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Fault diagnosis of processors plays a critical role in assessing the reliability of multiprocessor systems. The interconnection network's diagnosability is an important metric for measuring its self-diagnostic capability which has been extensively studied in many novel mutiprocessor systems. Permanent fault diagnosability for many mutiprocessor systems has been determined; however intermittent fault diagnosability is hard to obtain due to its crypticity. In this paper, we focus on the problem pertaining to the diagnosability in the intermittent fault situation. First, by learning the characteristics of intermittent fault diagnosis in PMC model, we propose some theorems and lemmas for intermittent fault diagnosability. Secondly, we give the range of intermittent fault diagnosability of product network. Lastly, by adopting the theorems, we propose an Auto-IFD algorithm to find the intermittent faults of the hypercube network and conduct experiments to verify the theorems. [ABSTRACT FROM AUTHOR]

Summary: In this article, the problem of intermittent fault (IF) detection is investigated for nonstationary processes in the multivariate statistics framework. By combining the moving window technique with maximum likelihood estimation (MLE), the moving window Wald‐based control chart is proposed to realize the detection of IFs in nonstationary processes. The computational efficiency and the convergence properties are discussed for the designed iterative algorithm of MLE. Then, necessary and sufficient conditions are presented to guarantee the detectability of IFs with the consideration of window lengths. Moreover, the alarm delays are analyzed for the appearance and disappearance of IFs. In virtue of the above analysis, the optimal window length is derived by minimizing the supremum of alarm delays. In order to estimate the time of IFs' appearance and disappearance, an algorithm is designed with the inspiration of simulated annealing strategy. Finally, a simulation on rotary steerable drilling tool system is provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Intermittent faults are widely present in aviation electronic devices, especially in various electrical connectors. It is usually hard to diagnose the source of the intermittent faults, which brings a huge challenge to the repair and maintenance of equipment. This paper focuses on the intermittent faults in typical aviation electrical connectors activated by shock test. The transient arc wave is observed on a nanosecond scale during the occurrence of the intermittent faults. An arc signal model is constructed to analyze the impact factors of the signal. Based on the arc wave characteristics, further intermittent fault diagnostic analyses are conducted on four types of connector components: damaged solder joints, cracked pin connections, loose wire connections and worn electrical connectors. The effective arc wave components of the raw signals are extracted using Variational Mode Decomposition (VMD), and a comparison is made between traditional diagnostic method and CNN-based deep learning method. The results show that the combination of VMD-CNN-SVM achieves the optimal diagnostic effect. The diagnostic results reflect that the proposed arc signal features are suitable for diagnosing intermittent faults in connector components. [ABSTRACT FROM AUTHOR]

For the objective of improving the reliability of converters in electric drives, research on a method for early detection of intermittent open-circuit faults of power valves is reported in this article. Intermittent open circuit condition is the incipient form of power valve open-circuit fault in power converters. Prompt detection of this fault allows for timely remediation of permanent open circuit defects that is a commonly subsequent process. This study introduces an investigation of this fault, which occurs in the voltage source inverter of induction motor drives. Intermittent faults are created through interference with the control pulse of the power valve. Wavelet transform with the Mexican hat mother function is utilized for signal processing. Appropriate ranges of the scale are selected to obtain a high magnitude of the wavelet coefficient at faulty instants. The analysis for the direct current recorded at the DC-link in simulation and the electromagnetic signal measured at the DC-bus of the inverter can be effectively used for the fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

Scan chains are essential for enhancing the testability of semiconductor circuits. While scan chains enhance the testing capability, defects can also occur in scan chains due to the hardware overhead caused by scan chains. To prevent a decrease in yield due to such defects, scan chain diagnosis is widely used in semiconductor manufacturing as an important system. Particularly, with the increasing complexity of semiconductor circuits, there is a growing necessity for the diagnosis of intermittent faults. Since the distinct patterns are shown in test results for intermittent faults compared to permanent faults, a decrease in diagnostic accuracy is caused by the occurrence of intermittent faults. To address this problem, this paper introduces a new deep-learning-based scan-chain diagnosis method, designed to diagnose both intermittent and permanent faults. The proposed method introduces two new concepts for diagnosing not only permanent but also intermittent faults. One is a CNN optimized for scan chain diagnosis, and the other is newly optimized input data tailored for this CNN architecture. The proposed CNN architecture is composed of multiple layers centered around the modified inception module adapted for scan chain diagnosis, maintaining spatial and local information while enabling additional feature extraction. Furthermore, the new input data is multi-channel data composed of subset failure vectors (SFVs), integer failure vectors (IFVs), and fan-out vectors, allowing for the maximization of CNN characteristics. The experimental results demonstrate that diagnostic accuracy for intermittent faults is significantly improved by the proposed method compared to previous works.

