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34 articles for “Fault Diagnosis”
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Condition monitoring and fault diagnosis of gears based on vibration signatures - A review
Abstract: Gearbox is widely used in industrial and military applications. It is subjected to high service load and harsh operating conditions or inevitable fatigue, due to which faults may develop in gears. Fault diagnosis plays an important role in condition monitoring to enhance the machine time. Vibration-based condition monitoring can be applied in gear systems to detect the origination and expansion of fault before the occurrence of failure. Eventually, occurrence of …
Published in Trends in Mechanical Engineering & Technology · Vol. 9, Issue 1, 2019 · pp. 39–45 Read article
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Enhancing Maintenance Decision-Making in Thermal Power Plants Using Generative AI-Based Fault Diagnosis
Abstract: The growing complexity of operation and power consumption of thermal power stations involve the need to have intelligent fault diagnosis systems that can be used to guarantee reliability and safety in operation. In this research, a Generative AI (GenAI)-based hybrid architecture of early fault detection and predictive maintenance is proposed to improve the decision-making process of the maintenance team. The data-driven analytic approach combines methods of data-driven analytics, Generative AI …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 25–33 Read article
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Fault Diagnosis of Air Compressor (AC) System using Local Mean Decomposition (LMD) and Logistic Regression (LR) Machine Learning Classifier
Abstract: This article presents a detailed and systematic procedure for performing fault diagnosis in an air compressor (AC) system by analyzing the audio signals generated during its operation. The analysis covers both normal (healthy) conditions and seven distinct types of faults, including bearing failure, flywheel malfunction, inlet valve leakage, outlet valve leakage, non-return valve failure, piston ring defect, and rider belt issues. To acquire the acoustic signals, the researchers utilized a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 416–427 Read article
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A Review On Various Faults And Fault Diagnosis Of Induction Motor
Abstract: Currently the utilization of Conditioning Monitoring of electrical automatic machines are expanding because of its capability to lessen working costs, upgrade the reliable quality of activity and enhance administration to clients. Distinctive contrasting options to identify and analyze faults in induction machines have been proposed and actualized in the most recent years. In this paper various strategies to analyze faults looked into over recent years are evaluated and this report …
Published in Trends in Mechanical Engineering & Technology · Vol. 8, Issue 2, 2018 · pp. 43–50 Read article
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Neuro-Fuzzy Technique Based Fault Diagnosis in Shell and Tube Heat Exchanger
Abstract: In order to transfer heat between two different medium or else the same medium heat exchanger is used. It is most widely used in oil refineries and other large chemical processes, and it is also suitable for high-pressure applications. Because of the change in process parameters, the general warmth exchange rate will be reduced in the heat exchanger. In order to rectify it, the possible faults developed in the heat …
Published in Journal of Instrumentation Technology & Innovations · Vol. 6, Issue 2, 2016 · pp. 21–27 Read article
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Impact of Thermal Image Preprocessing Methods on a ResNet50 Model Baseline Performance for Ball Bearing Fault Diagnosis
Abstract: Ball bearings are an important part for the operational rotating machinery. To prevent it from failing, traditional engineering uses many methods. One of these is infrared thermography (IRT). IRT is a useful tool that helps monitor the situation without physical contact. But in the current world, various fields use artificial intelligence to do it more accurately & for fast detection. Deep learning models play an important role in fault detection …
Published in Trends in Mechanical Engineering & Technology · Vol. 16, Issue 3, 2026 Read article
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Bearing Fault Diagnosis Using ANN and SVM with the Help of Wavelet Transform Based Features
Abstract: Many researchers use vibration signals as fault characteristics of any rotating bearing for fault detection and use various optimization techniques available for fault of bearing classification. The method of fault classification uses few neural network (NN) geometry and parameters necessary for the formulation. There is no derived formulation which can be used to select the optimal values for the network parameters. The parameters which are required to be calculated, impacts …
Published in Recent Trends in Electronics Communication Systems · Vol. 4, Issue 2, 2017 · pp. 26–34 Read article
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Fabrication Fault Diagnosis of Bracket for Air Borne Vehicle Using Experimental and Analytical Modal Analysis
Abstract: This paper presents the experimental and analytical modal analysis of bracket structures made of two different materials. Manual inspection of fabricated components may at-times tend to be much tedious and less economical approach in identifying any geometrical and dimensional flaws when the quantity is more and may also result in failure, particularly when the purpose for which the components are designed or fabricated is very sensitive and crucial. Effective methodology …
Published in Journal of Experimental & Applied Mechanics · Vol. 7, Issue 2, 2016 · pp. 19–29 Read article
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Early Fault Diagnosis of Vehicle EGR System Based on Support Vector Machine Technique
Abstract: It has been a challenge to the automotive world to manufacture the smart vehicle components that contribute to the performance of vehicle operations to adhere due to strict vehicle emission norms. In this context, engine operations need to carry systematically, which helps to maintain the function of the vehicle emission system properly. Now a day’s electronic sensors are playing a vital role in smarter vehicle component operation. In-vehicle exhaust gas …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 9, Issue 3, 2021 · pp. 1–11 Read article
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Real-Time Fault Detection and Diagnosis in Switched Reluctance Motor Using Advance Deep Neural Network for Electrical Vehicles and Industrial Applications
