Faulty Elements Research Articles

A fault detection and location algorithm for the LVDC interconnection network in rural area

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?

Exploit High-Dimensional RIS Information to Localization: What Is the Impact of Faulty Element?

Radiation Pattern Correction of Faulty Planar Phased Array using Genetic Algorithm

The probability of antenna array failure or malfunctioning cannot be ruled out, and hardware replacement of faulty elements is not always a viable solution. Therefore, academic and industrial interest in self-healing phased arrays are on the rise. In this work, the phase-only genetic algorithm (GA) optimization flow for the radiation pattern correction of a 4 × 4 phase faulty planar antenna array is proposed. Initially, a reference array pattern at the desired scan angle is generated. Then random phase faults are introduced across the 1 × 4 antenna elements in any one of 4 sub-arrays to produce maximum distortion in the reference radiation pattern of 4 × 4 planar array. The proposed GA re-computes the new excitation weights for the remaining non-faulty 3 sub-arrays to correct the overall radiation pattern of 4 × 4 array. This is achieved by calculating the array output power for reference and GA computed weights. The GA corrected patterns fairly follow the desired array patterns in terms of peak gain and reducing sidelobe levels for the desired scan angle. The efficiency of the optimized radiation patterns was evaluated in full-wave HFSS model and measurements validation. In this way, maintenance cost can be reduced with recovery of acceptable level of radiation pattern using software instead of physically replacing faulty antenna elements in the array.

Two-dimensional Array Coverage Pattern Recalculating under Faulty Elements

Two-dimensional Array Coverage Pattern Recalculating under Faulty Elements

Paired 2-disjoint path covers of burnt pancake graphs with faulty elements

The burnt pancake graph BPn is the Cayley graph of the hyperoctahedral group using prefix reversals as generators. Let {u,v} and {x,y} be any two pairs of distinct vertices of BPn for n≥4. We show that there are u−v and x−y paths whose vertices partition the vertex set of BPn even if BPn has up to n−4 faulty elements. On the other hand, for every n≥3 there is a set of n−2 faulty edges or faulty vertices for which such a fault-free disjoint path cover does not exist.

Spotting Faults Over the Spectrum: Fast and Online Antenna Array Fault Diagnosis for Multi-Carrier Precoding Systems

We present a new online method to diagnose faulty elements in an antenna array quickly and reliably while a wireless communication system is under regular operation. Existing methods for fault diagnosis do not usually allow simultaneous operation and diagnosis, leading to downtime, which becomes a severe constraint for modern wireless systems. Our approach is based on exploiting an extra degree of freedom available in the frequency domain in modern multicarrier hybrid/digital precoding architectures by allocating a very small part of the available spectrum for diagnosis while the rest is used for regular operation. Measurements can be made in a single shot by using optimized inputs, thereby minimally disrupting regular operation. We address both fully and partially connected RF precoding architectures of hybrid precoders and introduce one and two-stage fault diagnosis procedures, respectively. While the faulty antennas for fully connected hybrid architectures or digital architectures are diagnosed employing ideas from sparse recovery, additional ideas from block-sparse recovery are used in the case of a partially connected architecture. Our results indicate that online array fault diagnosis is possible in both digital and hybrid precoding architectures by introducing simple digital operations into the system and by using a single fixed receiver measurement setup.

Deep learning based structural damage identification for the strain field of a subway bolster

Strain-based structural health monitoring technology has been widely used in the field of transportation. The existing strain damage identification methods have defects such as complex process, lag in state evaluation, and low intelligence. This paper adopts the deep learning method to establish a network model that uses the strain field information to map directly to the damage information, and takes a subway bolster as the engineering background to realise the end-to-end automatic damage identification. Firstly, the problem of damage identification in strain field is described, combined with the idea of fully convolutional network. The basic structure of damage identification network is modularised, and the overall design framework is proposed. Then, the damage simulation method is determined, and the feasibility of using this method to construct a strain field damage dataset is verified. The batch random damage model generation and the random noise signal addition program are coded, and the datasets of the bolster under static/dynamic force are obtained. Finally, the deep learning model is applied to the bolster damage dataset, and a residual module BolRes_Att that integrates spatial attention and channel attention mechanism is proposed. It has better damage identification performance without an increase in model parameters. The average number of faulty elements on the two bolster test sets of the improved damage identification network is 3.02 and 2.92, respectively, accounting for about 0.016% of all elements. The average processing time for a set of data is only 0.014 s. The results show that the deep learning model constructed in this paper can accurately and quickly identify the damage information of elements according to the strain field information.