During the operation of large-scale multi-processor systems, intermittent failures have gradually become one of the major reliability challenge due to their unsustainable nature. The presence of intermittent faults usually complicates the diagnosis problem and may lead to incomplete diagnosis. As a result, the intermittent diagnosability of many interconnection networks are still uncertain, especially intermittent diagnosability under the MM* (Maeng and Malek) model. In this paper, under some conditions, we establish a new relationship between intermittent diagnosability and the minimum degree of a graph. As applications, the intermittent diagnosability of triangle-free graphs and graphs whose connectivity is equal to their minimum degree are determined, including BC (bijective connection) networks, exchanged hypercubes, exchanged crossed cubes, star graphs, folded hypercubes, augmented cubes, k-ary n-cubes, bubble-sort star graphs, split-star networks, etc., under the PMC (Preparata, Metze, and Chien) model and MM* model. [ABSTRACT FROM AUTHOR]

This paper is focused on the finite-time sliding mode fault-tolerant control problem for a class of discrete networked control systems with intermittent fault and time-varying delay under the event-triggered scheme. The intermittent fault is described as a randomly occurring fault obeying a Bernoulli distribution. The event-triggered mechanism is introduced to determine the transmission of signal, which can conserve communication resources and reduce network burdens. By means of the sliding mode observer method, a linear sliding mode function is constructed by employing the estimated state and fault information. Based on Lyapunov stability theory, sufficient criteria are derived, which can ensure the closed-loop system is finite-time bounded with pre-specified H∞ performance requirement satisfied. Then, the event-triggered observer-based sliding mode fault-tolerant controller is established to force the system state trajectories onto a pre-set sliding mode region. Afterward, the solvability of the non-convex problem is studied by testifying the feasibility of a minimization problem. Finally, a practice simulation result is provided to illustrate the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN. [ABSTRACT FROM AUTHOR]

In current days, sensor nodes are deployed in hostile environments for various military and commercial applications. Sensor nodes are becoming faulty and having adverse effects in the network if they are not diagnosed and inform the fault status to other nodes. Fault diagnosis is difficult when the nodes behave faulty some times and provide good data at other times. The intermittent disturbances may be random or kind of spikes either in regular or irregular intervals. In literature, the fault diagnosis algorithms are based on statistical methods using repeated testing or machine learning. To avoid more complex and time consuming repeated test processes and computationally complex machine learning methods, we proposed a one shot likelihood ratio test (LRT) here to determine the fault status of the sensor node. The proposed method measures the statistics of the received data over a certain period of time and then compares the likelihood ratio with the threshold value associated with a certain tolerance limit. The simulation results using a real time data set shows that the new method provides better detection accuracy (DA) with minimum false positive rate (FPR) and false alarm rate (FAR) over the modified three sigma test. LRT based hybrid fault diagnosis method detecting the fault status of a sensor node in wireless sensor network (WSN) for real time measured data with 100% DA, 0% FAR and 0% FPR if the probability of the data from faulty node exceeds 25%.

A novel fault-tolerant electric drive system based on dual-winding permanent magnet motor (DWPMM) has been proposed to enhance the reliability of electric drive system. The proposed electric drive system with strong fault-tolerant capability not only possesses the superiority of high reliability, but also can reduce the cost of drive system and adapt to single power supply. Due to temperature mutation, line aging, electromagnetic interference, and low hardware reliability, the intermittent open circuit fault will be caused in power switches by the failure of drive signal. The power switch intermittent open circuit fault is a fault between normal state and complete fault state, which is not a complete open circuit fault and cannot be diagnosed by the conventional fault diagnosis methods. The fuzzy logic means to imitate the way of judgment and reasoning of uncertain concepts in human brain, and reason to overcome the fuzzy information problem which is difficult to solve by regular methods. In this paper, the power switch open circuit fault diagnosis strategy for novel fault-tolerant electric drive system based on fuzzy logic is proposed and investigated. Through theoretical analysis and verification, the research results indicate that the proposed power switch open circuit fault diagnosis strategy based on fuzzy logic can detect and locate the general and intermittent open circuit fault in real time and accurately.

