Coupling Faults Research Articles

A rift‐scale view at strain partitioning during multiphase rifting: Insights from the Hailar Basin, northeast Asia

AbstractThe spatial strain variations while continental lithospheric extension progresses and leads to either a failed rift basin or a fully rifted passive margins have been extensively studied. However, determining the spatial kinematics of rift basins throughout their entire temporal evolution at rift scale is often difficult due to the sparsity of spatial data coverage; this limits our current understanding of rift evolution and our ability to ascertain the controlling factors on basin development. This study uses extensive high‐quality 2D and 3D seismic reflection data and borehole data from the Hailar Basin (northeast Asia) to investigate the spatial strain variations that occurred during multiphase rifting at both high resolution and rift scale. By coupling fault evolution and depocenter analysis, we demonstrate that the Hailar Basin experienced complex strain partitioning with a progressively eastward migration of strain and eventual strain localization onto rift axis. This strain localization occurred in the area that had been dominated by syn‐rift sagging. We propose that the observed eastward strain migration has been driven by the multiphase rollback of the Paleo‐Pacific slab. Contrastingly, during the final rift phase, crustal rheology played the dominant role in rift development, while the influence of geodynamic processes significantly decreased, resulting in the cessation of the eastward rift migration and the ultimate strain localization. This pattern of strain migration and localization is likely to be applicable to other multiphase rifts where the main controlling factors varied through time.

Model decomposition-based fault-tolerant control for interconnected wave partial differential equation systems

The fault-tolerant control (FTC) issue for a class of interconnected wave-distributed parameter systems is addressed by using the inclusion principle. Both subsystem faults and coupling faults are considered. First, the original system is expanded into an equivalent decoupled system. The main structure of the expanded system consists of multiple pair-wise subsystems. Second, the pair-wise fault-tolerant controllers are designed to stabilise the pair-wise subsystems, which can compensate for the effects of subsystem faults and major coupling faults. Third, a coordinated fault-tolerant controller with a suitable coordinated compensator for the expanded system is constructed according to the pair-wise fault-tolerant controllers of all subsystems. The coordinated compensator is used to compensate for the effects of other minor interconnections and coupling faults, and the coordinated controller is designed for the expanded system. Furthermore, the fault-tolerant controller is obtained to guarantee asymptotical stability of the faulty interconnected wave system by satisfying the contraction condition. The numerical experiments are presented to illustrate the effectiveness of the FTC method.

Vibration characteristics of rotor system with disk-shaft clearance-eccentric coupling faults

Clearance between the disk and shaft can lead to the aggravation of the system fault. To solve this problem, a disk-shaft dynamic model with the clearance-eccentric coupling faults is established by the finite element method. Based upon the model, the dynamic characteristics of the system with different influence factors such as clearance and rotating speed are acquired by comprehensive analysis. Meanwhile, the accuracy of the dynamic model is verified by experiment investigations. The results show that frequency components in the spectrum include the shaft’s rotating frequency and high frequency multiplication components. And the sawtooth phenomenon from the trajectory diagram of disk center can be observed. With the increase of rotating speed, the disk-shaft contact stress and strain energy first decrease and then increase, while the speed difference of disk to shaft and vibration amplitude increase, and the number of high frequency multiplication components in the spectrum decreases gradually. Furthermore, with the increase of clearance, the stable values of the disk-shaft contact stress and strain energy decrease, while the vibration amplitude, the number of high frequency multiplication components and speed difference increase gradually, and the trajectory shape of disk center changes from closed circle to hollow ring.

Demagnetization Fault Diagnosis for Magnetically Suspended PMSM Using Rotor Displacement Signals

Demagnetization fault diagnosis is a significant issue in magnetically suspended permanent magnet synchronous motor (PMSM). The occurrence of a permanent magnet (PM) demagnetization fault reduces the motor efficiency and even disables the whole power system. Unfortunately, it is challenging to extract the fault feature and evaluate the exact severity precisely due to the rich environmental noise and the coupling faults. This article proposes a novel strategy using the improved variational mode decomposition (VMD) and energy moment relative entropy (EMRE) to achieve the demagnetization fault diagnosis, facilitating the timely implementation of the corresponding control strategies. Firstly, the harmonic component caused by the demagnetization fault is studied. An improved VMD algorithm is proposed to preprocess the displacement signals and decompose them into a series of mutually-exclusive intrinsic mode functions (IMFs). Secondly, signal reconstruction is performed on the selected IMFs, and fault feature extraction is accomplished utilizing the Teager enhancement spectrum. Thirdly, EMRE is proposed as the evaluation indicator to evaluate the exact demagnetization severity. Finally, the system experimental platform with different demagnetization degree prototypes is constructed for validation.

