Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings

Elisabeth Källström, John Lindström, Lars Håkansson, Magnus Karlberg, and Jing Lin
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Full Paper
ijphm_19_014.pdf3.45 MBJune 20, 2019 - 12:07am

As more features are added to the heavy duty construction equipment, its complexity increases and early fault detection of certain components becomes more challenging due to too many fault codes generated when a failure occurs. Hence, there is a need to complement the present onboard diagnostics method with a more reliable diagnostics method for adequate condition monitoring of the heavy duty construction equipment in order to improve uptime.
Major components of the driveline (such as the gearbox, torque converter, bearings and axles) are components necessary to monitor. A failure among any of these major components of the driveline may result in the machine standing still until a repair is scheduled.

In this paper, vibration based condition monitoring methods are presented with the purpose to provide a diagnostic framework possible to implement onboard for monitoring of critical driveline parts in order to reduce service cost and improve uptime.
For the development of this diagnostic framework, sensor data from the gearbox, torque converter, bearings and axles are considered. Further, the feature extraction of the data collected has been carried out using adequate signal processing methods, which includes: Adaptive Line Enhancer and Order Power Spectrum respectively. In addition, Bayesian learning was utilized for adaptive learning of the extracted features for deviation detection. Bayesian learning is a powerful prediction method as it combines the prior information with knowledge measured to make updates. The results indicate that the vibration properties of the gearbox, torque converter, bearings and axle are relevant for early fault detection of the driveline. Furthermore, vibration provides information about the internal features of these components for detecting deviations from normal behavior.

In this way, the developed methods may be implemented onboard for the continuous monitoring of these critical driveline parts of the heavy duty construction equipment. Thus, if their health starts to degrade a service and/or repair may be scheduled well in advance of a potential failure and in that way the downtime of a machine may be reduced and costly replacements and repairs avoided.

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Submission Keywords: 
Automatic Transmission
Adaptive filtering
Adaptive Line Enhancer
Bayesian Learning
Order Analysis
Order Power Spectrum
Torque Converter and Vibration
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
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