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Data Driven Condition Monitoring for Transmission and Axles
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo CE.ORCID iD: 0000-0003-1691-4387
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
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: urn:nbn:se:ltu:diva-68180ISBN: 978-91-7790-090-0 (print)ISBN: 978-91-7790-091-7 (electronic)OAI: oai:DiVA.org:ltu-68180DiVA, id: diva2:1195338
Public defence
2018-05-21, E632, Luleå, 10:00 (English)
Supervisors
Available from: 2018-04-06 Created: 2018-04-05 Last updated: 2018-05-16Bibliographically approved
List of papers
1. Identification of Vibration Properties of Wheel Loader Driveline Parts as a Base for Adequate Condition Monitoring: Bearings
Open this publication in new window or tab >>Identification of Vibration Properties of Wheel Loader Driveline Parts as a Base for Adequate Condition Monitoring: Bearings
Show others...
2017 (English)In: / [ed] Gibbs B., International Institute of Acoustics and Vibration , 2017Conference paper, Published paper (Refereed)
Abstract [en]

In order to reduce costly downtime, adequate condition monitoring of the automatic transmission components in heavy duty construction equipment is necessary. The transmission in such equipment enables to change the gear ratio automatically. Further, the bearings in an automatic transmission provide low friction support to its rotating parts and act as an interface separating stationary from rotating components. Wear or other bearing faults may lead to an increase in energy consumption as well as failure of other related components in the automatic transmission, and thus costly downtime. In this study, different sensor data (particularly vibration) was collected on the automatic transmission during controlled test cycles in an automatic transmission test rig to enable adequate condition monitoring.

An analysis of the measured vibration data was carried out using signal processing methods. The results indicate that predictive maintenance information related to the automatic transmission bearings may be extracted from vibrations measured on an automatic transmission. This information may be used for early fault detection, thus improving uptime and availability of heavy duty construction equipment.

Place, publisher, year, edition, pages
International Institute of Acoustics and Vibration, 2017
Keyword
Automatic Transmission, Adaptive Line Enhancer (ALE), Bearings, Order Power Spectrum, Order Modulation Spectrum, Recursive Least Squares (RLS), and Vibration.
National Category
Engineering and Technology Signal Processing Other Mechanical Engineering
Research subject
Signal Processing; Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-63304 (URN)2-s2.0-85029451185 (Scopus ID)
Conference
24th International Congress on Sound and Vibration, London, 23-24 July, 2017
Available from: 2017-05-10 Created: 2017-05-10 Last updated: 2018-05-16Bibliographically approved
2. Analysis of automatic transmission vibration for clutch slippage detection
Open this publication in new window or tab >>Analysis of automatic transmission vibration for clutch slippage detection
Show others...
2015 (English)In: 22nd International Congress on Sound and Vibration: ICSV 2015, Florence, Italy, 12-16 July 2015, Florence: International Institute of Acoustics and Vibrations , 2015Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Florence: International Institute of Acoustics and Vibrations, 2015
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-34186 (URN)84971254996 (Scopus ID)84f9507c-5417-4e95-b36f-f043c07dcd85 (Local ID)9788888942483 (ISBN)84f9507c-5417-4e95-b36f-f043c07dcd85 (Archive number)84f9507c-5417-4e95-b36f-f043c07dcd85 (OAI)
Conference
International Congress on Sound and Vibration : 12/07/2015 - 16/07/2015
Projects
Fastelaboratoriet - VINNEXC
Note
Godkänd; 2015; 20150303 (jlm)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-16Bibliographically approved
3. On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
Open this publication in new window or tab >>On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
Show others...
2018 (English)In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 9, no 1, article id 007Article in journal (Refereed) Published
Abstract [en]

In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machinein combination with feature extraction and classification methods may be utilized.

This paper, based on a study at Volvo Construction Equipment,presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in a heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components,the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomalyis detected, the Case-Based diagnosis module is activated for fault severity estimation.

