Ayush Kimothi ¹, Ameet Singh ¹, V. Sugumaran ¹, M. Amarnath ²

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

Abstract

Objective: Gearbox being the backbone of transmission system is designed and manufactured very carefully so that there is minimum compliance in the system. However, there are still faults and failures which usually occur in the system. The failure in helical gearbox is more prominent in bearings rather than gears which are the main components of the system. Gearbox is susceptible to failures because of reasons like misalignment, vibration and shocks. In this paper wavelet feature extraction is used along with random forest algorithm to diagnose faults in gearbox. The vibration signals were used for extracting wavelet features. Features were selected using J48 Decision Tree and were classified using random forest algorithm. A detailed study has been done to ensure that the optimum number of features was used and the factor was iterated so that maximum classification accuracy is obtained. The results are presented along with the conclusion. Method Analysis: The classification accuracy is obtained by 3 steps namely, feature extraction, feature selection and feature classification. By obtaining the Decision Tree the most important factors are selected to obtain maximum classification accuracy at minimum number of features to reduce calculations in real time application. The number of features and depth of data is iterated to obtain the maximum classification accuracy. Findings: Through this research random forest algorithm was tested for fault diagnosis of gearbox and a better classification accuracy was obtained. These results can be further used for fault diagnosis in industries for any gearbox related problems. Application/Improvements: An extensive investigation is done by a random forest algorithm which produced better forecasting than the other algorithms. Based on the overall study, random forest was found as the most preferred classification algorithm that achieved the best classification accuracy of 93.08% which is better than the other algorithms.

Keywords

Fault Diagnostics, Gearbox Fault Diagnostics, J48 Decision Tree, Machine Learning, Random Forest, Vibration Signals, Wavelet Feature Extraction

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Nikhil Pawar ¹, V. Sugumaran ¹, Ameet Singh ¹, M. Amarnath ²

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, IIITDM, Jabalpur - 482005, Madhya Pradesh, IN

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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Abhi Srivastava ¹, Ameet Singh ¹, V. Sugumaran ¹, M. Amarnath ²

Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai - 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, IITDM, Jabalpur - 482005, Madhya Pradesh, IN

Abstract

Objectives: Gearbox, being an important component in the mechanism of many industrial machines can have a few faults mostly by fatigue cracking under cyclic contact stressing. Most of the implements presently being utilized in the industries for the gearbox fault diagnosis are dependent upon the vibration signals which are accumulated from the gearbox. Methods: A machine based learning approach has been utilized for the detection of faults with the utilization of vibration signals that have been acquired from helical gearbox setup. The features were extracted from the collected vibration signals using wavelets. The significant features were selected using a Decision Tree algorithm. The selected features from this approach were then classified using random tree algorithm and higher accuracy was achieved. Findings: The random tree algorithm used for the classification of the wavelets which were extracted from the vibration signals of the gearbox resulted in a classification accuracy of 90.4%. This classification accuracy is unique in terms of the vibration signals that have been acquired utilizing the accelerometer from the helical gearbox setup. The higher classification is achieved after feature extraction, selection and classification. Improvements/Applications: The classification accuracy achieved using the random tree algorithm was higher than the previously attained values for the gearbox. The higher accuracy would result in better fault diagnosis for the helical gearbox setup.

Keywords

Decision Tree, Fault Diagnosis, Helical Gearbox, Machine Learning, Random Tree Algorithm, Wavelet Features.

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Ameet Singh 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: Gear plays an efficient role in power transmission. Minor faults in gears can lead to severe faults. The vibration analysis can be used for determining the causes of the faults which are raised while ongoing operation. This study determines the usage of machine learning algorithm for condition monitoring of helical gearbox. Methods/Statistical Analysis: The vibration signals were taken by using accelerometers from helical gearbox in which artificial faults were incorporated before testing. By using Discrete Wavelet Transform (DWT) feature extraction was done. The feature selection and feature classification was done by using J48 algorithm and subsequent results were observed. Findings: The classification accuracy of helical gearbox using Discrete Wavelet Transform was observed to be 89.28% which itself shows its efficiency. In feature extraction maximum accuracy of 89.06% was obtained by sym 8 wavelet. During feature selection and classification many modifications in algorithm were made i.e. minimum number of object, confidence factor etc. Suitable readings of the modifications were applied and feature classification was done. Improvements: Different Discrete Wavelet Transforms were compared taken from vibration signal proved Sym 8 Discrete Wavelet Transform is the best one to be used in this scenario. The methodology yielded a satisfactory classification accuracy of 89.28%, which is higher than what was obtained by similar experiments with different methodology till date. The results and their analysis are discussed in the study. The performance of this methodology may be further improved by using different classifiers and different wavelets.

Keywords

Condition Monitoring, Discrete Wavelet Transform, J48 Algorithm, Vibration Signals.

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Nikhil Pawar ¹, V. Sugumaran ¹, Ameet Singh ¹, M. Amarnath ²

Affiliations
1 School of Mechanical and building Science (SMBS), VIT University, Chennai Campus, Chennai – 600127, Tamil Nadu, IN
2 Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing, Airport Rd, Jabalpur Campus, Khamaria, Jabalpur – 482005, Madhya Pradesh, IN

Abstract

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.

Keywords

Decision Tree, Gearbox Fault Diagnosis, J48 Classifier, J48 graft Classifier, Wavelet.

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