Application of statistical parameters and discrete wavelet transform to gear damage diagnosis

Abstract

Gear is one of the most commonly used and important components in machine system. Mainly gear failure may cause serious damage of the whole equipment, even huge economic losses. Therefore, it is important to detect the gear damage as early as possible. This paper provides a method of diagnosis and location for gear damage based on statistical approach and discrete wavelet transform (DWT). The vibration signals of gear box and bearing box are measured as analytical data. To emphasize the failure features of the measured signal, gear motion residual signal is obtained from the raw signal and provides a better indication of the existence of failures. Additionally, the method of discrete wavelet transform is employed to reduce the noise from the residual signal and decompose the signal into several decomposition levels. Because of the good sensitivity to the altering of vibration signal, statistical parameters such as standard deviation, kurtosis and so on are extracted from the raw signal and the reconstructed signal by DWT as failure features for detecting the gear damage. For a comparison of the raw signal and the reconstructed signal, the variation of statistical parameters among different kinds of test gears is also discussed by the significance test. The validity of the presented method is testified by some experiments under different conditions. Three kinds of gears namely normal gear, spot damaged gear and pitted gear are tested on the power circulating type gear testing machine, and the vibration acceleration on both gear box and bearing box is obtained. The experimental results show the effectiveness of this method on the diagnosis of gear damage.

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

“…With other wavelet transforms, a key advantage over Fourier transform is temporal resolution: it captures both frequency and location information. Fan et al [105] used discrete wavelet transform to reduce the noise and decompose the signal into several decomposition levels, and then statistical parameters were applied for detecting the gear damage. Rahman et al [106] used a discrete wavelet transform to extract fault information, and then envelope detection was used for online condition monitoring of unbalanced rotor induction motor.…”

“…Fan et al [95] Wavelet transform He et al [96] Wavelet transform Cui et al [97] Wavelet transform + time-frequency analysis + blind source Separation theory Morsy et al [111] Morlet wavelet Filter + envelope detection Yiakopoulos [112] Morphological + Complex Shifted Morlet Wavelets. Cui et al [98] High-frequency characteristics + self-adaptive wavelet de-noising Wang et al [114] Complex Morlet wavelet coefficients + sparsity measurement Tse et al [109] Wavelet transform + envelope analysis Wang et al [99] Adaptive wavelet stripping algorithm Morsy et al [113] Maximum Kurtosis + Morlet wavelet Combet et al [100] Wavelet bicoherence Moumene et al [101] Wavelets multiresolution analysis + the high-frequency resonance Fan et al [105] Discrete wavelet transform Karuppaiah et al [108] HAAR wavelet Rahman et al [106] Discrete wavelet transform Rangel-Magdaleno et al [107] Discrete wavelet transform + motor current signature analysis Chen et al [102] Adaptive redundant multiwavelet packet He et al [103] Adaptive multiwavelet Yang et al [110] EMD + autocorrelation de-noising + wavelet package decomposition Li et al [104] Intrinsic character-scale decomposition + tunable Q-factor wavelet transform.…”

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

“…This is because the training dataset was also acquired under 25 and 70 NÁm loads. Our previous studies 24 showed that the load torque and rotation speed will affect the vibration accelerations and feature parameters, thus reducing the robustness of the proposed method. How to reduce the effect of load torque and rotation speed on the extracted features is a problem in our study now.…”

Method of Predicting Pitting Fatigue Utilizing an Eddy Current Sensor

“…Pitting failure causes large vibrations, and downtime is needed for maintenance. An acceleration sensor has been used to detect pitting in practical applications [2,3]. However, the acceleration signals are largely constant during normal operation, and the signal immediately increases after pitting failure, making it difficult to predict pitting occurrences with this method.…”