scholarly journals Application of Reassigned Wavelet Scalogram in Wind Turbine Planetary Gearbox Fault Diagnosis under Nonstationary Conditions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaowang Chen ◽  
Zhipeng Feng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Frequency Resolution ◽  
Vibration Signals ◽  
Cross Term ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Gear Fault ◽  
Nonstationary Conditions

Wind turbine planetary gearboxes often run under nonstationary conditions due to volatile wind conditions, thus resulting in nonstationary vibration signals. Time-frequency analysis gives insight into the structure of an arbitrary nonstationary signal in joint time-frequency domain, but conventional time-frequency representations suffer from either time-frequency smearing or cross-term interferences. Reassigned wavelet scalogram has merits of fine time-frequency resolution and cross-term free nature but has very limited applications in machinery fault diagnosis. In this paper, we use reassigned wavelet scalogram to extract fault feature from wind turbine planetary gearbox vibration signals. Both experimental and in situ vibration signals are used to evaluate the effectiveness of reassigned wavelet scalogram in fault diagnosis of wind turbine planetary gearbox. For experimental evaluation, the gear characteristic instantaneous frequency curves on time-frequency plane are clearly pinpointed in both local and distributed sun gear fault cases. For in situ evaluation, the periodical impulses due to planet gear fault are also clearly identified. The results verify the feasibility and effectiveness of reassigned wavelet scalogram in planetary gearbox fault diagnosis under nonstationary conditions.

Fault diagnosis of internal combustion engine gearbox using vibration signals based on signal processing techniques

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ravikumar KN ◽  
Hemantha Kumar ◽  
Kumar GN ◽  
Gangadharan KV
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Classification Accuracy ◽  
Internal Combustion ◽  
Frequency Characteristics ◽  
Ic Engine ◽  
Vibration Signals ◽  
Content Type ◽  
Time Frequency ◽  
Gear Fault

PurposeThe purpose of this paper is to study the fault diagnosis of internal combustion (IC) engine gearbox using vibration signals with signal processing and machine learning (ML) techniques.Design/methodology/approachVibration signals from the gearbox are acquired for healthy and induced faulty conditions of the gear. In this study, 50% tooth fault and 100% tooth fault are chosen as gear faults in the driver gear. The acquired signals are processed and analyzed using signal processing and ML techniques.FindingsThe obtained results show that variation in the amplitude of the crankshaft rotational frequency (CRF) and gear mesh frequency (GMF) for different conditions of the gearbox with various load conditions. ML techniques were also employed in developing the fault diagnosis system using statistical features. J48 decision tree provides better classification accuracy about 85.1852% in identifying gearbox conditions.Practical implicationsThe proposed approach can be used effectively for fault diagnosis of IC engine gearbox. Spectrum and continuous wavelet transform (CWT) provide better information about gear fault conditions using time–frequency characteristics.Originality/valueIn this paper, experiments are conducted on real-time running condition of IC engine gearbox while considering combustion. Eddy current dynamometer is attached to output shaft of the engine for applying load. Spectrum, cepstrum, short-time Fourier transform (STFT) and wavelet analysis are performed. Spectrum, cepstrum and CWT provide better information about gear fault conditions using time–frequency characteristics. ML techniques were used in analyzing classification accuracy of the experimental data to detect the gearbox conditions using various classifiers. Hence, these techniques can be used for detection of faults in the IC engine gearbox and other reciprocating/rotating machineries.

Application of S transform and morphological pattern spectrum for gear fault diagnosis

2011 ◽  
Vol 225 (12) ◽  
pp. 2963-2972 ◽  
Author(s):  
B Li ◽  
P-L Zhang ◽  
Z-J Wang ◽  
S-S Mi ◽  
D-S Liu
Keyword(s):  
Fault Diagnosis ◽  
Wavelet Transforms ◽  
Morphological Pattern ◽  
Vibration Signals ◽  
Pattern Spectrum ◽  
Time Frequency ◽  
S Transform ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.

A Time-Frequency Analysis Approach Based on Plane Partition for Fault Diagnosis of the Planetary Gearbox Under Nonstationary Conditions

2018 ◽  
Author(s):  
Yue Hu ◽  
Xiaotong Tu ◽  
Fucai Li
Keyword(s):  
Fault Diagnosis ◽  
Frequency Analysis ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Repair Costs ◽  
Important Research Topic ◽  
Planet Gear ◽  
Nonstationary Conditions

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.

A velocity synchrosqueezing transform for fault diagnosis of planetary gearboxes under nonstationary conditions

2016 ◽  
Vol 231 (15) ◽  
pp. 2868-2884 ◽  
Author(s):  
Yunpeng Guan ◽  
Ming Liang ◽  
Dan-Sorin Necsulescu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Frequency Resolution ◽  
Time Frequency ◽  
Machinery Condition Monitoring ◽  
Frequency Representation ◽  
Nonstationary Vibration ◽  
Synchrosqueezing Transform ◽  
Fine Resolution ◽  
Nonstationary Conditions

Time–frequency analysis is widely used in the field of machinery condition monitoring and fault diagnosis under nonstationary conditions. Among the time–frequency methods synchrosqueezing transform outperforms others in providing fine-resolution time–frequency representation. However, it suffers from time–frequency smear when analysing nonstationary signals. To address this issue, this paper proposes a new synchrosqueezing-transform-based method which works by (1) mapping the raw nonstationary vibration signal into a corresponding stationary angle domain signal to meet the stationarity requirement of the synchrosqueezing transform, (2) performing the synchrosqueezing transform of the corresponding signal and (3) restoring the time–frequency representation of the raw signal from the synchrosqueezing transform result of the corresponding signal. As the synchrosqueezing transform is applied to the stationary corresponding signal, the time–frequency smear is eliminated in the synchrosqueezing transform result of the corresponding signal and the final signal time–frequency representation. As such the proposed method can generate a smear-free time–frequency representation with fine time–frequency resolution and thus provide more reliable diagnosis decisions. A fast implementation algorithm is also developed to simplify the implementation of the proposed method. The effectiveness of the proposed method is validated using both simulated and experimental vibration signals of planetary gearboxes.

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