Multiple Faults Research Articles

Adaptive mode decomposition method based on fault feature orientation and its application to compound fault diagnosis of planetary gearboxes

Abstract Planetary gearboxes often experience multiple component failures during service, which can accelerate the degradation and failure of industrial equipment. Accurate separation and identification of multiple faults is an important means of ensuring the safe and stable operation of equipment. However, different faults can interact with each other, along with the influence of background noise, making it challenging to accurately extract faults with relatively weak energy among multiple faults. This difficulty leads to the problems of potential misdiagnosis and underdiagnosis. To address this issue, an adaptive mode decomposition method based on fault feature orientation (AMD-FF) is proposed in this paper. Initially, a fault impact indicator (FII) is constructed based on period-weighted kurtosis of envelope spectral and correlated combination negentropy to effectively characterize the impulsiveness and periodicity of fault features. Furthermore, with the objective of maximizing the FII, an adaptive decomposition of the original signal is designed based on blind convolution theory using a finite-impulse response filter group. Subsequently, a variable weight particle swarm optimization is employed to adaptively optimize the key decomposition parameters. Finally, the data of industrial-grade planetary gear transmission test rig are collected to validate the proposed method for compound fault diagnosis of planetary gearboxes. The results indicate that the AFMD-FF can effectively separate and extract compound faults in planetary gearboxes, demonstrating superior fault separation and diagnostic performance compared to the fault mode decomposition (FMD) and adaptive FMD. This method offers a novel approach to diagnosing compound faults in rotating equipment in industrial scenarios.

Neuroadaptive event-triggered tracking control for nonlinear systems with dynamic fault feedback and prescribed time convergence

This paper investigates the problem of event-triggered adaptive prescribed time tracking control for nonlinear systems with combining multiplicative and additive dynamic sensor fault. A simple state observer is designed by fusing fault output and reference signal to estimate unmeasurable states, where the unknown unmeasurable uncertain functions are approximated by neural network technology. Then, the prescribed time performance function is constructed based on the time scale function, and a suitable function transformation is introduced so that the tracking error can reach the desired residual set in the prescribed time, where the initial value of the error can be arbitrarily selected regardless of the prescribed time performance function. Moreover, the desired accuracy and the prescribed time can be specified on request. The designed adaptive event-triggered controller can effectively compensate for the sensor fault in the output feedback control and reduce the communication burden, and the feasibility of the proposed method is verified by the simulation result.

On the Temperature and Composition Dependence of Non‐basal Stacking Faults in C14 Laves Phases

Activation of non‐basal slip is essential in improving the deformability of hexagonal crystals. However, for complex intermetallics such as Laves phase, the mechanism of non‐basal slip remains largely unknown. In this work, the prismatic slip systems of C14 Laves phase crystals and possible metastable states along the slip paths are assessed using atomistic simulations. Multiple prismatic stacking fault states with the same projected positions of atoms but different site occupancies and chemical distributions are identified at different inter‐atomic layers, temperatures and chemical compositions. The formation of energetically favorable prismatic stacking faults involves short‐range diffusion which implies the thermally activated nature of prismatic slip. The outcomes of this work advance the understanding of temperature and composition‐dependent non‐basal slip in Laves phases.

SURE: A Visualized Failure Indexing Approach using Program Memory Spectrum

Failure indexing is a longstanding crux in software debugging, the goal of which is to automatically divide failures (e.g., failed test cases) into distinct groups according to the culprit root causes, as such multiple faults residing in a faulty program can be handled independently and simultaneously. The community of failure indexing has long been plagued by two challenges: 1) The effectiveness of division is still far from promising. Specifically, existing failure indexing techniques only employ a limited source of software run-time data, for example, code coverage, to be failure proximity and further divide them, which typically delivers unsatisfactory results. 2) The outcome can be hardly comprehensible. Specifically, a developer who receives the division result is just aware of how all failures are divided, without knowing why they should be divided the way they are. This leads to difficulties for developers to be convinced by the division result, which in turn affects the adoption of the results. To tackle these two problems, in this paper, we propose SURE, a vi SU alized failu R e ind E xing approach using the program memory spectrum. We first collect the run-time memory information (i.e., variables’ names and values, as well as the depth of the stack frame) at several preset breakpoints during the execution of a failed test case, and transform the gathered memory information into a human-friendly image (called program memory spectrum, PMS). Then, any pair of PMS images that serve as proxies for two failures is fed to a trained Siamese convolutional neural network, to predict the likelihood of them being triggered by the same fault. Last, a clustering algorithm is adopted to divide all failures based on the mentioned likelihood. In the experiments, we use 30% of the simulated faults to train the neural network, and use 70% of the simulated faults as well as real-world faults to test. Results demonstrate the effectiveness of SURE: It achieves 101.20% and 41.38% improvements in faults number estimation, as well as 105.20% and 35.53% improvements in clustering, compared with the state-of-the-art technique in this field, in simulated and real-world environments, respectively. Moreover, we carry out a human study to quantitatively evaluate the comprehensibility of PMS, revealing that this novel type of representation can help developers better comprehend failure indexing results.

