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In recent times, advances in artificial intelligence (AI) and IoT haveenabled seamless and viable maintenance of appliances in home and buildingenvironments. Several studies have shown that AI has the potential to providepersonalized customer support which could predict and avoid errors morereliably than ever before. In this paper, we have analyzed the various buildingblocks needed to enable a successful AI-driven predictive maintenance use-case.Unlike, existing surveys which mostly provide a deep dive into the recent AIalgorithms for Predictive Maintenance (PdM), our survey provides the completeview; starting from business impact to recent technology advancements inalgorithms as well as systems research and model deployment. Furthermore, weprovide exemplar use-cases on predictive maintenance of appliances usingpublicly available data sets. Our survey can serve as a template needed todesign a successful predictive maintenance use-case. Finally, we touch uponexisting public data sources and provide a step-wise breakdown of an AI-drivenproactive customer care (PCC) use-case, starting from generic anomaly detectionto fault prediction and finally root-cause analysis. We highlight how such astep-wise approach can be advantageous for accurate model building and helpfulfor gaining insights into predictive maintenance of electromechanicalappliances.

A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it