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Prognostics and Health Management (PHM) is a fast growing domain in the industrial sector. PHM is concentrates on foreseeing failures and faults by investigating the critical health indicators. The modern technologies like Artificial Intelligence (AI), Block Chain Technology (BCT), Internet of Things (IoT), Edge computing, Internet of Everything (IoE) and cloud computing has facilitated the deployment of sensor in the industrial sector to impart smartness to the machineries. PHM has evolved as an effective tool that tracks the values of the health indicators whose failure is likely to happen at more than one point. The effect of the failures in the industrial machineries may be catastrophic and fatal. However, the industrial maintenance domain is accelerating to develop predictive maintenance frameworks by leveraging the disruptive computing technologies to foresee faults and failures. The track record of Block Chain Technology, with immutable record maintenance can be habituated in monitoring values of critical health parameters of any industrial equipment. The course of the maintenance activity would progress by investigating the degradation signals which are characterized by its temporal nature. Hence, BCT germinated as a natural solution to track the onset of faults and failures by maintaining digital records. The work proposes a versatile BCT based framework that integrates futuristic technologies for industrial predictive maintenance. The high fidelity of block chains ascertains that the happenstances at industrial site can be surveilled at any predefined points in industrial monitoring to detect abnormality in normal operating profile. [ABSTRACT FROM AUTHOR]

In the world of modern era we find many techniques for enhancement of reliability in distribution networks and meeting the consumer demand by provision of quality and incessant power supply. Amidst the various techniques, in this study, we opt for demand response (DR) and charging/discharging of electrical vehicles (EV's) are evaluated to ameliorating reliability for radially distributed power system by adopting teaching learning based optimization (TLBO) algorithm. This analysis is considered due to the contribution of technologies like are DR and EV's for reducing the usage of conventional sources and enhancing the air quality. Here in this paper we are adopting reliability evaluation by considering energy expected not supplied (EENS) and loss of load expected (LOLE)where the characteristics of distribution network contains load and voltage profile. This paper provides a new optimization approach for finding the optimal solution. [ABSTRACT FROM AUTHOR]