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Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden.ORCID iD: 0000-0003-1691-4387
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-2356-7830
Linnaeus University, Växjö, Sweden.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-2342-1647
Show others and affiliations
2018 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648Article in journal (Refereed) Submitted
Abstract [en]

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, the need to complement the present onboard diagnostic methods with more sophisticated diagnostic methods 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 such components. Failure of these major components of the driveline may results 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, Order Power Spectrum respectively. In addition, Bayesian learning was utilized for adaptively learning of the extracted features for deviation detection. Bayesian learning is a powerful prediction method as it combines the prior information with knowlegde measured to make update. 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 provide 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 so that 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.

Place, publisher, year, edition, pages
2018.
Keywords [en]
Automatic Transmission, Adaptive Filtering, Adaptive Line Enhancer, Axle, Bearings, Bayesian Learning, Gearbox, Order Analysis, Order Power Spectrum, Torque Converter and Vibration
National Category
Engineering and Technology Control Engineering Other Mechanical Engineering Other Civil Engineering
Research subject
Control Engineering; Computer Aided Design; Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-68353OAI: oai:DiVA.org:ltu-68353DiVA, id: diva2:1197834
Available from: 2018-04-15 Created: 2018-04-15 Last updated: 2018-05-16
In thesis
1. Data Driven Condition Monitoring for Transmission and Axles
Open this publication in new window or tab >>Data Driven Condition Monitoring for Transmission and Axles
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Datadriven övervakning för transmission och axlar
Abstract [en]

As the requirements to improve up-time and thus to reduce costly down-time con-tinuously increases, the construction equipment business focuses on more and newways to increase ability and sensitivity of early fault detection of critical compo-nents and parts in order to prevent failure. Failure of critical components in theheavy duty machine may lead to unnecessary stops and expensive downtime. Withmore features added to the heavy duty construction equipment, its complexity in-creases and early fault detection of certain components becomes more challengingdue to too many fault codes generated when a failure occurs. Hence, the need tocomplement the present onboard diagnostic methods with more sophisticated diag-nostic methods for adequate condition monitoring of the heavy duty constructionequipment in order to improve uptime. Further, reduced downtime leads to im-proved customer satisfaction, reduced warranty and service cost. In addition, thisupgrade result in the construction equipment business staying competitive with im-provement in sales and profit.

Heavy duty construction equipment is often equipped with a driveline whichconsists of major components, such as torque converter, gearbox, clutches, bearingsand axles. The driveline enables the transferring of torque from the engine to thegearbox, with the clutches enabling automatic gear ratio changes, and this drivingtorque from the gearbox is further transmitted to the wheels via the axles. Thesemajor components of a driveline may be considered as crucial components whosefailure may result in costly downtime. Since the current on-board diagnostic sys-tems use simple rules and maps to carry out diagnosis, most failures are not easy todiagnose as a result of too many fault codes being generated when there is a fail-ure. This means that, the engineers and technicians may have to spend substantialamount of time to identify the failure and root-cause. As a result, where major driv-eline parts are involved, this may cause the machine to stand still until the problemis identified and repaired, with a negative impact on customer satisfaction.

In this thesis, condition monitoring methods are presented with the purpose toprovide a diagnostic framework possible to implement onboard for monitoring ofcritical driveline parts in order to improve uptime.

In this thesis the gap in condition monitoring of major driveline components in an actual machine is addressed. A methodology for monitoring the health of theautomatic transmission and axles onboard the machine using vibration signals andavailable CAN-bus signals has been developed. Furthermore, this thesis presentsa vibration based diagnostic framework for the monitoring of the torque converter,gearbox, bearings and axles. 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 or-der analysis technique and adequate signal processing methods, which includes,Adaptive Line Enhancer, Order Power Spectrum and Order Modulation Spectrumrespectively. In addition, Bayesian learning was utilized for learning of the extractedfeatures onboard. The results indicate that the vibration properties of the gearbox,torque converter, bearings and axle are relevant for early fault detection of the driv-eline. Furthermore, vibration provides information about the internal features ofthese components for detecting deviations from normal behavior.

A different approach was utilized for the monitoring of the automatic transmis-sion clutches. The feature extraction methods utilized for the monitoring of theautomatic transmission clutches are based on moving average square value filter-ing and a measure of the fourth order statistical properties of the CAN-bus signals.Results show that the feature extraction methods provide an indication of clutchslippage deviations. This thesis also includes an investigation of clutch slippage de-tection from driveline vibrations based on spectrogram and spectral Kurtosis meth-ods.

In this way, the developed methods may be implemented onboard for the con-tinuous monitoring of these critical driveline parts of the heavy duty constructionequipment so that if their health starts to degrade a service and/or repair may bescheduled well in advance of a potential axle failure and in that way the downtimeof a machine may be reduced and costly replacements and repairs avoided.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018. p. 85
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-68180 (URN)978-91-7790-090-0 (ISBN)978-91-7790-091-7 (ISBN)
Public defence
2018-05-21, E632, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-04-06 Created: 2018-04-05 Last updated: 2018-05-24Bibliographically approved

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