review · Journal of Intelligent Manufacturing
Smart manufacturing systems are increasingly complex and costly, which leaves them vulnerable to faults and failures that can degrade entire operational processes. Because modern manufacturing environments have very little tolerance for reduced productivity, performance decline, or safety risks, detecting and resolving operational anomalies quickly is essential. While numerous techniques exist to address these issues, choosing the right method for a specific industrial setting remains challenging. To address this, a novel conceptual architecture integrates intelligent fault diagnosis and self-healing capabilities for smart manufacturing. This architectural framework serves as the basis for reviewing various strategies, including fault detection and diagnosis alongside self-healing fault-tolerant methods. An analysis of over 256 research articles published over the past decade examines these approaches and outlines future research pathways towards resilient industrial manufacturing.
Modern factories rely on expensive, interconnected equipment where unexpected faults can rapidly halt production and create safety hazards. Understanding how to detect anomalies and enable systems to automatically heal themselves helps industrial operations maintain high productivity and protect staff. Establishing clear frameworks to compare diagnostic methods allows engineers to choose the most reliable tools for preventing costly downtime.
This work presents a conceptual architectural model and analyses academic literature, placing it at an early conceptual stage rather than a deployable technology. It offers manufacturing engineers, system architects, and automation developers a structured way to evaluate fault detection and self-healing strategies. While direct commercial tools are not implemented in the work, the framework can inform the future design of automated monitoring and recovery software for industrial facilities.
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Abstract Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted.
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DOI: 10.1007/s10845-023-02165-6
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