Fault Detection - Science topic

Aircraft inspection ensures flight readiness by identifying faults. We benchmark state-of-the-art supervised and unsupervised models to evaluate suitability for aircraft fault detection. Supervised models are recommended for the real-time detection of known defects using an aerial multi-robot system (MRS). We developed an aircraft defect dataset to...

This article presents a state-of-the-art review of machine learning (ML) methods and applications used in smart grids to predict and optimise energy management. The article discusses the challenges facing smart grids, and how ML can help address them, using a new taxonomy to categorise ML models by method and domain. It describes the different ML t...

Solar energy has become the fastest growing renewable and alternative source of energy. However, there is little or no open-source datasets to advance research knowledge in photovoltaic related systems. The work presented in this article is a step towards deriving Photo-Voltaic Module Dataset (PVMD) of thermal images and ensuring they are publicly...

Renewable energy systems have become integral components of the electrical grid, offering environmental benefits and cost-effective power generation. Technological advancements have introduced internet of things (IoT) devices with robust data collection and execution capabilities. Solar photovoltaic systems, reliant on unpredictable solar radiation...

Fault-as-address-simulation (FAAS) is a simulation mechanism for testing combinations of circuit line faults, represented by the bit addresses of element logical vectors. The XOR relationship between the test set T and the truth table L of the element forms a deductive vector for fault simulation, using truth table addresses or the logic vector bit...

Air compressors are critical components in many industries whose catastrophic failure results in huge financial losses and downtime leading to accidents. Hence, real time fault diagnosis of air compressor is essential to predict the health condition of air compressor and plan scheduled maintenance thereby reducing financial losses and accidents. Fa...

Individuals across different industries, including but not limited to agriculture, drones, pharmaceuticals and manufacturing, are increasingly using thermal cameras to achieve various safety and security goals. This widespread adoption is made possible by advancements in thermal imaging sensor technology. The current literature provides an in-depth...

The identification, categorization, and localization of faults play a crucial role in maintaining the smooth operation of power systems. Distance relays possess a significant capability to withstand power fluctuations, thereby minimizing inadvertent disruptions in transmission lines. Addressing these challenges involves the adoption of advanced fau...

This paper proposes a new diagnostic approach for identifying bearing faults in induction motors, which are common issues that can affect the performance and durability of these motors. Although various methods have been developed to diagnose these faults, we propose a high-resolution technique based on stator current analysis, enabling more effect...

With the rapid advancements in very large scale integration (VLSI) and integrated circuit (IC) technology, the complexity of devices has escalated significantly. Designing a VLSI chip is essential for scaling up the capabilities of chips to meet the growing demands of modern applications, like artificial intelligence (AI), IoT, and high-performance...

This paper introduces an approach decentralized to fault detection and isolation (FDI) in manufacturing systems using a Boolean discrete event model. The method incorporates diverse information sources to create distinct models for plant systems and control. The objective is to enhance the understanding of process operations by employing various re...

The growing complexity of distributed network systems has brought forth the need for advanced software testing strategies to ensure reliability and high performance. Traditional manual testing approaches often struggle to meet the demands of these large, interconnected systems. Leveraging Machine Learning (ML) and Artificial Intelligence (AI) in te...

An artificial lift is a technique for pumping fluids in the petroleum industry today. Most artificial lift that is used nowadays is an electrical submersible pump (ESP). ESP is very convenient and reliable for lifting production. It applies under offshore and onshore industries, where it has high displacement capacity and flexibility to handle vari...

The wireless sensor network (WSN) has received significant recognition for its positive impact on environmental monitoring, yet its reliability remains prone to faults. Common factors contributing to faults include connectivity loss from malfunctioning node interfaces, disruptions caused by obstacles, and increased packet loss due to noise or conge...

The evolution of distributed networks and the increasing complexity of software systems have highlighted the need for advanced testing solutions. Machine learning (ML) and artificial intelligence (AI) offer transformative solutions, enhancing the efficiency, accuracy, and scalability of testing processes in distributed network systems. AI-powered t...

Implementation aspects of fault-ride-through (FRT) operation in voltage source converter with virtual synchronous generator (VSG) control are discussed in this paper. The phenomenon of voltage decline during fault periods is recognized to be intricately associated with the interplay between power angle movement and the trajectory of current saturat...

