International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Toward Trustworthy Edge Intelligence for Predictive Maintenance in Smart Manufacturing: A Critical Review of 30 Published Studies |
|---|---|
| ABSTRACT | Predictive maintenance is a key component of smart manufacturing, linking machine-health monitoring with reduced downtime, improved safety, quality, and maintenance cost. This review critically examines 30 published studies covering machine learning, deep learning, bearing diagnosis, vibration analysis, Edge AI, TinyML, Industrial IoT, explainable AI, and benchmark datasets. The literature shows a shift from conventional feature-based methods toward deep learning, transfer learning, transformers, and resource-aware edge deployment. However, industrial adoption remains limited by laboratory-based datasets, inconsistent validation, domain shifts, model drift, energy constraints, and limited human-centered evaluation of explanations. The review highlights the need for lightweight and uncertainty-aware models, multi-sensor fusion, secure edge-cloud collaboration, realistic datasets, explainable decision support, and standardized reporting. |
| AUTHOR | ADITYA ZADE, SHEETAL DEVI, RADHA TADAS, SUMEDH MESHRAM, PROF. DHIRAJ KUMAR GUPTA, HARSH KALBANDE Department of Computer Science and Engineering, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India Guide, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India Alumni Mentor, St. Vincent Pallotti College of Engineering and Technology, Nagpur, Maharashtra, India |
| VOLUME | 188 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1409014 |
| pdf/14_Toward Trustworthy Edge Intelligence for Predictive Maintenance in Smart Manufacturing A Critical Review of 30 Published Studies.pdf | |
| KEYWORDS | |
| References |
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