scholarly journals Research on the Electromagnetic Conversion Method of Stator Current for Local Fault Detection of a Planetary Gearbox

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 277
Author(s):  
Xiangyang Xu ◽  
Guanrui Liu ◽  
Xihui Liang
Keyword(s):  
Fault Detection ◽  
Planetary Gear ◽  
Magnetomotive Force ◽  
Planetary Gearbox ◽  
Transfer Path ◽  
Stator Current ◽  
Motor Current ◽  
Gear Fault ◽  
Motor Current Signature Analysis ◽  
Signal Processing Methods

Motor current signature analysis (MCSA) is a useful technique for planetary gear fault detection. Motor current signals have easier accessibility and are free from time-varying transfer path effects. If the fault symptoms in current signals are well understood, it will be more beneficial to develop effective current signal processing methods. Some researchers have developed mathematical models to study the characteristics of current signals. However, no one has considered the coupling of rotor eccentricity and gear failures, resulting in an inaccurate analysis of the current signals. This study considers the sun gear failure of a planetary gearbox and the eccentricity of the motor rotor. An improved induction motor model is proposed based on the magnetomotive force (MMF) to simulate the stator current. By analyzing the current, the modulation relationships of gearbox meshing frequency, fault frequency, power supply frequency, and gear rotating frequency are obtained. The proposed model is validated to some extent using experimental data.

Planetary Gear Fault Detection Based on Mechanical Torque and Stator Current Signatures of a Wound Rotor Induction Generator

2018 ◽  
Vol 33 (3) ◽  
pp. 1072-1085 ◽  
Author(s):  
Mohammad Hoseintabar Marzebali ◽  
Jawad Faiz ◽  
Gerard-Andre Capolino ◽  
Shahin Hedayati Kia ◽  
Humberto Henao
Keyword(s):  
Fault Detection ◽  
Planetary Gear ◽  
Induction Generator ◽  
Stator Current ◽  
Gear Fault ◽  
Current Signatures

A positive energy residual (PER) based planetary gear fault detection method under variable speed conditions

2019 ◽  
Vol 117 ◽  
pp. 347-360 ◽  
Author(s):  
Jungho Park ◽  
Moussa Hamadache ◽  
Jong M. Ha ◽  
Yunhan Kim ◽  
Kyumin Na ◽  
...  
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Planetary Gear ◽  
Positive Energy ◽  
Variable Speed ◽  
Gear Fault

Online Condition Monitoring of Traction Motor

2016 ◽  
pp. 489-523
Author(s):  
Anik Kumar Samanta ◽  
Arunava Naha ◽  
Devasish Basu ◽  
Aurobinda Routray ◽  
Alok Kanti Deb
Keyword(s):  
Fault Detection ◽  
Condition Monitoring ◽  
Traction Motor ◽  
Motor Current ◽  
Detection Technology ◽  
Current Voltage ◽  
Motor Current Signature Analysis ◽  
Complete Failure ◽  
Condition Monitoring System ◽  
The Cost

Squirrel Cage Induction Motors (SCIMs) are major workhorse of Indian Railways. Continuous online condition monitoring of the SCIMs like Traction Motor (TM) are essential to prevent unnecessary stoppage time in case of a complete failure. Before a complete failure, the TMs generally develop incipient or weak faults. Weak faults have minute influence on the motor performance but eventually leads to complete failure of the motor. If these weak faults are identified at the earliest then, a scheduled maintenance can be planned which will prevent any unplanned stoppage. The signals used for SCIM fault detection are motor current, voltage, vibration, temperature, voltage induced in search coil, etc. The most popular fault detection technology is based on Motor Current Signature Analysis (MCSA). MCSA based online and onboard TM condition monitoring system can be very useful for Indian railways to reduce the cost of operation and unplanned delay by shifting from unnecessary scheduled maintenance to condition-based maintenance of TM and other auxiliary SCIMs.

scholarly journals Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Manifold Learning ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Planetary Gearbox ◽  
Characteristic Extraction ◽  
Gear Fault ◽  
Planet Gear ◽  
Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

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