Coupling Faults Research Articles

Research on noise reduction method of centrifugal pump coupling fault signal based on LMD-FE-IWT

Abstract Aiming at the problem that the coupling fault signal of centrifugal pump presents the complexity of mutual coupling due to the mutual influence of various fault components, and it is easy to be affected by the surrounding environmental noise, which leads to the difficulty of noise reduction, the LMD-FE-IWT noise reduction method is proposed. This method first utilizes local mean decomposition (LMD) to adaptively decompose the vibration signal, and calculates the fuzzy entropy of each component. According to fuzzy entropy, the components are divided into two categories: disordered and ordered. After the improved wavelet threshold denoising of the disordered component, it is reconstructed with the ordered component to achieve the denoising effect. By analying the simulation signal and experimental data, the results show that, the proposed method outperforms other methods, with improvements in signal-to-noise ratio by 55.95%, 48.86%, and 31.71% for the simulation signal noise reduction. The effect is significant, enabling the effective extraction of characteristic frequencies associated with different faults for the experimental signal noise reduction.

A Review Paper on Memory Fault Models and its Algorithms

The significance of testing semiconductor memories has grown significantly in the semiconductor industry due to the increased density of modern memory chips. This paper aims to investigate and analyze different types of functional faults present in today's memory technology. These faults include stuck-at faults, transition faults, coupling faults, address decoder faults, and neighborhood pattern-sensitive faults. The paper also delves into the techniques utilized to identify and detect these faults. In particular, the focus is placed on the importance of zero-one, checkerboard, and March pattern tests, which are widely employed to assess functional memory defects at different levels, such as the chip level, array level, and board level. Furthermore, the study provides an in-depth exploration of various test algorithms and thoroughly examines their fault coverage capabilities. Overall, this review paper provides valuable insights into the challenges posed by the dense nature of modern memory chips and offers a comprehensive analysis of functional faults in memory technology. By emphasizing the importance of testing and presenting a detailed exploration of fault detection methods and test algorithms, this study contributes to the advancement of reliable and high-performance memory devices in the electronic industry suggesting that MARCH algorithms outperform others when considering factors like fault coverage, power efficiency, area optimization, and time complexity parameters, making them the preferable choice for reliable and high-performance memory devices in the electronic industry.

Coupling Fault Diagnosis of Bearings Based on Hypergraph Neural Network.

Coupling faults that simultaneously occur during the operation of mechanical equipment are widespread. These faults encompass a diverse range of high-order coupling relationships, involving multiple base fault types. Based on the advantages of hypergraphs for higher-order relationship descriptions, two coupling fault diagnosis architectures based on the hypergraph neural network are proposed in this paper: 1. In the coupling fault diagnosis framework based on feature generation, the base faults serve as the hypergraph nodes, and each hyperedge connects the base faults. The generator, which consists of the hypergraph neural network, generates coupling faults as negative samples to enforce regularization constraints for the discriminator training. 2. In the coupling fault diagnosis framework based on feature extraction, each node represents a fault mode, and each hyperedge connects nodes with common failure modes. The multi-head attention mechanism extracts the features of base faults, and the common fault features in a hyperedge are aggregated via the hypergraph neural network. The inner product correlation is used to diagnose the fault modes. The results show that the diagnostic accuracy for coupling faults with the two frameworks reaches 88.6% and 86.76%, respectively. Both frameworks can be used for the diagnosis and analysis of high-order coupling faults.

Near-optimal March Tests for Three-Cell and Four-Cell Coupling Fault Models in Random-Access Memories

This research work addresses the problem of testing n×1 RAMs in which complex models of unlinked static three or four-cell coupling faults are considered. As in other works, it is assumed that only physically neighboring memory cells could be involved in a three or four-cell coupling fault. For this reason, these fault models can also be considered to be of the neighborhood pattern sensitive type. As extensions of the well-known model of all unlinked static two-cell coupling faults, the fault models we address are complex including faults sensitized by a transition write operation as well as faults sensitized by a non-transition write or a read operation. For these complex models, near-optimal multirun march tests are proposed. This optimality assessment is based on the fact that, for any group of cells corresponding to the considered fault model, the state graph is completely covered, and each arc is traversed only once, which means that the graph is of the Eulerian type. Additional write operations are only required for data background changes.

Analysis of bending and torsional vibration characteristics of rotor under the influence of air gap eccentricity-rub impact coupling fault

Analysis of bending and torsional vibration characteristics of rotor under the influence of air gap eccentricity-rub impact coupling fault

Coupling Fault Diagnosis Based on Dynamic Vertex Interpretable Graph Neural Network.

Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.

