PARAMETERS OF TRAPPING CENTERS IN AMORPHOUS Se 70 Te 30-x Zn x THIN FILMS

Thermally stimulated currents have been measured at different heating rates (β) in amorphous thin films of Se 70 Te 30-x Zn x (x = 0, 2, 4, 6, 8). A clear TSC peak occurs at a particular temperature that shift towards higher temperatures as heating rates (β) is increased. The aim of this paper is to determine initial information about trap depth and trap concentration (N t ). The value of N t decreases with increases in Zn concentration upto 2 % and thereafter it increases with increase in Zn concentration

Predictive Maintenance using Machine Learning

Predictive maintenance (PdM) is a concept, which is implemented to effectively manage maintenance plans of the assets by predicting their failures with data driven techniques. In these scenarios, data is collected over a certain period of time to monitor the state of equipment. The objective is to find some correlations and patterns that can help predict and ultimately prevent failures. Equipment in manufacturing industry are often utilized without a planned maintenance approach. Such practise frequently results in unexpected downtime, owing to certain unexpected failures. In scheduled maintenance, the condition of the manufacturing equipment is checked after fixed time interval and if any fault occurs, the component is replaced to avoid unexpected equipment stoppages. On the flip side, this leads to increase in time for which machine is non-functioning and cost of carrying out the maintenance. The emergence of Industry 4.0 and smart systems have led to increasing emphasis on predictive maintenance (PdM) strategies that can reduce the cost of downtime and increase the availability (utilization rate) of manufacturing equipment. PdM also has the potential to bring about new sustainable practices in manufacturing by fully utilizing the useful lives of components