Application In Fault Diagnosis Research Articles

Research and application of power equipment monitoring and fault diagnosis

Power equipment monitoring and fault diagnosis are important parts of power system operation and maintenance. By monitoring the status and performance parameters of power equipment in real time, and timely discovering and diagnosing potential fault problems, the reliability, safety and economy of the power system can be improved. At present, this research has made significant progress, but there are still some research gaps that need to be further explored and resolved. Through the research and application of power equipment monitoring and fault diagnosis, this paper aims to improve the reliability, safety and economy of power grid, reduce the risk of failure, optimize equipment operation and maintenance strategies, and improve power supply quality and user satisfaction. Condition monitoring and fault diagnosis are very important for the normal operation of the power system equipment, which can not only strengthen the fault diagnosis of the power system equipment, but also avoid the problem of large-scale paralysis caused by the failure of the power system equipment.

Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference

Abstract Considerable attention has been garnered by the bearing fault diagnostic method, founded on sparse feature learning, for its exceptional robustness and adaptability to noise. Being an effective sparse learning method, parallel sparse filtering (PSF) has found successful applications in intelligent fault diagnosis. However, influenced by mechanical structural collisions and complex transmission paths, the obtained bearing signals come with abnormal impacts and noise, resulting in a reduced reliability of the PSF. Additionally, PSF constrains features and samples with l_(1⁄2) norm, which is significantly disturbed by noise. To address these issues, generalized nonlinear hybrid-norm parallel sparse filtering (GNHNPSF) is proposed. Firstly, the Sigmoid nonlinear function is examined to suppress the amplitude of the anomalous shock. Subsequently, the hybrid-norm constraints, which involve the application of a l_(1⁄2) norm to the column features of the input signal and a l_(1⁄3) norm to the row features, can be employed to effectively extract distinctive information from the original data. The GNHNPSF is demonstrated to improve the accuracy of bearing fault diagnosis under complex interference, as evidenced by the results of simulation and experiments.

Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis

Storage batteries with elevated energy density, superior safety and economic costs continues to escalate. Batteries can pose safety hazards due to internal short circuits, open circuits and other malfunctions during usage, hence real-time surveillance and error diagnosis of the battery’s operational state is imperative. In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern of the physical field such as magnetic field and temperature when the battery is operational is acquired. Furthermore, this manuscript also investigates the diagnostic methodology for defective batteries with internal short circuits and fissures, that is, the operational state of the battery is evaluated and diagnosed by the distribution of the magnetic field surrounding the battery. To substantiate the method’s practical viability, the present study extends its examination to the 18650-battery pack. We obtained the magnetic field images of the normal operation of the battery pack and the failure state of some batteries and analyzed the relationship between the magnetic field distribution characteristics and the performance of the battery pack, providing a new method for the health monitoring and fault diagnosis of the battery pack. This non-contact method incurs no damage to the battery, concurrently exhibiting elevated sensitivity and extremely rapid response time. Meanwhile, it provides an effective means for non-destructive research on the batteries and can be applied to areas such as battery safety screening and non-destructive testing. This research not only helps to facilitate our understanding of the battery’s operating mechanism, but also provides robust support for safe operation and optimal battery design.

A novel feature extraction method based on symbol-scale diversity entropy and its application for fault diagnosis of rotary machines

Multiscale entropy-based methods have made great progress in the field of health condition monitoring and fault diagnosis of machines due to their powerful feature representation capabilities. However, existing multiscale entropy methods suffer from three major obstacles: high fluctuation under large scale-factor, loss of high-frequency information, and poor robustness to noises. Thus, this work proposes a symbol-scale analysis method to deal with the above problems. In one aspect, to capture fault features from the time series over multiple time scales, time-delay process of different intervals is utilized to obtain long-term features and short-term features. In the other aspect, symbol-scale analysis introduces a symbolization procedure and maps time series into a corresponding sequence of symbols to overcome the limitation of weak fault extraction under a low-signal-to-noise ratio environment. Moreover, the symbol-scale entropy approach is developed by integrating with diversity entropy, called symbol-scale diversity entropy. The effectiveness of the proposed strategy is intensively validated using two simulated signals and experimental cases. Results demonstrate its advantages in dynamic change tracking ability and calculation efficiency by comparing it with other state-of-the-art entropy methods. Apart from diversity entropy, the versatility of incorporating the proposed symbol-scale analysis and other entropy methods is also verified using experimental data.

