Shantanu Gupta ¹, Siju K. Abraham ¹, V. Sugumaran ¹, M. Amarnath ²

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai – 600127, Tamil Nadu, IN
2 Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur - 482005, Madhya Pradesh, IN

Abstract

Objectives: Heart of the transmission system in most machineries are gears for efficient power transmission. Even minor faults in gear can lead to major losses in terms of energy as well as in terms of money. The unwanted by-product while operating gear box are vibration and acoustic signals, which can be used for the condition monitoring and fault diagnosis of the gearbox. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the acoustic signals produced by the gearbox. Methods/Analysis: The acoustic signals were captured using microphone from a gearbox with artificially created fault conditions. A comprehensive study was carried out using different discrete wavelet transformations for feature extraction which was further used in generating J48 decision tree algorithm and subsequently it was employed for selection and classification of the extracted features. Finding: Through this study the classification accuracy obtained is 97.619% by varying the different parameter to achieve the highest accuracy level. Data used in this study is exclusively obtained through experiment and subsequently through J48 decision tree and random tree classification accuracy level is studied to accomplish the highest accuracy. Novelty/Improvements: The comparison of different discrete wavelet transforms of the acoustic signals proved Daubechies 5 Discrete Wavelet Transform is the best suited one to use. The methodology yielded a satisfactory classification accuracy of 97.619%, which is higher than what was obtained by similar experiments with different methodology till date. The results and their analysis is discussed in the study. The performance of this methodology may be further improved by using different classifiers.

Keywords

Acoustic Signals, Gearbox, J48 Decision Tree, Random Tree, Wavelets

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Siju K. Abraham ¹, V. Sugumaran ¹, M. Amarnath ²

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, IN
2 Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur - 482005, Madhya Pradesh, IN

Abstract

Objectives: Most machineries employ gears for efficient power transmission. Even minor faults with the gear box can lead to severe losses both in terms of energy and money. The vibration and acoustic signals from the gear box, which usually are said to be as an unwanted by-product of the operation, can be used for the condition monitoring and fault diagnosis of the gearbox. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Methods/Analysis: The acoustic signals were captured using microphone from a gearbox with artificially created fault conditions. An exhaustive study using different discrete wavelet transformations for feature extraction from the acoustic signals was carried out and subsequently J48 Decision Tree algorithm was employed for selection and classification of the extracted features. Findings: The time domain acoustic signals were converted into frequency time domain data using different discrete wavelet transforms. Of all the wavelet ransforms, the Daubechies 5 Discrete Wavelet Transform was found to be the best suited for the current scenario. The methodology yielded a satisfactory classification accuracy of 97.6% when classified using J48 algorithm. Novelty/ Improvements: The classification accuracy yielded through this methodology is higher than what was obtained by similar experiments with different methodologies till date. The results and their analysis is discussed in the study. The whole methodology when put in a real time sytem will have the capability to monitor the condition and diagnose the faults in the gearbox quickly and effectively. The performance of this methodology may be further improved by using different classifier algorithms.

Keywords

Acoustic Signal, Condition Monitoring, Decision Tree Classifier, Gearbox, Wavelets.

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