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.
翻译:最近,人工智能(AI)和IoT的进步使得在家庭和建筑环境中对电器进行无缝和可行的维修。一些研究表明,AI有可能提供个性化的客户支持,这种支持可以比以往任何时候更加可靠地预测和避免错误。在本文件中,我们分析了成功实现由AI驱动的预测维护使用案例所需的各种构件。与现有的调查不同,这些调查主要为最近的预测维护(PdM)的人工智能算法提供了深入的下潜,我们的调查提供了完整的视角;从商业影响开始到最近在算法以及系统研究和模型部署方面的技术进步。此外,我们还提供了利用公开数据集预测维护电器的示范性使用案例。我们的调查可以作为设计成功的预测维护使用案例所需的模板。最后,我们联系现有的公共数据源,从一般异常检测到错误预测和最终的根源分析开始,对由AI驱动的主动客户护理(PCC)的使用案例进行分步分解。我们强调,这种渐进式方法对于精确的模型构建和对电子设备的预视是有用的。