Versatile Economic Risk Tool

The foundation of the Versatile Economic Risk Tool is based on the methodologies of the Generation Risk Assessment (GRA) and uses a systematic approach to prioritizing systems, structures, and components based on estimated operation reliability and the impact on future power generation. The GRA provides management with insight that helps with decisions on maintenance, component repair/replacement, design modifications, and other issues associated with electricity generation and equipment reliability programs. Although the benefit of the GRA is understood in the nuclear community, there are negative aspects associated with the current approaches. Traditional GRA modeling is time consuming, convoluted, and considers only the static portion of generation risks. The Versatile Economic Risk Tool addresses the drawbacks of the traditional GRA through simplifying and/or automating the requirements for an effective GRA model. The Versatile Economic Risk Tool also integrates in-depth parametric variability within models to evaluate system, structure, and component generation risks as a function of time. The Versatile Economic Risk Tool addresses the shortcomings of the GRA by automating the study of equipment reliability and degradation from an electricity generation perspective by coupling fault tree analysis and statistical analysis software that support repetitive, parametric analyses. Versatile Economic Risk Tool case studies are presented to provide insight into the potential benefit provided.

A Low Coupling Fault Tolerant PMA-SynRM With Mixed-Pitch Segregated Windings

Mutual coupling effect between different winding modules causes undesirable effects for the fault tolerant machine drives, such as current distortion, torque ripple, and increased fault current. In this article, a mixed-pitch segregated winding configuration is presented for a triple modular fault tolerant permanent magnet (PM)-assisted synchronous reluctance machine (PMA-SynRM). It exhibits less mutual coupling effect between the different winding sets, which leads to improved fault tolerance compared to the existing full-pitched segregated windings. The mutual coupling effect is described by deriving the magnetomotive force (MMF) profile, and the fault tolerant performance improvement is proven by detailed finite-element (FE) analysis and prototyping tests in healthy and various fault conditions. The results confirm that the presented winding machine has the same healthy performance as the conventional full-pitched windings, and it operates in a more balanced manner with less current distortion and lower turn fault current. It will become a more competing candidate for the safety critical applications.

Nonlinear dynamic behavior of a dual-rotor bearing system with coupling misalignment and rubbing faults

Taking the dual-rotor system in an aero-engine as a research object, the vibration behaviors of a dual-rotor bearing system that is fault-free or has misalignment, rub-impact or misalignment rub-impact coupling faults are studied, respectively. First, the three-dimensional model of the dual rotor bearing system is established, and then the first six modes and critical speeds of the dual rotor bearing system are obtained by the finite element method. Then, the dynamic equations of the dual rotor bearing system with and without faults are derived based on the Lagrange method. The Runge–Kutta method is used to solve the dynamic equation, and the nonlinear dynamic response of the system is obtained. The vibrational behaviors of the dual rotor system with misalignment, rub-impact and misalignment rub-impact coupling faults are analyzed and discussed through the time history, phase diagram and frequency spectrum of the rotor. The vibrational behaviors of the first six modes of the dual rotor system with and without faults are analyzed, respectively. The results provide theoretical guidance for the structural optimization design of an aero-engine rotor system and the prior decision information for misalignment and rub-impact coupling fault diagnosis.

Analysis of fault coupling vibration and transmission characteristics of multistage gear transmission system

In multi-stage gear transmission system, the accident is often not caused by a single fault, but by coupling fault. The decoupling and identification of coupling faults has always been a hot topic. Through the analysis of frequency characteristics and transmission characteristics, this paper explored the correlation of coupling faults in multistage gear transmission system, and analyzed the coupling mechanism of faults. According to the fault test-bed, a 3D model was built to analyze the contact force and vibration characteristics of gears at all stages. STFT and waterfall diagram were used to analyze the coupling correlation characteristics of experimental signals under two single faults and coupling faults: fixed-axis gear crack and planetary gear wear. The contact force transfer function of all stages of gears to the box was built by using the system identification theory. The source of the fault characteristic frequency component was found through the transfer function, and the transmission characteristic of the fault was revealed. It can be seen from the results that the fault coupling is not the superposition of frequency characteristics. In the coupled signal, the frequency characteristic with the largest amplitude increase is not the fault gear itself, but the intermediate gear connected with them and affected by them. This phenomenon is often misled and misjudged in the analysis.

