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Today, the industry is outfitted with robots, cameras and several kinds of other sensors that collectively perform picking, packing and transferring packages with no human presence or oversight. Without this, if 500 workers are required in a factory instead, only five technicians would be required to service the machines&

Root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. RCA is widely used in industrial process control, IT operations, health industry and accident analysis in aviation, nuclear plants and rail transport. Without delving in the intricate details of speci
c problems, several general conditions can make RCA more&.

Innovative fault prediction can play a crucial role in ensuring smooth and cost effective process management. In this project, we designed a solution for predicting failure events by leveraging modern bleeding edge AI techniques such as deep learning, meta-learning&.

Almost all kind of engineering industrial process and production is performed by rotating machinery like motors, pumps, gearbox, fans, and assembly lines, etc. The condition-based monitoring of these types of equipment helps to boost productivity and profitability. In recent years, the industry&

Semiconductor manufacturing is a complex process with hundred of subprocesses. Different sensors record diverse information including the profiles of tools used in each fabrication subprocess, temperatures, pressures, etc. The output of this process is a semiconductor wafer. Any malfunctioning subprocess can&.

Accurate failure time estimation of mechanical components plays a significant role in enhancing the reliability of the machines. Ball-bearing failures are the most probable failures in the industrial rotating machinery. Therefore, predicting the remaining useful life of ball bearing proves to&.