Search

In this paper, a novel method of intermittent fault diagnosis based on dynamic power current in digital circuit is proposed. The intermittent fault model in digital circuit is introduced. The influence of intermittent fault of different magnitudes and types on the dynamic power current of logic gate circuit is analyzed. Then, an intelligent diagnosis method of intermittent fault based on power current information is put forward, which has been verified on ISCAS85-C17 circuit. The test shows the feasibility of using dynamic power current for digital circuit intermittent fault diagnosis.

Wireless Sensor Networks (WSNs) have become a new information collection and monitoring solution for the various application. Faults occurring to sensor nodes are prevalent due to the sensor device itself and the harsh environment, where the sensor nodes are deployed. To ensure the quality of service and to avoid further degradation of service, it is necessary for the WSN to be able to tolerant of the faulty nodes present in the network. The fault diagnosis techniques are classified based on the methods they employ to determine the faults. In this paper, we have proposed a traversal-based diagnosis algorithm that seeks to diagnose both permanent as well as intermittent fault in WSN. The proposed algorithm employs a special node called an anchor node to traverse the field. The traversal of the field is decided by a proposed traversal algorithm taking into consideration the length and breadth of the sensor field, and the transmission range of the nodes. The anchor node stops at defined positions in the deployment field where it executes the fault diagnosis algorithm taking into consideration the normal sensor nodes which are in its range. The diagnosis algorithm uses a timeout mechanism to identify hard faults and adjusted boxplot method to identify permanent and intermittent faults in the network. The adjusted boxplot method takes into consideration the skewness of the data generated by the nodes in the sensor field. The faulty sensor nodes are classified by using a Feed Forward Neural Net (FFNN) model with Gravitational Search (GS) learning algorithm. The proposed algorithm is implemented in the Omnet++ environment which shows very promising results. The performance parameters, such as detection accuracy, false alarm rate, false positive rate, and energy consumption of the proposed algorithm show significant improvement over the existing algorithms.

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. This work was supported by the National Natural Science Foundation of China under Grants 62173119 and 61673154.

Static test plays a key role in accuracy evaluation in range equipment test. However, the ground station equipment in static test may inevitable encounter intermittent fault, which leads to inaccurate navigation result. In this paper, two improved intermittent fault algorithms are proposed by using sorting, iteration, and recursion techniques based on multivariate statistical analysis. These algorithms not only reduce significantly the computational complexity of fault detection based on the traditional detection statistics, but also improve the navigation accuracy. It will have a wide application prospect in the future flight test and final equipment test.

An intermittent fault identification approach based on empirical mode decomposition(EMD) and hidden Markov model(HMM) to restrain false alarm for built-in test(BIT) caused by intermittent fault is proposed. Firstly, EMD is employed to decompose the original BIT signals which can reflect the fault feature, several energy-dominating intrinsic mode functions (IMF) are selected to extract the feature vector. Then extracted feature value are sent to the trained HMM for state recognition. The results show that the intermittent fault state can be effectively identified by the approach.

In this paper, through the statistical analysis of the data of contact resistance changing with time when the circuit intermittent fault occurs, a model of intermittent fault is proposed, and an intermittent fault injector is built by the FPGA and high-speed programmable resistor, which use to inject the intermittent fault into the circuit, so as to carry out the research related to the intermittent fault. Then, the application of the intermittent fault injector is illustrated by an example of circuit health status recognition. In the example, intermittent fault injector is used to keep circuits in different health states, then FSV method is used to extract the feature of the fault, after training HSMM models, the health state of the circuit is identified. Finally, the correctness and effectiveness of the proposed method are verified by comparative experiments.

In this paper, the problem of intermittent fault diagnosis for linear discrete systems subjected to unknown disturbance is studied. An interval observer for fault diagnosis is designed, residual evaluation and threshold setting are not required, and the upper and the lower bounds of the residual generated by the interval observer can be directly used as adaptive thresholds for the intermittent sensor fault detection. In order to locate the faults, a set of interval isolation observers is designed to isolate sensor faults. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed diagnosis scheme.

