AI-DRIVEN FAULT DETECTION AND DIAGNOSTICS
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.68Keywords:
Artificial Intelligence, Internet of Things (IoT), fault detection and diagnosis (FDD), smart algorithms, machine learning (ML), deep learning (DL)Abstract
Artificial intelligence (AI) has changed the game when it comes to fault detection and diagnosis (FDD) because with AI, identifying and solving faults in machines, systems, and networks becomes easy and effective. Manual techniques have traditionally been used to perform these tasks; however, they are tedious and costly. Utilizing the concept of FDD using AI involves a lot of machine learning (ML), deep learning (DL), and data analysis techniques in order to detect and predict possible faults. The sensors continuously, in real time, parse enormous amounts of data, and automated AI readily identifies equipment errors, especially in manufacturing, healthcare, energy, automotive, cybersecurity, and other industries, where error prevention averts failure, enhances safety, and reduces expenses. AI-based systems help organisations optimise machine performance while reducing downtime. AI has the potential to reduce faults through the use of smart algorithms and Internet of Things (IoT) devices. It is important to note that concerns remain regarding data quality, high costs, and the interpretability of AI decisions. With the advancement in AI. The technology, reliability, and usability of AI are improving over time, and fault detection and diagnostics are becoming faster and more effective. This chapter provides a comprehensive review of the applications of artificial intelligence for fault detection and diagnosis, with a critical analysis of the advantages, challenges, and future scope of AI in this domain for improved system performance.
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[113] Dalal, P., Beniwal, G., Sharma, V., Garg, P., & Ahmed, K. (2025). Predicting Student Motivation and Engagement through Machine Learning Models. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 393-411.
[114] Gupta, A., Mund, A., Roy, S., Garg, P., & Yadav, D. K. (2025). Trust in AI Systems: A Social-psychological Investigation of Human–AI Collaboration. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 428-446.
[115] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2025). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analog Applications: Bhardwaj, Das, Garg, and Yadav. Journal of Electronic Materials, 1-12.
[116] Dalal, P., Sharma, B., Sharma, T., Garg, P., & Ahmed, K. (2025). Explainable AI for Understanding Human Decision Making Patterns. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 412-427.
[117] Sharma, K. K., Verma, P. K., Garg, P., & Shrotriya, V. K. (2025, October). Predicting costs and benefits of IoT-based energy management for optimizing sustainable energy storage in rural areas. In AIP Conference Proceedings (Vol. 3343, No. 1, p. 040017). AIP Publishing LLC.
[118] Ahmed, K., Baranwal, A., Sharma, N., Garg, P., & Singh, N. (2026). The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions. In Enabling Collaborative Health Intelligence With Federated Learning (pp. 421-448). IGI Global Scientific Publishing.
[119] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (2025). Federated learning based intelligent systems to handle issues and challenges in IoVs (Part 2). Bentham Science Publishers. https://doi.org/10.2174/97898153222241250301
[120] Garg, P., Pranav, S., & Prerna, A. (2021). Green internet of things (G-IoT): A solution for sustainable technological development. In Green Internet of Things for Smart Cities (pp. 23-46). CRC Press.
[121] Malik, A., Nandal, D., Gupta, V., Garg, P., & Nandal, V. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.
[122] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (Eds.). (2025). Federated learning based intelligent systems to handle issues and challenges in IoVs (Part 2).
[123] Garg, P., Bhatt, M., Parmar, R., & Arsalan, M. (2025). Generative AI: Evolution, Applications, Challenges And Future Prospects. Applications, Challenges And Future Prospects (May 17, 2025).
[124] Kumar, N., Kumar, Y., Khurana, D., Kumar, S., & Garg, P. (2025, November). A Hybrid Ensemble Learning Framework for Interpretable Student Performance Prediction Using Academic and Extracurricular Factors. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 666-672). IEEE.
[125] Khurana, D., Kumar, Y., Kumar, N., Kumar, S., & Garg, P. (2025, November). Transformer-Based Movie Recommendation System with Autoencoder-Enhanced Feature Compression. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 685-690). IEEE.
[126] Garg, P. (2025, November). Comparative Analysis of Various Neural Networks for Galaxies Classification. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 697-701). IEEE.
[127] Saggu, A. K., Babbar, N., & Garg, P. (2025, November). Health-Guard AI: Integrated Health Report Management and Analysis. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 614-623). IEEE.
