Domain generalization methods can effectively identify machinery faults under unseen new target working conditions. Nevertheless, most of them rely on data from multiple source domains that are available for model training. However, it is laborious difficult to collect complete monitoring data of machinery under multiple working conditions. Confronting the scenario that only one working condition is available, this paper proposes a novel single domain generalization model, termed multi-scale style generative and adversarial contrastive networks (MSG-ACN), which learns diagnosis knowledge from the single working condition and generalizes it to new working conditions. The main idea of the MSG-ACN model is to generate diverse samples in an extended domain via a domain generation module, and extract domain-invariant features from the source and extended domains via a diagnosis task module. A multi-scale style generation strategy is established to ensure that the generated samples contain abundant state information with the aids of multi-scale convolutional kernels and style learning. Furthermore, an adversarial contrastive learning strategy is designed to promote the learning of class-wise domain-invariant representations while maintaining the diversity of the generated samples. Extensive generalization diagnosis experiments on two datasets verify the superiority of the proposed method over the state-of-the-art fault diagnosis methods.

Multiple faults diagnosis for ocean-going marine diesel engines based on different neural network algorithms.

Fault diagnosis technologies for ocean-going marine diesel engines play an important role in the safety and reliability of ship navigation. Although many fault diagnosis technologies have achieved acceptable results for single fault of diesel engines, the diagnosis of multiple faults is rarely involved. Due to the strong correlation, non-linearity and randomness of multiple faults, it is extremely difficult to make an accurate diagnosis. In this study, diagnosis methods based on thermal parametric analysis combined with different neural network algorithms were established and used for the diagnosis of multiple faults in the ocean-going marine diesel engine. The results show that the Levenberg Marquardt back propagation neural network has the highest diagnostic accuracy rate of 88.89% and 100% for multiple faults and single faults, respectively, and its diagnostic time is also relatively short, 0.78 s. The Bayesian regularization back propagation neural network can give a diagnostic accuracy rate of 100% for single faults, but for multiple faults, the diagnostic accuracy rate is only 55.56%, and the diagnosis time for the entire sample is the longest. As for the probabilistic neural network, although it has the fastest diagnosis speed, it has the lowest diagnostic accuracy rate for both single faults and multiple faults. The results may provide references for the online diagnosis of single faults and multiple faults in ocean-going marine diesel engines.

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.

Diagnosis of single and multiple-source faults of chiller sensors using EWEEMD-ICKNN by time sequence denoising and non-Gaussian distribution feature extraction

Currently, fault detection of chiller sensors primarily focuses on single-source fault (SSF) sensors. These methods are typically ineffective in multiple source faults (MSF) cases. These methods assume a Gaussian distribution of the chiller data and ignore the coupling between the sensors. Therefore, we propose temporal feature denoising and non-Gaussian distribution-based methods in this study. First, the chiller sensor signals are denoised based on exponential weighted moving average ensemble empirical mode decomposition (EWEEMD). Subsequently, an independent component analysis k-nearest neighbour (ICKNN) classifier is proposed based on the non-Gaussian distribution features of the chiller sensor data. A residual vector combined with independent component analysis was used for feature extraction to propose the ICKNN classifier. Finally, according to their characteristics, different SSF and MSF were applied to the chiller sensors. Experimental comparisons with other methods were performed for four single-source faults and six multisource faults. The EWEEMD-ICKNN was experimentally confirmed to have excellent fault detection performance in the SSF and MSF chiller sensors. The experimental results demonstrate that the fault diagnosis rate of EWEEMD-ICKNN ranges from 99.90% to 86.80%. Additionally, the fault diagnosis efficiency of EWEEMD-ICKNN remains consistently stable across various fault states, exhibiting an average F1 score of 0.9589 with F1 score statistics of 0.038.

