  In recent times, advances in artificial intelligence (AI) and IoT have
enabled seamless and viable maintenance of appliances in home and building
environments. Several studies have shown that AI has the potential to provide
personalized customer support which could predict and avoid errors more
reliably than ever before. In this paper, we have analyzed the various building
blocks needed to enable a successful AI-driven predictive maintenance use-case.
Unlike, existing surveys which mostly provide a deep dive into the recent AI
algorithms for Predictive Maintenance (PdM), our survey provides the complete
view; starting from business impact to recent technology advancements in
algorithms as well as systems research and model deployment. Furthermore, we
provide exemplar use-cases on predictive maintenance of appliances using
publicly available data sets. Our survey can serve as a template needed to
design a successful predictive maintenance use-case. Finally, we touch upon
existing public data sources and provide a step-wise breakdown of an AI-driven
proactive customer care (PCC) use-case, starting from generic anomaly detection
to fault prediction and finally root-cause analysis. We highlight how such a
step-wise approach can be advantageous for accurate model building and helpful
for gaining insights into predictive maintenance of electromechanical
appliances.
