Stuck-at fault

About: Stuck-at fault is a research topic. Over the lifetime, 9707 publications have been published within this topic receiving 160254 citations.

Fault-Tolerant Control Using the GA Optimization Considering the Reluctance Torque of a Five-Phase Flux Switching Machine

Abstract: This paper deals with the fault tolerance of a five-phase flux switching machine. Short-circuit currents calculation considering inductances variation is developed. Machine behavior (torque quality, copper losses, and homopolar current) under a single short-circuit phase fault, two consecutive and nonconsecutive phases short-circuited, is simulated with a two-dimensional finite elements (2-D FE) model and validated experimentally. Then, a new method is developed to improve its performances in faulty mode, by reconfiguring reference currents. In fact, an accurate torque model is established and then used in a genetic algorithm to optimize reference currents in faulty mode. In this approach of reference currents computation, the used algorithm has multiobjectives and multiconstraints, thereby allowing choosing the appropriate fault-tolerant current solution according to our application. The torque model is considered to be more accurate and closer to the 2-D FE results in both healthy and faulty modes. Then, a comparison of machine performances in healthy, degraded, and reconfigured modes is presented. Experimental results corroborate the analysis.

A circuit level fault model for resistive bridges

Abstract: Delay faults are an increasingly important test challenge. Modeling bridge faults as delay faults helps delay tests to detect more bridge faults. Traditional bridge fault models are incomplete because these models only model the logic faults or these models are not efficient to use in delay tests for large circuits. In this article, we propose a physically realistic yet economical resistive bridge fault model to model delay faults as well as logic faults. An accurate yet simple delay calculation method is proposed. We also enumerate all possible fault behaviors and present the relationship between input patterns and output behaviors, which is useful in ATPG. Our fault simulation results show the benefit of at-speed tests.

Combined Fault Detector and Faulted Phase Selector for Transmission Lines Based on Adaptive Cumulative Sum Method

Abstract: In this paper, a novel approach is proposed for fault detection in transmission lines. This idea is based on the adaptive cumulative sum method (ACUSUM), whose structure is adaptive with the current passing through the corresponding line. The proposed ACUSUM algorithm can detect even low magnitude faults with high resistances. By using the proposed method, just a few milliseconds after fault inception, the fault detection unit permits the main protection algorithm to become activated. Moreover, ACUSUM output indices can discriminate faulted phases within only 1 ms after fault registration. This new faulted-phase selector can also be applied to double-circuit transmission lines to detect cross-faults as well as intercircuit faults. The results have shown that the proposed method has good performance in speed and accuracy as a combined fault detector and faulted-phase selector algorithm.

Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Wavelet Time-Frequency Entropy and One-Class Support Vector Machine

Abstract: Mechanical faults of high voltage circuit breakers (HVCBs) are one of the most important factors that affect the reliability of power system operation. Because of the limitation of a lack of samples of each fault type; some fault conditions can be recognized as a normal condition. The fault diagnosis results of HVCBs seriously affect the operation reliability of the entire power system. In order to improve the fault diagnosis accuracy of HVCBs; a method for mechanical fault diagnosis of HVCBs based on wavelet time-frequency entropy (WTFE) and one-class support vector machine (OCSVM) is proposed. In this method; the S-transform (ST) is proposed to analyze the energy time-frequency distribution of HVCBs’ vibration signals. Then; WTFE is selected as the feature vector that reflects the information characteristics of vibration signals in the time and frequency domains. OCSVM is used for judging whether a mechanical fault of HVCBs has occurred or not. In order to improve the fault detection accuracy; a particle swarm optimization (PSO) algorithm is employed to optimize the parameters of OCSVM; including the window width of the kernel function and error limit. If the mechanical fault is confirmed; a support vector machine (SVM)-based classifier will be used to recognize the fault type. The experiments carried on a real SF6 HVCB demonstrated the improved effectiveness of the new approach.

Hidden Markov model-based process monitoring system

Abstract: Sensor signals produced in industrial manufacturing processes contain valuable information about the condition of operations. However, extracting the appropriate feature for effective fault diagnosis is difficult. Moreover, the adaptability and flexibility of current fault diagnosis systems are often found wanting in real-world applications. Unfortunately, it is essential to rebuild most fault diagnosis systems when new fault types emerge. This paper presents an intelligent fault diagnosis system based on a hidden Markov model. Introducing the concepts of time marginal energy and frequency marginal energy, the features of which can be acquired by the wavelet packet technique satisfy the requirements for fault diagnosis. By utilizing the best tree principle, this method not only extracts the feature automatically without a priori experience but also compresses the data; both of which ensure a system that is practical for real-time application. The new diagnosis system developed here is efficient and effective, as demonstrated by the model developed and applied to a real-time sheet metal stamping process. Based on tests conducted during two experiments (one based on simple blanking, the other on progressive operations) and related comparisons, the proposed method is substantially more effective than other approaches. In addition, the new method requires that only related models be created for new fault types, which results show are ideal for shop floor applications.