Discrete Analysis and Operations Research

An approach to automatic fault detection in four-point system for knitted fabric with our benchmark dataset Isl-Knit

Electric motor drives with a wide variety of applications are usually derived with inverters, where the inverter switches are always prone to different types of faults. Short circuit faults can rapidly shut down systems, and open-circuit ones can lead to secondary damage if they are not detected and tolerated in time. Due to this fact, in this paper, a novel data-driven fault detection and diagnosis (FDD) method has been proposed to detect and locate all types of inverter switch faults. Three deep learning algorithms, including fully connected neural networks (FCNs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM), have been implemented and compared. The BiLSTM network with 98.45% accuracy outperforms the others and can detect all types of faults in less than half a fundamental period under different and variable speeds with the existence of noise. The results show that the proposed method is highly effective and is a great candidate for real-time applications.

The Matrix converters (MCs) are widely used in a large number of applications such as Aircraft, Submarines, and AC drives. In these applications, many chances of occurrence of a critical fault that leads to a halt of the system, and due to the failure of power electric switches, the MC operations can be compromised and there is a fear of burning the components caused by the whole system shutdown. Therefore, a Fault-Tolerant Control System (FTCS) is extremely necessary for the continued operations of the Three-Phase Matrix Converter (TPMC) to improve the reliability and productivity of the system under faulty conditions. In this paper, an advanced FTCS is proposed based on detection, and dual hardware redundancy. Two types of faults are injected into the system to observe the performance of the proposed system: internal open circuit faults on switches and external short circuit faults at the load side. A Fault Detection and Isolation (FDI) unit is used to detect and isolate the faulty switches using Artificial Neural Networks (ANN), and dual hardware redundancy in the switches has been proposed for fault tolerance. In case of external fault, a load-side fault detector is implemented using ANN. The simulation results in MATLAB/Simulink environment show the accurate and stable working of TPMC under faulty conditions, and hardware-in-the-loop is implemented with STM32-Nucleo-F103RB board to verify open circuit fault results. The proposed work dual redundant FTC with FDI unit offers an excellent solution for the continued performance of TPMC which enhances the reliability of the system.

Objectives: The manufactured Integrated circuits are usable only when they are free from any types of faults or error. This is even more essential in complex circuits like analog and mixed signal circuits. This paper focuses on the fault classification in the analog portion of mixed signal circuits using artificial neural networks. Methods/Statistical Analysis: All possible catastrophic possible faults in mixed signal circuits are introduced and simulated in an exhaustive manner using Cadence Simulation package. Faults are introduced one at a time and parameters were recorded. The parametric variations obtained through simulation were normalized and are used to suitably train artificial neural networks by creating them as database. The artificial neural network is trained such that it can identify correct functioning of the circuit from its faulty operation and also to further distinguish it into the component at fault, along with the type of catastrophic or hard fault. Findings: Comparative results of Feed forward neural networks trained with Levenberg-Marquardt algorithm and Radial basis function networks in the classification of faults are provided. Application/Improvements: This can be extended for the other mixed signal circuits by creating the training data using the software simulations. The hardware implementation is possible using the embedded controllers and other types of ANN can also be selected if necessary.

Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains.

Brushless DC (BLDC) motors are used extensively in the industry for a wide variety of applications. In the Aerospace industry, they are mainly used in the control actuation system in launch vehicles. Different types of faults can develop in a BLDC motor. It is essential to detect these faults in the early stage itself before they develop into system failures. In this paper, fault detection in BLDC motors using parameter estimation technique is presented. Detailed fault diagnosis is subsequently carried out by a fuzzy inference system. Discrete Square Root Filtering in Covariance form (DSFC) algorithm is used initially for estimating the physical parameters of the BLDC motor. These parameters are then passed on to the fuzzy inference system which carries out the detailed diagnosis. Simulation studies carried out indicates that the developed fault detection and isolation scheme is capable of isolating different types of faults like open circuit, short circuit faults, performance degradation of magnets etc. in BLDC motors.