The issue of quality-related fault detection in the industrial process has attracted much attention in recent years. The partial least squares (PLS) is considered an efficient tool for predicting and monitoring. The modified partial least squares (MPLS) is an extended algorithm for solving the oblique decomposition of PLS, however, the study indica...

The increasing complexity and scalability demands of modern distributed networks pose significant challenges for software testing. Traditional testing methods often fall short when dealing with large-scale systems that require high reliability, efficient resource management, and the ability to adapt to dynamic network conditions. Artificial Intelli...

The case dependency of vibration-based Structural Damage Detection (SDD) models on their training structure (data) has always been a setback in their application to new structures with no or limited data. Obtaining labeled data for both non-damage and damage conditions is an expensive and time-consuming process, which is nearly impractical for oper...

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient fo...

As humanity looks toward the Moon for future exploration and potential habitation, energy management becomes one of the most critical challenges to ensure the success of long-term lunar missions. Lunar microgrids, designed to power lunar habitats and scientific equipment, must operate autonomously due to the extreme remoteness and harsh environment...

Due to the factors such as high efficiency, power density and excellent torque speed characteristics, PMSM is being widely used in Electric Vehicles and other industrial applications. Hence, a lot of research works are going on to improves its control strategies. PMSM being costlier than induction motor, this paper focuses on quicker and real time...

The increasing complexity and scale of cloud data infrastructures necessitate advanced monitoring systems to ensure data reliability and integrity. Traditional monitoring approaches often fall short in addressing dynamic and intricate failure patterns inherent in modern cloud environments. This paper explores the enhancement of cloud data reliabili...

This paper is dedicated to solving the problem of concept drift in industrial plants using artificial intelligence methods. For this purpose, methodological approaches and procedures are considered and analyzed. Based on the findings, reference architectures were developed at different abstraction levels that can be used in an industrial environmen...

Field Programmable Gate Arrays are extensively used in space, military, and commercial sectors due to their reprogrammable nature. In high-safety environments, ensuring fault tolerance is crucial to improving the performance of electronic and computational systems. Common fault-tolerant methods include time redundancy, double modular redundancy, tr...

The rapid expansion of power distribution networks demands robust transmission line protection systems to maintain a continuous electricity supply and safeguard infrastructure. This project proposes an integrated system that combines transformer protection with an innovative power theft detection approach. It enhances power grid reliability by offe...

With the advent of the information age, the evolution of aerospace technology has rendered high-altitude flights increasingly common and vital. Nonetheless, the fault diagnosis of the pressure chamber, a crucial aspect of ensuring flight safety, remains an urgent challenge. The integration of segmented control technology in this domain further augm...

The increasing complexity and demand for efficient power system operation necessitate advanced modeling and analysis techniques to ensure the stability, reliability, and optimization of alternating current (AC) power systems. This paper explores the applications of machine learning (ML) and deep neural network (DNN)-based techniques in the dynamic...

Reliable fault detection is an essential requirement for safe and efficient operation of mechanical systems in various industrial applications. As machine complexity increases, the number of sensors required to measure over time to infer abnormal and normal behavior increases dramatically, while despite the abundance of existing approaches and the...

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity mark...

Advancements in pulse generation automation have significantly improved the efficiency and accuracy of circuit testing and fault detection in modern electronic systems. The increasing complexity of integrated circuits and the demand for high-performance systems necessitate more effective methods for identifying faults and ensuring optimal functiona...

This paper presents an original approach to error correction in real-time systems. The proposed solution is based on the original multitasking system architecture, which was recently analyzed for energy. The authors have added a structure to correct random errors and distortions at the signal level, increasing reliability. The authors overview thei...

Addressing the challenge of diagnosing incipient bearing faults amidst significant noise, a novel diagnostic approach is introduced, leveraging a Rank Constrained Low-Rank and Sparse Decomposition (RCLRSD) model tailored for weak fault detection in bearings. Initially, we raised the Autocorrelation Function of the Square Envelope in Frequency Domai...

