Intermittent Fault Diagnosis Method of Power System Based on HMM-SVM Characteristics

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.

Diagnosis of incipient parametric faults has been widely addressed and various model-based as well as data-based methods have been reported in the literature. In contrast to incipient faults, it is unknown if, when and for how long intermittent faults will reappear which makes their diagnosis and prognosis difficult. This paper addresses intermittent faults with a magnitude that increases over time. Such a sequence of intermittent faults may reach a failure alarm threshold and may eventually lead to a component or even a system failure. The proposed approach uses a diagnostic Bond Graph (DBG) model for an online detection, isolation and estimation of intermittent faults. Pulses of increasing height and varying width not equally spaced on the time evolution of residuals of Analytical Redundancy Relations (ARRs) obtained from an offline developed DBG indicate a degradation trend of a parameter or a variable. Certain values of this trend are concurrently extrapolated in a repeated failure prognosis resulting in a sequence of Remaining Useful Life (RUL) estimates. The proposed BG-based approach to a failure prognosis in the presence of intermittent faults can be combined with a previously presented BG-approach to incipient fault prognosis and is applicable to mode switching models. For illustration, it is applied in a case study to a small electronic circuit in an offline simulation. The case study also considers intermittent faults of constant magnitude.

Faulty switches, broken interswitch connections and certain bridging faults in a Benes switching network can be identified using a sequence of faulty paths across the network. The method described may use on-line faulty-path data and is suitable for use in the diagnosis of intermittent faults. It is shown that a single faulty switch can be identified in less than four random faulty paths on average, after presentation of the initial faulty path. The method applies to other types of switching network and provides a similar performance.

A method for the identification of stuck-at bits on a data bus is described. Faulty data words are compared to isolate the stuck-at bits. A similar method can be used to identify an instruction which does not execute correctly in a processor instruction set. Here, faulty programmes, or faulty programme segments, are compared to isolate the faulty instruction. For both bus and instruction fault cases, probabilistic analyses are given with explicit formulas and data for the mean number of faulty samples required for diagnosis, and also the associated standard deviation. Both of these procedures use on-line data and are suitable for the diagnosis of intermittent faults.

A new algorithm to diagnose intermittent scan chain fault in scan-based designs is proposed in this paper. An intermittent scan chain fault sometimes is triggered and sometimes is not triggered during scan chain shifting, which makes it very difficult to locate the fault sites. In this paper, we provide answers to three questions: (1) Why intermittent scan chain faults happen? (2) Why diagnosis of this type of faults is necessary? (3) How to diagnose this type of faults? The experimental results presented demonstrate that the proposed diagnosis algorithm is effective for large industrial designs with multiple intermittent scan chain faults.

A power system operation at minimum cost is no longer the only measure for electrical power dispatch. Combined Economic Emission Dispatch (CEED) problem is attained by considering both the economy and emission objectives with necessary constraints. The stability of the power system is also considered as an important factor in the performance of the power system. Several optimization techniques are slow for such complex optimization tasks and are not suitable for on-line use. This paper presents an effective optimization algorithm, for solving security constrained combined economic emission dispatch problem. Also, the power system stability plays an important role in power system. Power system stability criteria are also considered in this paper for better performance of the entire power system. In this paper, an efficient optimization technique called Modified Artificial Bee Colony Optimization is used to solve the CEED problem. The performance of the proposed approach is compared with the other optimization techniques. The experimental result shows that the proposed system results in better solution for CEED problem with the maintenance of system stability.

With the rapid increase of the number of processors in multiprocessor systems and the fast expansion of interconnection networks, the reliability of interconnection network is facing severe challenges, where the fast recognition of fault processors is crucial. In practice, most of the processor failures are intermittent faults. In this article, we first determine the intermittent fault diagnosability <inline-formula><tex-math notation="LaTeX">$t_{I}^{PMC}(S_{n}^{2})$</tex-math></inline-formula> of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional split-star network <inline-formula><tex-math notation="LaTeX">$S_{n}^{2}$</tex-math></inline-formula> under the PMC model. In addition, we propose a fast intermittent fault probabilistic diagnosis algorithm FIFPDPMC to identify the nodes with intermittent fault in the <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> -dimensional split-star network <inline-formula><tex-math notation="LaTeX">$S_{n}^{2}$</tex-math></inline-formula> under the PMC model, and we calculated the time complexity of the algorithm FIFPDPMC. Then we implement the algorithm FIFPDPMC in the IoT-based wireless sensor network (IoTWSN) and a randomly generated network (RGN) under different number of nodes with intermittent fault, and we evaluate the performance and efficiency of the algorithm FIFPDPMC in terms of accuracy, precision, recall (TPR), F1, G-mean, FPR, TNR and FNR. Experimental results show that, as the number of stages of executing the algorithm FIFPDPMC increases, the number of nodes with intermittent fault being diagnosed by the algorithm FIFPDPMC increases, which implies that the algorithm FIFPDPMC has good performance and efficiency in both IoTWSN and RGN.

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.

This article deals with fault detection and the classification of incipient and intermittent open-transistor faults in grid-connected three-level T-type inverters. Normally, open-transistor detection algorithms are developed for permanent faults. Nevertheless, the difficulty to detect incipient and intermittent faults is much greater, and appropriate methods are required. This requirement is due to the fact that over time, its repetition may lead to permanent failures that may lead to irreversible degradation. Therefore, the early detection of these failures is very important to ensure the reliability of the system and avoid unscheduled stops. For diagnosing these incipient and intermittent faults, a novel method based on a Walsh transform combined with a multilayer perceptron (MLP)-based classifier is proposed in this paper. This non-classical approach of using the Walsh transform not only allows accurate detections but is also very fast. This last characteristic is very important in these applications due to their practical implementation. The proposed method includes two main steps. First, the acquired AC currents are used by the control system and processed using the Walsh transform. This results in detailed information used to potentially identify open-transistor faults. Then, such information is processed using the MLP to finally determine whether a fault is present or not. Several experiments are conducted with different types of incipient transistor faults to create a relevant dataset.

