Proposed Fault Diagnosis Method Research Articles

Motor fault diagnosis based on composite multi-scale weighted reverse slope entropy and neighborhood preserving embedding

The motors are critical components of the electromechanical transmission in vehicles, and its operating status directly affects the maneuverability of vehicles. To quickly and accurately identify the operating status of motors, this paper proposes a new entropy - Composite Multi-scale Weighted Reverse Slope Entropy (CMWRSlE) for motor fault diagnosis, which is a more interpretable entropy due to its deep exploration of signals. Firstly, the composite multi-scale weighted reverse slope entropy values are extracted from the vibration signals of the motor in different states; Secondly, the extracted features are dimensionally reduced by the manifold learning algorithm – Neighbourhood Preserving Embedding (NPE) and classified by the hierarchical prototype-based approach (HPA) to achieve the fault diagnosis of the motor. Finally, the method proposed in this paper is validated through two sets of experimental data: motor rotor faults and motor bearing faults. The results show that the accuracy of the proposed method in motor fault diagnosis reaches 100 %, which indicates the effectiveness of the proposed method.

A data-driven hybrid sensor fault detection/diagnosis method with flight test data

Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of modern industrial systems, and accurate fault diagnosis of systems based on multi-source signals remains challenging. This study proposes a data-driven hybrid fault detection/diagnosis method to identify sensor faults in complex systems through interactions between multiple sensors. Dynamic mode decomposition with control is used to obtain the approximate model of the investigated system from multi-source signals, extract the underlying physical mechanisms, combined with the Kalman filter observer to generate the residual between the observed data and the predicted data. Then the residual (moving innovation covariance matrix V k ) is input into the k-nearest neighbor classification algorithm for fault detection and diagnosis. The effectiveness of the proposed method was evaluated using the flight test braking system dataset. The results showed that the accuracy of the proposed method in fault detection and diagnosis (with accuracies of 100% and 100%, respectively) was significantly improved than that of using raw signal data (76.6% and 6.38%) or raw signal data and Vk (80.85% and 42.55%). The analysis of different parameters including fault severity, algorithm hyperparameter k , and sensor type showed that the proposed method has high robustness, generalization ability, and practicality.

DDAGCN: an unsupervised cross-domain identification method for tie rod bolt loosening in a rod-fastening rotor system under different working conditions

The cross-domain diagnosis of tie rod bolt loosening is essential for guaranteeing the healthy operation of rod-fastening rotor (RFR) systems. The unsupervised domain adaptation (UDA) method effectively alleviates the impact of domain discrepancy and has been applied for cross-domain diagnosis. Traditional UDA methods mainly focus on the marginal and conditional distributions with fixed weights to adapt the domain distribution discrepancy. However, the fixed distribution combination cannot satisfy the requirement of feature domain alignment under different working conditions, and the relative importance of the two distributions cannot be evaluated quantitatively. This paper proposes an improved dynamic distribution adaptive graph convolutional network (DDAGCN) for the cross-domain diagnosis of tie rod bolt loosening under different working conditions. This method can quantitatively evaluate the relative significance of each distribution in representing the distribution discrepancy. First, it combines the convolutional neural network and the graph convolutional network to extract the features in the graph structure by using the connection relationship between nodes, and realizes the full extraction of neighbourhood information of nodes. Then, the dynamic distribution adaptive alignment strategy is introduced to construct the dynamic linear combination of marginal and conditional distributions, so as to measure the distribution discrepancy between domains. Meanwhile, the domain adversarial module is combined to further reduce the domain gap and finally realize feature alignment. The extracted domain invariant features can effectively enhance the generalization ability and fault identification ability of the model. The case of the public bearing dataset verifies that the effectiveness and generalization ability of the proposed method for cross-domain fault diagnosis under different working conditions is superior to other compared methods. In addition, the identification ability of the proposed method for the degree of tie rod bolt loosening is verified by the self-made bolt loosening dataset of the RFR system.