Faulty rolling bearing digital twin model and its application in fault diagnosis with imbalanced samples

The simulation signals generated by the bearing dynamics model have a big gap with the actual signals, which limits their efficacy in bearing fault diagnosis. Therefore, it is valuable to build an accurate digital twin model of faulty rolling bearing. Firstly, a multi-degree-of-freedom bearing fault dynamics model is constructed in the virtual space for generating the vibration responses of bearing parts. Then considering that the frequency spectrum contains more characteristic information than the time-domain signal, a frequency-domain bi-directional long short-term memory (Bi-LSTM) cycle generative adversarial network (CycleGAN) named FBC-GAN is proposed to construct the frequency-domain coupling mapping relationship between the multipart vibration responses and the measured signals. In the proposed network, Bi-LSTM is used for enhancing the feature extraction ability. Meantime, a new spectrum-constraint loss is proposed to ensure the frequency-domain mapping. Next, the simulated fault bearing signals close to the actual signals are generated by FBC-GAN and Fourier transform. Finally, the results of two experiments show the superiority of the proposed method over other advanced data augmentation methods in bearing fault diagnosis with the imbalanced samples.

An End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism for Multisensor Fault Diagnosis

Deep clustering methods have found successful applications in single-sensor data fault diagnosis. However, most of these methods employ separate optimization strategies that overlook the interaction between feature learning and clustering. Moreover, conventional deep learning methods for fault diagnosis often disregard the consistent and complementary information inherent in the multisensor data, resulting in unsatisfactory multisensor fault diagnosis performance. In this study, we introduce a novel End-to-end Deep Clustering Method with Consistency and Complementarity Attention Mechanism, termed EDCM-CCAM, tailored for multisensor fault diagnosis. Firstly, multiple deep autoencoder networks are utilized to concurrently extract the deep representation features from various sensor inputs. Secondly, we introduce a Consistency and Complementarity Attention Mechanism (CCAM) to facilitate multisensor feature fusion, accompanied by the design of two distinct loss functions to fully exploit the consistent and complementary information within multisensor data. Finally, fault pattern recognition in multisensor data is accomplished through Kullback-Leibler (KL) divergence-based clustering, while a joint optimization strategy is employed to simultaneously optimize all components of the EDCM-CCAM. The efficacy of the proposed method is validated on a gearbox dataset, demonstrating superior performance in multisensor fault diagnosis compared to alternative methods.

Parallel differential evolution paradigm for multilayer electromechanical device optimization

Design optimization of multilayer piezoelectric transducers is intended for efficient and practical usage of wideband transducers for fault diagnosis, biomedical, and underwater applications through adjusting layer thicknesses and volume fraction of piezoelectric material in each layer. In this context, we propose a parallel differential evolution (PDE) algorithm to mitigate the complexities of multivariate optimization as well as the computation time to achieve an optimized wideband transducer for the particular application. For lead magnesium niobate-lead titanate (PMN PT)- and PZT5h-based piezoelectric materials, the fitness function is formulated based on uniformity of mechanical pressure at the first three harmonics to achieve wide bandwidth in the required functional frequency range. It is carried out using a one-dimensional model (ODM), while input layer thicknesses and volume fractions of active material are evaluated using PDE. The simulation is performed on a parallel computing platform utilizing three different host machines to reduce computational time. Results of the proposed methodology for PDE are statistically represented in the form of minimum, maximum, mean, and standard deviation of fitness value, while graphically represented in terms of speedup and time. It can be observed that the execution time for parallel DE decreases with the increasing number of cores.

Adaptive minimum noise amplitude deconvolution and its application for early fault diagnosis of rolling bearings

To address the challenge of detecting early faults in rolling bearings, where weak fault features are occasionally obscured by background noise, an innovative early fault diagnosis method based on adaptive minimum noise amplitude deconvolution (MNAD) is introduced. Initially, a correlation Gini index function is defined to estimate the fault period, while leveraging the iterative advantages of MNAD to progressively approach the true fault period. This resolution effectively overcomes the requirement of prior knowledge regarding the fault period, thus reducing the number of input parameters. Subsequently, a composite index, combining square envelope Gini index and square envelope entropy, is constructed as the fitness function. The sand cat swarm optimization algorithm is employed to adaptively determine the optimal noise ratio and filter length for deconvolution, ensuring the acquisition of the finest filtered signal. Ultimately, envelope spectrum analysis is conducted on the filtered signal to extract fault features and facilitate early fault diagnosis. The effectiveness of the proposed method is validated through simulated and experimental data, highlighting its superior feature extraction capabilities and robustness compared to original MNAD and other sophisticated blind deconvolution methods.