Linked Coupling Faults Detection by Multirun March Tests

This paper addresses the problem of describing the complex linked coupling faults of memory devices and formulating the necessary and sufficient conditions for their detection. The main contribution of the proposed approach is based on using a new formal model of such faults, the critical element of which is the introduction of roles and scenarios performed by the cells involved in the fault. Three roles are defined such that the cells of the complex linked coupling faults perform, namely, the roles of the aggressor (A), the victim (V), and both (B), performed by two cells simultaneously in relation to each other. The memory march test and applied address sequence and background determine the scenario for implementing the roles of memory faulty cells. The necessary and sufficient conditions for detecting linked coupling faults are given based on a new formal model. Formally, the undetectable linked coupling faults are defined, and the conditions for their detection are formulated using multirun memory march tests. The experimental investigation confirmed the validity of the proposed formulated statements. Based on the example of a linked coupling fault, this study demonstrates the fulfillment of the necessary and sufficient conditions for its detection using a single march test. As demonstrated in this article, employing the approach proposed by the authors, a two-pass march C test, for instance, enables the attainment of 55.42% fault coverage for linked coupling faults, inclusive of undetectable faults identified by the single-pass march test.

Enhancing the Coupling of Real-Virtual Prototypes: A Method for Latency Compensation

Currently, innovations in mechatronic products often occur at the system level, requiring consideration of component interactions throughout the entire development process. In the earlier phases of development, this is accomplished by coupling virtual prototypes such as simulation models. As the development progresses and real prototypes of certain system components become available, real-virtual prototypes (RVPs) are established with the help of network communication. However, network effects—all of which can be interpreted as latencies in simplified terms—distort the system behavior of RVPs. To reduce these distortions, we propose a coupling method for RVPs that compensates for latencies. We present an easily applicable approach by introducing a generic coupling algorithm based on error space extrapolation. Furthermore, we enable online learning by transforming coupling algorithms into feedforward neural networks. Additionally, we conduct a frequency domain analysis to assess the impact of coupling faults and algorithms on the system behavior of RVPs and derive a method for optimally designing coupling algorithms. To demonstrate the effectiveness of the coupling method, we apply it to a hybrid vehicle that is productively used as an RVP in the industry. We show that the optimally designed and trained coupling algorithm significantly improves the credibility of the RVP.

Multi-label deep transfer learning method for coupling fault diagnosis

With the development of complexity and integration of machines, the multiple components of the system are prone to simultaneous failures and the multi-fault signals may be measured in limited sensors. This fault mode is defined as coupling fault in this paper and becomes more common and attracts great attention. Current coupling fault diagnosis approaches rely on labeled data under consistent working conditions. However, in practical varying working conditions, the data is difficult to label and the traditional methods perform poorly. To address the challenge problem, we utilize some related annotated data under other working conditions for auxiliary learning and abstract this problem as a multi-label transfer learning problem which is a new research topic in fault diagnosis. We proposed a novel deep transfer coupling fault diagnosis method from the global and local views to achieve the effect of transfer, which is the first attempt to address this practical problem as far as we know. At the global level, we used Maximum Mean Discrepancy (MMD) at multiple stages of the network to reduce the hypothesis space of the model. Furthermore, we design a special manifold framework with multi-level similarity to constrain the hierarchy of the distance of the sample and the hierarchy of the labels being consistent between the two domains, to further improve the effect of local alignment. The proposed method has high accuracy in multiple transfer tasks for both public and laboratory datasets, powerfully demonstrating its effectiveness.

A New Test Algorithm and Fault Simulator of Simplified Three-Cell Coupling Faults for Random Access Memories

A New Test Algorithm and Fault Simulator of Simplified Three-Cell Coupling Faults for Random Access Memories

Formal description model and conditions for detecting linked coupling faults of the memory devices

Objectives. The aim of the work is to develop and analyze a formal model for describing complex linked coupling faults of memory devices and to formulate the necessary and sufficient conditions for their detection. The relevance of these studies lies in the fact that modern memory devices, characterized by a large amount of stored data and manufactured according to the latest technological standards, are distinguished by the manifestation of complex types of faults in them.Methods. The presented results are based on the classical theory and practice of march tests (March tests) of memory devices. In particular, the paper uses formal mathematical models for describing memory faults and shows their limitations for representing complex linked coupling faults. The main idea of the approach proposed by the authors is based on the use of a new formal description of such faults, the key element of which is the introduction of roles performed by the cells involved in the fault.Results. Three main roles are defined that cells of the complex linked coupling faults perform, namely the role of the aggressor (A), the role of the victim (V), as well as the role of both the victim and the aggressor (B), performed by two cells simultaneously in relation to each other. It is shown that the scenario for the implementation of the roles of memory failure cells is determined by the marching test used, and, first of all, by the address sequence used to access the cells. The procedure for setting a formal model of a linked fault is given, the basis of which is the roles performed by the cells included in the fault and the scenario specified by the test. A statement is given that determines, on the basis of a new formal description of linked coupling faults, the necessary and sufficient conditions for the detection of such faults. The presence of undetectable linked coupling faults is shown, and the conditions for their detection are formulated using multiple March tests. The conducted experimental studies have confirmed the validity of the formulated provisions of the article. On the basis of the classical example of a linked coupling fault, the fulfillment of necessary and sufficient conditions for its detection by a single march test is shown.Conclusion. The results of the research confirm that the proposed formal mathematical model for describing linked coupling faults makes it possible to determine their detection by marching tests. Within the framework of the proposed model, the necessary and sufficient conditions for detecting linked coupling faults by marching tests that detect single coupled faults are determined.