An efficient approach based on a novel 1D-LBP for the detection of bearing failures with a hybrid deep learning method

Bearings serve as fundamental components in the transmission of motion for rotating machinery. The occurrence of mechanical wear and subsequent bearing failures within these rotating systems can lead to diminished operational efficiency and, if left unaddressed, may result in the complete cessation of the system's function. Hence, there exists a critical need for effective monitoring methodologies aimed at accurately detecting faults in such systems, preferably in their nascent stages. This study presents a novel approach to fault diagnosis leveraging vibration data obtained from bearings. Initially, a feature extraction technique is devised, which incorporates localized signal variations. Subsequently, these features, extracted via MM-1D-LBP, are utilized in conjunction with a hybrid deep learning network based on Long Short-Term Memory (LSTM) and one-dimensional Convolutional Neural Network (1D-CNN) architectures for diagnostic purposes. To assess the efficacy of the proposed methodology, experiments were conducted on two distinct datasets acquired from real-world bearing assemblies. In the first dataset, the aim was to predict various failure types (Inner Ring, Outer Ring, Ball). In the second dataset, the objective was to estimate defect sizes using bearing vibration signals corresponding to defects of different dimensions (0.15 cm, 0.5 cm, 0.9 cm) under consistent operating conditions. Remarkably high success rates of 99.31 % and 99.65 % were achieved for the two datasets, respectively, thus underscoring the efficacy of the proposed MM-1D-LBP+1D-CNN-LSTM approach. These findings not only demonstrate the feasibility of the proposed method for fault diagnosis in bearing systems but also suggest its potential applicability across diverse signal categories. Ultimately, this research contributes to advancing the state-of-the-art in fault diagnosis methodologies for rotating machinery, offering enhanced accuracy and early detection capabilities.

Reversed trend comprehensive assessment-based inverter fault diagnosis under various complex interferences in microgrid

The healthy operation of inverter is the basic issues to ensure the quality of engineering power supply. However, the various complex interferences from microgrid engineering applications are relatively a difficult problem for fault diagnosis of inverter, e.g. harmonic components and unbalanced state. It increases the difficulty of the failure analysis, model design, algorithm realization and thresholds selection, even may lead to false alarm and erroneous triggering of protection units. Hence, a new fault diagnosis algorithm is proposed. Firstly, the reversed coordinate two-dimensional processing (RCT-dP) method is proposed to obtain reversed two-dimensional data (RTD) by inserting a moving reversed coordinate, which is very convenient for data attenuation segmentation and reversed trend extraction. Secondly, the obtained RTD are used for a proposed reversed trend comprehensive assessment (RTCA) method to obtain assessment variables based on multiple reversed trends and corresponding reliability, which can effectively extract the fault component, effectively reduce the influence of harmonic components and unbalanced state. Thirdly, the relative location information of detected signals are extracted. Then, the obtained assessment variables and relative location information are processed by variable regularization processing. Finally, the diagnosis results are obtained through a fault decision. Compared with existing fault diagnosis methods, the proposed fault diagnosis method can accurately detect and locate fault for any switch of inverter under harmonic components and unbalanced state. Meanwhile, because the fault diagnosis algorithm is based on saltatory driving, it can realize very low number of start-up without affecting the accuracy of the diagnosis. The experimental results are shown to validate the proposed algorithm.

Internal short circuit fault diagnosis for the lithium-ion batteries with unknown parameters based on transfer learning optimized residual network by multi-label data processing

In recent years, internal short circuits have frequently been the cause of safety accidents, and traditional diagnosis methods for such problem have limitations that prevent their application under complex working conditions. Furthermore, due to the scarcity of real internal short circuit data, it is difficult to obtain a large amount of real vehicle data, which makes internal short circuit fault diagnosis even more challenging. In this paper, fault batteries with unknown parameters are diagnosed using a residual network based on multi-label processed battery data and transfer learning is utilized to optimize the diagnosis effect for fault localization and fault degree identification, thus increasing the information richness of the training model samples and improving the training effect. Accuracy, recall, and false positive rate are used to evaluate the diagnosis results of the residual network. The results demonstrate that the accuracy rate increased by 11.1% after transfer learning, indicating a significant reduction in misdiagnoses. The proposed fault diagnosis method using residual network and transfer learning has practical significance for enhancing battery fault detection accuracy and efficiency. Especially in areas such as electric vehicles and energy storage systems, it can provide effective technical support for battery health monitoring and early warning.

Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.

