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Amarnath, M.
- Fault Diagnosis of Helical Gearbox through Vibration Signals using Wavelet Features, J48 Decision Tree and Random Forest Classifiers
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: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- Fault Diagnosis of Roller Bearings with Sound Signals using Wavelets and Decision Tree Algorithm
Abstract Views :141 |
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Authors
Affiliations
1 School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN
2 IIITDM Jabalpur, Jabalpur - 482005 , Madhya Pradesh, IN
1 School of Mechanical and Building Sciences, VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, IN
2 IIITDM Jabalpur, Jabalpur - 482005 , Madhya Pradesh, IN
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination:Abstract
Objectives: Use of an appropriate fault diagnosis methods alerts in advance about malfunctioning and failure of bearings. Vibration and Sound signals of rotating machines contain the dynamic information about their operating conditions. There are many articles reporting suitability of vibration signals for fault diagnosis applications; however, the transducers ( accelerometers) and data acquisition equipment used for vibration signals analysis are costly. This prevents small scale industries and low cost equipment from using diagnostic tools on affordability ground. On the other hand, transducers used for acquiring sound signals (microphones) are relatively low cost or/and affordable. Hence, there is a need for studying the use of sound signal for fault diagnosis applications. This paper uses sound signals acquired from roller bearings in good and simulated faulty conditions for the fault diagnosis purpose. Methods/Analysis: Sound signals from bearings having defects on inner race and outer race have been considered for analysis. Since the characteristic sound signals of faulty bearings are complex and are struck in the noise and high frequency structural resonance, simple signal processing techniques cannot be used to detect bearing fault. Hence, wavelet features are used for extracting features from sound signals. The energy levels at various levels of wavelet decomposition are used to define features from sound signals. The most contributing features were selected and their classification is done using decision tree algorithm. This paper also discusses the effect of features, effect of various classifier parameters on classification accuracy. Findings: In feature classification of the fault signals the RBIO 2.4 wavelet has given the highest classification accuracy of 96.66%. Out of the 120 total instances, 116 (96.66%) were correctly identified while 4 instances were incorrectly classified with an error margin of (3.33%). Application/Improvements: An extensive investigation has been made by a J48 algorithm which produced better predictive performance than the other algorithms. The training and the optimization of J48 model with their essential parametric measures are reported. Based on the overall study, J48 with variation in number of objects (from 1 to 6) feature was found as the most successful classification algorithm that achieved the best classification accuracy of 96.66%. The classification accuracy of the proposed algorithm has been found better with only 4 misclassified features. The classification capability and the performance evaluation of J48 algorithm with confusion matrix and detailed classification accuracy is reported and discussed for further study.Keywords
Bearings, Classification Accuracy, Decision Tree, Fault Diagnosis, Feature Selection, Sound Signals, Wavelet Features.- Fault Diagnostics of a Gearbox via Acoustic Signal using Wavelet Features, J48 Decision Tree and Random Tree Classifier
Abstract Views :143 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination:Abstract
Objectives: Heart of the transmission system in most machineries are gears for efficient power transmission. Even minor faults in gear can lead to major losses in terms of energy as well as in terms of money. The unwanted by-product while operating gear box are vibration and acoustic signals, which can be used for the condition monitoring and fault diagnosis of the gearbox. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the acoustic signals produced by the gearbox. Methods/Analysis: The acoustic signals were captured using microphone from a gearbox with artificially created fault conditions. A comprehensive study was carried out using different discrete wavelet transformations for feature extraction which was further used in generating J48 decision tree algorithm and subsequently it was employed for selection and classification of the extracted features. Finding: Through this study the classification accuracy obtained is 97.619% by varying the different parameter to achieve the highest accuracy level. Data used in this study is exclusively obtained through experiment and subsequently through J48 decision tree and random tree classification accuracy level is studied to accomplish the highest accuracy. Novelty/Improvements: The comparison of different discrete wavelet transforms of the acoustic signals proved Daubechies 5 Discrete Wavelet Transform is the best suited one to use. The methodology yielded a satisfactory classification accuracy of 97.619%, which is higher than what was obtained by similar experiments with different methodology till date. The results and their analysis is discussed in the study. The performance of this methodology may be further improved by using different classifiers.Keywords
Acoustic Signals, Gearbox, J48 Decision Tree, Random Tree, Wavelets- Fault Diagnosis of Helical Gearbox Using Vibration Signals through K-Star Algorithm and Wavelet Features
