Open Access
ARTICLE
A Novel Integrated Machine & Business Intelligence Framework for Sensor Data Analysis
S. Kalyani*, A. Mary Sowjanya, K. Venkat Rao
Vignan’s Institute of Engineering for Women, Andhra University, Visakhapatnam, India
* Corresponding Author: S. Kalyani. Email:
Journal on Internet of Things 2021, 3(1), 27-38. https://doi.org/DOI:10.32604/jiot.2021.013163
Received 12 October 2020; Accepted 07 December 2020; Issue published 16 March 2021
Abstract
Increased smart devices in various industries are 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
cclassification 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
Cite This Article
S. Kalyani, A. M. Sowjanya and K. V. Rao, "A novel integrated machine & business intelligence framework for sensor data analysis,"
Journal on Internet of Things, vol. 3, no.1, pp. 27–38, 2021.