Simultaneous Faults Research Articles

Multi-fault diagnosis of industrial rotating machines using advanced sliding window and WSST-CNN

Accurate fault diagnosis of rotating machines is the most important part of industries, as it helps manage the workforce and inventory within the stipulated time. In industries, when the fault occurs at one component, that may propagate to other components due to excessive speed and load, leading to the occurrence of a simultaneous fault condition. Identification of multi-component faults is relatively more difficult than single-component faults due to the possibility of characteristic frequency overlapping that is observed during signal processing. This work considered various single and complex multi-component fault conditions of a rotating machine, and data is acquired at different speed and load conditions using a standard acoustic array setup. The signals are then converted into signal segments using a continuously moving advanced sliding window. From the extracted segments time-frequency spectrums are generated by using Wavelet Synchrosqueezed Transform (WSST) followed by the training of deep neural networks. The performance analysis of the model is carried out, and it is found that the proposed methodology helped diagnose multi-faults at different speed and load conditions. It mitigates the complexity of high-end signal processing techniques for distinguishing the fault characteristics. This study will be beneficial for industries to diagnose multi-faults in incipiently.

Online Diagnosis of Multiple-Switch Simultaneous Faults in SRM Drives Based on Current Errors

Online Diagnosis of Multiple-Switch Simultaneous Faults in SRM Drives Based on Current Errors

Event-triggered distributed consensus control of heterogeneous multi-agent system under communication and actuator faults

In this paper, a heterogeneous leader-follower multi-agent system is studied under simultaneous time-varying communication faults and actuator faults. First, the state of the leader is modeled as the closed-loop reference model (CRM) where the states of the direct-connected followers are fed to the leader to improve the leader-follower tracking capability. An event-triggered communication mechanism is designed for the agent information sharing among its neighbors so as to reduce the communication burden. The designed event-triggered mechanism, capable of adjusting the triggering interval threshold within a certain range, can be applied in practical multi-robot collaborative control to accommodate the varying requirements for different triggering intervals. Considering the time-varying communication link failures, a new distributed event-triggered observer is designed for each follower to estimate the system states so as to reduce the state error, whereas the adaptive distributed event-triggered estimators are further designed for the non-directly connected followers to estimate the coefficient matrix of the leader system. Then, an estimator is designed for the actuator fault estimation to mitigate their impact on the system consistency. Finally, an adaptive control strategy is proposed to ensure the consistency of the leader-follower system under the time-varying communication link faults and actuator faults. It is also shown that Zeno behavior is excluded for each agent and the effectiveness of the proposed adaptive event-triggered control strategy is verified on a heterogeneous multi-agent system.

Adaptive signal fusion for swashplate pump fault detection using bidirectional long short-term memory and wavelet scattering transform

In construction equipment, swashplate pumps are vital for uninterrupted operations. However, these pumps often experience multiple simultaneous faults. Current detection methods typically rely on a single signal, insufficient for accurately identifying multiple faults in this hydraulic system. To improve accuracy, we utilize multiple signals that provide comprehensive information, using an adaptive signal-level fusion technique. This method involves analyzing pressure, shaft torque, and swashplate torque signals to identify potential faults. The study employs a Bidirectional Long Short-Term Memory (BiLSTM) model to analyze each type of signal, assessing the model's ability to classify different faults. Once detection accuracy for each fault type is assessed, we propose using a weight matrix to adaptively fuse signals based on their assigned weights. Additionally, the Wavelet Scattering Transform (WST) is utilized for feature extraction from signals with low variance, and Bayesian optimization is applied to find the optimal settings for the BiLSTM model. The proposed approach is tested under various scenarios, including the impact of white Gaussian noise, to evaluate its effectiveness and stability. The results show that the adaptive signal-level fusion approach outperforms traditional methods and individual signal analyses in accurately and robustly detecting swashplate pump faults, highlighting its potential to significantly improve the reliability of construction equipment.

