A. Joshuva ¹, V. Sugumaran ², M. Amarnath ³, Sang-Kwon Lee ⁴

Affiliations
1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu, IN
2 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu
3 Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing,Airport Road, IIITDM Jabalpur Campus, Khamaria, Jabalpur – 482005, Madhya Pradesh, IN
4 Department of Mechanical Engineering, Inha University, KR

Abstract

Objectives: The main objective of this study is to develop a model which can able to predict the remaining life time working of a gearbox using vibration signals. Method: This study is considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. In this research, histogram features are extracted from vibration signals, feature selection are carried out using J48 algorithm and different regression models were built to predict the reaming lifetime assessment of a gearbox. Findings: In this study, the J48 algorithm was used and the regression was found to be 0.8944 for Gaussian model. This is a novel approach to finding the life prediction of gearbox using histogram and regression model. Improvements: This algorithm is applicable for real-time analysis and further the condition monitoring can be carried out using different algorithms with less computation time.

Keywords

Assessment, Fault Diagnosis, Gearbox, Histogram Features, Life Time, Multiple Regression, Sound Signals.

Full Text

   

A. Joshuva ¹, V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur-Kelambakkam Road,Chennai – 600127, Tamil Nadu, IN

Abstract

Objectives: The main objective of this research is to develop a model which can able to predict the various blade faults occurs in the wind turbine blade while the turbine in operating condition using vibration signals. Method: This study is considered as a machine learning problem which consist of three phases, namely feature extraction, feature selection and feature classification. In this research, statistical features are extracted from vibration signals, feature selection are carried out using J48 algorithm and different parameters of J48 algorithm were optimized to build a better classifier. Findings: In this study, the J48 algorithm was used and the classification accuracy was found to be 85.33% for multiclass problem. This is a novel approach of finding the different problem occurs in wind turbine blade at once. Improvements: This algorithm is applicable for real-time analysis and further the condition monitoring can be made as a portable device with less computation time.

Keywords

Fault Diagnosis, J48 Algorithm, Statistical Feature, Structural Health Monitoring, Vibration Signal, Wind Turbine Blade.

Full Text