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Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Axle
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling.ORCID-id: 0000-0003-1691-4387
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0002-2356-7830
Department of Applied Signal Processing, Blekinge Institute of Technology.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Produkt- och produktionsutveckling.ORCID-id: 0000-0002-2342-1647
Vise andre og tillknytning
Rekke forfattare: 62016 (engelsk)Inngår i: ICSV 2016 - 23rd International Congress on Sound and Vibration: From Ancient to Modern Acoustics / [ed] Vogiatzis, K; Kouroussis, G; Crocker, M; Pawelczyk, M, 2016Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

With increasing complexities in the heavy duty construction equipment, early fault detection of certain components in the machine becomes more and more challenging due to too many fault code generated when a failure occurs. The axle is one of such component. The axle transfers driving torque from the transmission to the wheels and axle failure may result in costly downtime of construction equipment. To reduce service cost and to improve uptime, adequate condition monitoring based on sensor data from the axle is considered. Vibration is measured on the axle. Analysis of the data has been carried out using adequate signal processing methods. The results indicate that the vibration properties of the axle are relevant for early fault detection of the axle. In this way; the health of the axle may be continuously monitored on-board using the vibration information and if the axle health starts to degrade a service and/or repair may be scheduled well in advance of a potential axle failure and in that way the downtime of a machine may be reduced.

sted, utgiver, år, opplag, sider
2016.
Serie
Proceedings of the International Congress on Sound and Vibration, ISSN 2329-3675
Emneord [en]
Order Power Spectrum, Order Modulation Spectrum, Product Development, Information technology - Signal processing
Emneord [sv]
Informationsteknik - Signalbehandling
HSV kategori
Forskningsprogram
Datorstödd maskinkonstruktion
Identifikatorer
URN: urn:nbn:se:ltu:diva-27469ISI: 000388480402101Scopus ID: 2-s2.0-84987920136Lokal ID: 0ef78cf3-88bb-4c49-ae23-ca4971f5e423ISBN: 9789609922623 (tryckt)OAI: oai:DiVA.org:ltu-27469DiVA, id: diva2:1000653
Konferanse
International Congress on Sound & Vibration : 10/07/2016 - 14/07/2016
Prosjekter
Fastelaboratoriet - VINNEXCTilgjengelig fra: 2016-09-30 Laget: 2016-09-30 Sist oppdatert: 2018-05-16bibliografisk kontrollert
Inngår i avhandling
1. Data Driven Condition Monitoring for Transmission and Axles
Åpne denne publikasjonen i ny fane eller vindu >>Data Driven Condition Monitoring for Transmission and Axles
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Alternativ tittel[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.

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2018. s. 85
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
HSV kategori
Forskningsprogram
Datorstödd maskinkonstruktion
Identifikatorer
urn:nbn:se:ltu:diva-68180 (URN)978-91-7790-090-0 (ISBN)978-91-7790-091-7 (ISBN)
Disputas
2018-05-21, E632, Luleå, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-04-06 Laget: 2018-04-05 Sist oppdatert: 2018-05-24bibliografisk kontrollert

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