Vibrational resonance mechanism in the high-order-degradation bistable system and its application in fault diagnosis

Bearings play an important role in the rotating machinery. Timely fault detection and maintenance can prevent catastrophic incidents caused by bearing faults. As one of the advanced techniques to extract the weak characteristics of bearing fault, the methods based on the vibrational resonance (VR) mechanism can effectively amplify the weak characteristics. However, first, the effect of the barrier width and the barrier height on the VR mechanism has not been investigated. Second, the application of the VR mechanism requires accurate prior knowledge, which limits its application. In this paper, the high-order-degradation bistable potential is decoupled so that the effect of the barrier width or the barrier height on the VR mechanism can be easily analyzed. Then, the initial frequency detection technique based on the 1.5-dimension envelope spectrum and the logical operation of sets is proposed, which helps to adaptively provide accurate prior knowledge for the application of the VR mechanism in the high-order-degradation bistable system (HBSVR). Finally, experimental results show that the proposed method is more advantageous in detecting single and compound faults compared with other state-of-the-art methods.

An improved empirical Fourier decomposition method and its application in fault diagnosis of rolling bearing

An improved empirical Fourier decomposition method and its application in fault diagnosis of rolling bearing

Research on feature extraction method for different levels of cracks and pitting in spur gear based on dynamic characteristic templates

Health monitoring and fault diagnosis play a pivotal role in ensuring the safety of helicopter flights. Dynamic analysis has proven to be effective in accurately capturing the characteristics of crack and pitting signals. However, its application in fault diagnosis has limitations. This study introduces a feature extraction method based on the dynamic characteristic templates (DCTs) for different levels of cracks and pitting in spur gears. This method serves as a bridge between dynamic analysis and fault diagnosis, facilitating a comprehensive assessment of gear health. The integration of improved sparrow search algorithm (ISSA) and variational mode decomposition matching (VMDM) techniques enhances the search and matching capabilities of target signal based on DCTs. Based on above research, this study reveals distinctive morphological characteristics in the signals associated with crack and pitting under different levels and these characteristics serve as valuable index for fault diagnosis.

A new parameter-free entropy based on fragment oscillation and its application in fault diagnosis

For finite time series, it is difficult to provide a convincing determination scheme for specifying relevant parameters of entropy. This article proposes a new entropy, σkEn, based on the amplitude fluctuations of time series segments. It uses the optimal variance estimation under the principle of minimizing AMSE(σˆ2) to adaptively select the window width, and then realizes the no-argument calculation process of volatility entropy through kernel density estimation. By testing with artificial data and applying to real-world data, it has been verified anti-noise and stability properties of σkEn. In a background noise environment with an intensity of 20%, σkEn distinguish various chaotic systems and random signals efficiently. In addition, this manuscript also proposes the plane of (σkEn,NkEn) and (σ̄,σkEn) , which also successfully identifies 6 types of chaotic systems, and 3 random noise signals. Furthermore, (<σ̄>,<σkEn>) curve exhibits different dynamical behavior patterns for these chaotic systems. Finally, we employ (σ̄,σkEn) as a feature and take 8 usual machine learning models, such as Linear Discriminant, Gaussian Naive Bayes and Subspace Discriminant, etc, to perform anomaly detection and fault classification tests on 6 open datasets. Given the appropriate classifiers, our method can achieve a classification accuracy of 100% for four tasks, and an average accuracy of over 96% for the remaining two. This is significantly superior than the accuracy obtained by using Permutation Entropy as a feature.

A high-resolution time-frequency analysis technique based on bi-directional squeezing and its application in fault diagnosis of rotating machinery

Most of the signals in the field of industrial engineering are nonstationary signals, and the accurate description of the time-frequency (TF) characteristics of nonstationary signals is important for the advancement of industrial engineering. Instantaneous frequency (IF) and group delay (GD) are common TF features used to describe nonstationary signals. Time-reassigned synchrosqueezing transform and synchrosqueezing transform are two TF analysis tools that can accurately characterize the GD and IF of nonstationary signals by squeezing the TF coefficients in the time direction and frequency direction, respectively. However, it is difficult for such two techniques to accurately characterize IF and GD simultaneously. A novel method called time-frequency squeezing transform is introduced in this paper to conquer this drawback. The technique first uses the short-time Fourier transform to calculate the time-frequency representation (TFR) of a signal, and then divides the TFR into two parts according to a chirp rate estimator. The subdivided TFR parts are then squeezed in the frequency and time directions to accurately characterize the IF and GD, respectively, and the two squeezed results are added to form a high-resolution result. The effectiveness of the proposed technique is demonstrated with numerical and experimental signals.

