International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Advanced Data Monitoring and Predictive Maintenance of Valves using IOT and Data Analytics Anantbhat Sachin Narayan1, Akilesh Revankar2, Enriq Dsouza3 Gourish Katti4 Mahesh Sattigeri5 N.B. Rajeev Rao6 1234Undergraduate Students B.E(Mechanical Engineering) K.L.S Gogte Institute of Technology, Belagavi, Karnataka 5Assistant Professor Dept of Mechanical Engineering K.L.S Gogte Institute of Technology, Belagavi, Karnataka
6Technical Director, Kaavata Valves (India) Pvt Ltd, Belagavi Karnataka ---------------------------------------------------------------------***--------------------------------------------------------------------insights. It helps in getting the relationship between the Abstract - This research paper will cover the monitoring of independent and dependent variables which gives a clear data through use of advanced technology and also the linear representation about the dataset predictive maintenance of a mechanical equipment along with
prediction model using data analytics with which the mechanical equipment can be continuously monitored and also predictions can be made based on the data available thus enabling us to predict the failures and thus avoid any breakdown. This research paper is mainly divided into parts which are IOT and Data Analytics. Efforts have been to put forth the entire work in terms of literature and thus will help the reader to have a clear and effective understanding of the concept and thus contribute to the knowledge of the reader .
How data analytics can be implemented on control equipment: Implementing data analytics on any control device helps in continuously monitoring and evaluating the data which helps to analyze the current condition and predict the future scenario of device. It helps in remotely monitor the data allowing to get the performance of device and take the preventive action on it. Data analytics has helped for analyzing the data and converting the raw dataset in multiple ways.
Key Words: IOT, Valves, Fluid Machinery, Predictive Maintenance, Data Analytics, Mechatronics, Mechanical Device, Control Engineering, Instrumentation Engineering, Electronics Engineering.
Manipulating data: Data analytics helped selecting the true row and column removing the duplicate data reordering the data and also adding new data statistical summering the data
1.INTRODUCTION
Regression analysis:
In this research paper exhibits the application of IOT and Data Analytics onto a mechanical device which helps to control, monitor the behavior and the working conditions of a mechanical control device and also with data available a prediction model is been setup in order to predict the failure and thus avoid any breakdowns or accidents. This research paper will give an idea on use of these technologies on conventional mechanical control equipment with help of electronic sensors, programming of various parts has been done in order to facilitate wireless technology and thus achieving an interface which monitors, controls, predicts the actual working of the control equipment. In conclusion this research paper will showcase the application of wireless technology that can be implemented on mechanical device and also data analysis of the data that can be extracted through IOT.
Finding the residuals in the data which allows us knowing errors in the raw dataset and also fitting the linear equation to observed data evaluating the model Failure prediction: With the use of linear regression and applying algorithms such as random forest on the datasets. Data analytics helped us in the predicting the further scenario of valve on which corrective action would be implemented. The equation used for linear regression model Y=A+BX+E Where, Y - Dependent variable
2. Data Analytics
A - Intercept
What is data analytics? Data analytics is process of
B - Slope that change in dependent variable for unit change in independent variable
analyzing the available dataset to find the insights of data and historical trends for solving problem and data driven decision making. It involves use of math, statistic, and computer techniques for converting raw data into actionable
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X- Independent variable E - Error.
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