Open AccessIntelligent system to monitor and diagnose performance deviation in Industrial Equipment
Author(s) -
Arun Subramanian,
M. Udayakumar,
V. Indragandhi,
R. Ramkumar
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/623/1/012012
Subject(s) - production (economics) , clearance , reliability engineering , fault (geology) , industrial production , work (physics) , margin (machine learning) , computer science , production line , engineering , mechanical engineering , machine learning , economics , medicine , seismology , keynesian economics , macroeconomics , geology , urology
As per ISO norms, industries used to calibrate the meters which they are using, but performance deviations of the working equipment which are not affecting the economy or production of industries are not given significant importance. So, this work aims at concentrating on fine performance deviation in induction machine which if avoided without taking corrective action may even stop production. This is done by taking a set of reference parameters and the newly taken values are compared with the reference ones and error margin is determined. Various sensors are interfaced for measuring the physical parameters of the machines. Thus, with all these the performance of industrial induction motor with different loading conditions are carried out and the possible damage that could occur if the fault in the machine is not cleared, and also will show the preventive measures to be taken to avoid the failure of the drives are framed. In industries the machines used will be of higher ratings. And if there occurs any error or fault then its production will be greatly affected, which in turn will create huge production and economic losses, so to avoid that the proposed system to forecast the performance deviations of the drives will be of great commercial significance. With this system, the production and economic losses can be reduced to greater extent and also can prevent the machines from damage.