In this paper, the problem of fixed-time active fault-tolerant control is studied for systems with sector-bounded nonlinearities, intermittent faults, and input saturation. Since intermittent faults appear and disappear randomly, a fixed-time scheme is considered in the active fault-tolerant control algorithm, composed of detection, isolation, estimation, and the control unit. Utilizing homogeneity-based observers, the fixed-time state estimation is available in the presence of unknown but bounded disturbances, and a fault diagnosis unit is proposed. An input saturation compensator is introduced to analyze the effect of input saturation, and its auxiliary variables are used in the reconfigurable control law. The fault-tolerant controller, which is constructed via the information provided by the fault diagnosis unit and saturation compensator, has two switching modes. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, the example of a heat control system is exploited to demonstrate the effectiveness of the developed active fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

The accurate detection and identification of intermittent cable faults are helpful in improving the reliability of the distribution system. This paper proposes intermittent fault detection and identification for distribution networks based on machine-learning (ML) techniques. For this reason, the IEEE 33 bus system is simulated in the radial and mesh topologies by considering all possible single- and three-phase electrical faults and limitations to collect high-resolution voltage and current waveforms. Moreover, this simulation investigates and considers various cases including low-impedance faults (LIFs) and high-impedance faults (HIFs) with a short and long duration. The collected data from the simulation are used for high-impedance intermittent fault detection, classification, and branch identification using eight supervised learning methods. A comparison between the accuracy and error of these ML classifiers shows that gradient booster (GB) and K-nearest neighbors (KNN) have the best performance for all three objectives. However, GB has a very high computation time compared to KNN.

In this article, the problem of fixed-time active fault-tolerant control is investigated for dynamical linear systems with intermittent faults and unknown disturbances. Unlike traditional active fault-tolerant control, fixed-time control is taken into account in this article since intermittent faults appear and disappear within a certain period of time. The entire active fault-tolerant control framework is composed of fault detection, fault isolation, fault and state estimation as well as the reconfigurable controller. Using the homogeneity-based observers, states and faults are well estimated and a fault diagnosis scheme is proposed for the sake of detecting and isolating intermittent faults in a fixed time. The fault-tolerant controller, which provides global practical fixed-time stability of the closed-loop system, has two switching states corresponding to the appearance and disappearance of intermittent faults. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, two examples are exploited to demonstrate the effectiveness of theoretical results. • Active fault-tolerant controller is designed for systems with intermittent faults (IFs). • Fault diagnosis unit for IF is realized with diagnosis time bounded in a fixed time. • The global practical fixed-time stability of the closed-loop system is guaranteed. [ABSTRACT FROM AUTHOR]

Intermittent faults are very common problems in various kinds of electronic devices. This article is focused on the intermittent fault phenomenon in degraded electrical connectors in vibration environments. To analyze the intermittent faults in degraded electrical connectors, a micro-state contact model is constructed to simulate the resistance variation behavior in vibration environments. The relationship between the intermittent fault behavior and the degradation state is studied, and a health indicator parameter is extracted from the signals to act as a symptom for degradation. A test platform is built to collect the intermittent fault signals from electrical connectors at different degradation states in vibration environments. The degradation curve of the health indicator is used to predict the remaining useful life (RUL) of the electrical connectors. The method of extreme learning machine (ELM) is adopted to conduct the prediction of RUL. The genetic algorithm (GA) is used to optimize the weight and bias parameters in ELM. The results reflect that the intermittent fault characteristic is a proper degradation index in the prediction of RUL for degraded electrical connectors. [ABSTRACT FROM AUTHOR]

Prognosis of failing components under intermittent faults is challenging since intermittent faults gradually deteriorate in duration and magnitude over time meanwhile show the stochasticity of fault appearance and disappearance. To address the problem, this paper proposes an intelligent prognosis method for intermittently faulty components in electric scooter based on dual degradation processes. Firstly, fault detection and isolation is used to identify discrete faults and isolate possible intermittent faults in continuous components, where the fault isolation performance under multiple faults condition is improved by developing an extended fault signature matrix. Secondly, the adaptive competitive swarm optimization is proposed to identify the magnitude, appearing and disappearing instants of each intermittent fault for faulty components. After that, the dual degradation processes are established with the aid of tumbling window (TW), where the duration degradation process describes the evolutionary trend of the ratio of fault duration to the length of duration-TW, while the magnitude degradation process captures the evolutionary trend of maximum feature in magnitude-TW. With dual degradation processes and predefined failure thresholds, the remaining useful life of the faulty component is jointly predicted, where the degradation speed difference between duration and magnitude is considered. Finally, the proposed methods are validated by experiment results. [ABSTRACT FROM AUTHOR]