A novel multi-label classification deep learning method for hybrid fault diagnosis in complex industrial processes

Hybrid Fault Detection and Diagnosis, encompassing both individual and simultaneous faults, is an important solution needed in chemical process safety practice. We systematically examine the two perspectives of simultaneous faults in previous studies: multi-class and multi-label classification, and highlight the limitations of the former while demonstrating the efficacy of the latter. Then, a novel multi-label classification Hybrid Fault Transformer (mcHFT) model was put forward to address hybrid faults. Our model is capable of learning not only intrinsic features of individual faults but also their coupled relationships. Importantly, this work constitutes the first comprehensive evaluation of Hybrid FDD on the Tennessee Eastman (TE) process to our knowledge. The mcHFT model significantly enhances key performance indicators over existing models and introduces an adaptive strategy to reduce false positives. The dataset developed for this research is made available under an MIT license, contributing a valuable resource for future exploration in this field.

Simultaneous state and fault estimation for a class of nonlinear Markov jump systems by distributed fault-tolerant observers

This paper investigates the estimation problem of a class of nonlinear Markov jump systems with actuator and sensor faults. The main goal is to design a distributed fault-tolerant observer to estimate system states and actuator faults simultaneously. Firstly, a novel distributed observer network is constructed to compensate the missing information of unobservable nodes of the system. Next, a class of new redundant sensors are set up on each distributed observer node to obtain more output measurement samples. More importantly, when some of the mentioned sensors occur faults, an index of sensor heath level is constructed to characterize the quality of the faulty sensor information. Further, a novel algorithm is designed to mask low quality output information and filter out relatively healthy one automatically. Based on the selected healthy output information, the system state and actuator fault are estimated in the case of sensor failure. Finally, an example is provided to demonstrate the effectiveness of the proposed method.

Command filtered‐based fixed‐time fault‐tolerant tracking control for nonlinear systems

AbstractAs for a class of strict‐feedback nonlinear systems with simultaneous sensor and actuator faults, the article focuses on the problem of fixed‐time fault‐tolerant control. Both fixed‐time command filters and a compensation mechanism are utilized to cope with these issues of differential explosion and filter errors. Meanwhile, through the fusion of fixed‐time control, fuzzy logic systems, adaptive control and the backstepping technology, a ‐step design scheme is presented. Our proposed controller ensures the fixed‐time convergence of all the signals in the system even with sensor/actuator faults, and allows the designers to acquire the upper limit value of the fixed convergence time only by tuning the design parameters. And this scheme takes into account rapidity, adaptability and fault tolerance of the system simultaneously. Based on the numerical simulation analysis, it is shown that the better tracking performance can be achieved. So it is concluded that our strategy is effective.

Dynamic partitioning of island smart distribution systems in emergencies

AbstractWhen a severe fault occurs in the distribution network, all or parts of it may be disconnected from the upstream network. Partitioning of these islanded areas is a solution to supplying the affected loads. Due to the variable nature of loads and renewable distributed generation (DG), the static model of partitioning with a fixed nature during island operation cannot be suitable. Therefore, in this article, considering the variable nature of loads and renewable distributed generation, a dynamic model is presented for the island partitioning to restore more valuable loads, which is suitable for quick decision‐making in emergencies. Also, a method for deciding on the mode of charging and discharging storage systems in emergencies is proposed. This model considers time limitation, uncontrollable DGs, controllable DGs and their control, controllability, and priority of loads, tie‐switches, storage systems, simultaneous faults, different situations of unintentional islanding of the distribution network, position of switches, and variable nature of loads and distributed generations. So, it is more comprehensive than the previous methods. Applying the proposed model to the modified IEEE 69‐bus system with controllable and uncontrollable generation and storage systems assuming different scenarios shows the effectiveness of the proposed scheme.

Robust distance protection for cross-country faults in power grids with high penetration of inverter- based resources

Cross-country faults can adversely affect the operation of protection devices. Relays whose pickup is based on overcurrent or impedance elements are more likely to fail during cross-country faults. The failure can be expressed as an unnecessary trip of all phases during a single-phase fault or even blocked relay functions. Relays may also fail to operate due to low fault currents, which is the case when power grids make use of many inverter-based resources (IBR) or when two simultaneous faults occur in the same phase and in different locations. This paper proposes a Recursive Discrete Stockwell Transform (RDST) method for addressing the mentioned challenges. The energy content computed by the RDST is used for fault detection and faulty phase selection. A robust distance relay model is proposed to ensure correct relay performance and is combined with a directional and an impedance trajectory module. The proposed method performance is thoroughly evaluated by applying real-time simulations for 6480 different cross-country faults, including three repetitions, four different generators (Synchronous generators, Wind turbine type-III, Wind turbine type-IV), two rating powers, three different fault distances, five different types of faults, and six grid codes. Ninety cases are also tested with real devices in hardware in the loop. Simulation results confirm the method's ability to detect different fault types, even during low fault currents, and to ensure high accuracy in phase selection.

