Version 1
: Received: 27 July 2020 / Approved: 29 July 2020 / Online: 29 July 2020 (12:34:24 CEST)
How to cite:
akiri, K.; b, V.R. A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis. Preprints2020, 2020070697. https://doi.org/10.20944/preprints202007.0697.v1
akiri, K.; b, V.R. A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis. Preprints 2020, 2020070697. https://doi.org/10.20944/preprints202007.0697.v1
akiri, K.; b, V.R. A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis. Preprints2020, 2020070697. https://doi.org/10.20944/preprints202007.0697.v1
APA Style
akiri, K., & b, V.R. (2020). A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis. Preprints. https://doi.org/10.20944/preprints202007.0697.v1
Chicago/Turabian Style
akiri, K. and Venkat Rao b. 2020 "A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis" Preprints. https://doi.org/10.20944/preprints202007.0697.v1
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. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction should be useful for decision maker to drive the business decision; hence Binary classification is performed, and business case analysis is performed. 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.
Keywords
RUL prediction; sensors; IOT; aircraft engine; business intelligence
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
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.