Digital twin-assisted dual transfer: A novel information-model adaptation method for rolling bearing fault diagnosis

Rolling bearing fault diagnosis is of great importance to the safety management of mechanical equipment. The scarcity of labelled fault data makes it difficult to adequately perform the training process of intelligent diagnosis models, and this will result in these intelligent models not being effectively and widely used in practice. Although some recent studies have verified that the addition of dynamic model response to the training process will greatly improve the ability of the model with low cost and high efficiency, it is still stuck in poor effect caused by large information distribution difference between dynamic model response and real measured data. Focusing on this issue, a digital twin-assisted dual transfer (DTa-DT) method with information and model adaptation was proposed for rolling bearing fault diagnosis. Different from the traditional digital-analogue driven transfer methods, the proposed DTa-DT aims to simultaneously synthesize data information transfer and feature model transfer together with domain transfer error minimization. In particular, it should be noted that the DTa-DT architecture consists of a dual transfer learning process, including digital twin-driven information transfer (DTd-IT) and digital-analogue-driven model transfer (DAd-MT), where the information is collaborated with the model to improve the integrated transfer diagnosis effect under sampling. On one aspect, with the employment of bearing dynamic model responses, DTd-IT is innovatively designed to establish the transfer of dynamic information and measured information. The information distribution difference between these twin data and real measured data is effetely adjusted with the introduced actual inference components, where the twin data with low information distribution difference can be well fusion generated by the information transfer digital twin (ITDT) model. On the other aspect, considering the truth that there are still small sample cases of real measured data and information distribution differences will affect the quality of the twin data, a digital-analogue driven model transfer (DAd-MT) method is further proposed, where the deep branch transfer network (DBTN) model with improved convolutional neural network (CNN) is used to achieve an accurate fault diagnosis effect with the help of digital twin data. Experiments and wear analysis verified that the proposed DTa-DT can significantly reduce the distribution difference between the dynamic model response and the real measured data, thus achieving low-cost and efficient rolling bearing transfer diagnosis compared to other ten state-of-the-art deep learning models. It can be predicted that the proposed dual transfer architecture provides more opportunities for the practical application of intelligent fault diagnosis under small sample sizes.

An adaptive optimization machine of mass function for conflict management

Dempster–Shafer evidence theory uses mass functions to represent uncertain information, which is widely applied in information fusion. However, when it is used to combine highly conflicting mass functions, the results will be against common sense. Therefore, how to effectively deal with the conflicts is a project with research significance. Reconstructing mass functions is an effective method at present. However, a number of existing methods reconstruct them through a series of transformation, which are very cumbersome. Inspired by optimization ideas, we design a loss function LossA and propose an adaptive optimization machine (AOM) to reconstruct mass functions. The results of ablation experiments validate the effectiveness of our designed method. As far as we know, this is the first work that loss function is applied to Dempster–Shafer evidence theory. Subsequently, we use a large number of numerical examples to discuss in detail the process of AOM dealing with uncertainty problems. Finally, the applications in fault diagnosis and real-world target classification show that our proposed method outperforms the state-of-the-art methods.

Data augmentation using improved conditional GAN under extremely limited fault samples and its application in fault diagnosis of electric submersible pump

Electric submersible pump (ESP) in offshore oilfields is one of the important artificial lifting methods to achieve high and stable production. The complexity of the ESP system and the long pumping cycle result in data having the typical characteristics of "a large amount of data and a small amount of information". Therefore, the scarcity of valid samples causes a major challenge for ESP fault diagnosis. To address these practical problems, we propose an intelligent virtual sample generation method that introduces the idea of multi-distribution mega trend diffusion (MD-MTD) into conditional generative adversarial networks (MCGAN-VSG). In the MCGAN-VSG method, the acceptable diffusion range of the sample attributes is first obtained by estimating the samples using the triangular probability distribution model constructed in MD-MTD. Secondly, the Borderline-SMOTE and uniform distribution were added to describe the small sample properties, and suitable output samples are generated to fill the information gap between samples for resampling with Bootstrap. Thirdly, CGAN is used to generate the input samples corresponding to the output samples. Finally, the accuracy of the classification model is improved by generating a large number of virtual samples with an extremely limited number of fault samples. In order to verify the advantages of the proposed MCGAN-VSG, the quality of the input and output virtual samples generated via the method is investigated through a two-dimensional standard function. The proposed method is further applied to the fault diagnosis of ESP in an offshore oil field, and the effectiveness of MCGAN-VSG is verified with actual industrial data. The MCGAN-VSG was compared with most advanced methods such as MTD, TTD, Bootstrap and MD-MTD, and the experimental results show that the proposed method is superior to all other methods.