Objectives: Scheduling is an optimization problem in computer science and operation research in which ideal jobs are assigned to particular times. Nowadays, the problem is presented as an online problem, that is, each job is presented and the online algorithm needs to make a decision about the job before the next job is presented. Methods and Statstical Analysis: The proposed approach is used to solve the open job scheduling problem in which any job can be connected with the available machine. That was implemented in matlab to available best results with hybrid evolutionary algorithm. Findings: In this paper, a hybrid algorithm based on the particle swarm optimization is proposed, for flexible, open shop scheduling problem, to minimize the make-span. First an effective new approach using two decisions based on parallel priories dispatching rules is applied. Next we develop a hybridizing HPSO, that presents new components for updating velocity and position using evolutionary operators, with an adaptive neighbourhood procedure based on the insert-interchange fitness function, selection, mutation, crossover. Application/Improvements: The performance of the proposed a new hybrid algorithm is compared to other benchmark problems. Keywords: Dispatching Rules, Evolutionary Operators, Flexible Open Shop Problem, Local Search, Particle Swarm Optimization

Wind turbine fault detection and identification using a two-tier machine learning framework

A practical fault detection approach for PV systems intended for online implementation is developed. The fault detection model here is built using artificial neural network. initially the photovoltaic system is simulated using MATLAB software and output power is collected for various combinations of irradiance and temperature. Data is first collected for normal operating condition and then four types of faults are simulated and data are collected for faulty conditions. Four faults are considered here and they are: Line to Line faults with a small voltage difference, Line to line faults with a large voltage difference, degradation fault and open-circuit fault. This data is then used to train the neural network and to develop the fault detection model. The fault detection model takes irradiance, temperature and power as the input and accurately gives the type of fault in the PV system as the output. This system is a generalized one as any PV module datasheet can be used to simulate the Photovoltaic system and also this fault detection system can be implemented online with the use of data acquisition system.

The demand for the Internet of Things (IoT) has raised in today's era significantly. Wireless Sensor Networks (WSNs) is the main component of IoT. However, the sensor nodes are prone to failures due to their deployment in hostile environments. In WSNs, fault in sensor nodes extensively degrades the network performances. The existing fault detection approaches are unable to diagnosis different types of faults in the sensor. Also, the fault detection accuracy of the existing approaches is very less. In this paper, a deep Bidirectional LSTM model is used to detect the fault in nodes. The proposed methodology can detect various types of faults such as soft permanent, intermittent and transient faults. The results show that the proposed algorithm outperforms the other state-of-the- art fault detection approaches in terms of fault detection accuracy, false alarm rate, and false-positive rate.

A k-dominating set of a graph, G, is a set of vertices, D⊆ V(G), such that for every vertex v ∈ V(G), either v ∈ D or there exists a vertex u ∈ D that is at distance at most k from v in G. We are interested in finding k-dominating sets of small cardinality. In this paper we consider a simple, yet efficient, randomised greedy algorithm for finding a small k-dominating set of regular graphs. We analyse the average-case performance of this heuristic by analysing its performance on random regular graphs using differential equations. This, in turn, proves an upper bound on the size of a minimum k-dominating set of random regular graphs.

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems.

With the increased densities of integrated circuits, several different types of faults can occur. Faults in digital circuits resulting from random defects can introduce DC (stuck-at) faults as well as AC (delay) faults. Previous work in statistical modeling and analysis for delay fault testing generally assumes that at most a single delay fault can occur along any given path in the circuit under test. In this paper we investigate the statistical effect of multiple delay faults along any path in a circuit under test, and predict the path delay fault probabilities as well as the maximum number of path delay faults for both combinational and sequential benchmark circuits. We begin with the development of a statistical model for path delay faults in VLSI circuits [4], which takes into account multiple delay faults along any signal path.

In Permanent Magnet Synchronous Motors (PMSMs) with multistrands windings, inter-turn short circuit (ITSC) faults occur in two types. One is that the ITSC fault occurs between two spots of a single strand and the other is that the ITSC fault occurs between two different strands. We proposed a fault indicator to detect two types of inter-turn short circuit faults in PMSMs that have multistrands windings. The fault indicator is calculated from stationary dq currents and their Hilbert transforms. The fault indicator converges to zero in healthy state but increases proportionally to the severity of the fault in faulty state. We validated the fault indicator with finite element method (FEM) simulation data. The proposed fault indicator only uses current data, so the indicator is simple and efficient tool to detect ITSC faults in PMSMs with multistrands windings.

Consumption of electrical energy has increased many folds in the recent time due to its use for industrial, commercial, agricultural, domestic and social purposes. HVDC technology is increasingly used for bulk power transmission due to advantages like low loss, ease of control, etc. However, the protection system for the HVDC system is not as mature as AC system. HVDC system requires fast and accurate fault detection. Recently, various time- and frequency-domain algorithms are proposed. In this paper, Stockwell (S-) transform is utilized for real-time fault detection. Different type of faults in HVDC transmission line are simulated using PSCAD. The generated fault signals are analyzed using MATLAB. S-transform can detect the transient signal effectively in time-frequency domain. It can be used to robustly differentiate the different type of faults and the load variation.