An Open-Circuit Fault Diagnosis Method for LLC Converters

In electrified transportation systems, power system failures can lead to greater disasters. Therefore, the reliability of converters in transportation systems has been a concern. Fault-tolerant techniques are widely applied to ensure that converters can continue to supply loads under fault conditions. Fault diagnosis as a prerequisite for fault tolerance has also become a research hotspot. This paper proposes a fast method for fault diagnosis of high-frequency LLC converters. The proposed fault diagnosis method is based on the observation of the voltage across the resonant capacitor to determine and locate the faulty power switch, providing a basis for fault tolerance. This diagnosis method requires a voltage sensor, which is also necessary for some control methods. When applying these control methods, the proposed fault diagnosis method can be used without additional sensors, beneficial for cost reduction. A full-bridge LLC converter controlled by a digital signal processor was used as an experimental platform to verify the effectiveness and speed of the proposed diagnostic method. The results show that the proposed fault diagnosis method can achieve the fast diagnosis of high-frequency LLC converters in a short time and with only minimal computational resources.

A novel fault diagnosis method for second-order bandpass filter circuit based on TQWT-CNN.

To accurately locate faulty components in analog circuits, an analog circuit fault diagnosis method based on Tunable Q-factor Wavelet Transform(TQWT) and Convolutional Neural Network (CNN) is proposed in this paper. Firstly, the Grey Wolf algorithm (GWO) is used to improve the TQWT. The improved TQWT can adaptively determine the parameters Q-factor and decomposition level. Secondly, The signal is decomposed, and single-branch reconstruction is conducted with TQWT to facilitate adequate feature extraction. Thirdly, to capture the time-frequency features in the signal, a CNN-LSTM network is built by combining CNN and LSTM for feature extraction. Finally, CNN, which introduces Fully Convolutional Network (FCN) layers and a Batch Normalization layer, is used to fault diagnosis. The method was comprehensively evaluated with a second-order bandpass filter circuit. The experimental results illustrate that the proposed fault diagnosis method can achieve excellent fault diagnosis accuracy, and the average accuracy is 98.96%.

An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection

A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition (SGMD) and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the SGMD SGMD is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved SGMD is more effective compared to traditional SGMD. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.

Dynamic Feature Extraction-Based Quadratic Discriminant Analysis for Industrial Process Fault Classification and Diagnosis.

This paper introduces a novel method for enhancing fault classification and diagnosis in dynamic nonlinear processes. The method focuses on dynamic feature extraction within multivariate time series data and utilizes dynamic reconstruction errors to augment the feature set. A fault classification procedure is then developed, using the weighted maximum scatter difference (WMSD) dimensionality reduction criterion and quadratic discriminant analysis (QDA) classifier. This method addresses the challenge of high-dimensional, sample-limited fault classification, offering early diagnosis capabilities for online samples with smaller amplitudes than the training set. Validation is conducted using a cold rolling mill simulation model, with performance compared to classical methods like linear discriminant analysis (LDA) and kernel Fisher discriminant analysis (KFD). The results demonstrate the superiority of the proposed method for reliable industrial process monitoring and fault diagnosis.

Mutual dimensionless improved bearing fault diagnosis based on Bp-increment broad learning system in computer vision

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial for ensuring normal machinery operation. However, the nonlinear and non-stationary vibration signals generated by machinery in harsh environments pose significant challenges in distinguishing fault signals from normal ones. Although several fault diagnosis methods based on mutual dimensionless indicators (MDI) have been proposed, they often fail to achieve effective and accurate health monitoring. Hence, this paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model based on the combined synergistic of two modules, to address the existing challenges. Firstly, a new mutual dimensionless indicator (VMDI) with high sensitivity and low overlap is refactored. Secondly, leveraging the advantages of incremental learning algorithms, a novel Broad Learning System (BLS) model for quickly identifying different fault types is constructed. Finally, the proposed method is validated using multiple datasets and verified through a comparative analysis with a published method based on dimensionless indicators (DI). The results demonstrate the effectiveness of the proposed method in fault diagnosis.