This study investigates the nonlinear fault detection and control allocation of a hypersonic re‐entry vehicle with intermittent faults and disturbances. The uncertain external disturbances in the inner loop system are processed on the basis of the studied intermittent fault mode. A set of residuals is generated by sliding time windows, and two hypothesis tests based on the residual mean are proposed to detect the appearance and disappearance of faults. An augmented observer is used to estimate the faults subject to the L2‐gain disturbance rejection constraint. A nonlinear backstepping fault‐tolerant control is designed to generate the desired control torque and make the output attitude track the given signal. An optimization control allocation algorithm distributes the desired torque to aerodynamic surfaces and reaction control system jets. The stability of the schemes is analyzed by Lyapunov and linear matrix inequality theories. Numerical simulation and rapid prototyping results are presented to demonstrate the validity of the proposed methods. [ABSTRACT FROM AUTHOR]

In this article, a new event-triggering mechanism based prognosis scheme is proposed for discrete components in hybrid systems using degradation model selection. As an advantage over existing prognosis methods for hybrid systems, a more challenging case that multiple discrete components suffer from intermittent faults with unknown degradations is investigated. By introducing a hybrid structure where a centralized structure is used for fault detection and isolation and a distributed structure is adopted for fault estimation, the fault isolation performance under multiple discrete faults condition can be enhanced using the IMCSM and the fault estimation can be implemented with less computational burden using the decomposed submodels. With the aid of tumbling window, the total duration of intermittent fault in tumbling window can be treated as the intermittent fault feature. Since the degradation model describing the feature evolution is unknown in practice and may vary with the usage condition, an event-triggered prognosis method is proposed where a degradation model selection method is developed to find the best fit model under various usage conditions and thus remaining useful life prediction can be achieved. Experimental results on an electrical hybrid system validate the developed scheme. [ABSTRACT FROM AUTHOR]

Prognosis of discrete component with intermittent fault in hybrid systems is challenging since the component has only two states (i.e., ON and OFF) and no associated physical parameter in the model can quantify the degradation. This article aims to solve the discrete component prognosis problem under the model-based paradigm. First, the fault detection and isolation module help find the possible faulty discrete components. Based on the isolated possible faulty discrete components, Levy flight biogeography-based optimization is proposed to identify the faulty discrete component states, as well as the fault appearing and fault disappearing instants. Second, a Weibull function-based degradation model which can capture the duration evolution of intermittent fault of discrete component in observation window (OW) is developed using coordinate reconstruction approach, and the degradation model coefficients can be calculated from the fault identification results. After that, the concept of failure threshold for faulty discrete component is defined based on the ratio of fault duration to OW, which enables the prognosis of intermittent fault in discrete component. Finally, the proposed methodologies are validated by experiment results. Note to Practitioners—This article is motivated by the intermittent fault prognosis problem of discrete components (e.g., relays and hydraulic valves) in hybrid systems. Existing fault prognosis researches do not consider discrete component which is an important part of hybrid systems. For the intermittent fault prognosis of discrete component, the observation window (OW) concept and coordinate reconstruction (CR) method are proposed to establish the degradation model, and the ratio of fault duration to OW is used to define the failure threshold of discrete component. To show the effectiveness of the proposed methods, an application on a hybrid circuit system is considered. It is noted that the degradation pattern (e.g., increase of frequency or duration of intermittent fault) of discrete components may vary in different systems, while the degradation process can be quantified by the OW and CR methods developed in this article, which enables the prognosis of intermittent fault in discrete component for various hybrid industrial systems. The proposed approach can be applied to industrial hybrid systems if the following conditions are satisfied: 1) the hybrid bond graph model of the monitored system can be established, based on which the fault detection and isolation can be implemented and 2) the monitored system contains multiple discrete components suffering from intermittent faults whose appearing and disappearing instants can be identified by certain method. [ABSTRACT FROM AUTHOR]

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency. [ABSTRACT FROM AUTHOR]

