Application In Fault Diagnosis Research Articles

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

Implementation of supervised machine learning classification for detection and severity determination of electrified power train noise and vibration fault diagnosis

Vehicles equipped with electrified powertrains produce lower sound and vibration levels compared to those equipped with internal combustion engine powertrains. This makes noise and vibration (N&V) from other non-engine components more perceptible. Gear growl is one of the newly observed N&V that brings concerns by the passengers and manufacturers. The understanding of signal characteristics and the threshold for determining whether gear growl requires attention remains limited. To address this, supervised machine learning classification is employed. Root-mean-square (RMS) and spectral entropy values are sufficient for the classification of vibration data with test accuracy of 0.983. However, the acoustic signal required more features due to background noise, making data linearly inseparable. Features that describe the characteristics of acoustic data are studied, extracted, and selected. Utilizing a support vector machine (SVM) for classification, the study achieves an average test accuracy of 0.918. Further, a multi-class classification model is implemented based on preliminary subjective listening studies, classifying different severities of gear growl. Further listening studies are suggested for improving multi-class classification performance. Methods described in this study mainly focus on the analysis of gear growl, but they can be generalized for N&V signal-based fault diagnosis applications.

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.

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.

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.

Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition

Digital real-time fault diagnosis is an effective way to ensure the reliable long-term operation of the diesel engine, but there is still a lack of systematic methods with high integrity and practicability. Therefore, a digital twin-driven diesel engine fault diagnosis method based on the combination of the classification algorithm and the optimization algorithm is proposed and a case study of fuel injection system fault diagnosis is used to illustrate and verify the proposed method. This method closely links the physical system, virtual model, database, and diagnosis system through data transmission and the diagnostic process consists of three parts: classification, diagnosis, and decision. The fault classification part can preliminarily lock the possible types and degrees of faults, and point out the key classification features for each fault type by using classification algorithms such as Random Forest. The fault diagnosis part can diagnose and reproduce the diesel engine faults by using an optimization-simulation joint calculation model, where the virtual model variables and optimization algorithm are determined according to the possible fault types, and the optimization target depends on the importance of classification features. Then the maintenance decision can be made according to the fault detailed information. The proposed method reduces the requirement of covering the fault degree of the database, and the obtained fault model provides the possibility for subsequent online optimization and also facilitates the development of intelligent engine management.

Verification and Validation of Rotating Machinery Using Digital Twin

Abstract Rotating machinery has extensive usage in industrial applications, either as leading equipment (power plants) or as auxiliary equipment (oil and gas exploitation). These highly complex systems demand expensive maintenance programs due to the high costs of eventual shutdown. Consequently, critical fault diagnosis and prognosis are essential during the operation of those systems. Fault identification and classification demand robust verification of codes and calculations and a discerning validation of numerical models used for rotating machinery. Hence, verification and validation (V&amp;V) are an essential initial service for a digital twin (DT), offering some advantages in this application. In this context, the following research question is proposed: Does V&amp;V using DT improve data access and reduce the effort of data exchange? The following objectives are created to address the research question: perform a code verification, conduct the calculation verification, validate the models using two different validation approaches, and demonstrate easy access to asset data. For this study, two identical hydrodynamic bearings and a noncentral disk were considered, representing a laboratory experimental setup. The validation metric requirement is promisingly satisfied for the disk and bearings according to validation Approaches 1 and 2. Furthermore, validation Approach 2 generates even more successful results than approach 1. Accurate estimation and reliable interpretation of the numerical model outcomes encourage the application of DT for future fault diagnosis and prognosis.

Improved Multi-Distance ARMF Integrated With LTSA Based Pattern Matching Method and Its Application in Fault Diagnosis

Effective dimensionality reduction (DR) and classification in fault diagnosis remain a significant challenge, primarily due to the increasing scale of industrial processes and the non-linear and high-dimensional features of process data. To address this challenge, we present local tangent space alignment (LTSA) integrated with a multi-distance adaptive order morphological filter (MARMF) fault diagnosis method (LTSA-MARMF). In LTSA-MARMF, LTSA that preserves the local manifold structure using tangent space is first utilized for DR to provide the required feature space data for ARMF. Next, the cosine distance and dynamic time warping distance are introduced into the distance error of ARMF, considering the spatial similarity and dynamic features to improve classification accuracy. Finally, the distance-matching result of the pattern is applied to determine the type of fault. Through simulations, it is evident that LTSA-MARMF can achieve more satisfactory fault diagnosis accuracy than other related methods on the Tennessee-Eastman process (TEP) and the actual Grid-connected PV System (GPVS). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is inspired by the difficult-to-handle high-dimensional and non-linear features of process data but is also applicable to high-dimensional and non-linear data from other industrial processes. The DR and classification are important aspects of fault diagnosis. In this paper, a novel pattern-matching method is utilized for fault diagnosis, which uses LTSA and the modified multi-distance ARMF for DR and classification, respectively. In terms of mathematics, the distance error of ARMF is analyzed. The combination of multi-distance is used to enhance the accuracy of fault diagnosis. The preliminary experiments show that LTSA-MARMF is feasible, but has not been tested in the plant. We will consider testing LTSA-MARMF in an actual plant in future research.

Fault diagnosis technology of CNC electromechanical system in mechanical engineering equipment manufacturing under structural coupling

Abstract This study addresses the fault diagnosis technology of CNC electromechanical systems in mechanical engineering equipment manufacturing, and explores the fault detection methods under the influence of structural coupling to improve the accuracy and efficiency of fault diagnosis. The study first analyzes the time-domain and frequency-domain features for fault diagnosis, including quantitative and dimensionless features used to identify different types of faults. Subsequently, the study explores feature dimensionality reduction methods, including algorithms such as PCA, LLE and t-SNE, and compares the effectiveness of their application in fault diagnosis. The research focuses on proposing a lightweight deep learning fault diagnosis framework called LTCN-BLS, which combines 2-DLTCN and 1-DLTCN branches, and an ILAEN-based BLS classifier to effectively extract and fuse time-domain and time-frequency-domain features of the data. The experimental results show that the LTCN-BLS framework has high accuracy and low network complexity in fault diagnosis, and has obvious advantages in early fault monitoring, degradation assessment, and robustness compared with traditional methods.