A Hypothesis Method for T-Type Three-Level Inverters Open-Circuit Fault Diagnosis Based on Output Phase Voltage Model

The open-circuit (OC) faults for T-type three-level inverters often share similar distorted output features, which are usually distinguished by adopting invasive methods. The purpose of this article is to locate the OC fault by rebuilding the established phase voltage model according to the hypothesis method. Based on the vector decomposition principle and voltage-second balancing theory, the output phase voltage model is established considering neutral point voltage unbalance and time-offset injection. The hypothetical phase voltage vector and residual for fault reasoning can be obtained by inputting the suspected OC faults positions to the model. The hierarchical fault diagnosis scheme involves two steps. First, the group-level fault can be located according to voltage residual vector amplitude and angle. Considering the impact of parameter error, dead time, and delay time, an adaptive residual vector amplitude threshold is designed. Next, the device-level fault can be located by building two suspected phase voltage models to approximate the real system. Furthermore, the fault location rules under zero-crossing (ZC) and non-ZC conditions are summarized. Especially, the proposed hypothesis method has correctness verification ability to avoid misdiagnosis. Experimental results show the robustness and effectiveness of the proposed low coupling fault diagnosis method.

Adaptive feature selection method with FF-FC-MIC for the detection of mutual faults in rotating machinery

In the coupling state of rotor unbalance fault and bearing defect fault for rotor system, the signals contain multiple fault components, and the fault diagnosis of the rotor system needs to contain comprehensive multidimensional feature quantities. However, irrelevant feature information in the multi-dimensional feature quantities increases the complexity of classification calculation and affects the efficiency and accuracy of diagnosis. In order to eliminate redundant and irrelevant features in the feature information, and achieve the goal of fewer diagnostic features and good diagnostic results, this paper proposes an adaptive feature selection based on the maximum information coefficient FF-FC-MIC (Feature-to-Feature and Feature-to-Category Maximum Information Coefficient) method. Firstly, the sparse representation algorithm is used to reconstruct the original signal to improve the signal-to-noise ratio, and the multi-dimensional feature quantity of the reconstructed signal is calculated; Secondly, calculate the correlation between features and features through MIC to obtain a feature set of weak correlation between features; thirdly, use MIC to calculate the correlation between features and signal categories to obtain a feature set with strong correlation between features and signals; Finally, the FF-FC-MIC feature selection method is used for feature adaptive selection and input into SVM to complete fault diagnosis. The method is analyzed by simulation signals and the real experiment signals. The results show that the method can effectively remove redundant and disclosed features in the coupling fault, reducing the characteristic dimension to reduce the fault classification time, and improve classification accuracy. Different experimental cases and various feature selection comparison methods further verify the accuracy and applicability of the proposed method.

Comprehensive diagnosis and analysis of spur gears with pitting-crack coupling faults

As an important part of power transmission in mechanical system, the health status of gear needs to be judged accurately. However, most of researchers focus on single fault gear, and very few papers are reported on the performance of multiple faults. So the gear mathematical model with pitting-crack coupling faults is derived in this study, and it is validated by a three-dimensional finite element model. Furthermore, several types of fault combinations from health to severity are presented to investigate the effect of coupling fault evolution on gear time-varying meshing stiffness. The stiffness excitation is applied into an 8 degrees of freedom model to obtain the dynamic characteristics of the gear-rotor system in terms of displacement and acceleration, and a reasonable diagnosis of the gear status can be made by analyzing the dynamic characteristics in time domain and frequency domain. In the next step of research, the statistical indicators are applied to further reveal the degree of coupling faults, which provides effective reference on the diagnosis of coupling fault gears.

F2G: A hybrid fault-function graphical model for reliability analysis of complex equipment with coupled faults

Reliability analysis plays a crucial role in revealing the failure causes and determining the improvement measures for reliability growth. However, reliability analysis of complex equipment with coupled faults still corresponds to a challenging task, due to unclear coupling mechanism and unsuitable analysis model. A down-top, deductive modeling method, named as fault-function graph (F2G), is proposed. First, the meta models are defined to normalize all the atomic faults, coupling relations and coupling forms in modeling. Next, an initial fault model is constructed based on typical fault-relations and coupling forms. Furthermore, the functional hierarchy of fault determined by IDEF0 is appended. Lastly, the rigorous modeling rules and computing processes are explained based on an actual case. As a graphical modeling method, it handles the coupling faults by integrating the system functional and fault information. Exploiting the advantages of conventional models, the coupling relations are quantified, and the false relations are detected based on functional constraints. Therefore, the proposed method can be used flexibly in the reliability modeling of coupled faults. Moreover, it provides a foundation for the comprehensive and dynamic reliability analysis and the failure mechanism mining of complex equipment, and it can be used in other engineering applications as well.

Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions

Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis of rolling bearing under variable working conditions. The collected vibration signals of rolling bearing are divided into long time series samples, including normal samples, single fault samples, and coupling fault samples. Then, variational autoencoder (VAE) is utilized for data augmentation of time series samples, and the generated samples are brought into one-dimensional deep convolutional neural network (1-DCNN) for further classification of multiple coupling faults. Subsequently, the trained 1-DCNN is regarded as embedding model. Training samples and other working condition samples are defined as support set and query set. Based on metric-based meta-learning method, sample pairs composed of support set and query set are constructed and brought into the embedding model to get the category with shortest metric distance as the classification result. In addition, the embedding model can be optimized by minimizing the contrastive loss among these sample pairs. The case study shows that the DML-VAE can achieve accurate classification results under the coupling of two faults and three faults, and maintain high diagnostic accuracy under variable working conditions. Compared with other models, the proposed model can also get the most accurate fault diagnosis results for all categories under unbalanced samples.

A Novel Multilabel Classification Framework for Coupling Faults in Hot Rolling Processes

Compared with a single fault, the occurrence, evolution, and composition of coupling faults have more uncertainties and diversities, which make coupling fault classification a challenging topic in academic research and industrial application areas. This brief addresses the classification problems of coupling faults from a new perspective. Specifically, the main innovations are: 1) a classification framework for coupling faults is first proposed, which integrates multiple kernel learning and multilabel dimensionality reduction; 2) label correlations and nonlinear characteristics among coupling faults are fully explored aiming at improving classification performance; and 3) a trace ratio form of l_1 norm-based objective function is designed for improving the robustness of multilabel classifier. Extensive experiments on the hot rolling process (HRP) are finally given to validate the effectiveness of the proposed scheme.

Multi-fault Classification of Train Wheelset System

Abstract The condition monitoring and faults diagnosis of wheelset are of great significance for the safe and stable operation of trains. Aiming at the axle crack, wheel flat, wheel out-of-roundness and their coupling faults in wheelset system, this paper proposes a method to diagnose these multiple faults simultaneously. Firstly, the dynamic model of a wheelset system with flat and out-of-roundness is established in SIMPACK software to analyze the fault characteristics of these two typical wheel damages. And the wheel flat and out-of-roundness are equivalent to displacement excitation and then loaded into the finite element (FE) model of the cracked wheelset. The vibration responses of the cracked wheelset with flat and out-of-roundness are solved in ABAQUS software. Then, the simulated multi-fault vibration data of wheelset are input into a Light-GBM model to realize the faults classification of axle crack, wheel flat, wheel out-of-roundness and their coupling faults. Finally, the wheelset vibration signals under the conditions of health, axle crack, wheel flat, and crack-flat coupling fault are collected from an experimental bench to verify the proposed method. This work can provide a basis for multi-fault classification and on-line health monitoring of trains.

Rotor crack and bearing fault diagnosis based onVMD-ICA

Abstract The rotor-bearing system is vulnerable to rotor cracks, bearing failures, or even multiple failures at the same time during long-term service, and it is more difficult to diagnose the faults under rotor crack and bearing fault coupling conditions. This paper proposed a signal reconstruction method to classify the rotor crack, the bearing outer ring fault and their coupling faults based on Variational Mode Decomposition (VMD) and Independent Component Analysis (ICA). Firstly, the original vibration signal collected on a rotor-bearing test bench is decomposed with VMD, and several intrinsic mode functions (IMF) are obtained. Then, the correlation kurtosis CK M which introduces the characteristics of periodic correlation function is to evaluate the decomposed IMF component, and screen the rotor crack information component. Finally, the rotor crack information component is input into a virtual channel signal and separated with the original vibration signal with ICA, and then the separated two sets of signals are superimposed to obtain a vibration signal that simultaneously highlights the information of the rotor crack and the bearing outer ring fault. In addition, the time domain signal is directly input into a LightGBM to diagnose different faults. The research results show that this method can simultaneously diagnose the rotor crack, the bearing outer ring fault and the coupling fault.

Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review

With the increase of the scale and complexity of mechanical equipment, traditional intelligent fault diagnosis (IFD) based on shallow machine learning methods is unable to meet the demand of coupling faults. In the past decades, the vigorous development of deep learning (DL) brings new opportunities for IFD, especially the representation learning based on Autoencoder (AE) theory has been widely applied. To provide a more comprehensive reference, the theoretical foundations of multi-type AEs and the training method of stacked autoencoder (SAE) are briefly introduced. Then the application advances of AE are reviewed from optimization and combination aspects, which are aiming at improving the representation learning ability. To provide ways for the application of AE-based methods, two typical study cases for ideal and complex engineering systems are illustrated respectively. Finally, the challenges and prospects of AE-based representation learning are reported from four aspects, which give a guidance for the future research direction.

Analysis on vibration response of bearing coupling faults in rotor-bearing system

It is well known that the coupled fault diagnosis of rotating machinery is a challenging task, which is mainly due to the complexity of the vibration signals and the interaction of multiple fault components. In order to study the coupling vibration characteristics from unbalance and bearing fault in rotor-bearing system, A dynamic model of the coupled fault is performed to explore the phenomena of multi-fault in the rotor-bearing system in this paper. An experimental study, including seven working conditions, is also examined to further indicate the vibration response of coupling fault with unbalance and bearing fault. The influence of the rotor unbalance on the bearing vibration characteristics and the vibration rules of the coupling fault are proposed in this paper.

Fault Modeling and Efficient Testing of Memristor-Based Memory

Memristor-based memory technology is one of the emerging memory technologies, which is a potential candidate to replace traditional memories. Efficient test solutions are required to enable the quality and reliability of such products. In previous works, fault models are caused by open, short and bridge defects and parametric variations during the fabrication. However, these fault models cannot describe the bridge defects that cause the state of the faulty cell to an undefined state. In this paper, we analyze the different effects of bridge defects and aggregate their faulty behavior into new fault models, undefined coupling fault and dynamic undefined coupling fault. In addition, an enhanced March algorithm is designed to detect all the modeled faults. In one resistor crossbar with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> memristors, the enhanced March algorithm requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8N$ </tex-math></inline-formula> write and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7N$ </tex-math></inline-formula> read operations with negligible hardware overhead. To reduce the test time, a March RC algorithm is proposed based on read operations with new reference currents, which requires <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4N+2$ </tex-math></inline-formula> write and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6N$ </tex-math></inline-formula> read operations. Analytical results show that the proposed test algorithms can detect all the modeled faults outperforming all the previous methods. Subsequently, a Design-for-Testability scheme is proposed to implement March RC algorithm with a little area overhead.

Transparent memory tests with even repeating addresses for storage devices

The urgency of the problem of memory testing of modern computing systems is shown. Mathematical models describing the faulty states of storage devices and the methods used for their detection are investigated. The concept of address sequences (pA) with an even repetition of addresses is introduced, which are the basis of the basic element included in the structure of the new transparent march tests March _pA_1 and March _pA_2. Algorithms for the formation of such sequences and examples of their implementations are given. The maximum diagnostic ability of new tests is shown for the case of the simplest faults, such as constant (SAF) and transition faults (TF), as well as for complex pattern sensitive faults (PNPSFk). There is a significantly lower time complexity of the March_pA_1 and March_pA_2 tests compared to classical transparent tests, which is achieved at the expense of less time spent on obtaining a reference signature. New distance metrics are introduced to quantitatively compare the effectiveness of the applied pA address sequences in a single implementation of the March_pA_1 and March_pA_2 tests. The basis of new metrics is the distance D(A(j), pA) determined by the difference between the indices of repeated addresses A(j) in the sequence pA. The properties of new characteristics of the pA sequences are investigated and their applicability is evaluated for choosing the optimal test pA sequences that ensure the high efficiency of new transparent tests. Examples of calculating distance metrics are given and the dependence of the effectiveness of new tests on the numerical values of the distance metrics is shown. As well as in the case of classical transparent tests, multiple applications of new March_pA_1 and March_pA_2 tests are considered. The characteristic V(pA) is introduced, which is numerically equal to the number of different values of the distance D(A(j), pA) of addresses A(j) of the sequence pA. The validity of analytical estimates is experimentally shown and high efficiency of fault detection by the tests March_pA_1 and March_pA_2 is confirmed by the example of coupling faults for p = 2.