A novel multi-resolution network for the open-circuit faults diagnosis of automatic ramming drive system

The open-circuit fault is one of the most common faults of the automatic ramming drive system (ARDS), and it can be categorized into the open-phase faults of Permanent Magnet Synchronous Motor (PMSM) and the open-circuit faults of Voltage Source Inverter (VSI). The stator current serves as a common indicator for detecting open-circuit faults. Due to the identical changes of the stator current between the open-phase faults in the PMSM and failures of double switches within the same leg of the VSI, this paper utilizes the zero-sequence voltage component as an additional diagnostic criterion to differentiate them. Considering the variable conditions and substantial noise of the ARDS, a novel Multi-resolution Network (MrNet) is proposed, which can extract multi-resolution perceptual information and enhance robustness to the noise. Meanwhile, a feature weighted layer is introduced to allocate higher weights to characteristics situated near the feature frequency. Both simulation and experiment results validate that the proposed fault diagnosis method can diagnose 25 types of open-circuit faults and achieve more than 98.28% diagnostic accuracy. In addition, the experiment results also demonstrate that MrNet has the capability of diagnosing the fault types accurately under the interference of noise signals (Laplace noise and Gaussian noise).

Enhanced robust diagnosis of multiple‐type demagnetisation fault for permanent magnet synchronous motor based on D‐axis magnetic network model using magnetic field reconstruction

AbstractDemagnetisation fault (DF) is a common rotor fault in the permanent magnet synchronous motor (PMSM). DF can cause obvious changes in the magnetic field of PMSMs. As a result, previous DF diagnosis methods mainly depends on magnetic field analysis. However, conventional analysis methods based on the magnetic potential‐permeability method, sub‐domain method and magnetic equivalent circuit method are not suitable for DF condition. In addition, DF diagnosis is further complicated by various operating conditions. To address these issues, a robust DF diagnosis method is proposed for PMSM based on d‐axis magnetic network model using magnetic field reconstruction. The proposed fault diagnosis method employs magnetic field reconstruction to obtain the radial air‐gap flux density under the open‐circuit condition. Subsequently, a d‐axis magnetic network is established to solve the demagnetisation coefficient matrix in the DF state. Finally, the effectiveness of the proposed method is validated through simulations and experiments. Both results demonstrate that the proposed method can accurately recognise uniform DF and partial DF with multiple demagnetised permanent magnets, exhibiting great robustness against different operating conditions.

Few-Shot Fault Diagnosis Method of Rotating Machinery Using Novel MCGM Based CNN

The existing fault diagnosis methods can achieve good results when various status fault data are available. However, the construction of the diagnosis model is often unachievable in the actual application because only normal data are available, which is actually a few-shot fault diagnosis problem. Therefore, a novel intelligent few-shot fault diagnosis method of rotating machinery based on the convolutional neural network (CNN) using virtual samples generated by the mechanism character generative model (MCGM) integrating the generative adversarial network (GAN) is proposed. The distribution pattern of common parameters that reflect the fault category is learned using the GAN and source domain fault data. Then, the normal state data of the target domain is combined with the distribution common parameters to generate virtual samples in target domain based on the MCGM. Moreover, the fault diagnosis model is trained by virtual samples based on the CNN. Finally, the proposed fault diagnosis method is validated using the laboratory bearing data, the industrial data and the public data of the rotating machinery, respectively. The results show that the proposed method achieves an average accuracy of 93.38% in the diagnostic task, exhibiting at least 4.56% better performance than other comparison methods.

Intelligent fault diagnosis of hydroelectric units based on radar maps and improved GoogleNet by depthwise separate convolution

Abstract Fault diagnosis plays an essential role in maintaining the safe and stable operation of hydroelectric units. In this paper, an intelligent fault diagnosis method based on radar maps and improved GoogleNet by depthwise separate convolution (DSC) is proposed to address the problems of low recognition accuracy and weak computing speed of fault diagnosis models in the field of hydroelectric unit fault diagnosis at present. Firstly, a one-dimensional signal sequence is obtained and denoised. Secondly, five time-domain features are extracted and radar maps are plotted. Then, an improved GoogleNet intelligent fault diagnosis model based on DSC (DSC-GoogLeNet) is constructed for training and validation. To assess the effectiveness of the proposed model, two case studies are conducted using the simulated dataset of the rotor experimental bench and the actual measured dataset of a domestic hydroelectric power plant. The results demonstrate that the average recognition accuracy of the fault diagnosis method proposed in this paper is as high as 99.04% on the simulated dataset, and even though the recognition accuracy decreases on the actually measurement dataset, it still has a recognition rate of 98.79%. The fault diagnosis performance is better than the other types of comparison models. The results demonstrate that the proposed fault diagnosis method holds significant engineering applicability in the domain of safe operation of hydroelectric units. It effectively addresses the existing challenges in fault diagnosis within this field with accuracy, stability, and efficiency.

