Big Data Quality for Reliable Industrial Internet of Things Based Systems
The development of modern sensors and information technologies make possible to collect and process a large amount of reliability data to predict the system health of a monitored item. This work emphasizes on conceptual issues and methodological aspects related to data registration, filtering, smoothing, analyzing in order to predict important indicators of the quality of life and describes new practical strategies to analyze reliability data in context Big Data. Industrial Internet of Things (IIoT) is considered and architectures of IIoT are discussed from the reliability computational available approaches. The following hypothesis are validated: 1) Big data is an opportunity for reliability engineers when study/analyze big networks of sensors, large grids, or very large smart cities; 2) There is at least one reference architecture supporting high connectivity when working according to the Industry 4.0 framework; 3) There are developed many platforms, frameworks, and standards to serve as main vectors in implementing large scale applications supporting integrated Big Data technologies, Industrial Data and Sensors oriented protocols.
Keywords: Big Reliability Data, Industrial Internet of Things, Industry 4.0