Fault-tolerant consensus of nonlinear multi-agent systems with channel noises under multiple faults and denial-of-service attacks

Fault-tolerant consensus of nonlinear multi-agent systems with channel noises under multiple faults and denial-of-service attacks

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Nonstrict-Feedback Nonlinear Systems With Stochastic Noises

This paper discusses the problem of fault-tolerant tracking control for discrete-time nonstrict-feedback nonlinear systems in the presence of stochastic noises and actuator faults. The system is characterized by a discrete-time nonstrict-feedback structure and multiplicative stochastic noise related to the states, which poses a challenge for the design and analysis of the fault-tolerant controller. Moreover, the considered actuator faults include multiplicative and additive faults without prior information. By integrating the properties of the backstepping framework and neural networks, and by introducing an adaptive fault compensation term, a novel fault-tolerant tracking control strategy is proposed. The effects of faults are compensated and the difficulties caused by the system structure are overcome, avoiding the algebraic loop problem and overcoming the causal contradiction. Given specific parameters, the designed fault-tolerant controller can ensure that the tracking error converges to an adjustable region regarding the origin and that all system signals are uniformly bounded concerning the mean-square sense. The simulation examples illustrate the effectiveness of the designed fault-tolerant control method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —As system complexity has been increasing, practical systems are often affected by faults when performing tracking control. Fault-tolerant control can provide acceptable robustness and improve system safety. On the one hand, many practical systems are nonlinear, difficult to model mathematically and accurately, and affected by stochastic noises. On the other hand, with digital control, the object is a discrete-time system. In practical applications, the upper bounds of the faults and the noises are often unknown. In this paper, a novel adaptive fault-tolerant control method is proposed for the more general uncertain discrete-time nonlinear systems, i.e., nonstrict-feedback nonlinear systems, with stochastic noises and actuator faults. Neural networks and an adaptive fault compensation term are integrated into the fault-tolerant controller. By adjusting the controller parameters properly, it is ensured that the tracking error converges to a small neighborhood of zero in the event of a fault occurring. In future research, sensor faults, input constraints, optimal control design, and applications in multi-agent systems will be considered.

Adaptive Anti-Disturbance Control for a Class of Uncertain Nonlinear Systems With Composite Disturbances.

High-precision and safety control in face of disturbances and uncertainties is a challenging issue of both theoretical and practical importance. In this article, new adaptive anti-disturbance control schemes are proposed for a class of uncertain nonlinear systems with composite disturbances, including additive disturbances, multiplicative actuator faults, and implicit disturbances deeply coupled with system states. Both the cases with known and unknown control/fault directions are investigated. By properly fusing the techniques of disturbance observers and adaptive compensation, it is shown that all closed-loop signals are globally uniformly bounded and the tracking error converges to zero asymptotically, no matter the control/fault directions are known or not. In the case of known directions, the proposed control scheme, for the first time, guarantees asymptotic tracking and L∞ tracking performance simultaneously in face of disturbances and actuator faults. Moreover, novel Nussbaum functions and a contradiction argument are introduced, which allow the system to have multiple unknown nonidentical control directions and unknown time-varying fault direction. Simulation results illustrate the effectiveness of the proposed control schemes.