Developing reliable protection systems is critical for the advancement of medium-voltage direct-current (MVDC) grids. This paper highlights the significance of fault detection in MVDC grids, especially in ensuring the reliability and efficiency of renewable energy systems. This paper provides a comparative analysis of fault detection algorithms, in...

Microgrids are the most popular power generation technology in recent years due to advancements in power semiconductor technology, but protection is a crucial task when a critical fault occurs in the microgrid. Fault detection under critical conditions is a complex task due to the dissimilar magnitude of the fault current which can be small or larg...

In order to improve the self-healing control ability of AC-DC hybrid distribution network and ensure the stability of distribution network operation, a coordinated oscillation control strategy for AC-DC hybrid distribution network based on mixed-integer linear programming (MILP) is proposed. By constructing a multiscale reactive power optimization...

Efficiency of fault detection in rolling element bearings is heavily influenced by the quality of data. In controlled environments, such as test rigs designed for bearing diagnostics, data quality is relatively good. Similarly, diagnosing bearings that support shafts in industrial machinery is relatively straightforward. However, diagnosing bearing...

This systematic review provides a comprehensive analysis of the applications, challenges, and future directions of Artificial Neural Networks (ANNs) in the field of power electronics. Over recent decades, ANNs have gained significant traction as powerful tools for solving complex problems in power electronics systems, including control, optimizatio...

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate ca...

Idlers are essential to conveyor systems, as well as supporting and guiding belts to ensure production efficiency. Proper idler maintenance prevents failures, reduces downtime, cuts costs, and improves reliability. Most studies on idler fault detection rely on supervised methods, which depend on large labelled datasets for training. However, acquir...

In the development of future automotive systems, safety and performance are crucial considerations. The reliable operation of Drum-type Electromechanical Brakes (D-EMBs), key components responsible for vehicle braking, is essential. Previous research has predominantly focused on post-fault response strategies, emphasizing fault detection and diagno...

This paper presents an advanced automatic fault classification method for detecting rotating rectifier faults in brushless synchronous machines (BSMs). The proposed approach employs a multilayer perceptron (MLP) neural network to classify the operational states of the rotating rectifier, including healthy conditions and common fault types: open-dio...

Linear Matrix Inequalities (LMIs) have recently gained momentum due to the increasing performance of computing hardware. Many current research activities rely on the advantages of this growth in order to design controllers with provable stability and performance guarantees. To guarantee robustness despite actuator faults, model uncertainty, nonline...

O artigo intitulado “Priorização dos casos de testes de software: uma abordagem utilizando Clustering e AHP-gaussiano” tem como objetivo otimizar a priorização de casos de testes de software para maximizar a detecção de falhas. Foram empregadas duas abordagens principais: 1) AHP-Gaussiano: Um método de apoio à decisão que prioriza casos de teste co...

With the growing need for precise campus electricity management, understanding load patterns is crucial for improving energy efficiency and optimizing energy use. However, detailed electricity load data for campus buildings and their internal equipment is often lacking, hindering research. This paper introduces an energy consumption monitoring data...

Effective fault identification and diagnosis in photovoltaic (PV) arrays is vital for improving the effectiveness and safety of solar energy systems. While various artificial intelligence methods have successfully established fault detection and diagnosis models, introducing inefficiencies and potentially overlooking useful features. Moreover, thes...

It is well known that seismic data (or seismic volumes/images) are one of the primary work materials of the oil and gas industry. Nevertheless, the manual interpretation of such data has become increasingly time-consuming and prone to errors. This problem arises due to the massive amount of data and the complexity and variability of geological patt...

This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation i...

This study presents a tracking and fault-tolerant controller architecture for uncertain steer-by-wire (SbW) systems using model predictive control in the presence of actuator malfunction and the nonlinear properties of tire lateral stiffness coefficients. By changing the internal model, the model predictive control (MPC) technique was used to achie...

Modular deep learning model for improved fault detection in industrial datasets. • Addresses data sparsity and imbalance in industrial diagnostics using multimodal data. • A new stratification algorithm for highly imbalanced multimodal mul-tilabel datasets. • Application of proposed methodology to real world dataset from hy-drogenerators. • Scalabl...