Speech emotion recognition as a significant part has become a challenge to artificial emotion. It is particularly difficult to recognize emotion independent of the person concentrating on the speech channel. In the paper, an integrated system of hidden Markov model (HMM) and support vector machine (SVM), which combining advantages on capability to dynamic time warping of HMM and pattern recognition of SVM, has been proposed to implement speaker independent emotion classification. Firstly, all emotions are divided into two groups by SVM. Then, HMMs are used to discriminate emotions from each group. For a more robust estimation, we also combine four HMMs classifiers into a system. The recognition result of the fusion system has been compared with the isolated HMMs using Mandarin database. Experimental results demonstrate that comparing with the method based on only HMMs, the proposed system is more effective and the average recognition rate reaches 76.1% when speaker is independent.

Diagnosis of intermittent scan chain failures still remains a hard problem. In this article, we demonstrate that the use of artificial neural networks (ANNs) can lead to significantly higher accuracy. The key of this method is a multistage process incorporating ANNs with gradually refined focuses. During this process, the final fault suspect is elected through multiple rounds of ANN inference, instead of just one round. At each stage, identification of a proper Affine Group, used as the “candidate set of scan cells for the next round of ANN inference,” will influence the final diagnostic accuracy. Thus, we propose a validation-based learning procedure for Affine Group derivation to further boost the final diagnostic accuracy. The experimental results on benchmark circuits have shown that this method is, on the average, 17.46% more accurate than a state-of-the-art commercial tool for intermittent stuck-at-0 faults.

The Internet and associated network technologies are an increasingly integral part of modem day working practices. With this increase in use comes an increase in dependence. For some time, commentators have noted that given the level of reliance on data networks, there is a paucity of monitoring tools and techniques to support them. As this area is addressed, more data regarding network performance becomes available. However, a need to automatically analyse and interpret this performance data now becomes imperative. The paper takes one-way latency as an example performance metric. The term 'data exception' is employed to describe delay data that is unusual or unexpected. Data exceptions can be used by network operators to assess the effect of network modifications, failures and usage, and can also help in the diagnosis of intermittent network faults. Automating the detection of data exceptions is a non-trivial process that is not well suited to a rule-based solution. The paper shows that data exceptions can be identified by the use of a two-stage approach. The Kolmogorov-Smirnov test can initially be applied to detect general changes in the delay distribution, a neural network can then be used to categorise the change. The approach is evaluated using a network simulation.

Controller Area Networks (CANs) play an important role in many safety-critical industrial systems, which places high demands on their reliability performance. However, the intermittent connection (IC) of network cables, a random and transient connectivity problem, is a common but hard troubleshooting fault that can cause network performance degradation, system-level failures, and even safety issues. Therefore, to ensure the reliability of CANs, a fault symptom association model-based IC fault diagnosis method is proposed. Firstly, the symptoms are defined by examining the error records, and the domains of the symptoms are derived to represent the causal relationship between the fault locations and the symptoms. Secondly, the fault probability for each location is calculated by minimizing the difference between the symptom probabilities calculated from the count information and those fitted by the total probability formula. Then, the fault symptom association model is designed to synthesize the causal and the probabilistic diagnostic information. Finally, a model-based maximal contribution diagnosis algorithm is developed to locate the IC faults. Experimental results of three case studies show that the proposed method can accurately and efficiently identify various IC fault location scenarios in networks.

Focusing on the challenges posed by the strong randomness, weak manifestation, and difficulty in diagnosing intermittent faults (IFs) in high-density integrated circuits (HDICs)—often induced by bonding wire defects—this paper takes the GPIO interfaces of a typical DSP chip as the research object. It systematically analyzes the response characteristics of intermittent short-circuit and open-circuit faults and proposes a hybrid intelligent diagnosis method based on the Sparrow Search Algorithm-optimized Variational Mode Decomposition and Attention-based Support Vector Machine (SSA–VMD–Attention–SVM). A dedicated fault injection circuit is designed to accurately replicate IFs and acquire the power supply current response signals. The Sparrow Search Algorithm (SSA) is employed to adaptively optimize the parameters of Variational Mode Decomposition (VMD) for effective extraction of frequency-domain features from fault signals. A three-level attention mechanism is introduced to adaptively weight multi-domain features, thereby highlighting the key fault components. Finally, the Support Vector Machine (SVM) is utilized to achieve high-precision fault classification under small-sample conditions. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 97.78% for intermittent short-circuit and open-circuit faults in the GPIO interfaces of the DSP chip, significantly outperforming traditional methods and exhibiting notable advantages in terms of diagnostic accuracy, robustness, and interpretability.

BIT design improvement is possible by using the proposed method. This method uses a Markov's Three State Model to identify the intermittent faults. This method sets double threshold values for intermittent faults, thus providing a technique to categorize them independently so as to allow for further design analysis and risk mitigation. This technique could be of pragmatic value during BIT design development. In future, we hope to collect, DVT based, HALT sample data to generate an accurate picture of the parameter variance due to environmental extremes and margin erosion or over time. Optimizing BIT parameter variance during the BIT design phase provides an opportunity to eliminate intermittent faults. HALT augmented BIT characterization will open a path forward for identification and mitigation of intermittent faults.