A high‐accuracy and robust diagnostic tool for gearbox faults in wind turbines

AbstractFaulty gears are a common cause of wind turbine failures. For this sake, this work was developed as a reliable diagnostic tool for wind turbines to improve wind power stability accordingly. A convolutional extension neural network (CENN) was proposed to identify vibration and audio signals captured from a gearbox. According to the status of the contained faulty gears, a gearbox was categorised as one of the three types: (i) broken, (ii) rusty and (iii) a combination of (i) and (ii). It was further assigned one of the three severity levels: mild, moderate and severe. Therefore, there were a total of nine combinations for identification. Captured raw vibration and audio signals were applied to a chaotic synchronisation detector by which 3D chaotic error scatter feature images were generated to train and test the CENN. The recognition rate provided by CENN and the majority rule reached 99.6%, and then slightly fell to 97.4% in a noise robustness test, and consequently CENN outperformed counterparts in terms of the recognition rate and the robustness against noise. Accordingly, multiple gearbox faults can be well diagnosed for the first time in the literature. Finally, this paper concludes with a simplified version of the original proposal.

Research on Transformer Multiple Fault Detection Based on Pulse Coupled Injection

Abstract The fault diagnosis method of winding deformation based on pulse coupling injection is becoming more and more popular due to its advantages of online detection. In order to further improve the method, a pulse generator with adjustable parameters is developed in this paper to provide stable excitation. Its amplitude is adjustable from 600v to 5000v, pulse width is adjustable from 100 to 1000ns, and frequency is adjustable from 1 to 20kHz. In the diagnosis of winding deformation, the current research mainly focuses on the single deformation fault, and rarely involves the mixed deformation fault. Therefore, based on the pulse coupling injection method, field experiments are carried out to verify the feasibility of the method and find out the important reasons affecting the curve. At the same time, the detection experiment of multi-winding deformation fault superposition of a transformer is carried out, and the RVM frequency divider is used to accurately diagnose the mixed fault types of transformer windings.

Fault-Tolerant Control Study of Four-Wheel Independent Drive Electric Vehicles Based on Drive Actuator Faults

Failure of any of the drive systems in a Four-Wheel Independent Drive (4WID) electric vehicle may affect the control performance and driving safety of the whole vehicle. Therefore, in this paper, a fault-tolerant controller (FTC) for 4WID electric vehicles considering drive actuator failures is proposed. First, a comprehensive characterization of multiple fault types is achieved by establishing a generalized fault model and designing a comprehensive fault factor. Second, based on the comprehensive fault factor, an LPV model with faults is constructed. Further, a fault-tolerant controller based on LPV/H∞ output feedback is designed by combining the weighting function. Finally, the effectiveness of the FTC in this paper is verified by simulation and hardware-in-the-loop (HIL) experiments. The experimental results show that the FTC designed in this paper can improve the stability of the vehicle traveling while ensuring tracking accuracy when the drive system fails.

Digital Twin Modeling for Hydropower System Based on Radio Frequency Identification Data Collection

The safe and steady operation of hydropower generation systems is crucial for electricity output in the grid. However, hydropower stations have complicated interior structures, making defect detection difficult without disassembly inspections. The application of digital modeling to hydropower stations will effectively promote the intelligent transformation of hydropower stations as well as reduce the maintenance costs of the system. This study provides a model of the power generating and transmission system for hydropower plants, with an emphasis on primary equipment and measured data. The model utilizes PSCAD to digitalize state response in hydropower plants with various short-circuit faults. The fault information is identified and learned using the Adaptive Time–Frequency Memory (AD-TFM) deep learning model. It is demonstrated that our proposed method can effectively obtain the fault information through radio frequency identification (RFID). The accuracy of the traditional method is 0.90, while the results for AD-TFM show a fault classification accuracy of 0.92, which is more than enough to identify multiple fault types compared to the existing methods.

Impact of Multiple Faults on the Maximum Credible Ground-Motion Parameters of Large Earthquakes at a Near-Field Site

The ground-motion simulation of regional-specific earthquake scenarios is crucial for the seismic design of key facilities. Herein, we considered parameter uncertainty in ground-motion simulations and the impact of multiple faults when determining the maximum credible ground-motion parameters of large earthquakes at a near-field dam. The source models of the Daju–Lijiang, Xiaozhongdian–Daju, and Longpan–Qiaohou faults were established based on geological and geophysical data. Although the method for identifying asperity is not yet mature and still faces many difficulties, it provides an opportunity to identify the non-uniform slip distribution on the rupture plane by earthquake scenarios. A multi-scheme stochastic finite-fault simulation method was then used to estimate the minimum; mean; maximum; and 50th-, 84th-, and 95th-percentile values of the peak ground acceleration and pseudo-spectral acceleration response spectra. The results showed that the Longpan–Qiaohou fault can generate the largest ground-motion parameters compared with the other two faults. Moreover, this result was supported by the statistical analysis of the results of twelve thousand simulations of these three faults. Thus, it can be concluded that the maximum credible ground-motion parameters are represented by the 84th-percentile pseudo-spectral acceleration response spectrum of the Longpan–Qiaohou fault. This finding will benefit the seismic safety design of the target dam. More importantly, this multi-scheme method can be applied to other key facilities to obtain reasonable ground-motion parameters.