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In this article, the finite-time sliding mode fault-tolerant control problem is addressed for discrete-time cyber-physical systems with intermittent faults and denial-of-service (DoS) attacks. To model the intermittent nature of faults, two sequences of shifted gate functions are employed to depict the fault appearance and disappearance moments. Considering that DoS attacks can prevent transmission of signals, resulting in measurement data losses over the sensor to observer channel, switching-like observer and sliding mode fault-tolerant controller are presented to accommodate both situations with and without attacks. The designed sliding mode fault-tolerant controller can ensure the finite-time reachability of the sliding surface such that it can enter the quasi-sliding mode domain within finite time from any initial state. Moreover, based on the Lyapunov theory, sufficient criteria are derived to guarantee the finite-time boundedness of the closed-loop systems with intermittent faults and DoS attacks in both the reaching and sliding motion phases. Finally, the effectiveness of the proposed sliding mode fault-tolerant control scheme is verified by a numerical example and a practical example. • A switching-like observer is constructed to estimate unknown state and fault information. • The designed reaching law can force the sliding surface function enter quasi-sliding mode domain within a finite time. • An upper bound on the number of arrival steps in the reaching phase is given. • The finite-time boundedness of the closed-loop systems is analyzed in both the reaching phase and the sliding motion phase. [ABSTRACT FROM AUTHOR]

Intermittent faults of analog circuits in the core area of electronic systems may induce false alarms of built-in test (BIT) system and even cause catastrophic accidents. Due to notable non-repeatability and high randomness, intermittent faults are arduous to be detected. In order to enhance the safety and reliability of electronic systems, an end-to-end diagnostic framework based on multi-scale enhanced convolution Transformer network (MSECTN) with novel token mixing and fusion strategy has been developed. Intermittent fault is a kind of random fault from global perspective, and there is also abundant local information inherent in the fault interval. Wholly composed of self-attention mechanisms, Transformer network possesses prominent capability of representing global features and modelling distant temporal associations, thus can be applied to identify intermittent faults. Meanwhile, introduce the local perception of convolution operation into Transformer network. One convolution embedding and convolutional multi-head self-attention module are designed to capture more abundant local details. Since the information at single scale cannot sufficiently reflect the circuit status, multi-scale convolution embedding with kernels in different sizes is performed to enrich the feature representation. Moreover, one token mixing strategy incorporating Fourier transformation, pooling operation, and depth-wise convolution is proposed to simultaneously model the frequency correlation and enhance the information interaction among spatially adjacent tokens. In order to excavate more inherent and distinguishing features, a novel token fusion strategy is designed to eliminate the effects of redundant feature information. Experimental results on two typical analog circuits demonstrated that the proposed approach can effectively identify different intermittent faults. [ABSTRACT FROM AUTHOR]

Real life experience has shown that intermittent faults are among the most challenging kinds of faults to detect and isolate, being present in the majority of production systems. Such a concern has made intermittent fault an active area of research in both discrete event and continuous-variable dynamic systems. In this paper, we present a review of the state-of-the art of intermittent fault diagnosability of discrete event systems modeled by finite state automata. To this end, we revisit the main definitions of diagnosability of intermittent faults, and present comparisons between them, consider verification and analysis techniques, and discuss available complexity results. Examples are used throughout the paper to illustrate the reviewed concepts and verification algorithms. We also look ahead, by suggesting some perspectives for future research. [ABSTRACT FROM AUTHOR]

Modern avionics can account for around 30% of the total cost of the aircraft. Therefore, it is essential to reduce the operational cost of avionics during a lifetime. This article addresses the critical scientific problem of creating the appropriate maintenance models for digital avionics systems that significantly increase their operational effectiveness. In this research, we propose the lifecycle cost equations to select the best option for the maintenance of digital avionics. The proposed cost equations consider permanent failures, intermittent faults, and false-positives occurred during the flight. The lifecycle cost equations are determined for the warranty and the post-warranty interval of aircraft operation. We model several maintenance options for each period of service. The cost equations consider the characteristics of the permanent failures and intermittent faults, conditional probabilities of in-flight false-positive and true-positive as well as the cost of different maintenance operations, duration of the flight, and some other parameters. We have demonstrated that a three-level post-warranty maintenance variant with a detector of intermittent faults is the best because it minimizes the total expected maintenance cost several folds compared to other maintenance options. [ABSTRACT FROM AUTHOR]

Traditional machine learning methods work well under a general assumption that the training data and the testing data should be drawn from the same distribution. However, this assumption does not hold in many real-world applications. For two structure-similar but specification-different connectors with intermittent fault, one connector has sufficient intermittent fault data with severity labeled but another connector has no labeled intermittent fault data. In this situation, the severity evaluation model trained by the samples from the former connector will have large evaluation errors for the samples from the latter connector. For realizing the transfer of the intermittent fault severity evaluation model between two similar but different connectors, this article proposes a transfer learning long short term memory (LSTM) network-based severity evaluation method for intermittent faults of an electrical connector. Conventional transfer learning methods with maximum mean discrepancy (MMD) have two shortcomings: 1) MMD only considers the mean discrepancy between the source and target domains and 2) MMD only measures the marginal distribution discrepancy of the source and target domains but ignored the conditional distribution discrepancy of different labels. Therefore, this article proposes a modified MMD metric which not only considers the higher order statistics but also the label discrepancy information to enhance the severity evaluation accuracy. Finally, this transfer learning model is applied in the intermittent fault experiment of three electrical connectors, and the effectiveness of the proposed method is verified using comparisons with four state-of-art transfer learning methods. [ABSTRACT FROM AUTHOR]

