scholarly journals A Review of Gear Fault Diagnosis of Planetary Gearboxes Using Acoustic Emissions

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
New Technologies ◽  
Acoustic Emissions ◽  
Mechanical Transmission ◽  
Machine Condition ◽  
Machine Condition Monitoring ◽  
Gear Fault ◽  
Advanced Analysis ◽  
Monitoring Technologies

AbstractDespite the progress made in the last decades in the field of machine condition monitoring, there are still cases where the current state of the art is not enough and new technologies and advanced analysis methods are required to prevent unexpected failures. One example is planetary gearboxes (PGs), which are one of the main components of mechanical transmission systems in heavy-duty machines such as off-highway trucks, electric rope shovels, helicopters and wind turbines. Although those machines are usually equipped with vibration and temperature sensors to detect faults in mechanical components, these technologies might not be able to perform well under certain circumstances. Therefore, the applied investigation on new monitoring technologies in the field of machine condition monitoring is a necessary step. Among those, in this review the acoustic emission technology will be addressed as a tool for fault diagnosis of gear faults in PG.

A Correlative Method of Machine Condition Monitoring Based on the Choi-Williams Distribution

2005 ◽  
Vol 293-294 ◽  
pp. 777-784
Author(s):  
Guoan Yang ◽  
Zhenhuan Wu ◽  
Jin Ji Gao
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Condition Monitoring ◽  
New Method ◽  
Time Varying ◽  
Machine Condition ◽  
Time Frequency ◽  
Machine Condition Monitoring ◽  
Machine Fault Diagnosis ◽  
Feature Extractor

In this paper, a new method for time-varying machine condition monitoring is proposed. By Choi-Williams distribution, the interference terms produced by the bilinear time-frequency transform are reduced and the fault signal is processed by the correlation analysis of the Choi-Williams distribution. For machine fault diagnosis, both the feature extractor and classifier are combined to make a decision. It is particularly suited to those who are not experts in the field. Satisfactory results have been obtained from a real example and the effectiveness of the proposed method is demonstrated.

Acoustic Emission Signal Analysis and Artificial Intelligence Techniques in Machine Condition Monitoring and Fault Diagnosis: A Review

2014 ◽  
Vol 69 (2) ◽  
Author(s):  
Yasir Hassan Ali ◽  
Roslan Abd Rahman ◽  
Raja Ishak Raja Hamzah
Keyword(s):  
Artificial Intelligence ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Acoustic Emission Signal ◽  
Signal Analysis ◽  
Machine Condition ◽  
Artificial Intelligence Techniques ◽  
Emission Signal ◽  
Machine Condition Monitoring

Acoustic Emission technique is a successful method in machinery condition monitoring and fault diagnosis due to its high sensitivity on locating micro cracks in high frequency domain. A recently developed method is by using artificial intelligence techniques as tools for routine maintenance. This paper presents a review of recent literature in the field of acoustic emission signal analysis through artificial intelligence in machine conditioning monitoring and fault diagnosis. Many different methods have been previously developed on the basis of intelligent systems such as artificial neural network, fuzzy logic system, Genetic Algorithms, and Support Vector Machine. However, the use of Acoustic Emission signal analysis and artificial intelligence techniques for machine condition monitoring and fault diagnosis is still rare. Although many papers have been written in area of artificial intelligence methods, this paper puts emphasis on Acoustic Emission signal analysis and limits the scope to artificial intelligence methods. In the future, the applications of artificial intelligence in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature.

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