Dynamic modeling of spur gear transmission system with evolutive coupling fault of fatigue crack and wear

Parallel shaft spur gear system is widely used in various machineries because of its simple structure, high transmission efficiency and strong adaptability. The health status of spur gear greatly affects the safety and reliability of equipment in service, so it is quite necessary to reveal the evolution law of gear fault and clarify the fault dynamic response. Fatigue crack is a typical gear fault, and it is often accompanied by wear which is generally unavoidable in the life cycle of gear transmission system. The two typical gear faults reduce the meshing stiffness of the gear pair, whereupon a calculation approach of time-varying meshing stiffness for the coupling fault of fatigue crack and wear is proposed. First, the prediction models of fatigue crack and wear are respectively investigated, then a coupling fault evolution model is built by combining them. Using this model, a new approach for calculating the time-varying meshing stiffness is put forward. Via embedding the obtained meshing stiffness into the eight-degree-of-freedom dynamics model of spur gear transmission system as the fault excitation, an evolutive coupling fault dynamics model is constructed. Finally, the effectiveness and accuracy of the proposed evolutive fault dynamics model is verified by the experiment on a faulty single-stage gearbox. The proposed model has important guiding significance for the fault diagnosis and prognosis of spur gearbox.

Semi-supervised diagnosis method for coupling faults of key rotating components based on EEMD-KPCA under cross working conditions

To address the challenges of extracting coupled fault features from key rotating components and classifying them under changeable operating conditions, a semi-supervised fault diagnosis method is proposed. First, Ensemble Empirical Mode Decomposition and Kernel Principal Component Analysis are employed to decompose the original coupled fault signals and reduce feature dimensionality. Experiments are conducted on labeled datasets, yielding an average classification accuracy of 92.43%. To further classify unlabeled datasets under various working conditions, a probability distribution estimation function is incorporated and a confidence threshold is set. For unlabeled data with probabilities greater than the confidence threshold, a pseudo-label is added to increase the labeled data quantity. Thus, it makes learning from these unlabeled data possible. A comparison with the other three methods under cross working conditions showcases the superiority of the proposed approach.

An efficient sensor and thermal coupling fault diagnosis methodology for building energy systems

An efficient sensor and thermal coupling fault diagnosis methodology for building energy systems

Nonlinear Dynamic Behavior Analysis of Dual-Rotor-Bearing Systems with Looseness and Rub–Impact Faults

The nonlinear dynamic behaviors of dual-rotor-bearing systems with looseness and rub–impact faults are discussed in this paper. The dual-rotor-bearing system with looseness and rub–impact coupling faults is established by the finite element method. The component mode synthesis (CMS) and proper orthogonal decomposition (POD) methods are introduced. The CMS and POD methods are used to reduce the original rotor system model, and the efficiency of the order reduction method is verified by comparing the dynamic behaviors of the original and reduced systems. The frequency spectrum and amplitude–frequency behaviors of rotor systems are studied. The results can provide qualitative guidance to structural design optimization of large rotating machines and prior information for looseness and rub–impact coupling faults.

Multi-stage collaborative resilient enhancement strategy for coupling faults in distribution cyber physical systems

Due to the strong interdependence of cyber systems and physical systems, cyber-physical systems (CPS) often face serious coupling faults in case of extreme natural disasters. To improve the ability of the distribution CPS to withstand extreme events and rapidly resume normal operation, it is critical to consider effective recovery processes and formulate the collaborative resilience enhancement strategy of CPS. In this paper, a sequential multi-stage collaborative recovery strategy from CPS faults caused by natural disasters is proposed, considering the interdependence of the cyber system and physical system at the layers of facilities, topologies and functions, and the coupling relationship of power grid integration in the recovery process. In the implementation of this strategy, on the cyber side, based on the demand of power grid integration recovery, the communication optical fiber aims to be repaired as quickly as possible to control the power grid Remote Controlled Switches (RCS). On the physical side, the power grid is restored with the objective to reduce loss of load, considering the whole process of degradation, response and recovery during fault handling in coordination. Resilience metrics based on the proposed method are introduced to evaluate the resilience level, as the absorption rate, adaptation rate and energy supply rate. IEEE-33 system is used as an example to show that compared with the traditional recovery strategy that considers only the power grid, the proposed strategy can significantly improve the system's ability to face disasters at the early stage, maintain power supply after the disaster, and speed up the overall load recovery.

