Fault Diagnosis of Helical Gear Box Using Vibration Signals through J-48 Graft Algorithm and Wavelet Features

Objectives: In this paper, machine learning approach, grounded on vibrations, has been used for helical gear box and holds a vital position in the industry. This approach has three steps namely feature extraction, feature selection and feature classification. Firstly, feature extraction was carried out using Matrix Laboratory (MATLAB) software. Feature selection was done using J48 classifier. The nodes with highest classification accuracy were further tested using J48 graft classifier and the results obtained were very promising. Methods/Analysis: Vibration signals were obtained from the experimental set up of the helical gear box. The recorded signals were then used for feature extraction using MATLAB through different wavelet features. The total numbers of signals extracted were 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 (SYM). In wavelet selection, signals were dissected into various frequencies and each was analyzed with appropriate resolution.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. Findings: Feature classification was carried out by J48 graft algorithm. Using the grafting technique, the classifier achieved the highest accuracy for pruned data for 10 times cross validation. It gave maximum accuracy for pruned data (40%) and the results were satisfactory. Novelty/Improvements: The J48 graft algorithm uses grafting to infer from previous decision trees. This helps in reducing prediction errors.