Intermittent fault of electronic equipment is an essential problem in the industry, and its diagnosis is paid more attention. The data-driven approach is commonly utilized, but it focuses on the mapping relation more than the inner data structure characteristics. In this paper, a method for intermittent fault diagnosis based on SOM. SOM topology is obtained based on unsupervised learning, and it is labeled using the majority voting strategy in the associated samples of every neuro. Inputs are judged as states excluding intermittent fault are processed again using SVM, and two new features are proposed for the SVM training. The “one-versus-one” is utilized to establish multi-classification SVM. Voltage conversion board is taken as an example, in which the presented method is applied and compared with ANN and SVM. The result shows that the proposed method is effective.

The dependability of highly dependable systems relies on the reliability of its components and interconnections. One of the most challenging faults that threatens the reliability of interconnections in a system are intermittent resistive faults (IRFs). They may occur randomly in time, duration and amplitude in every interconnection. The occurrence rate can vary from a few nanoseconds to months. As a result, evoking and detecting such faults is a major challenge. In this paper, IRF detection at the chip level has been tackled by utilising a fully digital insitu IRF monitor. This paper introduces a new algorithm for inserting IRF monitors in a design. The goal of this algorithm is to minimise the number of IRF monitors while providing a high fault coverage for IRFs. The algorithm has been validated using software-based fault injection. The simulation results show that the proposed algorithm improves the IRF coverage at the chip level at the cost of a small area and power-consumption overhead.

Intermittent faults are a critical reliability threat in deep submicron VLSI circuits. These faults occur non-deterministically due to unstable hardware and unpredictable operating conditions; they are activated/deactivated with changes in the runtime environment. Online fault prediction models are commonly used to predict soft errors and aging effects. A small set of flip-flops, whose states constitute the signature, conveys information about the fine-grained behavior of the circuit, and serves as the input to a machine-learning (ML) model. The nondeterministic failure mechanisms of intermittent faults, however, result in temporally- and spatially-correlated signatures (TSC-signatures). Moreover, the high-dimensional time-series features impede the use of traditional ML models for intermittent-fault detection. To cope with this challenge, we adapt the TSC-signatures to existing ML detection models. Moreover, we propose a novel detection model based on Recurrent Neural Network with Long Short-Term Memory (LSTM) that is inherently suitable for this problem. Simulation results for the ITC99 benchmark circuits highlight the effectiveness of the proposed model.

In order to clear the obstacles of detecting intermittent faults and distinguishing between permanent faults, a Petri net model of normal, intermittent and failure modes is established. On the basis of this model, a test monitoring device for IGBT intermittent fault is designed. With the function of recording intermittent fault data and comparing fault signals with LabVIEW data processing module, the device can also conduct intermittent fault tests of IGBT. Finally, the test results show that the device facilitates the monitoring of IGBT intermittent faults, and is capable of predicting the permanent faults.

Sensor nodes in wireless sensor networks (WSNs) are randomly deployed in hostile environments. Real-time experience shows that sensor nodes are prone to faulty. Different faults of sensor nodes are inevitable due to internal and external influences such as adverse environmental conditions, low battery, calibration and sensor ageing effect. Since WSNs applications rely on the fidelity of data reported by the sensor nodes, it is important to detect a faulty sensor and isolate them. Most of the existing fault detection techniques in literature are statistical based which demands sensor domain knowledge and the data from the neighbouring sensors. There may be a problem of detecting a sensor fault by analyzing the sensor data in distributed approach is non-trivial since a faulty sensor reading could mimic non-faulty sensor data. Currently, machine learning algorithms have been successfully used to identify and classify various types of faults in WSNs to avoid such kind of problems. However, the application of deep learning (DL) methods has sparked great interest in both the industry and academia in the last few years. In this chapter, neural network methods will be used in fault diagnosis in WSN with DL algorithms. The focus on diagnosis of fault includes hard, soft, intermittent and transient types.