Multi-target domain adaptation intelligent diagnosis method for rotating machinery based on multi-source attention mechanism and mixup feature augmentation

Intelligent diagnostic methods for identifying faults in rotating machinery, based on domain adaptation, have garnered significant attention. However, most current domain adaptation approaches are primarily designed for single-source domain and single-target domain (SSST) applications. There is a dearth of domain adaptation approaches tailored for single-source to multi-target domains (SSMT). In contrast to SSST, SSMT takes a more comprehensive approach by considering relationships across multiple target domains. This approach offers increased versatility and a broader range of potential applications. To address this, an end-to-end multi-target adversarial subdomain adaptation method is proposed that leverages attention mechanism data fusion and mixup feature augmentation. Firstly, the attention mechanism is used to fuse data from different sensors in both channel and spatial dimensions. Subsequently, a mixup-based feature augmentation method is proposed for multi-target domain adaptation. The method is combined with subdomain adaptation and domain discrimination to further reduce the distributional differences between the source and various target domains while relieving the overfitting problem during domain adaptation. Finally, with the above approach, a robust and stable model for multiple target domain fault diagnosis can be trained. Our experimental results illustrate that our approach has higher accuracy and robustness compared to several popular domain adaptation methods.

Adaptive fault-tolerant control for a class of nonlinear multi-agent systems with multiple unknown time-varying control directions

In this paper, we investigate the consensus tracking control for a class of heterogeneous multi-agent systems with multiple unknown time-varying control directions and unknown direction actuator faults. Different from existing work, the directions of the multiple time-varying control coefficients are subject to fault-induced sign-switching. To address this challenge, a series of high-order Lyapunov functions and differentiable functions are introduced to avoid non-integrable terms. Then, a novel contradiction statement and some Nussbaum functions are used to handle the summation of multiple unknown control coefficients with time-varying amplitudes and directions. Meanwhile, a novel distributed observer with quantized communication is introduced to track a reference trajectory with unknown dynamics. It is shown that all closed-loop signals are globally uniformly bounded and the tracking errors converge to residual sets that can be made arbitrarily small. Simulation results illustrate the effectiveness of the proposed scheme.

Roles of heat and stress transfer in triggering fault instability in conjugate faulted reservoirs

The presence of multiple conjugate but non-intersecting faults in geothermal reservoirs presents issues related to fault interaction in the presence of complex coupled thermo-hydro-mechanical (THM) processes in influencing the triggering of seismicity. We examine alternate strategies in stimulating such a conjugate-faulted geothermal reservoir analogous to that hosting the Mw 5.5 Pohang earthquake (2017). We evaluate the response of the reservoir to both short-term stimulation (1y) and long-term production (10y), both with and without thermal effects – for a large fault (F1) adjacent to a non-intersecting smaller fault (F2) and in a reverse faulting stress regime. Results suggest that the slip on either fault impacts the stress state on the other fault through stress transfer. Reactivation of the minor fault (F2) transfers stress towards to the upper part of primary fault (F1), inducing instability. The slip of the major fault is delayed by positioning the location of injection away from the junction between the two faults – decreasing the injection depth from 4087.5 m to 3712.5 m delays the time to slip by 2.61 y. Furthermore, thermal stress plays a decisive role in prompting late-stage fault reactivation for long-term fluid circulation where pore pressures have already reached steady state. The pattern of thermal unloading follows the path of fluid transport and heat transfer along the faults. Overall, this study not only advances our understanding of mechanisms of injection-induced fault instability in EGS reservoirs with multiple and closely-interacting faults, but also provides insights into how different injection strategies can delay or mitigate induced seismicity.