Abstract: In today world, electric motor play very big role in industry and daily life. Switched reluctance motor (SRM) is one of the important types of motor because it is simple in construction, low cost and high efficiency but SRM also faces many problems like noise, vibration and fault in power converter or winding. When these faults come, performance of motor goes down and sometime full system stops working. To solve …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 3, 2026 Read article
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Real Time Ai Based Fault Detection in EV
Abstract: Electric vehicles (EVs) are rapidly increasing in usage, but motor faults can affect their performance and safety. This paper presents a real- time AI-based fault detection system using sensor data and embedded processing. The system utilizes an ESP32 microcontroller along with current, vibration, and temperature sensors to continuously monitor motor conditions. An intelligent algorithm analyzes real-time data to detect abnormalities such as overheating, overcurrent, and mechanical faults. When a fault …
Published in Journal of Microcontroller Engineering and Applications · Vol. 13, Issue 3, 2026 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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AI-Driven Predictive Maintenance Framework for Intelligent Vehicle Health Monitoring
Abstract: The accelerated development of smart and connected car systems made the necessity to find the accurate and real-time predictive maintenance solutions which would minimize the number of unexpected failures as well as increase the cars on-road safety. The current paper proposes an artificial intelligence-based hybrid predictive maintenance system that combines Long Short-Memory (LSTM) networks and the XGBoost predictor to provide a potent vehicle fault diagnosis, Remaining Useful Life (RUL) prediction, …
Published in Trends in Machine design · Vol. 13, Issue 1, 2026 · pp. 1–17 Read article
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Digital Twin and Machine Learning Framework for Predictive Maintenance of Nuclear Power Systems
Abstract: Nuclear power systems contain safety-critical components whose degradation can affect availability, reliability, and maintenance planning. Conventional maintenance strategies are largely based on scheduled inspection, threshold monitoring, and condition-based assessment, which may not fully exploit the large volume of continuously generated plant data. Digital Twin (DT) technology provides a mechanism for maintaining a synchronized virtual representation of a physical nuclear system, while machine learning (ML) can extract degradation patterns from multivariate …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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Feature Extraction and Analysis of Bearing Faults: A Review
Abstract: One of the most important steps in identifying bearing problems is feature extraction. In order to provide a more meaningful dataset, it entails locating and extracting pertinent features from raw bearing vibration signals. Tasks involving categorization and prediction can then make use of these attributes. In many practical applications, such as monitoring rotating machinery or electronic components, the raw signals collected (e.g., vibration, current, temperature) are often complex, high-dimensional, and …
Published in Trends in Mechanical Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 20–28 Read article
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IOT and algorithmic intelligent motor health monitoring as well as maintenance prediction
Abstract: Manufacturing, transportation, and energy systems rely largely on industrial electric motors, and their untimely failure can result in expensive downtime, safety hazards, and decreased operational efficiency. The majority of traditional motor maintenance procedures rely on reactive methods or routine inspections, which frequently miss early-stage problems and lead to needless maintenance or unexpected breakdowns. This project offers an Intelligent Motor Health Monitoring and Predictive Maintenance System that combines Internet of Things …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 28–37 Read article
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Bearing Defect Diagnosis: An Approach for Manufacturing Industries Using Wavelet Transform Based Features
Abstract: To maintain reliability in the manufacturing units, industries have concentrated their attention on the condition based maintenance. Fault detection and diagnosis are the two of three condition based maintenance mainstays. Bearing is one of the most important and essential components of the rotating machines. Hence, the researchers have shown their interest in bearing fault detection and diagnosis from the last few years. They mainly use bearing vibration as fault characteristics …
Published in Journal of Microelectronics and Solid State Devices · Vol. 4, Issue 1, 2017 · pp. 15–21 Read article
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To Diagnose the Broken Tooth Fault, Signal Processing Techniques and Machine Learning Technique are Applied
Abstract: The objective of this research is to study the diagnosis of broken tooth fault of spur gear using vibration signals with signal processing and machine learning techniques. In this study, experiment has been performed and analyse on the broken tooth fault and healthy spur gear conditions. This paper describes two approaches to signal processing techniques from acquired vibration signals, which are time-domain and frequency domain, involving statistical characteristics of vibration …
Published in Trends in Mechanical Engineering & Technology · Vol. 13, Issue 3, 2023 · pp. 45–53 Read article
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AI Based Electrical Safety and Predictive Maintenance System
Abstract: This review examines twenty research papers on artificial intelligence (AI), Internet of Things (IoT), condition monitoring, fault diagnosis and predictive maintenance, with emphasis on their relevance to electrical safety. Electrical equipment such as induction motors, contactors, transformers, batteries and renewable-energy systems can develop abnormal conditions that lead to unexpected shutdowns, equipment damage or unsafe operation. Conventional maintenance based only on fixed schedules or after-failure repair may not identify every developing …
Published in Recent Trends in Sensor Research & Technology · Vol. 13, Issue 2, 2026 Read article
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A Review on AI and Machine Learning for Predictive Maintenance and FDD in RAC Systems
Abstract: The paper reviews the existing AI/ML methods first in the general context of predictive maintenance and FDD of RAC systems, then specifically focusing on granular cooling appliances. Perspectives and insights are provided on the reasons why potentially valuable models do not make it into practice more often, and where future research and development should be headed. New emerging topics for decision support systems to include domain knowledge and physics-based modeling …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 13, Issue 1, 2026 · pp. 15–25 Read article