Poor working conditions can cause intermittent faults in analog circuit to occur frequently. Thus, an intermittent fault diagnosis method for analog circuit based on wavelet packet transform and stacked auto-encoder (WPT-SAE) is proposed. The voltage signals of the output node in the analog circuit are decomposed by WPT, obtaining reconstructed signals of different frequencies. Feature extraction is performed on the reconstructed signals with obvious intermittent fault characteristics, and their kurtosis, impulse factor and so on are calculated to constitute the feature vectors. The feature vectors are transmitted as input vectors to the SAE for training and recognition. The diagnostic results show that this method can identify different types of intermittent faults in the analog circuit and achieve higher diagnostic accuracy. The effectiveness is verified by comparative experiments.

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.

PurposeThis paper aims to reproduce the electrical connector intermittent fault behaviours with step-up vibration stress while maintaining the integrity of the product. Design/methodology/approachA dynamic model of an electrical connector under vibration is established for contact resistance analysis. Next, the dynamic characteristics of contact resistance are analysed, and cumulative damage theory is used to calculate the damage under different stresses during the intermittent fault reproduction test. To reduce damage and improve efficiency, the step-up stress is used for the reproduction test. FindingsThe proposed method can reproduce the intermittent fault behaviour, and the step-up stress test is more efficient than the constant stress test. Research limitations/implicationsStep-up stress is used for intermittent fault reproduction, and the quantitative relationships between intermittent fault and product damage can be further studied. Practical implicationsIt is expected that the proposed methodology can help engineers to reproduce the intermittent fault behaviours to facilitate the detection and diagnosis of intermittent fault and to improve equipment safety. Originality/valueThe mechanism of electrical connector reproduction is analysed and the step-up stress test is used for intermittent fault reproduction.

The online monitoring method based on the spread spectrum time domain reflectometry (SSTDR) theory is studied to detect the intermittent fault of the aircraft ARINC825 bus cable. Firstly, the impedance characteristics and reflection characteristics of the aircraft ARINC825 bus cable fault are analyzed, and the online monitoring method model is constructed. Secondly, the frequency and amplitude of the monitoring signal is designed according to the ARINC825 protocol and airworthiness standard. Finally, the experimental platform is built to verify the proposed method. On-line monitoring experiments are carried out with multiple sets of ARINC825 bus cables. The results show that the positioning accuracy and fault type judgment accuracy of fault online monitoring are very high. Therefore, the method has proved to be feasible.

The problem of permanent fault diagnosis has been discussed widely, and the diagnosability of many well-known networks have been explored. Faults of a multiprocessor system generally include permanent and intermittent, with intermittent faults regarded as the most challenging to diagnose. In this paper, we investigate the intermittent fault diagnosability of hyper Petersen networks. First, we derive that an $$n$$ -dimensional hyper Petersen network $$HP_{n}$$ with fault-free edges is $$(n - 1)_{i}$$ -diagnosable under the PMC model. Then, we investigate the intermittent fault diagnosability of $$HP_{n}$$ with faulty edges under the PMC model. Finally, we prove that an $$n$$ -dimensional hyper Petersen network $$HP_{n}$$ is $$(n - 2)_{i}$$ -diagnosable under the MM* model.

Static electrical contact resistance (ECR) has been well researched. To investigate the dynamic performance of electrical connector under vibration, a test apparatus is built to measure the ECR with high precision. The ECRs are tiny and suffer from noise. Therefore, a wavelet packet transform-based denoising method is proposed. This method can filter noise with frequency components ranging from low to high. The dynamic characteristics of ECR are presented after an analysis of the measured results. Moreover, the connector intermittent fault (IF) performance under vibration is characterized and analyzed. Finally, an ECR model for rough surfaces under vibration is proposed, based on which the discovered laws are theoretically analyzed. The detected dynamic characteristic of ECR and IF can support an online diagnosis of connectors in further research.

This work is concerned with the filtering and intermittent fault detection problems for a class of time-varying systems with stochastic parameter uncertainty and sensor saturation. A robust filter is proposed which fully considers both stochastic parameter uncertainty and sensor saturation, but does not depend on any specific uncertainty structure. A special effort is made to obtain the upper bound of the estimation error covariance and the filter gain is designed to minimize it at each time step. Based on the proposed filter, a residual is generated and the corresponding fault detection strategy is given. Finally, a simulation study is carried out to illustrate the validity of the proposed methods.