Cyclostationary Feature Mode Decomposition and Its Application in Fault Diagnosis of Planetary Gearboxes via Built-In Information

Cyclostationary Feature Mode Decomposition and Its Application in Fault Diagnosis of Planetary Gearboxes via Built-In Information

Refined composite multiscale dynamic causality diagram: a novel time-series analysis approach and its application in fault diagnosis of rotating machinery

Entropy-based feature extraction methods have been widely used in the fault diagnosis of rotating machinery, but the entropy-based methods still have the defects of poor noise robustness, weak feature extraction, and low computational efficiency. To solve this problem, this article proposes a fault diagnosis method based on refined composite multiscale dynamic causal diagram (RCMSDCD) and local receptive field extreme learning machine (LRFELM). First, a novel feature extraction method, named dynamic causal diagram (DCD), is proposed to comprehensively quantify static and dynamic complexity. DCD is obtained by combining generalized inverse fractional order entropy with complexity–entropy causal plane. Then, combined with the coarse-graining process, DCD is extended to a multiscale analysis called RCMSDCD to complement the feature description at cross-time scales. Third, RCMSDCD features are input into LRFELM classifier for fault recognition of rotating machinery. The effectiveness of the proposed RCMSDCD-LRFELM method is verified by the Paderborn University bearing test and real wind turbine gear signals. The results show that this method has the highest classification accuracy of 100% with high computational efficiency, good stability, and strong generalization ability.

Quaternion empirical Ramanujan Fourier decomposition and its application in gear fault diagnosis

In the field of gear health monitoring, the measured signals are traditionally derived from a single sensor or a single direction. However, with the increasing complexity and size of equipment structures nowadays, a single-channel signal often falls short in providing comprehensive status information. Furthermore, fault-induced vibrations are randomly indeterminate in both location and direction, which indicates the necessity of using multi-channel signals for effective gear fault diagnosis. As a single-channel method, Ramanujan Fourier transform (RFT) has been recognized for its effectiveness in extracting periodic components from signals. However, the RFT is limited to handling single-channel signals and lacks the ability to analyze nonstationary signals. To overcome these challenges, this paper proposes a novel multi-channel signal processing method called quaternion empirical Ramanujan Fourier decomposition (QERFD), which extends the RFT to the multi-channel field using the quaternion method. By fusing multi-channel signals while preserving the advantages of RFT, the proposed QERFD method enhances fault information extraction. In addition, QERFD shows high computational efficiency. Therefore, QERFD is of great practical value for the health monitoring of complex equipment in the present industrial context of big data. To substantiate the superiority of QERFD, simulations and experiments are carried out and compared against both single-channel and multi-channel methods.

A step-by-step parameter-adaptive FMD method and its application in fault diagnosis

A newly proposed method, feature mode decomposition (FMD), can effectively enhance signal features while decomposing the signal. This feature is beneficial for analyzing weak vibration signals. However, the input parameters (the segment number K, the filter length L, and the mode number n,) significantly influence the decomposition performance and efficiency. Based on the analysis of filter properties and decomposition performance of the FMD method, a step-by-step parameter-adaptive FMD method is proposed. First, parameters K and L are optimized; second, the parameter n is determined. In addition, a comprehensive evaluation indicator, the ratio of sample entropy and ensemble kurtosis, is constructed considering both the periodic impact characteristics of fault signals and the noise intensity to create objective functions for each step. Compared with the methods of variational mode decomposition spectral kurtosis method and the wavelet packet decomposition, the proposed method exhibits better decomposition performance: the amplitude has increased by nearly ten times for the simulation data and six times for the actual engineering data, and three evaluation factors (the crest factor, the impulse factor, and the kurtosis) have higher value. Therefore, it can be concluded that the proposed method has better superiority in identifying weak periodic fault features.