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Fault Diagnosis of Helical Gearbox Using Vibration Signals through K-Star Algorithm and Wavelet Features

Affiliations

  • School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, India
  • Department of Mechanical Engineering, IIITDM, Jabalpur - 482005, Madhya Pradesh, India

Abstract


Objectives: Gears are machine elements that transmit motion by successively engaging teeth. In technical terms, gears are used to transmit motion. Fault in gears can lead to major problems which may end up in affecting the gear’s functionality. Hence, fault diagnosis at an initial stage is of utmost importance to reduce losses that might occur. Continuous monitoring of the gears is very necessary. Vibration signals recorded for good and faulty conditions are used for fault detection in the helical gearbox. The fault diagnosis is done using feature extraction, feature selection and feature classification. Firstly, feature extraction was carried out using MATLAB software. Feature selection was done using J48 classifier. The classification accuracies for different conditions were calculated and compared by using K-Star classifier and the results obtained were very promising. Methods/Analysis: Vibration signals were obtained from the experimental set up of the helical gearbox. The recorded signals were then used for feature extraction using MATLAB through different wavelet features. The total number of signals extracted was 448 with each class consisting of 64 signals. The families of wavelets taken into account for fault diagnosis were Haar, Discrete Mayer, Daubechies, Biorthogonal, Reverse Biorthogonal, Coiflet and Symlets. In wavelet selection, signals were split into different frequency components and each component was studied with a resolution matched to its scale. J48 classifier was used to carry out the feature selection process and decision tree was obtained for Sym 8 wavelet. The best combination of nodes was visualized and further feature classification was done on these nodes. By varying the global blends the optimum number of objects was selected to obtain the highest classification accuracy. Finding: The classification accuracy for the built model was 91.74%. The data extracted from the vibration signal is used for the classification purpose. This maximum classification accuracy was obtained with K star algorithm. Novelty/Improvements: Wavelet selection was different from Fourier methods in analyzing physical situations where the signal contains discontinuities and sharp spikes. K Star algorithm was used to carry out the fault diagnosis.

Keywords

Decision Tree, Gearbox Fault Diagnosis, J48 Classifier, K-Star Classifier, Wavelet

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