Affiliations: [a] School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India
| [b] School of Electrical Engineering, Vellore Institute of Technology, Vellore, India
Correspondence:
[*]
Corresponding author. T. Narendiranath Babu, School of Mechanical Engineering, Vellore Institute of Technology, Vellore, India. E-mail: narendiranathbabu.t@vit.ac.in.
Abstract: In the modern world, condition monitoring is crucial to the predictive maintenance of machinery. Gearboxes are widely used in machineries and auto motives to achieve the variable speeds. The major problem in gearbox is catastrophic failure due to heavy loads, corrosion and erosion, results in economic loss and creates high safety risks. So, it is necessary to provide condition monitoring technique to detect and diagnose failures, to achieve cost benefits to industry. The main purpose of this study to use Machine Learning (ML) algorithms and Artificial Neural Network (ANN) which are very powerful and reliable tool for fault detection and its most important attribute is its ability to efficiently detect non-stationary, non-periodic, transient features of the vibration signal. To do the vibration study, an experimental setup was created, and various faults were induced faults of various kinds that usually occurred in the gearbox. The gear in the gear train was subjected to vibration analysis which was captured via a sensor. Signal processing was carried out using MATLAB Toolbox. To automatically identify the flaws in the helical gearbox, an artificial neural network (ANN) and several machines learning methods, including KNN, decision tree, random forest, and SMV, were trained by creating a database from the experiment conducted. The outcomes showed potential in accurately classifying the faults. The results show that ANN has the highest accuracy of 99.6% with a 6.5662 seconds computational time while SVM has the lowest accuracy of 96% among them along with the highest computational time of 21.324 seconds.
Keywords: Helical gearbox, vibration analysis, signal processing, fault diagnosis, artificial neural network, K-nearest neighbor, support vector machine, decision tree, random forest