Fault Diagnosis in Analog Circuits Using Swarm Intelligence.

Open or short-circuit faults, as well as discrete parameter faults, are the most commonly used models in the simulation prior to testing methodology. However, since analog circuits exhibit continuous responses to input signals, faults in specific circuit elements may not fully capture all potential component faults. Consequently, diagnosing faults in analog circuits requires three key aspects: identifying faulty components, determining faulty element values, and considering circuit tolerance constraints. To tackle this problem, a methodology is proposed and implemented for fault diagnosis using swarm intelligence. The investigated optimization techniques are Particle Swarm Optimization (PSO) and the Bat Algorithm (BA). In this methodology, the nonlinear equations of the tested circuit are employed to calculate its parameters. The primary objective is to identify the specific circuit component that could potentially exhibit the fault by comparing the responses obtained from the actual circuit and the responses obtained through the optimization process. Two circuits are used as case studies to evaluate the performance of the proposed methodologies: the Tow-Thomas Biquad filter (case study 1) and the Butterworth filter (case study 2). The proposed methodologies are able to identify or at least reduce the number of possible faulty components. Four main performance metrics are extracted: accuracy, precision, sensitivity, and specificity. The BA technique demonstrates superior performance by utilizing the maximum combination of accessible nodes in the tested circuit, with an average accuracy of 95.5%, while PSO achieved only 93.9%. Additionally, the BA technique outperforms in terms of execution time, with an average time reduction of 7.95% reduction for the faultless circuit and an 8.12% reduction for the faulty cases. Compared to the machine-learning-based approach, using BA with the proposed methodology achieves similar accuracy rates but does not require any datasets nor any time-demanding training to proceed with circuit diagnostic.

A Novel Conditional Connectivity and Hamiltonian Connectivity of BCube with Various Faulty Elements

BCube is one of the main data center networks because it has many attractive features. In practical applications, the failure of components or physical connections is inevitable. In data center networks in particular, switch failures are unavoidable. Fault-tolerance capability is one main aspect to measure the performance of data center networks. Connectivity, fault tolerance Hamiltonian connectivity, and fault tolerance Hamiltonicity are important parameters that assess the fault tolerance of networks. In general, the distribution of fault elements is scattered, and it is necessary to consider the distribution of fault elements in different dimensions. We research the fault tolerance of BCube when considering faulty switches and faulty links/edges that distribute in different dimensions. We also investigate the connectivity, fault tolerance Hamiltonian connectivity, and Hamiltonicity. This study better evaluates the fault-tolerant performance of data center networks.

Fault Isolation and Estimation in Networks of Linear Process Systems.

Fault detection and isolation is a ubiquitous task in current complex systems even in the linear networked case when the complexity is mainly caused by the complex network structure. A simple yet practically important special case of networked linear process systems is considered in this paper with only a single conserved extensive quantity but with a network structure containing loops. These loops make fault detection and isolation challenging to perform because the effect of fault is propagated back to where it first occurred. As a dynamic model of network elements, a two input single output (2ISO) LTI state-space model is proposed for fault detection and isolation where the fault enters as an additive linear term into the equations. No simultaneously occurring faults are considered. A steady state analysis and superposition principle are used to analyse the effect of faults in a subsystem that propagates to the sensors' measurements at different positions. This analysis is the basis of our fault detection and isolation procedure that provides the position of the faulty element in a given loop of the network. A disturbance observer is also proposed to estimate the magnitude of the fault inspired by a proportional-integral (PI) observer. The proposed fault isolation and fault estimation methods have been verified and validated by using two simulation case studies in the MATLAB/Simulink environment.

