Maintenance Assessment of an Industrial System Using Artificial Intelligence

Abstract

In the (electricity plants, water desalination plants) whose parts are connected in a row, there are no fixed maintenance procedures for them because of the happening of faults at different times. To fulfill the research aims is developed a predictive maintenance program, using A.I. techniques. Al-fifth Dora's unit data. Two stages of research were conducted. First, missing data without temporal sequences were processed. The data was filled using time series hour after hour and consideration filling times as system working hours, making the volume of data relatively high for 2015-2016-2017, and 2018 was utilized as a test year to assess the modelling work and validate experimental results. In the second step, the artificial neural networks approach employed the python program as an A.I., and the affinity ratio to real data using the performance measurement (Root Mean Square Error) R. M.S.E. (0.507). 2019-2030 showed the program's long-term predictability.