Place, publisher, year, edition, pages
PHM Society, 2018
National Category
Engineering and Technology Control Engineering Other Mechanical Engineering
Research subject
Computer Aided Design; Control Engineering
Identifiers
urn:nbn:se:ltu:diva-67976 (URN)2-s2.0-85044281699 (Scopus ID)
Note

Validerad;2018;Nivå 1;2018-03-19 (rokbeg)

Available from: 2018-03-17 Created: 2018-03-17 Last updated: 2018-05-16Bibliographically approved
4. Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings
Open this publication in new window or tab >>Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings
Show others...
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.

Keyword
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:nbn:se:ltu:diva-68353 (URN)
Available from: 2018-04-15 Created: 2018-04-15 Last updated: 2018-05-16
5. Scalable Validation of Industrial Equipment using a Functional DSMS
Open this publication in new window or tab >>Scalable Validation of Industrial Equipment using a Functional DSMS
Show others...
2017 (English)In: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, Vol. 48, no 3, p. 553-577Article in journal (Refereed) Published
Abstract [en]

A stream validation system called SVALI is developed in order to continuouslyvalidate data streams from industrial equipment. The functional data model of SVALI allows the user to dene meta-data and queries about the equipment in terms of types and functions. The two system functions model-andvalidate and learn-and-validate provide such validation functionality. The experiments show that parallel stream processing enables SVALI to scale very well with respect to response time and system throughput. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment.

Keyword
Data Stream Management, Distributed Stream Systems, Data Stream Validator, Parallelization, Anomaly Detection, Statistics, computer and systems science - Informatics, computer and systems science, Statistik, data- och systemvetenskap - Informatik, data- och systemvetenskap
National Category
Control Engineering Other Mechanical Engineering
Research subject
Control Engineering; Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-13842 (URN)10.1007/s10844-016-0427-2 (DOI)000401468300004 ()2-s2.0-84982266338 (Scopus ID)d21d4080-6c31-465e-bee2-816ea19dc2a3 (Local ID)d21d4080-6c31-465e-bee2-816ea19dc2a3 (Archive number)d21d4080-6c31-465e-bee2-816ea19dc2a3 (OAI)
Projects
Fastelaboratoriet - VINNEXC
Note

Validerad; 2017; Nivå 2; 2017-05-17 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-05-16Bibliographically approved
6. Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Axle
Open this publication in new window or tab >>Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Axle
Show others...
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]

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.

Series
Proceedings of the International Congress on Sound and Vibration, ISSN 2329-3675
Keyword
Order Power Spectrum, Order Modulation Spectrum, Product Development, Information technology - Signal processing, Informationsteknik - Signalbehandling
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-27469 (URN)000388480402101 ()2-s2.0-84987920136 (Scopus ID)0ef78cf3-88bb-4c49-ae23-ca4971f5e423 (Local ID)9789609922623 (ISBN)0ef78cf3-88bb-4c49-ae23-ca4971f5e423 (Archive number)0ef78cf3-88bb-4c49-ae23-ca4971f5e423 (OAI)
Conference
International Congress on Sound & Vibration : 10/07/2016 - 14/07/2016
Projects
Fastelaboratoriet - VINNEXC
Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-05-16Bibliographically approved
7. Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Torque Converter
Open this publication in new window or tab >>Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Torque Converter
Show others...
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.

Series
Proceedings of the International Congress on Sound and Vibration, ISSN 2329-3675
Keyword
Order Power Spectrum, Order Modulation Spectrum, Torque Converter, Product Development, Information technology - Signal processing, Informationsteknik - Signalbehandling
National Category
Other Mechanical Engineering
Research subject
Computer Aided Design
Identifiers
urn:nbn:se:ltu:diva-40279 (URN)000388480402086 ()2-s2.0-84987896981 (Scopus ID)f5a03c2c-018a-403c-96bf-6d799be06eb9 (Local ID)9789609922623 (ISBN)f5a03c2c-018a-403c-96bf-6d799be06eb9 (Archive number)f5a03c2c-018a-403c-96bf-6d799be06eb9 (OAI)
Conference
International Congress on Sound & Vibration : 10/07/2016 - 14/07/2016
Projects
Fastelaboratoriet - VINNEXC
Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-05-16Bibliographically approved

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Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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