The partial diagnosability of interconnection networks under the Hybrid PMC model

System level diagnosis is a primary method to identify the faulty elements in a multiprocessor system. Different from the traditional system level fault diagnosis models, the new fault diagnosis model, hybrid PMC model (short for HPMC model), is suitable to the circumstances that the processor and link faults occur simultaneously. Under the HPMC model, the q-edge restricted diagnosability of a system H, denoted by tqe(H), is the maximum number p such that H can correctly identify all the faulty elements containing p faulty processors and at most q faulty links. Similarly, the p-vertex restricted edge-diagnosability of a system H, denoted by spv(H), is the maximum number q such that H can correctly identify all the faulty elements containing q faulty links and at most p faulty processors. These two diagnosability are collectively referred to as partial diagnosability. We investigate and determine the q-edge restricted diagnosability of a system H under the HPMC model for q≥η(H), where η(H)=max⁡{|N(u)∩N(v)||uv∈E(H)}. In addition, we investigate the relationship between tqe(H) and spv(H) under the HPMC model, and show that under some conditions, spv(H)=q if tqe(H)=p. Applying our results, the two kinds of partial diagnosability of k-ary n-cubes, bijective connection networks and undirected Kautz graphs are established.

A Comparison of Faulty Antenna Detection Methodologies in Planar Array

Broadcasting, radar, sonar and space telecommunication systems use phased arrays to produce directed signals to be transmitted at the desired angle. This system requires a large number of antenna elements. The presence of faulty element(s) in an array causes asymmetry, which results in a deformed radiation pattern with higher sidelobe levels. Higher sidelobe levels indicate waste of energy by transmitting and receiving signals in unwanted directions. Hence, it is important to develop a method that detects faulty elements and corrects the radiation pattern. To correct the failed radiation pattern, failed elements in an array must be identified first. There have been various studies conducted on linear array failed radiation pattern correction and the finding of faulty elements, but investigation on the planar array is limited. Further, the optimization suggested for linear arrays does not necessarily work for the planar array. In this study, planar array faulty antenna detection was developed with pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO) methods by reducing the Signal to Noise Ratio (SNR) as the objective function. The analysis was varied for 8 × 8 and 6 × 6 planar arrays with different types of failures. The results were compared to find the best method to identify the faulty element’s location in a planar array. The pattern search method produced outstanding results in finding the faulty element’s locations by providing 100% accuracy for all types of failure, while other methods failed to do the same.

Phased array antenna diagnosis from amplitude-only data using parallel deep learning models

There is a need for a reliable diagnostic process that can provide the information required to maintain optimal operating conditions of phased array antennas. The phase and amplitude information can be retrieved from the complex radiated field. However, phase measurement is laborious and costly at high frequencies. To overcome this challenge, it is of great relevance to create diagnostics methods that solely utilize the amplitude data. The shape and characteristics of the radiated field of a phased array antenna are predicated on the number of operating elements in the array. This implies that a failed element in the array will change the nature of the radiated pattern. We can locate a failed element using a learning algorithm to map the location of a failed element to the radiated field. We propose a technique for finding failed elements using only amplitude data by dividing the array into multiple subarrays. Each subarray is trained with dedicated deep convolutional neural networks for faulty element identification. We evaluated the proposed approach using simulated data for 20 × 20 and 30 × 30 array structures. The results demonstrate that the proposed approach can effectively diagnose and locate failed elements in a phased array antenna using amplitude-only data.

ПРИСТРІЙ ДЛЯ ВИЗНАЧЕННЯ ТЕХНІЧНОГО СТАНУ ЦИФРОВИХ ТЕЗЩО ВИКОРИСТОВУЄ ПАРАМЕТРИ ЕНЕРГОДИНАМІЧНОГО ПРОЦЕСУ

The article examines the structural and functional diagrams of the device for diagnosing existing and prospective typical digital replacement elements, which contain microprocessor-based on large integrated circuits and are part of the radio-electronic equipment of the Armed Forces of Ukraine. The principle of operation of the device is based on the use as diagnostic information of initial reactions (IR) of object of diagnosis and parameters of the energy-dynamic process. The use of two power supply voltages and the step-by-step construction of the test sequence (TS) makes it possible to make a decision about the operational efficiency of the object of diagnosis with reliability no lower than specified for an acceptable time. Decision-making about the technical condition of a typical replacement element and localization of defects is carried out with the help of a personal computer based on the theory of fuzzy logic. The computer is the core that provides the analysis of the available and received diagnostic information, the formation of test sequences (TS) and decision-making about the technical condition of the object of diagnosis. It allows you to quickly process a large amount of diagnostic information, carry out its analysis, storage and replenishment due to the knowledge of experts and the accumulation of statistics. This makes it possible to exclude some elementary test effects, the need for which is eliminated based on the results of the obtained data, as well as to reduce the number of sets in the TS, which leads to a reduction in diagnosis time. Step-by-step execution of commands and current analysis of the situation allows not only to carry out diagnostics up to the first faulty element, but also makes it possible to conduct a further search for faulty elements. The advantage of the proposed device is the possibility of increasing the reliability of diagnosis without increasing its duration.

