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Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Torque Converter
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.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
Håkansson Lars, Department of Applied Signal Processing, Blekinge Institute of Technology.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.ORCID iD: 0000-0002-2342-1647
Show others and affiliations
2016 (English)In: ICSV 2016 - 23rd International Congress on Sound and Vibration: From Ancient to Modern Acoustics / [ed] Vogiatzis, K; Kouroussis, G; Crocker, M; Pawelczyk, M, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Improving uptime is paramount in the heavy duty construction equipment business. Failure ofcritical components in the heavy duty machine may lead to unnecessary stops and expensive downtime. The torque converter, a complex omponent of the driveline, transmits and multiplies torque from the engine to the gearbox, and its failure may not only lead to the machine standing still but may also lead to damage of other parts of the automatic transmission. For adequate condition monitoring of the torque converter, different sensor data are measured on a construction equipment machine during controlled driving sessions. Vibration has been measured on the torque converter. An initial investigation of the vibration measured on the torque converter has been carried out to identify its vibration properties in order to enable its health monitoring to prevent failure. Initial signal analysis of the data have been carried out using Order Power Spectrum and Order Modulation Spectrum methods. The results indicate that the torque converter vibration properties contain information relevant for early fault detection.

Place, publisher, year, edition, pages
2016.
Series
Proceedings of the International Congress on Sound and Vibration, ISSN 2329-3675
Keywords [en]
Order Power Spectrum, Order Modulation Spectrum, Torque Converter, Product Development, Information technology - Signal processing
Keywords [sv]
Informationsteknik - Signalbehandling
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
URN: urn:nbn:se:ltu:diva-40279ISI: 000388480402086Scopus ID: 2-s2.0-84987896981Local ID: f5a03c2c-018a-403c-96bf-6d799be06eb9ISBN: 9789609922623 (print)OAI: oai:DiVA.org:ltu-40279DiVA, id: diva2:1013801
Conference
International Congress on Sound & Vibration : 10/07/2016 - 14/07/2016
Projects
Fastelaboratoriet - VINNEXCAvailable from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-05-16Bibliographically approved
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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Källström, ElisabethLindström, JohnKarlberg, Magnus

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