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13 articles for “Bearing faults”
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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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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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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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Bearing Fault Analysis using Variational Mode Decomposition
Abstract: Bearing health analysis plays a significant role in industry to improve reliability and performance of critical processes by alarming the faults at early stages. Conventional techniques do not guarantee to detect the faults at early stages because the low energy bearing frequencies get suppressed by stern noise and higher vibrations. The fast Fourier transform (FFT) fails to analyze the transient and nonstationary signals directly. This paper performs the signal analysis …
Published in Journal of Instrumentation Technology & Innovations · Vol. 4, Issue 2, 2014 · pp. 20–27 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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TinyML-Based Real-Time Industrial Fault Detection Using Vibration Signals
Abstract: Industrial rotating machinery is commonly monitored through vibration measurements because changes in bearing condition can alter signal amplitude, impulsiveness, and spectral distribution. Cloud-centred analytics may introduce communication dependence, bandwidth demand, and response delay. Tiny Machine Learning (TinyML) offers a complementary approach by executing compact inference locally on resource-constrained edge hardware. This paper presents a reproducible, deployment-oriented workflow for vibration-based machine-state classification using statistical and spectral features with small classifiers suitable …
Published in Journal of Instrumentation Technology & Innovations · Vol. 16, Issue 3, 2026 Read article
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Fault Investigation of Rolling Element Bearing using Soft Computing
Abstract: AbstractRolling element bearing is the most significant mechanical component of any rotating machinery and used in vast applications from the surgical machine tool to aircraft etc. In the present research, an experimental investigation has been done using vibration analysis, continuous wavelet transform (CWT), and soft computing, i.e., support vector machine (SVM) and k-nearest neighbor (k-NN) for fault detection and diagnosis of rolling element bearing. Vibration signatures in the time domain …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 1, 2019 · pp. 8–22 Read article
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Fault Identification by Wavelet Feature Extraction in Self Aligning Rolling Element Bearings Using Neural Networks
Abstract: This paper aims to present a comparison of different individual defects in self aligning ball bearings by the use of statistical tools and machine learning techniques like artificial neural network (ANN). The results generated are analyzed, and more realistic conformance to the theoretical observations has been drawn. Vibration analysis of a fault affected component gives a good understanding of machine diagnostics. Inner race and outer race defects have been studied …
Published in Trends in Mechanical Engineering & Technology · Vol. 7, Issue 2, 2017 · pp. 11–17 Read article
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CONDITION BASED MONITORING AND FAULT DIAGONISTICS OF A MACHINE
Abstract: This piece of study shows the existing condition based monitoring and fault diagnostics techniques in a machine. Vibrations are produced in every machine and their amplitude depends upon the type of faults present in the machine, for example, gear fault, journal bearing, rolling contact bearing, flexible coupling and electrical machine faults. The main challenge for the condition monitoring of the machines is the cost and complexity of condition machine monitoring …
Published in Journal of Experimental & Applied Mechanics · Vol. 9, Issue 2, 2018 · pp. 84–89 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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Defect Diagnosis and Modelling for A Rotating Machine Running at A Steady Pace
Abstract: Rotary equipment, such as gears, shafts, pumps, and bearings, are widely used across various rotary machine in industries, often operating under different loading conditions. The fluctuations in loading can lead to fatigue failures in rotating components, significantly affecting machinery performance. To investigate the behavior of such rotating equipment under diverse operational scenarios, a numerical model has been created. This approach can replace costly and often challenging experimental methods. However, it …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 154–164 Read article
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Advanced Quantitative Analysis of Dynamic Recovery during Warm Working of FCC Alloys
Abstract: Metal working processes of warm working require an advanced quantitative analysis of dynamic and static recovery of dislocations to control microstructures and crucial shape forming properties. Feasibility of dynamic recovery by thermal glide and static recovery by climb of constricted jogs, applicable to ausforming, nano bulk forming by SPD and normalizing has been addressed to design new steels, used for hulls, and tribologically superior bearings. Predictive dislocation recovery analysis shows …
Published in Journal of Materials & Metallurgical Engineering · Vol. 12, Issue 4, 2022 · pp. 1–14 Read article
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Advanced Quantitative Analysis of Dynamic Recovery During Warm Working of FCC Alloys
Abstract: Metal working processes of warm working require an advanced quantitative analysis of dynamic and static recovery of dislocations to control microstructures and crucial shape forming properties. Feasibility of dynamic recovery by thermal glide and static recovery by climb of constricted jogs, applicable to ausforming, nano bulk forming by SPD and normalizing has been addressed to design new steels, used for hulls and tribologically superior bearings. Predictive dislocation recovery analysis shows …
Published in Journal of Materials & Metallurgical Engineering · Vol. 12, Issue 1, 2022 · pp. 1–14 Read article