Data-driven fault detection and diagnosis in automated manufacturing systems
Vinay Kumar NASSA, Nilesh PARIHAR, Pinkey CHOUHAN, Anupam PAREEK, C. VIJAI, Ritesh Kumar SINGHAL
Abstract. The introduction of a data-based fault detection and diagnosis (FDD) has radically changed the concept of automated manufacturing systems as it provides the capability to monitor real-time in an intelligent manner, diagnose faults beforehand, and predictive maintenance schedules. Data-driven methods, in contrast to traditional reactive ones, make use of continuous sensor data streams to anticipate anomalies before they can result in critical failures using advanced analytics. This proactive feature has a great impact in minimizing unplanned downtime, increasing the safety of operations, and enhancing the overall efficiency of equipment. The FDD systems have been made effective with the help of the artificial intelligence (AI) and the Industrial Internet of Things (IoT). Sensors that have the ability to collect data through IoT can easily get data on various changes of the manufacturing facilities and the AI algorithms can help extract meaningful information data and support in making automated decisions. The other technologies such as cloud platform and edge computing, enable real-time processing of data and scalable analytics, thereby improving responsiveness and efficiency in the system. This implies that manufacturing systems are turning to be more adaptable, independent and robust.
Keywords
Fault Detection, Fault Diagnosis, Machine Learning, Industry 4.0, Predictive Maintenance, Smart Manufacturing
Published online 8/15/2026, 7 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Vinay Kumar NASSA, Nilesh PARIHAR, Pinkey CHOUHAN, Anupam PAREEK, C. VIJAI, Ritesh Kumar SINGHAL, Data-driven fault detection and diagnosis in automated manufacturing systems, Materials Research Proceedings, Vol. 71, pp 227-233, 2026
DOI: https://doi.org/10.21741/97816449042312-31
The article was published as article 31 of the book AI-Driven Innovations in Mechanical, Manufacturing, and Energy Engineering
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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