crossrefInternational Research Journal on Advanced Science Hub2026-07-24Cited by 0

Predicting Faults in Robotic Arms Using Machine Learning: A Study On Passive Detection Methods

Balaji Periasamy, P. Rajalakshmy, R.Madhanraj

Robotic arms are widely used in industries and keeping them reliable is very important. Traditional maintenance often waits until problems become visible, but new methods use data from sensors to detect faults early. This article reviews research from 2020 to 2025 that studies how signals from sensors such as accelerometers, gyroscopes, and motor currents, can be used with machine learning to find faults. The main types of faults like gear, bearing, motor, and collision issues and the techniques used to detect them are compared. The review shows that deep learning methods are becoming popular, but classical approaches and hybrid models are still useful. Overall, this study highlights how passive sensors combined with machine learning can improve predictive maintenance and make robotic arms safer and more dependable.

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AF-HQCNN: Adaptive Federated Hybrid Quantum Convolutional Neural Network for Privacy-Preserving Medical Image Classification

Velkumar K, Dr.Mathalairaj J, Bhavani M, Archana R, Pavithra M, Venkatalakshmi M

Medical image classification is an important task in clinical decision support systems. Although classical deep learning models can represent massive amounts of data, they are not very scalable DEEP LEARNING REVIEW and stand each in their own right for privacy loss and computatio…

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openalexInternational Research Journal on Advanced Science Hub2026-07-24

Threat Sense-XAI: A Context-Aware Threat Intelligence Framework for Real-Time Weapon, Violence, and Behavioural Risk Assessment

Megha Saha, R.Velvizhi Ramya

Classic approaches to security monitoring often encounter problems when dealing with detecting and resolving security threats in real time. This paper introduces a system that uses context-aware threat intelligence to detect weapons, recognize violent actions, analyze time-based…

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openalexInternational Research Journal on Advanced Science Hub2026-07-24

Design and Implement a Flexible, Lightweight Deep Learning Model for Restoring and Enhancing Leukemia Blood-Smear Images.

Mohit Kumar Saini, Sanjeev Patwa, Somil Jain, Dhanna Ram

Diagnosis of leukemia can be difficult using peripheral blood smears because the smears can have artifacts that pathologists may not be able to recognize, such as sensor noise, optical blur, uneven illumination, and low contrast that can obscure the fine nuclear and cytoplasmic d…

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openalexInternational Research Journal on Advanced Science Hub2026-07-24

Robust Multi Agent Coordination: Integrating Causal Inference and Evolutionary Strategies in Adversarial Environments

Jayasurya K, Mrs. Snigdha Kesh, Mrs. V. Mareeswari

Autonomous multi-agent systems operating in safety-critical settings—bordersurveillance, infrastructure protection, maritime patrol, and contested reconnaissance—must sustain effective coordination even when actively targeted by adversaries. UAV swarms face a particular challenge…

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Predicting Faults in Robotic Arms Using Machine Learning: A Study On Passive Detection Methods

Related papers

AF-HQCNN: Adaptive Federated Hybrid Quantum Convolutional Neural Network for Privacy-Preserving Medical Image Classification

Threat Sense-XAI: A Context-Aware Threat Intelligence Framework for Real-Time Weapon, Violence, and Behavioural Risk Assessment

Design and Implement a Flexible, Lightweight Deep Learning Model for Restoring and Enhancing Leukemia Blood-Smear Images.

Robust Multi Agent Coordination: Integrating Causal Inference and Evolutionary Strategies in Adversarial Environments