Integrated Analytics for IIoT Predictive Maintenance using IoT Big Data Cloud Systems

For predictive maintenance of equipment with Industrial Internet of Things (IIoT) technologies, existing IoT Cloud systems provide strong monitoring and data analysis capabilities for detecting and predicting status of equipment. However, we need to support complex interactions among different software components and human activities to provide an integrated analytics, as software algorithms alone cannot deal with the complexity and scale of data collection and analysis and the diversity of equipment, due to the difficulties of capturing and modeling uncertainties and domain knowledge in predictive maintenance. In this paper, we describe how we design and augment complex IoT big data cloud systems for integrated analytics of IIoT predictive maintenance. Our approach is to identify various complex interactions for solving system incidents together with relevant critical analytics results about equipment. We incorporate humans into various parts of complex IoT Cloud systems to enable situational data collection, services management, and data analytics. We leverage serverless functions, cloud services, and domain knowledge to support dynamic interactions between human and software for maintaining equipment. We use a real-world maintenance of Base Transceiver Stations to illustrate our engineering approach which we have prototyped with state-of-the art cloud and IoT technologies, such as Apache Nifi, Hadoop, Spark and Google Cloud Functions.