Robust multiple fault detection and estimation approaches based on adaptive finite‐time observer for nonlinear systems

AbstractThis article investigates robust multiple fault detection (FD) and fault estimation (FE) schemes, based on adaptive finite‐time observer (AFTO), applied to nonlinear systems with disturbances, actuator faults, and sensor faults. The AFTO is designed, and a parameter adaptive law is constructed to continuously optimize the residual based performance function to improve the AFTO robustness. The schemes are able to simultaneously detect and estimate unknown states, disturbances, and faults. The AFTO's design, finite‐time convergence and stability conditions are guaranteed in terms of linear matrix inequalities. Finally, simulation results, based on two typical practical systems, are given to illustrate the effectiveness of the proposed approaches. The simulation results also show that the designed AFTO can offer faster convergence speed, higher estimation accuracy, and stronger robustness than conventional observer and sliding mode observer.

A Virtual Reality Environment Based on Infrared Thermography for the Detection of Multiple Faults in Kinematic Chains

Kinematic chains are crucial in numerous industrial settings, playing a key role in various processes. Over recent years, several methods have been developed to monitor and maintain these systems effectively. One notable method is the analysis of infrared thermal images, which serves as a non-invasive and effective approach for identifying various electromechanical issues. Additionally, Virtual Reality (VR) is a burgeoning technology that, despite its limited use in industrial contexts, offers a cost-effective and accessible solution for the training and education of industrial workers on specialized engineering subjects. Nevertheless, most virtual environments are based on numerical simulations. This paper presents the design and development of a Virtual Reality training module for the detection of fourteen electromechanical fault cases in a kinematic chain. The VR training tool developed is based on actual thermographic data derived from experiments conducted on an authentic kinematic chain. During these experiments, thermal images were captured using an low-cost infrared sensor. The thermographic images were processed by calculating the histogram and fifteen statistical indicators, which served to differentiate fault cases in the VR application. A comprehensive evaluation was carried out with a group of vocational students specialized in electrical and automation installations to determine the effectiveness and practicality of the VR training module.

Incremental fully actuated system approach-based prescribed-time fault-tolerant formation control of helicopters under multiple faults

Helicopter formation is characterized by intricate dynamics, complex mechanics, and challenging scenarios, raising the necessity for agile, robust, and convenient controller design. This paper explores the prescribed-time fault-tolerant formation control of multiple helicopters through the incremental fully actuated system approach, which addresses multiple faults in sensors and swash plate struts. First, the helicopter model encompassing aerodynamic, flapping dynamic, swash plate dynamic, actuator faults, and sensor faults is established and further partitioned into attitude and position subsystems. Then, in the attitude subsystem, a prescribed-time attitude controller based on the incremental fully actuated system approach is proposed to track the desired attitude with robustness against actuator faults, aerodynamic drags, inter-axis coupling, and non-linearity. Next, in the position subsystem, the faults of velocity and position sensors are estimated and compensated through the prescribed-time fault-estimation observer, and a prescribed-time formation controller through the incremental fully actuated system approach is designed to achieve distributed leader-follower formation control. A Task-Fault dual-driven scheduling mechanism of the parameters of the prescribed-time technique is developed to promptly activate the prescribed-time function, and the control scheme is proven to be prescribed-time convergent via Lyapunov stability theory. Finally, numerical simulations are conducted to illustrate the efficiency of the algorithm.

Advanced impedance mismatch technique for detecting faults in photovoltaic systems

AbstractThis paper presents a comprehensive exploration of the Advanced Impedance Mismatch Technique (AIMT), a novel approach designed for the accurate detection of simultaneous and varied faults within photovoltaic (PV) systems. This investigation integrates a spectrum of fault detection strategies, pinpointing reflectometry as a notably effective tool. Despite its utility, conventional reflectometry applications face critical constraints, notably the limitation to identify only the primary fault location within a PV array and the inability to distinguish between different fault types. This work introduces an innovative mathematical model that estimates the impedance of PV modules, enhancing the reflectometry method to enable the precise identification and localization of multiple defective modules within a string. The proposed technique exhibits a remarkable sensitivity to detect slight impedance differences between a functional PV string and one with defective modules. The validity of the AIMT's mathematical model is corroborated through simulation experiments on a string of seven PV modules afflicted with multiple simultaneous faults. These experiments rigorously evaluate the technique's accuracy in pinpointing the locations of defective modules within a PV string. The outcomes of our proposed ‐times faster AIMT reveal a strong concordance between the simulated reflective signals and the impedance values forecasted by the model, highlighting the proposed method's proficiency in the detailed detection and diagnosis of progressive faults within PV systems.