[128] Kumar, S., Kumar, Y., Kumar, N., Khurana, D., & Garg, P. (2025, November). Hybrid FCM-DNN Model for Uncertainty-Aware Air Quality Classification Using Multi-Pollutant Data. In 2025 International Conference on Innovations and Emerging Technologies In AI & Communication Systems (IETACS) (pp. 679-684). IEEE.
[129] Babbar, N., Singh, H. V., Bendale, S., & Garg, P. (2025, November). Stock Market Price Prediction Using Big Data Analysis: A Performance Evaluation Study. In 2025 3rd International Conference on Computational Intelligence and Network Systems (CINS) (pp. 1-6). IEEE.
[130] Singh, A. K., Kori, G., Garg, P., & Srivastava, G. (2025, November). Bank Churn Prediction Using Machine Learning. In 2025 IEEE 7th International Conference on Computing, Communication and Automation (ICCCA) (pp. 1-6). IEEE.
[131] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2026). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analog Applications. Journal of Electronic Materials, 55(1), 1099-1110.
[132] Srivastava, A. K., Shankdhar, D., Ror, R., & Garg, P. (2026). Harnessing YOLOv5 for real-time object detection: A cloud-based approach. In Recent Advances in Computational Methods in Science and Technology (pp. 441-450). CRC Press.
[133] Srivastava, A. K., Shukla, A., Gupta, H., Saxena, K., & Garg, P. (2026). Towards intelligent attendance management system with face recognition using LBPH algorithm. In Recent Advances in Computational Methods in Science and Technology (pp. 8-15). CRC Press.
[134] Srivastava, A. K., Garg, P., & Pandey, H. (2026). Vedcure: Towards intelligent ayurvedic drug recommendation and disease prediction. In Recent Advances in Computational Methods in Science and Technology (pp. 16-23). CRC Press.
[135] Upadhyay, D., Garg, P., & Babbar, N. (2026). Blockchain and IoT based smart contract framework for efficient and secure product life management. Discover Internet of Things.
[136] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[137] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[138] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[139] Garg, P., Arora, K., Bawane, R., Gupta, C., & Ahmed, K. (2025). Detection and Prevention of Cyber Attack and Threat using AI.
[140] Ahmed, K., Ahmed, A., Khan, J., Garg, P., Seth, S., & Mallik, S. (2025). Principal Component Analysis Based Clustering of Insecticides and Molecular Docking of Pyrethroids Insecticides.
[141] Kumar, B., Kumar, A., Nanwal, J., Garg, P., & Patnaik, P. (2025, November). Ensemble of YOLOv5 and Segment Anything Model for Brain Tumor Detection. In 2025 2nd International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-5). IEEE.
[142] Arsalan, M., Anas, M., & Garg, P. (2025). Transparent AI for Drug Discovery and Development. Available at SSRN 5844242.
[143] Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026). Introduction to explainable artificial intelligence in healthcare. In Explainable AI in Clinical Practice (pp. 23-44). Academic Press.
[144] Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026). Explainable artificial intelligence in a diagnostic support system. In Explainable AI in Clinical Practice (pp. 131-145). Academic Press.
[145] Ahmed, K., Anas, M., & Garg, P. (2026). Case studies on unlocking the potential of Industry 4.0 for sustainable manufacturing through generative AI-driven innovations. Available at SSRN 6356958.
[146] Garg, P., & Oruganti, S. K. (2026, March). AI Assisted Routing Optimization in Opportunistic IoT Networks using Machine Learning: A Comprehensive review on Protocols & Simulators. In Sustainable Global Societies Initiative (Vol. 1, No. 4). Vibrasphere Technologies.
[147] Arsalan, M., Pokhrel, L., & Garg, P. (2026). Architecture, Components, and tools in Integrated AI-Augmented Intelligence: A design perspective. Components, and tools in Integrated AI-Augmented Intelligence: A design perspective (March 19, 2026).
[148] Singh, H., Ahmed, K., & Garg, P. (2026). Human Versus Machine Customer Behaviour and Functional Differences. Available at SSRN 6441098.
[149] Saraswat, P., & Garg, P. (2026). Soft Computing in AI Agents.
[150] Saraswat, P., & Garg, P. (2026). Water Quality Prediction Using IOT Sensors and Deep Networks.