Метод углубленного диагностирования главного тормозного цилиндра автотранспортного средства

The use of new methods for element-by-element diagnostics of the brake system increases the probability of detecting malfunctions, helps to identify faulty elements in the nodes of the brake system in advance, which helps to reduce accidents on the roads of the country. The development of a method for in-depth diagnosis of the master brake cylinder is an urgent task. Earlier, as part of the development of a new method for diagnosing the main brake cylinder of a hydraulic brake system, a set of diagnostic parameters was determined by the dynamic parameters of the resistance force to pressing the brake pedal. The need to introduce the method for practical use at road transport enterprises set the authors the task of determining the maximum permissible values of diagnostic parameters based on the comparison results, with which a conclusion will be made about the technical condition of the object with subsequent diagnosis. The aim of the work is to determine the maximum allowable values of diagnostic parameters, and to develop an algorithm for making a diagnosis. The maximum permissible values of the diagnostic parameters of the main brake cylinder were determined on the basis of a statistical method. The main provisions of the method are that a one-time sample of N values of the diagnostic parameter was considered for representatives of the set of diagnostic objects (both serviceable and faulty). At the same time, it was assumed that the values corresponding to the serviceable state would obey a different pattern than the values corresponding to the faulty one. Having singled out the distribution function f(S) of the parameter values for the healthy state of the object from the total population, the range of acceptable values of the diagnostic parameter in operation can be limited by some limiting dispersion relative to the nominal (reference) value of the parameter. For the braking system affecting traffic safety, the restriction was applied with 85% probability. The values were found for the entire range of diagnostic parameters. In the course of the study, the maximum permissible values of diagnostic parameters were determined. A diagnostic matrix of the main brake cylinder has been built. An algorithm for making a diagnosis has been developed. The scientific novelty lies in the difference in their implementation in the range of service braking, which makes it possible to maximize the informative possibility of each of the diagnostic parameters.

Method of Using radio-electronic Equipment Diagnostics Durable Systems and Devices for Localization of Defective Elements

This article focuses on enhancing fault tolerance and reliability in electronic systems by providing recommendations for diagnosing and repairing basic semiconductor devices. The study introduces a novel method for diagnosing radio-electronic equipment using a device that localizes faulty elements. The goals of the research include improving fault localization accuracy and reducing repair time. The proposed method is based on the principles of fault isolation and utilizes advanced diagnostic techniques. Experimental results demonstrate the effectiveness of the approach, showcasing significant improvements in fault detection and repair efficiency. The implications of this research suggest that implementing the proposed method can enhance the reliability and maintainability of electronic systems, leading to reduced downtime and improved overall system performance. 
 Highlights:
 
 
 Improved fault localization accuracy: The article presents a novel method for diagnosing radio-electronic equipment that focuses on accurately localizing faulty elements, leading to enhanced fault detection and diagnosis precision.
 
 
 Reduced repair time: The research aims to provide recommendations for repairing basic semiconductor devices, resulting in reduced repair time and improved system availability, ultimately minimizing downtime and improving overall system efficiency.
 
 
 Enhanced system reliability: By implementing the proposed method and incorporating advanced diagnostic techniques, the article highlights the potential for improving the reliability of electronic systems, leading to increased system performance and reduced maintenance efforts.
 
 
 Keywords: fault tolerance, reliability, electronic systems, diagnostics, repair.