Summary: In this article, the finite‐time fault tolerant control problem is investigated for a class of discrete‐time stochastic parameter systems subject to censored measurements. For the sake of relieving the communication burden, a stochastic communication protocol governed by a Markov chain is employed to determine which actuator has the access to the network at each transmission instant. Moreover, an improved performance index dependent on the predetermined censored threshold is constructed to evaluate the disturbance rejection level of the fault tolerant controller in the simultaneous presence of both external disturbances and censoring effects. The main aim of the addressed problem is to design a fault tolerant controller such that the closed‐loop system satisfies both the stochastically finite‐time boundedness and H∞ performance requirements. In light of the Lyapunov theory combined with matrix inequalities, some sufficient conditions are derived skillfully, and the desired controller gains are calculated by solving a set of linear matrix inequalities. Finally, two simulation examples are utilized to demonstrate the effectiveness of the developed controller design method. [ABSTRACT FROM AUTHOR]

So far, problems of intermittent fault (IF) detection and detectability have not been fully investigated in the multivariate statistics framework. The characteristics of IFs are small magnitudes and short durations, and consequently traditional multivariate statistical methods using only a single observation are no longer effective. Thus in this paper, moving average T 2 control charts (MA-TCCs) with multiple window lengths, which simultaneously employ a bank of MA-TCCs with different window lengths, are proposed to address the IF detection problem. Methods to reduce false/missing alarms and infer the IFs' appearing and disappearing time instances are presented. In order to analyze the detection capability for IFs, definitions of guaranteed detectability are introduced, which is an extension and generalization of the original fault detectability concept focused on permanent faults (PFs). Then, necessary and sufficient conditions are derived for the detectability of IFs, which may appear and disappear several times with different magnitudes and durations. Based on these conditions, some optimal properties of two important window lengths are further discussed. In this way, a theoretical framework for the analysis of IFs' detectability is established as well as extended discussions on how the theoretical results can be adapted to real-world applications. Finally, simulation studies on a numerical example and the continuous stirred tank reactor (CSTR) process are carried out to show the effectiveness of the developed methods. • Moving average T 2 control charts with multiple window lengths are developed to detect intermittent faults (IFs). • The IF detectability is systematically studied in the MSPM framework for the first time. • Methods to exclude/compensate false/missing alarms and infer the appearing/disappearing time instances of IFs are presented. [ABSTRACT FROM AUTHOR]

As semiconductor technology scales into the nano regime, hardware faults have been threats against computational devices. Cloud systems are incorporating more and more computing density and energy into themselves; thus, fundamental research on topics such as dependability validation is needed, in order to verify the robustness of clouds for sensor networks. However, dependability evaluation studies have often been carried out beyond isolated physical systems, such as processors, sensors, and single boards with or without operating system hosts. These studies have been performed using inaccurate simulations instead of validating complete cloud software stacks (firmware, hypervisor, operating system hosts and workloads) as a whole. In this article, we describe the implementation of a fault injection tool, which validates the dependability of a commercial cloud software stack. Hardware faults induced by high energy density environments can be injected; the fault propagation through the cloud software stack is traced, and quantitatively evaluated. Experimental results show that the integrated fault detection mechanism of the cloud system, such as fatal trap detectors, has left a detection margin of 20% silent data corruption to narrow down. We additionally propose two detection mechanisms, which proved good performance in fault detection of cloud systems. [ABSTRACT FROM AUTHOR]

In the past decades, with the rapid development of modern technologies, a special kind of fault, the intermittent fault (IF) has become a serious threat to system securities. Although fruitful research results have been obtained for fault diagnosis, most of them are concerned with permanent faults. Furthermore, up to now, there has been no overview on the diagnosis techniques for IFs in dynamic systems. In fact, IFs are common in a variety of fields, whereas their research is far from mature and many open issues still remain to be solved. Therefore, in this overview, our main objective is to attract direct attentions from more researchers and engineers into the fascinating area of IF diagnosis. In this paper, the concept, research background, challenges and research motivations of IFs are introduced. Then, current research into diagnostics of IFs is reviewed, along with discussions on their strengths, limitations, and future directions. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.