Fault estimation for a class of nonlinear algebro-differential parameter-varying systems

This paper presents a methodology to simultaneous estimate state variables and actuator fault for descriptor nonlinear parameter-varying systems. This class of systems is composed of an interpolated parameter-varying linear part and Lipschitz nonlinear one. The proposed observer structure allows some robustness and steady state accuracy in face to parametric uncertainties. The conditions of existence and stability are given in terms of LMIs (Linear Matrix Inequalities). The obtained results are illustrated on the heat exchanger system with two countercurrent cells model with actuator fault.

Effects of simultaneous soft faults in a reversible and variable-speed air-to-water heat pump

The widespread adoption of air-to-water heat pumps is driven by their high heating efficiency, with some systems offering reversibility for cooling applications. Despite their benefits, these systems are susceptible to soft faults such as undercharge, overcharge, and outdoor unit airflow restrictions (condenser fouling in cooling mode or evaporator fouling in heating mode). Detecting and addressing these faults before they escalate into hard faults is critical. While previous experimental studies have focused on residential heat pumps with imposed faults, most were conducted on air-to-air systems, primarily in cooling mode and with fixed-speed compressors. This study addresses the gap by investigating a reversible air-to-water heat pump employing R290 and a variable-speed compressor. The experimental work consists of imposing faults of undercharge, overcharge, condenser and evaporator fouling, individually and in combination. The investigation analysis considers the system’s behavior under these faults in cooling and heating modes and how the control strategy (subcooling or superheat) and a compressor suction accumulator affect it. In cooling mode, the combination of 85% undercharge and 50% condenser fouling worsens the EER up to 39% if the superheat is controlled and up to 10% if the subcooling is controlled. In heating mode with subcooling control, 85% undercharge combined with a 50% evaporator fouling can worsen the COP by 32%. However, there are cases where those same fault levels only impact the COP by 4% due to the charge requirements of the condition and that the excess charge is stored in the compressor suction accumulator. As compressor speed can significantly affect the refrigerant charge requirements of the system, this work shows its potential for fault detection, particularly for undercharge. These findings can provide a basis for developing Fault Detection and Diagnosis methodologies for reversible air-to-water heat pumps with variable-speed compressors.

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.

Design of finite‐time convergent functional observer for descriptor systems: Application to fault estimation

AbstractIn this paper, a finite‐time functional observer design for linear time‐invariant descriptor systems is presented. A generalized observer based on additional degrees of freedom is developed. This observer can be considered of a general structure since the existing proportional observer (PO) and proportional‐integral observer (PIO) constitute its particular cases. The main contribution is to generate a finite‐time convergent functional observer for descriptor systems by combining two generalized functional observers. The obtained results are applied to the simultaneous state, sensor, and actuator faults estimation. A numerical example is given to illustrate the approach. The obtained results show that the observer is robust in presence of uncertainties, compared to its particular cases.

Deep transfer learning strategy in intelligent fault diagnosis of gas turbines based on the Koopman operator

The gas turbine engine is a predominant prime mover in the transport and energy sectors, and ensuring its reliable operation holds paramount significance. While intelligent fault diagnosis (FD) approaches have seen successful advancements within the gas turbine FD landscape, many existing methods operate under the assumption of identical health states during both data collection and the FD process. Moreover, most previous studies have overlooked the diagnosis of both sensors and actuators. Another critical challenge lies in isolating simultaneous and multiple faults and providing compromising FD performance, especially in the face of continued system performance degradation. Aiming at these problems, this study develops a novel unsupervised data-driven FD strategy based on leveraging the potential of transfer learning and the Koopman operator. A deep neural network-based transfer learning framework is proposed for realizing a precise adaptive linear model called the deep transfer linear (DTL) model enabling reliable prediction of the system’s behavior in various situations and designing structured fault residuals. To this end, a deep neural network framework is used to obtain a precise Koopman model realization using the richly collected data in the source domain. Subsequently, the realized model is fine-tuned for the target domains associated with the degraded system, mitigating the adverse effects of domain shift and addressing the rich data scarcity problem in the target domain. In addition, the dedicated and generalized residual sets are designed and generated employing the geometric approach for fault isolation and a decision-making analysis is developed to diagnose simultaneous faults. The reliability of the proposed strategy is demonstrated through various experiments in the presence of noise and performance degradation, and a comparative performance analysis is conducted between the proposed strategy and another data-driven method showcasing the superiority of the proposed approach.

