Total views : 285
Fault Diagnosis of Roller Bearings with Sound Signals using Wavelets and Decision Tree Algorithm
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.
Bearings, Classification Accuracy, Decision Tree, Fault Diagnosis, Feature Selection, Sound Signals, Wavelet Features.
- Al-Badour F, Sunar M, Cheded L. Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing. 2011 Aug; 25(6):2083–101.
- Al-Ghamd AM, Mba D. A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mechanical Systems and Signal Processing. 2006 Oct; 20(7):1537–71.
- Sugumaran V, Ramachandran KI. Fault diagnosis of roller bearing using fuzzy classifier and histogram features with focus on automatic rule learning. Expert Systems with Applications. 2011 May; 38(5):4901–7.
- Bentley D. Predictive maintenance through the monitoring and diagnostics of rolling element bearings. Bently Nevada Co. Application note. 1989; 44:2–8.
- Sugumaran V, Ramachandran KI. Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mechanical Systems and Signal Processing. 2007 Jul; 21(5):2237–47.
- Jegadeeshwaran R, Sugumaran V. Vibration based fault diagnosis study of an automobile brake system using K-STAR (K*) Algorithm – A Statistical approach. Recent Patents on Signal Processing. 2014 Apr; 4(1):44–56.
- Nikolaou NG, Antoniadis IA. Roller bearing fault analysis using wavelet packets. NDT and E International. 2002; 35(3):197–205.
- Mori K, Kasashima N, Yoshioka T, Ueno Y. Prediction of spalling on a ball bearing by applying Discrete Wavelet Transform to vibration signals. Wear. 1996 Jul; 195(1-2):162–8.
- Lin J, Qu L. Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration. 2000 Jun; 234(1):135–48.
- Amarnath M, Sugumaran V, Hemantha Kumar R. Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement. 2013 Apr; 46(3):1250–6.
- Sugumaran V, Ramachandran KI. Effect of number of features on classification of roller bearing faults using SVM and PSVM. Expert Systems with Applications. 2011 Apr; 38(4):4088–96.
- Li Z, Zhu J, Shen X, Zhang C, Guo J. Fault diagnosis of motor bearing based on the Bayesian network. International Workshop on Automobile, Power and Energy Engineering. Procedia Engineering. 2011 Nov; 16:18–26.
- Sakthivel NR, Sugumaran V, Nair BB. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mechanical Systems and Signal Processing. 2010 Aug; 24(6):1887–906.
- Jegadeeshwaran R, Sugumaran V. Comparative study of using decision tree and best first tree as a classifier for fault diagnosis in automobile hydraulic brake system using statistical features. Measurements. 2013 Nov; 46(9):3247–60.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.