Diagnosis for gear tooth surface damage by empirical mode decomposition in cyclic fatigue test

Abstract

Gear is one of the most important and commonly used components in machine system. Some gear failure may lead to fatal damage of the entire system, or even huge losses in industrial production. Early detection of gear damage is crucial to prevent the machine system from malfunction. This paper provides an intelligent diagnosis method for gear damage based on techniques of empirical mode decomposition and support vector machines. By the data processing of empirical mode decomposition, the original signal are decomposed into a finite set of intrinsic mode functions with frequency bands ranging from high to low. The characteristic energy ratios of intrinsic mode functions are acquired as representative parameters of the signal. Furthermore, statistical parameters of standard deviation, root mean square value, kurtosis and skewness are extracted from the original signal. Characteristic energy ratios and statistical parameters are combined as failure feature vectors to be input to the support vector machines classifiers for gear damage diagnosis. The validity of the presented method is confirmed by the application of monitoring gear conditions during the cyclic fatigue test. The vibration accelerations of gear box are acquired to illustrate the progression of pitting damage. Most of the gear conditions are identified, indicating the effectiveness of the proposed method.

Gear tooth surface damage diagnosis based on analyzing the vibration signal of an individual gear tooth

“…The diagnosis accuracy is a percentage of the correctly diagnosed data divided by the number of test data. 12 The result shows that most of the teeth conditions can be correctly diagnosed using this method. This proves the effectiveness of the proposed method.…”

“…SVMs were developed based on statistical learning theory. 12 This can improve the generalization ability of the learning machine by minimizing the structural risk. Therefore, SVMs are effective in solving the learning, classification, and prediction problem of small samples.…”

“…In recent years, with the development of information technology, many artificial intelligent methods have been developed and used in health on-line monitoring and failure diagnosis systems. 12 Support vector machine (SVM) is a new artificial intelligent method that can analyze the data, recognize the pattern, and used in classification and regression analysis. SVM was developed by Vapnik 13 in the early 1990s.…”

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

“…The SVM was used to diagnose the fault types and fault severities for motor bearing. Fan et al [161] used EMD to decompose the signal into IMFs, then the characteristic energy ratios of IMFs and other statistical parameters were used as input of SVM for gear tooth surface damage diagnosis. Tabrizi et al [162] used WPD to denoise the signal, the fault features extracted by EEMD were put into SVM to detect small defects on roller bearings under different operating conditions.…”

Fault Diagnosis on Bevel Gearbox with Neural Networks and Feature Extraction

“…In 2014, Hemantha Kumar and colleagues used FFT and Bayes net classifier for the fault detection on bearings and stated the results to be encouraging [21]. In 2014, an intelligent diagnostic method for gear fault detection was presented by Qingrong Fan and colleagues, and they were able to classify 82 % of the gear pitting faults correctly [22]. Saravanan used a combined approach, Hilbert transform and Support Vector Machine, for the fault detection in bevel gear with 100 % accuracy [23].…”