Multivariate statistical analysis using the Hotellings T2 statistic (T2 control chart) is one of the most effective data-driven methods for fault detection. However, existing T2 control charts in the literature mainly focus on permanent faults, and intermittent faults are relatively rarely considered. In fact, due to the small magnitude and short duration characteristics, traditional multivariate statistical methods are not sensitive to intermittent faults, resulting in much missed detection. Thus in this paper, the T2 control chart with consecutive observations is designed for permanent fault detection and intermittent fault detection respectively. In order to analyze the detection ability of proposed methods, definitions of being guaranteed detectable are introduced both for permanent faults and intermittent faults. Then, sufficient conditions for the defined fault detectability are derived for both kinds of faults respectively. Moreover, optimal window length for intermittent fault detection is discussed. Finally a simulation study is carried out to show the effectiveness of the proposed methods.

In this paper, the intermittent fault (IF) detection problem is investigated for a class of linear discrete-time stochastic multi-agent systems (MASs). By using the reduced-order observer method and introducing a sliding-time window, the truncated residuals are designed to detect the appearing time and disappearing time of the IF. Moreover, two sets of hypothesis tests are proposed to determine the detection and location thresholds of the IF, and the detectability of the IF is discussed in the framework of statistical analysis. Finally, a simulation example is provided to validate the feasibility and effectiveness of the proposed method.

An interconnection network’s diagnosability is an important metric for measuring its self-diagnostic capability. Permanent fault and intermittent fault are two different fault models that exist in an interconnection network. In this paper, we focus on the problem pertaining to the diagnosability of interconnection networks in an intermittent fault situation. First, we study a class of interconnection networks called crisp three-cycle networks, in which the cn in -number (the number of common vertices each pair of vertices share) is no more than one. Necessary and sufficient conditions are derived for the diagnosability of crisp three-cycle networks under the PMC (Preparata, Metze, and Chien) model. A simple check can show that many well-known interconnection networks are crisp three-cycle networks. Second, we prove that an interconnection network S is a t i -fault diagnosable system without repair if and only if its minimum in-degree is greater than t i under the BGM (Barsi, Grandoni, and Masetrini) model. Finally, we extend the necessary and sufficient conditions to determine whether an interconnection network S is t i -fault diagnosable without repair under the MM (Maeng and Malek) model from the permanent fault situation to the intermittent fault situation.

This paper provided an approach to design feed-forward observer for nonlinear systems with Lipchitz nonlinearity and bounded unknown inputs (disturbances/uncertainties) to ensure the sensitivity against intermittent faults. The proposed observer design guarantees the system error stability. Some variables and scalars are also introduced to design observer's parameters, which bring more degrees of flexibility available to the designer. The designed observer is used to propose a precision fault detection scheme including adaptive threshold design to detect intermittent faults. The efficiency of the considered approach is examined by the intermittent failure case in the suspension system of a vehicle. Simulation results show that the accurate state estimation and fault detection are achieved successfully.

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

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%.

Despite various studies that have been conducted so far on the failure of high power cables, failure progression in low power cables, wires and interconnections have not been well understood yet. In general, it is hypothesised that failures of wires are progressed from random intermittent failures that are gradually developed as hard faults. This paper aims to present a test rig and possible test techniques that can be used for testing the failure progression of wires and interconnections. Research presented in this paper is based on tools, equipment and techniques that facilitate various ageing mechanisms needed to capture proper and right failure patterns from low power cables, wires and interconnections. Paper originally presented at the 5th International Conference in Through-life Engineering Services Cranfield University, 1st and 2nd November 2016.

Aiming at the problem of solder joint intermittent fault reproduction, an intermittent fault reproduction method based on sweep frequency vibration test is proposed. Firstly, the dynamic model is constructed for intermittent fault mechanism analysis, and the environmental factors that affect intermittent faults are obtained. Then, sweep frequency vibration test is used to perform intermittent fault reproduction, and intermittent fault signals are obtained for intermittent fault detection.

