Abstract
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
Highlights
Being a competitive business, the construction equipment business tries to find new ways to increase customer satisfac-International Journal of Prognostics and Health Management, ISSN2153-2648, 2019 014INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT tion
The results indicate that the vibration properties of the gearbox, torque converter, bearings and axle are relevant for early fault detection of the driveline
The order analysis technique transforms a signals non-stationary components whose vibration frequencies are related to the rotation speed in the time domain into stationary signal components in the angular domain and provides information about the vibration components related to the changing rotational speed (Junsheng et al, 2010)
Summary
The construction equipment business tries to find new ways to increase customer satisfac-International Journal of Prognostics and Health Management, ISSN2153-2648, 2019 014INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT tion. The construction equipment business tries to find new ways to increase customer satisfac-. International Journal of Prognostics and Health Management, ISSN2153-2648, 2019 014. INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT tion. An important way to achieve this is by reducing unwanted stops due to failure of critical mechanical components of the driveline, for instance the gearbox, the torque converter, the bearings and the axles. In the last decade, monitoring of machinery with changing rotational speed has attracted a lot of research contributions. This section comprises a review of some of the previous research carried out with focus on the Gearbox, Bearings, Axles and the Torque Converter. Randall (Randall, 2004a), discussed how dif-
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