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

A temporal and spatial EWMA-based monitoring and diagnosing approach for image data

ABSTRACT With the increasing application of machine vision systems, it is crucial to monitor images of industrial products for potential anomalies in the production process. In current literature, the research interest mainly lies in either detection of fault occurrence or identification of fault size/location. Simultaneous fault detection and identification, however, have received limited attention. This paper focuses on fault detection and identification based on images of products whose quality is characterized by uniformity or conformity to a specific pattern. A temporal and spatial EWMA-based control charting method is proposed, where spatial correlation is incorporated to efficiently screen out the suspicious region. Results from extensive simulations show that the proposed control chart is superior especially in detecting and estimating small-area faults, and performs competitively well in monitoring and diagnosing large-area faults. An experimental case is analyzed to illustrate the application of the proposed method in practice.

Distributed Event-Triggered Simultaneous Fault Detection and Consensus Control for Multiple Lur'e Systems

Distributed Event-Triggered Simultaneous Fault Detection and Consensus Control for Multiple Lur'e Systems

A reasoning approach-based pattern graph for analyzing the risk level of correlations among catenary components considering time distribution

The catenary system is a crucial part of the traction power supply system, consisting of multiple components interconnected through mechanical coupling. To reveal the risk characteristics of fault propagation between catenary components, this paper presents a reasoning approach-based pattern graph for analyzing the risk level of correlations of components considering time distribution from a statistical perspective. Initially, we define simultaneous fault correlations and sequential fault correlations among faulty components based on the different time distributions to capture the risk propagation features among components. Then, the MYCIN model is introduced to construct a certainty factor considering the belief and disbelief of fault correlations to calculate the risk levels of simultaneous/sequential fault correlations. Finally, we develop a risk pattern graph by linking the virtual paths to assess the risk level of inexplicit correlations hidden within the historical dataset. Simulation results, conducted based on the fault database of the Chengdu Railway Bureau, show the proposed method can effectively assess the risk level of correlations among faulty components to reveal the fault propagation features, which provides valuable references for proactive maintenance.

Fault Tolerance Exploration and SDN Implementation for de Bruijn Topology based on betweenness Coefficient

This article considers the method of analyze potentially vulnerable places during development of topology for fault-tolerant systems based on using betweenness coefficient. Parameters of different topological organizations using De Bruijn code transformation are observed. This method, assessing the risk for possible faults, is proposed for other topological organizations that are analyzed for their fault tolerance and to predict the consequences of simultaneous faults on more significant fragments of this topology.

Universal Adaptive Differential Wavelet Energy-Based Protection Scheme for an AC Microgrid

A protection engineer’s top priorities are the detection of faults and the recognition of faulty phases. Modern distribution networks are interconnected; thus, any breakdown poses a hazard to the entire system. One of the challenges with microgrids is developing an effective protection scheme. In this paper, a new scheme has been proposed that effectively detects faults in the grid-connected, as well as off-grid modes also radial and looped topology of the microgrid. The scheme can adapt to the system changes and even detects the high resistance faults in a low-voltage distributed feeder. Moreover, various non-fault events such as load switching, capacitor switching, DG outage, section cutoff, and external faults have also been considered for evaluation. The performance of the scheme has also been tested for changes in fault inception angles, fault types, fault locations, simultaneous, evolving, and composite faults. The suggested method has also been tested using real-time data from the OPAL-RT simulator. The results demonstrate that the suggested scheme is suitable for real-time practical applications for the protection of microgrid feeders. Further, a comparative study has been done to prove that the proposed scheme is better than several existing protection schemes considered in this paper.