Intermittent faults usually occur under the condition of vibration stress, and are often accompanied with damage degradation of the component. Based on the electrical contact structure of electrical connectors, the influence impacts on intermittent fault of electrical connector have been analyzed. Under vibration stress, the mechanical model of the electrical connector is built considering the connection relationship among its components. The dynamic characteristic of the electrical connector working state has been studied through simulation analysis and experiments. The results have shown that the working state of the electrical connector is strong related to the environment vibration stress.

In recent years, the method of time stress monitoring to improve the ability of fault prediction and false alarm recognition has attracted more and more attention. It is found that intermittent failure will increase with the aging of helicopter gyroscope. The time stress of helicopter gyroscope in the process of manufacture, transportation and use is the main reason of helicopter gyroscope aging. The intermittent failure characteristics of helicopter gyroscope can represent its healthy state. This paper briefly introduces the concepts of time stress and health management system. Then, a new design method of helicopter gyro health management system based on intermittent fault characteristics and in-flight testing is proposed. The system structure consists of five modules: sensor module, intermittent fault detection module, permanent fault diagnosis module, permanent fault prediction module and health management module. Finally, the structure and function of each module of helicopter gyroscope health management system are introduced in detail. Experiments show that this method can effectively improve the fault prediction and false alarm recognition ability of helicopter gyroscope.

In recent past, wireless sensor networks (WSNs) are used in various real-life applications where the nodes are randomly deployed in hostile environments. Different faults of sensors are inevitable due to adverse environmental conditions, low battery, and aging effect. Therefore, one major research focus in WSNs is to diagnose the sensor nodes regularly and to get the status of each of them. This helps to provide continuous service of the network despite the occurrence of failure of few nodes in the network. Some of the burning issues related to distributed fault diagnosis of intermittent faulty sensor in WSNs is addressed in this chapter. Further, how the performance of the fault diagnosis algorithm has been improved by employing robust statistical methods presented. The issue of intermittent fault diagnosis in WSNs is discussed using statistical methods which is the main focus of this chapter.

So far, the problem of detecting intermittent faults has not been fully investigated in the multivariate statistical framework. The intermittent fault is a kind of non-permanent fault which lasts within a limited period of time and then disappears itself. Generally speaking, intermittent faults have small magnitudes and short durations, which consequently makes them even more difficult to detect than incipient faults and causes losing efficacy for many existing methods. In this paper, a generic quadratic-form index, combined with the moving average technique, is utilized to detect intermittent faults. In order to determine the window length, fault detectability is studied and a sufficient condition is derived. Moreover, compared with the permanent fault case, false alarms have a worse influence on the detection of intermittent faults. Thus, a method to exclude false alarms is proposed based on the detectability analyses. Finally, a numerical simulation is carried out to verify the theoretical results and illustrate the effectiveness of the method.

Unit self-diagnosis is considered at system level. As distinct from system level self-diagnosis based on units mutual tests, we have researched the method based on the tests which a unit performs on other system units. Taking into account the obtained test results, a unit evaluates its own state. In our research, we have considered different faulty assumptions and testing procedures. Diagnosis model was developed and analyzed. Computer simulation is performed by using the web application developed for this research. Results of simulation were analyzed and assessed. Some recommendations were made for achieving better diagnosis results.

For the wind turbine vibration signal periodic intermittent fault detection problem, we put forward a new detection method: first to extract the wind turbine vibration original signal fundamental frequency based on Morlet wavelet analysis, and then get a new set of signal baseband removed as the fractional terminable vibration system input. Whether the wind turbine has periodic intermittent faults or not is judged by whether the phase trajectory is in a terminable state and the fault signal amplitude can be detected. The system is highly sensitive to the periodic signal but insensitive to the random perturbation. The experiment proves that the method has good function of early fault prediction.

Intermittent faults are widely found in various types of systems. For dynamic systems, the diagnosis of intermittent faults becomes more difficult. Based on the analysis of the characteristics of intermittent fault trend, this paper constructs a Status Tendency Recognition Convolutional Neural Network (STRCNN), which realizes the efficient identification of intermittent faults. The effectiveness of the proposed method is verified by a case based on the aeronautical power module circuit.