Single and multiple fault diagnosis based on PCA with relative variation of contribution strategy

Data-driven methods have been recognized as an efficient tool for multivariate statistical process control. Contribution plots are also well known as a popular tool in principal component analysis, which is used for isolating sensor faults without the need for any prior information. However, studies carried out in the literature have unified contribution plots in three general approaches. Furthermore, they demonstrated that correct diagnosis based on contribution plots is not guaranteed for either single or multiple sensor faults. Therefore, to deal with this issue, the present paper highlights a new contribution formula called relative variation of contribution. Simulation results show that the proposed method of contribution can successfully perform the fault isolation task, in comparison with partial decomposition contribution and its relative version (rPDC) based on their fault isolation rate.

Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests

A train traction system is an important part of an urban rail transit system. However, a train traction system has many components and a high risk of internal faults. How to systematically evaluate the fault coverage and diagnosis ability of testing equipment is a fundamental problem in the technical field of train operation. In response to this problem, this study attempts to apply testability technology to the test capability analysis of train traction systems for rail transit. In view of the uncertainty in actual tests, a method for constructing a fault diagnosis strategy for a traction system under unreliable testing is proposed. The concept of test credibility is introduced for the first time, and the quantitative evaluation of test credibility is realized using a cloud model, so as to construct a new “fault-test” credibility correlation matrix. On this basis, a single-fault diagnosis strategy of the traction system is constructed and compared based on information theory. The results show that a using a fault diagnosis strategy under the condition of unreliable testing is more similar to actual maintenance work, proving the significance of the diagnosis strategy constructed using this method for the practical application of the project.

Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm

Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural network (BPNN) is a commonly used intelligent algorithm for fault location research in the DN. To improve the accuracy of dual fault diagnosis in the DN, this study optimizes BPNN by combining the genetic algorithm (GA) and cloud theory. The two types of BPNN before and after optimization are used for single fault and dual fault diagnosis of the DN, respectively. The experimental results show that the optimized BPNN has certain effectiveness and stability. The optimized BPNN requires 25.65 ms of runtime and 365 simulation steps. And in diagnosis and positioning of dual faults, the optimized BPNN exhibits a higher fault diagnosis rate, with an accuracy of 89%. In comparison to ROC curves, the optimized BPNN has a larger area under the curve and its curve is smoother. The results confirm that the optimized BPNN has high efficiency and accuracy.

An adaptive morphological filtering and feature enhancement method for spindle motor bearing fault diagnosis

The unbalanced electromagnetic force and unqualified assembly of the spindle motor lead to weak vibration energy and the difference in fault features in different life cycles and different bearing individuals. Compared with single fault diagnosis and the datasets that come from the test bench, compound faults diagnosis of the spindle motor is a challenging task. To solve it, an improved filtering and feature enhancement method combined with the merits of AMF and TEO is proposed. Firstly, the adaptive morphological filtering (AMF) method is developed by adaptively constructing the size of the structural element (SE) for remaining in corresponding SE with the component of interest, which can reduce interference from background noise. Still, the periodic fault impulse, especially the incipient fault signals, is easily modulated by other fault-unrelated harmonic components. Thus, the filtered signals are processed by the Teager energy operator (TEO) for enhancing the faint transient impact compositions. Finally, the effectiveness of the method is verified by simulation and fault motor matched with NTN ceramic bearing and FAG metal bearing respectively, where the motor matched with NTN ceramic bearing is an early failure case and the other is a late failure case. Besides, compared with some traditional methods, the result proves that the proposed method has better performance under the actual engineering scenarios for different degrees of fault feature identification.

A novel integrated fault diagnosis method of chemical processes based on deep learning and information propagation hysteresis analysis

BackgroundTo guarantee the stationary operation of chemical plant, the anomaly symptoms of equipment should be discovered as soon as possible. Due to the limited ability of single fault diagnosis method in the increasingly complex industrial process, the integration of different methods has become a hot research direction. MethodsA three-layer fusion fault diagnosis method is proposed in this paper. This method consists of three layers. Time domain features of time series process variables are first extracted by sliding window. Then, the bidirectional long short-term memory network based on time domain feature extraction (T-BiLSTM) is formed to accomplish the online fault identification task. At last, the causalities of fault variables are obtained based on improved signed directed graph model with dynamic time warping (TD-SDG) to locate the fault sources. FindingsThe application to Tennessee Eastman (TE) shows that the average false positive rate (FPR) of T-BiLSTM is 1.98%, and the average fault diagnosis rate (FDR) is 96.6%. TD-SDG model realizes fault inference and location. This novel integrated fault diagnosis method thus can identify fault types and locate fault sources quickly based on graphical scenario object model with a high application capacity.