Electrical connectors are widely used in many kinds of main equipment. It is very difficult, costly, and time consuming for the off-line diagnosis of intermittent faults (IFs) in connectors. To realize the on-line diagnosis by the traditional method, the voltage drop between two ends of the connector should be measured continuously at a high sample rate. This continuous measurement requires enormous testing resources, which is impractical in engineering fields. To solve this problem, this paper proposes an on-line diagnosis method in condition of the single-end test for the electrical connector IFs. To determine the fault threshold of the electrical contact resistance (ECR), first the impact of the ECR on high-speed transmission is studied, and then the connector fault feature in condition of the single-end test, which is called the insertion loss increment (ILI), is extracted. The measurement method of the ILI is also presented. Based on the above analysis, an ILI-based on-line diagnosis method for the connector IFs is specified. According to the qualitative and quantitative tests, the proposed diagnosis method is verified. The verification results show that the cumulative insertion loss increment, which is calculated from the ILI can truly exhibit the variation of ECR, and the error of the ILI-based diagnosis method is only about 1%.

The electrical connector is an important basic element, and its electrical contact performance is easily changed by ambient factors, such as temperature, resulting in various intermittent and permanent faults of the electrical connector. The intermittent faults of the electrical connector are difficult to reproduce, raising serious challenges to equipment availability and mission success. This paper addresses the problem of the intermittent faults of electrical connectors caused by ambient temperature stress and provides qualitative analyses of the impacts of temperature stress on electrical contacts. A finite element simulation model of the contact pairs of electrical connectors is established. The characteristics of intermittent faults at different temperature stress levels for electrical connectors with different contact pair sizes and materials are simulated and analysed. Based on this simulation analysis, experimental studies are conducted on the mechanisms of different temperature stress levels, different contact pair sizes and materials on the intermittent faults of electrical connectors. The results show that external temperature stress and temperature impacts obviously influence the contact performance of the electrical connector and that random intermittent faults occur. The more extreme the temperature and the higher the temperature change rate are, the more obvious their influences. [ABSTRACT FROM AUTHOR]

• A novel scheme for fault detection, isolation and diagnosis (FDI/FDD) of multi-axle speed sensors. • A modified fault Petri Net and an idea of quantification of combined indicator. • Cases study through test-rig experiment and true fault records of train operation. • Great accuracy and practically suitable for fault diagnosis with intermittent or time-varying characteristics. This paper presents a novel scheme for fault detection, isolation and diagnosis of multi-axle speed sensors on high-speed trains. Firstly, the steady features are extracted from dynamic signal measurement of multi-axle speed sensors, and then principal component analysis (PCA) is utilized for condition monitoring. Once an anomaly is detected, fault isolation is conducted using a reconstruction-based contribution (RBC) method. Moreover, a modified fault Petri net (PN) model is developed, and an indicator quantification idea is proposed to deduce an incidence matrix and discover diagnostic rules, which is practically suitable for diagnosing intermittent or time-varying fault. This proposed approach was demonstrated by cases study through test-rig experiments and actual fault records of train operations. Compared with traditional strategies, the results show that the developed approach can not only guarantee effective fault detection but also provide reliable fault isolation and diagnosis of multi-axle speed sensors even if when suffering axle-lock. [ABSTRACT FROM AUTHOR]

This paper addresses the prognosis problem for hybrid systems with intermittent faults and uncertain parameters. First, a diagnostic hybrid bond graph in linear fractional form is used to model the uncertain hybrid system to generate the mode-dependent adaptive thresholds for fault detection purpose. Then, a global combinative fault signature matrix integrating independent and dependent augmented global analytical redundancy relations is proposed to improve the system fault isolability under the multiple-fault condition. After the possible fault set is isolated, an adaptive reinforcement unscented Kalman filter is introduced to identify the intermittent fault magnitude, where an auxiliary indicator is used to capture the fault appearing and disappearing time steps. To describe the degradation trend of the intermittent fault, a dynamic model with a mode-dependent degradation coefficient is used. Taking the variation of the degradation coefficient into account, an event-based sequential prognosis method is proposed, where the prognoser is only reactivated if the discrete event representing mode change is observed and its associated conditions are satisfied. Finally, the key concept of the proposed method is verified by experimental studies. [ABSTRACT FROM AUTHOR]

