ABSTRACT
Industrial machinery is an hourly necessity for every manufacturing sector. Thus, in order to ensure its reliable functionality, continuous monitoring of health conditions can be adapted to avoid any susceptible faults in due time to avoid catastrophic failures. Continuous monitoring of health conditions can greatly improve reliability as well as reduce maintenance costs to the greater extent because less human intervention is needed. This chapter proposes a modular Zigbee-based Internet of Things (IoT) platform for reliable health monitoring of industrial machines using a remote fault signature analyzer (ReFSA). ReFSA uses a computer-aided advanced diagnostic system to acquire current signatures from the industrial machines to identify the fault at its incipient stage and to generate a fault diagnostic report for the maintenance engineer. A computer-aided advanced diagnostic system with pattern recognition uses the analysis of variance (ANOVA) test to find the best possible model features. The extracted model features' current data is then transmitted at 180 Bps via a Zigbee-based IoT platform to generate a fault diagnostic report.
