Integrated Intelligent Framework for Sensor Data Analysis

Kalyani S.¹ , Venkat Rao K.² , A. Mary Sowjanya³

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.3 , pp.52-59, Sep-2020

Online published on Sep 30, 2020

Copyright © Kalyani S., Venkat Rao K., A. Mary Sowjanya . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Increased smart devices in various industries is creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. Time to failure data is generated using the end of life information of assets available from the historical data. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study focusses on prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction is useful for decision maker to drive the business decision, hence Binary & Multi class classification is performed to solve business challenges. Labels for Binary & Multi class classification can be generated from the asset operation needs and maintenance manuals. Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset. Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit. Multi Class classification is developed to find the classes based on multiple time periods for asset maintenance.

Key-Words / Index Term :
IoT, Life Estimation, RUL, Sensor Data

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