A Diagnostic Method for Open-Circuit Faults of Loads and Semiconductors in 3L-NPC Inverters

Open-circuit (OC) faults of inverter switch, clamping diode and load are easily misdiagnosed by most of the existing methods due to the extremely similar fault features. In this regard, a diagnostic method based on line voltage over-modulation and amplitude features is proposed in this paper to accurately locate coupling faults of three-level neutral-point-clamped (3L-NPC) inverter and load. Only two line voltages are used as diagnosis variables, and additional sensor is not required. The proposed diagnosis method consists of two parts: fault detection and fault classification. The fault detective algorithm only requires a small amount of computation and is suitable for uninterrupted execution. The classification algorithm starts immediately when the fault is detected, and the over-modulated voltage features are used to locate the fault. The proposed method can usually complete the diagnosis in 1/4 of fundamental period. The proposed method is also robust to different modulations, carrier frequencies and load variations. Experiments demonstrate that the proposed method can distinguish the OC faults of semiconductors (including switches and clamping diodes) and load effectively.

Electric vehicle charging path planning considering couped faults in distribution-transportation network

Once a distribution network failure occurs, it can spread to the traffic network through the coupling point, causing electric vehicles (EVs) to change their charging paths. To address this problem, this paper presents an EV charging path planning approach that considers coupled faults in the distribution-transportation network. First, the cascading failure model of the distribution-transportation network and the model for choosing charging stations are presented to transfer the information of coupling faults propagation and coupling points power interaction to the follow-up path planning scheme. Second, a time occupancy road resistance model that considers congested and unobstructed traffic states is proposed to calculate the road section travel time, based on the analysis results of the evolution process of road traffic flow queuing using traffic wave theory. For the speed and density parameters in the traffic wave model, values are calculated using the logistics speed-density model and the time occupancy model. Third, a multi-objective optimization function that integrates travel cost and coupling network operation state is determined from the perspective of hindering the propagation of coupling faults. The function is solved to recommend optimal charging paths using an improved A* searching algorithm. Finally, a 90-bus road network and three 33-bus distribution networks are selected as examples to verify the veracity and validity of the proposed model and method. The research results demonstrate that the proposed method can alleviate traffic congestion.

Numerical and experimental analysis of rotor-bearing system for axial piston pump with misalignment–rubbing coupling fault

Rotor-bearing system is widely utilised in axial piston pumps in which misalignment–rubbing mixed faults commonly occur, as rubbing and misalignment are both frequent and can occur simultaneously owing to the different working conditions. The dynamic behaviour of a rotor-bearing system misalignment–rubbing coupling defect supported by a ball bearing is complex. To examine the vibration characteristics of such faults, a rotor-bearing system model is established, and the dynamic responses of the rotor-bearing system with a misalignment–rubbing mixed fault are investigated under different parameters. The effects of the rotation speed, tilt misalignment, rubbing clearance, and rubbing stiffness on the rotor-bearing system with a misalignment–rubbing defect are examined via numerical simulations. Additionally, validation studies are conducted, and the results indicate that the distinctive frequency components may be useful for the detection of hybrid faults in rotor-bearing system. The degree of tilt misalignment has little influence on the rubbing frequencies. In the coupling fault, the rubbing fault is prominent. The frequency components of the system are dominated by high frequencies as the speed increases. The major fault feature in the frequency band is a double frequency. The numerical results are in agreement with the experiment. These results reveal the vibration characteristics of the rotor-bearing system for axial piston pump with misalignment-rubbing coupling fault.

Analysis of Time-Varying Mesh Stiffness and Dynamic Response of Gear Transmission System with Pitting and Cracking Coupling Faults

The gear transmission system is an important part of the mechanical system, so it is essential to judge its running state accurately. To solve the difficult problem of identifying the components of coupling faults, this paper derives the calculation method of gear time-varying mesh stiffness for coupling faults of pitting and cracking based on the energy method and considering the coupling between teeth, establishes the dynamics model of two-stage gear transmission system with coupling faults and studies the influence of coupling faults on gear time-varying mesh stiffness and dynamic characteristics. The accuracy of the proposed method is verified by experiments. The results show that both pitting and cracking can lead to a reduction in mesh stiffness. The stiffness of pitting will fluctuate irregularly due to the influence of pitting on the tooth surface, while the stiffness of cracked teeth is relatively smooth. The coupling fault stiffness is dominated by more serious faults. By analyzing the periodic impact components in time domain and the sideband components around the harmonics in frequency domain the faulty gears in the transmission system can be distinguished. It provides an effective reference for the diagnosis of faulty gears.