S. Aravinth ¹, R. Jegadeeshwaran ¹, V. Sugumaran ¹

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN

Abstract

Objectives: The brake system is one of the major components used in automobiles which inhibits motion by absorbing energy from a moving system. So regular monitoring is essential in brake system which ensures not only vehicle safety but also human lives. Methods/Statistical Analysis: In this study, a vibration based fault diagnosis approach has been reported through machine learning approach. A hydraulic brake setup was fabricated and vibration signals under various fault conditions were extracted using accelerometer sensor with suitable frequency. These signals were compared with good range of signals and variation is analyzed through histogram feature extraction, selection and classification of machine learing scenario. Findings: Histogram features were extracted by separation of signals into different bin ranges among which bin with highest accuracy level is further processed through selection process of Decision Tree and 87.78% was the achieved accuracy in fault determination. In Voting Feature Interval (VFI) 85.64% was the accuracy attained in error identification. Application/Improvements: Since Decision Tree gives the better result in fault identification in brake fault diagnosis of this study, it can be further improved by varying the frequency ranges of signals, so each and every variation in signals are noted. Moreover improvement in accuracy level can also be achieved in future by increasing number of samples percondtion of brake system.

Keywords

Decision Tree, Histogram Features, Machine Learning, Vibration Signals, Voting Feature Interval

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S. Aravinth ¹, K. Rajesh Kanna ², V. Sugumaran ²

Affiliations
1 Bannari Amman Institute of Technology, Sathyamangalam - 638401, Tamil Nadu, IN
2 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN

Abstract

Objectives: The fault diagnosis in reciprocating air compressor system was done through this article using vibration signals from accelerometer for both healthy and faulty conditions. Methods/Analysis: This article presents a condition monitoring strategy for compressor through vibration signals using accelerometer data in identifying five common faults of air compressor these were simulated manually. These vibration signals were processed through machine learning technique, where statistical features were extracted and the features contributing to the maximum classification accuracy were selected. The J48 decision tree algorithm is used in predicting the compressor faults in early stages. Findings: High classification accuracy of 98.33% was obtained for fault detection in compressor system. Application/Improvements: The proposed model can be used for regular monitoring of air compressor.

Keywords

Fault Diagnosis, J48 Algorithms, Reciprocating Air Compressor, Vibration Signals.

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