Spectral structure inducing efficient variational model for enhancing bearing fault feature

Variational mode extraction (VME) has attracted increasingly attentions due to its characteristic of extracting a specific mode from the complicated signal. Nevertheless, its performance depends on the initial center frequency (ICF) and the balance parameter. To address the issue of setting ICF, a convergence property of VME is first explored by feeding different ICFs into the variational model. During this exploration, a convergence tendency phenomenon of center frequency (CF) is found from the optimization procedure of VME and a mathematical proof is successfully conducted on this phenomenon. As a result, a CF identification strategy is designed to determine the ICFs for the expected modes automatically, where a calibration operation is synchronously built to improve the identification accuracy of ICFs. In addition, to determine the balance parameter efficiently, a bandwidth estimation strategy with a solid theoretical basis is constructed motivated by the filtering structure of VME model. Finally, a spectral structure inducing efficient variational model is proposed to adaptively extract the faulty modes from the complicated signal by combining the CF identification strategy and the bandwidth estimation strategy. The effectiveness of the proposed method for bearings fault diagnosis is verified by a simulated and two experimental cases and its superiority is also confirmed by comparing with some classical and advanced methods.

FASER: Fault-affected signal energy ratio for fault diagnosis of gearboxes under repetitive operating conditions

Vibration signal analysis is crucial for gearbox fault diagnosis, yet its inherent stochastic nature can challenge the identification of fault-induced changes amidst signal variability. This paper introduces a novel fault diagnosis approach, the Fault-Affected Signal Energy Ratio (FASER), designed to account for the uncertainty in vibration signals under repetitive operating conditions. The uncertainty is quantified using probability distributions of coefficients derived from Short-Time Fourier Transform (STFT) applied to normal state signals, employing Kernel Density Estimation and Kullback-Leibler divergence. This combined approach enables the quantification of uncertainty in signals with non-constant uncertainty, dependent on time and frequency, when converted to STFT. These distributions facilitate the assessment of the FASER in new signals extracted under synchronized operating conditions using our proposed method. This newly proposed FASER, accompanied by adaptive threshold to address feature randomness from STFT indices’ correlation, serves as a robust health indicator. Experimental validation demonstrates the effectiveness of the proposed method in accurate gearbox fault diagnosis under repetitive operating conditions, along with the capability to estimate fault severity levels.

Refined composite moving average fluctuation dispersion entropy and its application on rolling bearing fault diagnosis.

Vibration signal analysis based on multiscale entropy is one of the important means to realize rotating machinery fault diagnosis. However, the length of the time series will be shortened during the coarse-graining process with the increase of the scale factor, which makes the calculated entropy values unstable. This inherent drawback of the coarse-graining method limits its application in fault feature extraction. This paper presents a novel feature extraction method for vibration signals called refined composite moving average fluctuation dispersion entropy (RCMAFDE). It is verified by simulation experiments that RCMAFDE has high stability of entropy values under different time series lengths as well as different disturbances. The RCMAFDE was applied to the fault diagnosis of rolling bearings, and a new fault diagnosis method of rolling bearings was proposed by combining RCMAFDE and kernel extreme learning machine (KELM) optimized by the chaos sparrow search optimization algorithm (CSSOA). First, the vibration signal is preprocessed to form a sample set, and then, the fault feature vector is calculated by RCMAFDE. Finally, the feature vector set is input into the CSSOA-KELM model for training and testing, and the fault diagnosis result is output. To demonstrate the effectiveness and feasibility of the fault diagnosis method, two publicly available datasets and a self-collected dataset are used for experimental validation. The experimental results show that the proposed fault diagnosis method can extract the nonlinear dynamic complexity information of vibration signals more effectively compared with the comparison methods and obtain the highest fault identification accuracy under different datasets.