scholarly journals APPLICATION OF A CONTINUOUS WAVELET TRANSFORMS AND ANFIS TO FAULT DIAGNOSIS OF A GEARBOX OF TRACTOR

2017 ◽  
Vol 46 (1) ◽  
pp. 9-15
Author(s):  
Mostafa Bahrami ◽  
Hossein Javadikia ◽  
Ebrahim Ebrahimi
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Transforms ◽  
Fuzzy Inference ◽  
Vibration Signal ◽  
Continuous Wavelet ◽  
Inference System ◽  
Continuous Wavelet Transforms ◽  
Gearbox Vibration ◽  
Gear Fault ◽  
Tractor Gear

This study proposes a technique based on continuous wavelet transform added by adaptive-neural-fuzzy inference system (ANFIS) for fault diagnosis of gearbox of MF285 tractor. Varying fault can cause changes in a measured gearbox vibration signal. A gear motion vibration signal in speed of 1500 r/min is analyzed as source data. Energy of coefficients wavelet is proposed to evaluate tractor gear fault by ANFIS. We found that this technique can correctly indicate early gear fault.

scholarly journals Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFIS

2018 ◽  
Author(s):  
Moshen Kuai ◽  
Gang Cheng ◽  
Yusong Pang ◽  
Yong Li
Keyword(s):  
Fault Diagnosis ◽  
High Speed ◽  
Fuzzy Inference ◽  
Recognition Rate ◽  
Planetary Gear ◽  
Permutation Entropy ◽  
Intrinsic Mode Functions ◽  
Inference System ◽  
Gear Fault ◽  
Gear Fault Diagnosis

For planetary gear has the characteristics of small volume, light weight and large transmission ratio, it is widely used in high speed and high power mechanical system. Poor working conditions result in frequent failures of planetary gear. A method is proposed for diagnosing faults in planetary gear based on permutation entropy of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) Adaptive Neuro-fuzzy Inference System (ANFIS) in this paper. The original signal is decomposed into 6 intrinsic mode functions (IMF) and residual components by CEEMDAN. Since the IMF contains the main characteristic information of planetary gear faults, time complexity of IMFs are reflected by permutation entropies to quantify the fault features. The permutation entropies of each IMF component are defined as the input of ANFIS, and its parameters and membership functions are adaptively adjusted according to training samples. Finally, the fuzzy inference rules are determined, and the optimal ANFIS is obtained. The overall recognition rate of the test sample used for ANFIS is 90.75%, and the recognition rate of gear with one missing tooth is relatively high. The recognition rates of different fault gears based on the method can also achieve better results. Therefore, the proposed method can be applied to planetary gear fault diagnosis effectively.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

Gear Fault Diagnosis Methods Based on EMD and HMM

2013 ◽  
Vol 333-335 ◽  
pp. 1684-1687
Author(s):  
Bin Wu ◽  
Song He Zhang ◽  
Yue Gang Luo ◽  
Shan Ping Yu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Modulation Amplitude ◽  
Double Frequency ◽  
Gear Mesh ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Modulation Signal ◽  
Gear Fault Diagnosis ◽  
Modulation Sideband

Due to the feature and the forms of motion of the gears, the vibration signal of the gear is mainly the frequency modulation, amplitude modulation, or hybrid modulation signal corresponding to the gear-mesh frequency and its double frequency signal. When faults arise on the gears, the number and shape of the modulation sideband will be changed. The structures and forms of the FM composition differ according to the type of faults. According to the above mentioned characteristic, this essay raises a method to disassemble the gear vibrate signal, points out the formulas to build up characteristic vector, on that basis, the essay raised a gear fault diagnosis method based on EMD and Hidden Markov Model (HMM), this method can identify the working condition of the normal gears, snaggletooth gears, and pitting gears.

The Wavelet Analysis and Processing of Weak Vibration Signal

2011 ◽  
Vol 189-193 ◽  
pp. 1426-1431
Author(s):  
Ze Ning Xu ◽  
Hong Yu Liu ◽  
Yong Guo Zhang
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Basic Feature ◽  
Wavelet Function ◽  
Fault Analysis ◽  
Bearing Fault Diagnosis ◽  
Gear Fault ◽  
Vibratory Signals

Signal measuring is an important link in machine fault diagnosis. Accurate and reliable fault signals can be achieved by reasonable signal measuring. When the distance between sensor and measuring gear or bearing is comparatively far, the collected signals became weak and disturbed by other vibratory signals in equipments on bearing and gear fault analysis. Useful signals often were submerged in powerful noise, so caused difficult in extracting fault feature. In this paper, according to the feature of vibratory signals in machine test, wavelet analysis basic theory was applied on researching basic feature of wavelet analysis. By selecting suitable wavelet function and applying wavelet elimination noise technology the signal to noise ratio of signal was raised, thus the vibratory impact component can be measured in weak signals. Finally, wavelet analysis was applied on bearing fault diagnosis.

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