Review of Methods for Addressing Challenging Issues in the Operation of Protection Devices in Microgrids with Voltages of up to 1 kV That Integrates Distributed Energy Resources

With the large-scale integration of distributed energy resources (DER) into passive distribution networks with voltages of up to 1 kV, these networks are being converted into microgrids. When the topology and operating conditions change, several challenging issues arise related to the functioning of the protection devices (PD) that are in operation. Most DERs, including renewable generators, are integrated into microgrids by means of inverters. In the event of short circuits (SC) in microgrids, these DERs provide a fault current contribution of no more than 1.2–2.0 Irated at the fault location. This makes it difficult to identify the fault location and to carry out the selective disconnection of the faulty element by means of conventional PDs. This article provides an overview of engineering solutions for improving conventional protection schemes that have been historically used in passive distribution networks, as well as for creating modern protection schemes based on innovative principles and new methods. The use of adaptive protections built on decentralized and centralized principles in most cases ensures the reliable protection of microgrids. Modern intelligent electronic devices (IEDs), where protection functions are implemented, rank higher with respect to their technical perfection in terms of reliability, sensitivity, selectivity, and speed performance. The use of multi-agent systems in the implementation of modern protection schemes requires the availability of broadband communication channels, which hinders their use because of the high cost. The combined use of fault current limiters (FCL) and energy storage systems (ESS) allows for the reliable operation of microgrid protections. The use of modern PDs ensures the reliable operation of DERs and power supply to consumers in microgrids, both in the case of grid-connected and islanded operation modes. Since there is no unified concept of designing protection schemes for microgrids with DERs, the choice of specific approaches to the design of protection schemes should be based on the results of a comparative technical and economic analysis of different options.

Fast Antenna Array Diagnosis Based on Shifted Base Mode Character From Sparse Measurements

A novel fast antenna array diagnosis based on the shifted base mode character is presented. The problem is solved by the following steps: first, mathematically shift the reference phase origin (RPO) of the damaged far-field (FF) to the position of each element of the array one by one, then represent the shifted FFs with orthogonal mode, and then extract the base mode. In this way, the mapping from the element positions to the shifted base modes is established. Because the base mode is a sinc function of the off-center distance, the base mode shifted to the position of the faulty element will change significantly compared with the one shifted to its adjacent element. Since the larger sampling interval will not cause the base modes aliasing, the method can fast locate the faulty elements from sparse sampling (close to but less than twice the Nyquist interval). Compared with the existing methods based on compressive sensing and genetic algorithms, the proposed method avoids the prior knowledge of the failure-free array radiation pattern and the iteration solution, which can remarkably reduce the time of the measurement and data postprocessing. A wide set of full-wave simulations is done to assess the efficiency of the method.

Methodology for the development of diagnostic support of multiple-output objects during their design and operation

Despite the rapid increase in the reliability of the elemental base of modern means of special communication, the issue of ensuring their maintainability, the value of which indicators are regulated by governing documents, is an urgent issue. Searching for faulty elements takes up a significant amount of time in the current repair of special communication equipment, so improving the diagnostic support is quite important. This is achieved by using effective algorithms for the search activity of craftsmen, which reduce the required number of checks for damaged equipment. It was established that up to 30% of failures of special communication means are caused by malfunctions of their secondary power supply, which belong to the class of multi-output objects. In the article, with the use of modern achievements of technical diagnostics and metrology, which were not taken into account before, possible options for constructing conditional algorithms of diagnostics are investigated. Also, their quality indicators are studied depending on the structural features of the object of diagnosis and the results of comparison are given. The conditions for the preferential selection of defect search algorithms based on the criterion of the minimum average recovery time have been established, and the procedure for solving this task has been formalized.
 Approaches to ensuring the necessary level of reliability of radio-electronic means during the design of the design of the products are also considered, taking into account the metrological and diagnostic support for their operation. It is known that up to 80% of the time of current repairs is spent searching for defects, so special attention is paid to the effect of product design on minimizing the average time of diagnosis. Considered possible options for restoring the functionality of multi-output objects, which include power supply subsystems. It is shown that a well-reasoned choice of design, metrological and diagnostic support reduces the time of current repair by up to 30%.

Model calibration for compressive sensing based linear antenna array fault diagnosis

ABSTRACT The Compressive Sensing (CS) theory has been widely studied as it requires only a small number of measurements to identify faulty elements of antenna arrays. However, the model for linear antenna array fault diagnosis based on CS is much sensitive to model mismatch and the identification results usually turn out to be much worse than expected in the practical situation. In this study, the model mismatch for linear antenna array fault diagnosis is first analyzed and then a CS model calibration method is proposed to improve the performance of the CS-based linear antenna array fault diagnostic method by solving a convex optimization model. Numerical results demonstrate that the calibrated CS model is much more accurate in identifying faulty antenna elements under model mismatch and noise than the traditional one.