Abstract Views :146 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: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- Fault Diagnosis of Helical Gear Box using Vibration Signals through Random Tree and Wavelet Features
Abstract Views :127 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: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.- Acoustic Signal Based Condition Monitoring of Gearbox using Wavelets and Decision Tree Classifier
Abstract Views :155 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination:Abstract
Objectives: Most machineries employ gears for efficient power transmission. Even minor faults with the gear box can lead to severe losses both in terms of energy and money. The vibration and acoustic signals from the gear box, which usually are said to be as an unwanted by-product of the operation, can be used for the condition monitoring and fault diagnosis of the gearbox. This study proposes the usage of machine learning algorithm for condition monitoring of a helical gearbox by using the sound signals produced by the gearbox. Methods/Analysis: The acoustic signals were captured using microphone from a gearbox with artificially created fault conditions. An exhaustive study using different discrete wavelet transformations for feature extraction from the acoustic signals was carried out and subsequently J48 Decision Tree algorithm was employed for selection and classification of the extracted features. Findings: The time domain acoustic signals were converted into frequency time domain data using different discrete wavelet transforms. Of all the wavelet ransforms, the Daubechies 5 Discrete Wavelet Transform was found to be the best suited for the current scenario. The methodology yielded a satisfactory classification accuracy of 97.6% when classified using J48 algorithm. Novelty/ Improvements: The classification accuracy yielded through this methodology is higher than what was obtained by similar experiments with different methodologies till date. The results and their analysis is discussed in the study. The whole methodology when put in a real time sytem will have the capability to monitor the condition and diagnose the faults in the gearbox quickly and effectively. The performance of this methodology may be further improved by using different classifier algorithms.Keywords
Acoustic Signal, Condition Monitoring, Decision Tree Classifier, Gearbox, Wavelets.- Fault Diagnosis of Helical Gearbox through Vibration Signals using J48 Decision Tree and Wavelet
Abstract Views :133 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 33 (2016), Pagination: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.- Remaining Life-Time Assessment of Gear Box Using Regression Model
Abstract Views :171 |
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Authors
Affiliations
1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu, IN
2 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu
3 Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing,Airport Road, IIITDM Jabalpur Campus, Khamaria, Jabalpur – 482005, Madhya Pradesh, IN
4 Department of Mechanical Engineering, Inha University, KR
1 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu, IN
2 School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Vandalur Kelambakkam Road, Chennai – 600127, Tamil Nadu
3 Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing,Airport Road, IIITDM Jabalpur Campus, Khamaria, Jabalpur – 482005, Madhya Pradesh, IN
4 Department of Mechanical Engineering, Inha University, KR
Source
Indian Journal of Science and Technology, Vol 9, No 47 (2016), Pagination:Abstract
Objectives: The main objective of this study is to develop a model which can able to predict the remaining life time working of a gearbox using vibration signals. Method: This study is considered as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. In this research, histogram features are extracted from vibration signals, feature selection are carried out using J48 algorithm and different regression models were built to predict the reaming lifetime assessment of a gearbox. Findings: In this study, the J48 algorithm was used and the regression was found to be 0.8944 for Gaussian model. This is a novel approach to finding the life prediction of gearbox using histogram and regression model. Improvements: This algorithm is applicable for real-time analysis and further the condition monitoring can be carried out using different algorithms with less computation time.Keywords
Assessment, Fault Diagnosis, Gearbox, Histogram Features, Life Time, Multiple Regression, Sound Signals.- Fault Diagnosis of Helical Gear Box Using Vibration Signals through J-48 Graft Algorithm and Wavelet Features
Abstract Views :172 |
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Authors
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
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
Source
Indian Journal of Science and Technology, Vol 9, No 47 (2016), Pagination: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.- Fault Diagnostics of a Gearbox with Acoustic Signals Using Wavelets and Decision Tree
Abstract Views :174 |
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Authors
Affiliations
1 School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus,Chennai - 600127, IN
2 1School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus, Chennai - 600127, IN
3 School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus, Chennai - 600127,, IN
4 Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur - 482005, Madhya Pradesh, IN
1 School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus,Chennai - 600127, IN
2 1School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus, Chennai - 600127, IN
3 School of Mechanical and Building Sciences, Vellore Institute of Technology, Chennai Campus, Chennai - 600127,, IN
4 Indian Institute of Information Technology Design and Manufacturing Jabalpur, Jabalpur - 482005, Madhya Pradesh, IN