Article: Fault detection and diagnosis of industrial systems using artificial intelligence-based prognostic approach: a review Journal: International Journal of Advanced Mechatronic Systems (IJAMECHS) 2026 Vol.13 No.3 pp.143 - 155 Abstract: Moving to Industries 4.0 changes the need for preventative maintenance into the need for predictive maintenance. The research shows that artificial intelligence (AI) and machine learning (ML) are changing fault detection and diagnosis (FDD) in complicated industrial systems almost in real-time. AI-based models can analyse the high-dimensional and nonlinear features of a system when other methods do not work. AI-based solutions can be used to find problems early and make accurate predictions about how the system will work in the future. We show how these advanced forecasting tools are important in history and improve the quality, effectiveness, and range of mechanical diagnostics. The most recent improvement shows that an AI-driven FDD is necessary to make maintenance more efficient, make operations more reliable, and make equipment last longer. To sum up, we outline the current problems and suggest new areas of research for smart defect identification and diagnosis in a changing industrial setting. Inderscience Publishers - linking academia, business and industry through research

Title: Fault detection and diagnosis of industrial systems using artificial intelligence-based prognostic approach: a review

Authors: Nilesh Dhobale; Amol Bhanage; Nilesh Satonkar

Addresses: Department of Robotics and AI, Marathwada Mitramandal's Institute of Technology Lohgaon, Pune, Sr. No. 35, Plot No. 5/6, Lohgaon, Pune – 411047, India ' Department of Robotics and AI, Marathwada Mitramandal's Institute of Technology Lohgaon, Pune, Sr. No. 35, Plot No. 5/6, Lohgaon, Pune – 411047, India ' Department of Robotics and AI, Marathwada Mitramandal's Institute of Technology Lohgaon, Pune, Sr. No. 35, Plot No. 5/6, Lohgaon, Pune – 411047, India

Abstract: Moving to Industries 4.0 changes the need for preventative maintenance into the need for predictive maintenance. The research shows that artificial intelligence (AI) and machine learning (ML) are changing fault detection and diagnosis (FDD) in complicated industrial systems almost in real-time. AI-based models can analyse the high-dimensional and nonlinear features of a system when other methods do not work. AI-based solutions can be used to find problems early and make accurate predictions about how the system will work in the future. We show how these advanced forecasting tools are important in history and improve the quality, effectiveness, and range of mechanical diagnostics. The most recent improvement shows that an AI-driven FDD is necessary to make maintenance more efficient, make operations more reliable, and make equipment last longer. To sum up, we outline the current problems and suggest new areas of research for smart defect identification and diagnosis in a changing industrial setting.

Keywords: industrial systems; fault diagnosis; artificial intelligence; machine learning.

DOI: 10.1504/IJAMECHS.2026.155370

International Journal of Advanced Mechatronic Systems, 2026 Vol.13 No.3, pp.143 - 155

Received: 17 Jun 2025
Accepted: 16 Dec 2025
Published online: 30 Jul 2026 *

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Title: Fault detection and diagnosis of industrial systems using artificial intelligence-based prognostic approach: a review

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