Wind energy is considered a sustainable renewable energy source; however, it faces the challenge of significant operating and maintenance costs. The research proposes a hybrid fault detection method to combine the physical domain knowledge with the machine learning models to provide an overview of the health of wind turbine drivetrain components. S...

This paper concentrates on asynchronous thruster fault detection for unmanned marine vehicles (UMVs) under multiple cyber attacks, external disturbances and thruster fault. A novel model of multiple attacks is constructed using an asynchronous switched method by considering aperiodic denial-of-service (DoS) attacks and stochastic false data injecti...

The presence of missing values in the data poses challenges for fault detection tasks in wind power processes. The conventional data filling methods commonly focus on the process data with a single mode, disregarding the multimodal properties arising from time-varying characteristics in wind power processes. In this paper, to address the challenge...

Early fault detection plays a crucial role in the field of predictive maintenance for wind turbines, yet the comparison of different algorithms poses a difficult task because domain-specific public datasets are scarce. Many comparisons of different approaches either use benchmarks composed of data from many different domains, inaccessible data, or...

This study investigates the integration of machine learning (ML) algorithms with smart sensor technologies across manufacturing, energy, and healthcare sectors, focusing on their impact on real-time industrial monitoring, predictive maintenance, and operational efficiency. By utilizing data from the UCI Machine Learning Repository and Kaggle, this...

A method is proposed for fault classification in milling machines using advanced image processing and machine learning. First, raw data are obtained from real-world industries, representing various fault types (tool, bearing, and gear faults) and normal conditions. These data are converted into two-dimensional continuous wavelet transform (CWT) ima...

Fault detection technology based on parameter identification is advancing rapidly. For power electronic circuits, parameter identification is essential for accurately evaluating performance. The commonly used identification algorithms in engineering include least squares method and particle swarm optimization algorithm, etc. The least squares metho...

This study proposes a fault diagnosis method for harmonic drive systems based on multi-sensor data fusion and the gravitational search algorithm (GSA). As a critical component in robotic arms, harmonic drives are prone to failures due to wear, less grease, or improper loading, which can compromise system stability and production efficiency. To enha...

Considering the problems of difficult target detection and recognition and low accuracy caused by factors such as uneven illumination, poor working conditions, complex structure of tank guide and narrow space in coal mine. This paper simulates the complex working environment of the underground mine to carry out different fault conditions experiment...

The rapid evolution of Internet of Things (IoT) environments has created an urgent need for secure and trustworthy distributed computing systems, particularly when dealing with heterogeneous devices and applications where centralized trust cannot be assumed. This paper proposes TrustMesh, a novel blockchain-enabled framework that addresses these ch...

In this study, we discussed the reliability assessment for Open Source Software by introducing a new software reliability growth model based on stochastic differential equations. We considered that the software failure rate depends on time (t), and the reporting of faults is irregular. First, we determined its probability distribution by transformi...

Accurate monitoring of complex industrial plants is crucial for ensuring safe operations and reliable management of desired quality. Early detection of abnormal events is essential to preempt serious consequences, enhance system performance, and reduce manufacturing costs. In this work, we propose a novel methodology for fault detection based on Sl...

As a crucial component of CRH (China Railway High-speed) trains, the safety and stability of the suspension system are of paramount importance to the overall vehicle system. Based on the framework of probabilistic relevant principal component analysis (PRPCA), this paper proposes a novel method for incipient fault diagnosis in the CRH suspension sy...

Machine Learning-based fault detection approaches in energy systems have gained prominence for their superior performance. These automated approaches can assist operators by highlighting anomalies and faults, providing a robust framework for improving Situation Awareness. However, existing approaches predominantly rely on monolithic models, which s...

Fault diagnosis in rolling element bearings is critical for ensuring machinery reliability. This study improves machine learning techniques for predictive fault detection using the benchmark CWRU bearing dataset. Vibration signal data is preprocessed via balancing and graph-based feature engineering is performed to enable effective model training....

Smart grids (SGs) are essential for the efficient and distributed management of electrical distribution networks. A key task in SG management is fault detection and subsequently, network reconfiguration to minimize power losses and balance loads. This process should minimize power losses while optimizing distribution by balancing loads across the g...