Abstract
Abstract Gearbox is one of the vital components in aircraft engines. If any small damage to gearbox, it can cause the breakdown of aircraft engine. Thus it is significant to study fault diagnosis in gearbox system. In this paper, two deep learning models (Long short term memory (LSTM) and Bi-directional long short term memory (BLSTM)) are proposed to classify the condition of gearbox into good or bad. These models are applied on aircraft gearbox vibration data in both time and frequency domain. A publicly available aircraft gearbox vibration dataset is used to evaluate the performance of proposed models. The results proved that accuracy achieved by LSTM and BLSTM are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain. Also, to show the superiority of proposed models for aircraft gearbox fault diagnosis, performance is compared with classical machine learning models.
Highlights
Gears are the vital part of the mechanical transmission systems
The results proved that accuracy achieved by LSTM and BRNN version of LSTM (BLSTM) are highly reliable and applicable in health monitoring of aircraft gearbox system in time domain as compared to frequency domain
We have proposed a BLSTM model which is LSTM version Bidirectional recurrent neural network (RNN) (BRNN) structure
Summary
Gears are the vital part of the mechanical transmission systems. In the field of rotating machinery and mechanical transmission systems, the application of gears is essential. It is very essential to detect the faults of gears proactively to prevent breakdowns, accidents and to ensure the operation of mechanical transmission systems with no faults. Cracks in a gearbox system will occur due to the continuous usage over a period of time This will leads to defects in a gearbox. Vibration data analysis is a process of looking for deviations from the standard condition of the mechanical devices. These defects can be identified by analysing the vibration data of gearbox system collected through electronic sensors. Some of the attempts have been traced on diagnosis of gearbox system using machine learning and deep learning techniques
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