Deep branch attention network and extreme multi-scale entropy based single vibration signal-driven variable speed fault diagnosis scheme for rolling bearing

In view of the difficulty in measuring the speed signal and integrating the vibration and speed information flexibly in actual variable speed bearing fault diagnosis, a single vibration signal-driven variable speed intelligent fault diagnosis scheme for rolling bearings is developed to guarantee the reliability and safety of the equipment in this paper. In the proposed fault diagnosis scheme, the extreme multi-scale entropy (EMSEn) of the raw vibration signal is employed as the alternative characterization parameter of the speed information, and an intelligent diagnosis model named deep branch attention network (DBANet) is developed to integrate the vibration and speed information more flexibly. The developed DBANet contains 2 parallel and relatively independent forward propagation channels, and the attention mechanism is introduced into the deep architecture at branch level to adjust the importance of different branches, which endow the model with the ability of fusing the vibration and speed information autonomously. The effectiveness of the proposed method is verified by experiments, and the experimental results show that, compared with the methods relying on external information fusion, the suggested DBANet can integrate the vibration and speed information more flexibly. Besides, in the case of no speed signal, the proposed diagnosis scheme can achieve more outstanding results compared with the methods of using other multi-scale entropy features as the alternative characterization parameter of the speed information.

Research on the Safety Fault Diagnosis System of Mine Based on Information Fusion

The failure of any equipment in coal mine will directly affect the safety of coal mine staff and directly threaten the safe operation of the mine. When coal mine equipment is running in a complex and changeable environment, a single fault feature is difficult to accurately reflect the fault, which is easy to lead to misjudgment and missed judgment. Using a combination of AHP and evidence theory can improve the accuracy and reliability of the evaluation. This method determines the weight of each evaluation factor through AHP. In this process, D-S evidence theory is used to fuse and modify the expert scoring data. The synthesis rule of D-S evidence theory is used to determine the risk value of coal mine equipment. The experimental results show that: compared with the single signal fault diagnosis method, this method can diagnose the equipment fault more effectively, improve the accuracy of fault diagnosis, and be applied in the fields with higher requirements for fault identification accuracy. The method is applied to the safety risk assessment of the mine. The results show that the mine construction is at a good level, which is consistent with the mine construction and verifies the model's credibility.

Research on the Safety Fault Diagnosis System of Mine Based on Information Fusion

The failure of any equipment in coal mine will directly affect the safety of coal mine staff and directly threaten the safe operation of the mine. When coal mine equipment is running in a complex and changeable environment, a single fault feature is difficult to accurately reflect the fault, which is easy to lead to misjudgment and missed judgment. Using a combination of AHP and evidence theory can improve the accuracy and reliability of the evaluation. This method determines the weight of each evaluation factor through AHP. In this process, D-S evidence theory is used to fuse and modify the expert scoring data. The synthesis rule of D-S evidence theory is used to determine the risk value of coal mine equipment. The experimental results show that: compared with the single signal fault diagnosis method, this method can diagnose the equipment fault more effectively, improve the accuracy of fault diagnosis, and be applied in the fields with higher requirements for fault identification accuracy. The method is applied to the safety risk assessment of the mine. The results show that the mine construction is at a good level, which is consistent with the mine construction and verifies the model's credibility.

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstructed signal twice. The experimental results show that the proposed method can achieve the enhancement of weak features and the removal of noise to extract the fault feature frequency adaptively. Compared with EMD, VMD, MOMEDA, MCKD and other methods, the proposed method has better performance in fault feature frequency extraction, and it is effective for the diagnosis of single faults and composite faults.

A composite learning approach for multiple fault diagnosis in gears

A major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.