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Industry 4.0 is an era of smart manufacturing. Manufacturing is impossiblewithout the use of machinery. Majority of these machines comprise rotatingcomponents and are called rotating machines. The engineers' top priority is tomaintain these critical machines to reduce the unplanned shutdown and increasethe useful life of machinery. Predictive maintenance (PDM) is the current trendof smart maintenance. The challenging task in PDM is to diagnose the type offault. With Artificial Intelligence (AI) advancement, data-driven approach forpredictive maintenance is taking a new flight towards smart manufacturing.Several researchers have published work related to fault diagnosis in rotatingmachines, mainly exploring a single type of fault. However, a consolidatedreview of literature that focuses more on multi-fault diagnosis of rotatingmachines is lacking. There is a need to systematically cover all the aspectsright from sensor selection, data acquisition, feature extraction, multi-sensordata fusion to the systematic review of AI techniques employed in multi-faultdiagnosis. In this regard, this paper attempts to achieve the same byimplementing a systematic literature review on a Data-driven approach formulti-fault diagnosis of Industrial Rotating Machines using Preferred ReportingItems for Systematic Reviews and Meta-Analysis (PRISMA) method. The PRISMAmethod is a collection of guidelines for the composition and structure ofsystematic reviews and other meta-analyses. This paper identifies thefoundational work done in the field and gives a comparative study of differentaspects related to multi-fault diagnosis of industrial rotating machines. Thepaper also identifies the major challenges, research gap. It gives solutionsusing recent advancements in AI in implementing multi-fault diagnosis, giving astrong base for future research in this field.

Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research