With the aggressive scaling of submicron technology, intermittent faults are becoming one of the limiting factors in achieving high reliability in Network-on-Chip (NoC). Increasing test frequency is necessary to detect intermittent faults, which in turn interrupts the execution of applications. On the other hand, the primary goal of traditional mapping algorithms is to allocate applications to the NoC platform, ignoring the test requirement. In this paper, we propose a novel testing-aware mapping algorithm (TAMA) for NoC, targeting intermittent faults on the paths between crossbars. In this approach, the idle paths are identified, and the components between two crossbars are tested when the application is mapped to the platform. The components can be tested if there is enough time from the time when the application leaves the platform to the time when a new application enters it. The mapping algorithm is tuned to give a higher priority to the tested paths in the next application mapping, which leaves enough time to test the links and the belonging components that have not been tested in the expected time. Experiment results show that the proposed testing-aware mapping algorithm leads to a significant improvement over FF(Fiexitrst Free), NN(Nearest Neighbor), CoNA(Contiguous Neighborhood Allocation), and WeNA(Weighted-based Neighborhood Allocation). • The link utilization is quite low under real applications and the unused links could be tested at runtime. • The components tightly coupled to the link are named "path" and should be tested together. • A testing aware mapping algorithm can reduce the performance loss and increase testing frequency. • The testing frequency has been significantly improved without increasing transmission latency. [ABSTRACT FROM AUTHOR]

Intermittent fault is a special kind of fault form, which often occurs in industry processes but has been less studied. This study focuses on the distributed fault‐tolerant model predictive control (DFTMPC) problem for an intermittent fault in actuators or sensors. A novel state observer and DFTMPC controller integral framework are presented. The proposed distributed system state observer can both deal with the unknown inputs and compensate for the interconnected variables' estimation errors, achieving a more accurate estimation. A fault mode‐switched model is constructed to describe the system with multi‐intermittent faults in a hidden way. This allows us to design a distributed fault‐tolerant controller without reconfiguration. For the proposed distributed model predictive controller, asymptotically stable conditions are given, meanwhile, the whole algorithm for multi‐intermittent fault‐tolerant control is concluded. A numerical example is simulated to verify the proposed method. [ABSTRACT FROM AUTHOR]

Abstract The non-fragile fault-tolerant control for a class of nonlinear Markovian jump systems with intermittent multiple actuator faults is addressed in this study. The nonlinearity in the considered system is assumed to satisfy sector constraint. Multiple Markov chains are introduced to model the multiple intermittent actuator faults, which is in a multiplicative form. To ensure the fault tolerance of the closed-loop system in the presence of potential controller perturbation, a new non-fragile fault-tolerant controller is designed, which can stabilize the resulting closed-loop system and further satisfy a prescribed performance index. After appropriately synthesizing the fault model that dominated by multiple Markov chains and the underlying nonlinear Markovian jump system, a set of sufficient conditions for the considered problem focusing on the controller design is derived with both known and partially known transition probabilities, where the controller can be determined via a convex optimization procedure. An example is given to illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on chaotic spread spectrum sequence for synchronous online diagnosis of intermittent faults for complex and cable. networks, such as the aircraft power system. The novelties of the proposed method include: 1) since chaotic spread spectrum signals are sensitive to initial values, the number of chaotic sequences are not limited and the proposed method is not limited by the number of cables under monitoring; 2) with the self-correlation characteristic and cross-correlation characteristic of chaotic sequence, the proposed method can be extended to online diagnosis of multiple cable intermittent faults; and 3) the signal source impedance amplification method is proposed for intermittent arc faults of multiple cables, which improves the reflection coefficient and increases the accuracy of online detection and location of intermittent arc faults. A series of simulations and experiments on multiple cables with different intermittent fault modes are conducted to verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Modern avionics can account for around 30% of the total cost of the aircraft. Therefore, it is essential to reduce the operational cost of avionics during a lifetime. This article addresses the critical scientific problem of creating the appropriate maintenance models for digital avionics systems that significantly increase their operational effectiveness. In this research, we propose the lifecycle cost equations to select the best option for the maintenance of digital avionics. The proposed cost equations consider permanent failures, intermittent faults, and false-positives occurred during the flight. The lifecycle cost equations are determined for the warranty and the post-warranty interval of aircraft operation. We model several maintenance options for each period of service. The cost equations consider the characteristics of the permanent failures and intermittent faults, conditional probabilities of in-flight false-positive and true-positive as well as the cost of different maintenance operations, duration of the flight, and some other parameters. We have demonstrated that a three-level post-warranty maintenance variant with a detector of intermittent faults is the best because it minimizes the total expected maintenance cost several folds compared to other maintenance options.

This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results.