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  • 1.
    Källström, Elisabeth
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo CE.
    Data Driven Condition Monitoring for Transmission and Axles2018Doctoral 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.

  • 2.
    Källström, Elisabeth
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    On-board Feature Extraction for Clutch Slippage Deviation Detection2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Construction equipment companies continuously upgrade their products to meetcustomer demands, staying competitive with market challenges as well as improving sales and profits. With increased complexities in heavy duty machines today, up-time is considered an important aspect of the construction equipment business because it reduces warranty and service cost, while increasing sales and overall customer satisfaction. Therefore, a substantial amount of research is directed towards the development of intelligent machines which are capable of automatically monitoring the health of different components in the machine.Heavy duty construction equipment is often equipped with automatic transmissions, with multiple disc wet clutches, transferring torque from the engine to the gearbox enabling automatic gear ratio changes. The wet clutches in Volvo Construction Equipment vehicles may be considered as a crucial component of the driveline and failure may result in costly downtime. The frictional characteristics of wet clutches are crucial for the ultimate performance because they define clutch slip time during engagement. Furthermore, a wet clutch is considered to have failed when it can no longer transmit the desired torque. The level of torque transfer in wet clutches is controlled by the generated friction. Hence, clutch slippage is the result of diminishing frictional characteristics of wet clutches. Accurately monitoring slippage in wet clutches provides an indication of the health of the clutch material.However, many of the factors that influence the frictional characteristics of the wet clutch are only possible to measure in a test rig and not in actual machines.In this thesis the gap in condition monitoring of automatic transmission clutchesin an actual machine is addressed. A methodology for monitoring the health of the clutch material on-board the machine using the available CAN-bus signals via an on-board Data Stream Management System (DSMS) has been developed. The feature extraction methods utilized in the condition monitoring are based on Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the CAN-bus signals implemented as continuous queries over data streams. Results show that the feature extraction methods provide an indication of clutch slippage deviations.This thesis also includes an investigation of clutch slippage detection from driveline vibrations based on spectrogram and spectral Kurtosis methods.

  • 3.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Department of Applied Signal Processing, Blekinge Institute of Technology.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Bellgran, David
    Volvo Construction Equipment, Eskilstuna.
    Frenne, Nicklas
    Volvo Construction Equipment, Eskilstuna.
    Renderstedt, Reza
    Volvo Construction Equipment, Eskilstuna.
    Lundin, Joakim
    Volvo Construction Equipment.
    Larsson, Jonas
    Volvo Construction Equipment, Eskilstuna.
    Analysis of automatic transmission vibration for clutch slippage detection2015In: 22nd International Congress on Sound and Vibration: ICSV 2015, Florence, Italy, 12-16 July 2015, Florence: International Institute of Acoustics and Vibrations , 2015Conference paper (Refereed)
  • 4.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems.
    Håkansson, Lars
    Linnaeus University, Växjö, Sweden.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Lin, Jing
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings2019In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 10, article id 014Article in journal (Refereed)
    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.

  • 5.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Department of Applied Signal Processing, Blekinge Institute of Technology.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Renderstedt, Reza
    Volvo Construction Equipment.
    Larsson, Jonas
    Volvo Construction Equipment.
    Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Axle2016In: 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 (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.

  • 6.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Linnaeus University, Växjö, Sweden.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Öberg, Olof
    Volvo Construction Equipment, Eskilstuna, Sweden.
    Identification of Vibration Properties of Wheel Loader Driveline Parts as a Base for Adequate Condition Monitoring: Bearings2017In: / [ed] Gibbs B., International Institute of Acoustics and Vibration , 2017Conference 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.

  • 7.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Håkansson Lars, Department of Applied Signal Processing, Blekinge Institute of Technology.
    Karlberg, Magnus
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Öberg, Olof
    Volvo Construction Equipment.
    Renderstedt, Reza
    Volvo Construction Equipment.
    Larsson, Jonas
    Volvo Construction Equipment.
    Identification of Vibration Properties of Heavy Duty Machine Driveline Parts as a Base for Adequate Condition Monitoring: Torque Converter2016In: 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 (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.

  • 8.
    Källström, Elisabeth
    et al.
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment, Eskilstuna, Sweden.
    Olsson, Tomas
    RISE SICS Västerås, Sweden.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Department of Applied Signal Processing, Blekinge Institute of Technology, Karlskrona, Sweden.
    Larsson, Jonas
    Volvo Construction Equipment, Eskilstuna, Sweden.
    On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine2018In: International Journal of Prognostics and Health Management, ISSN 2153-2648, E-ISSN 2153-2648, Vol. 9, no 1, article id 007Article in journal (Refereed)
    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.

  • 9.
    Lindström, John
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Källström, Elisabeth
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Kyösti, Petter
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Development and Operation of Functional Products: Improving knowledge on availability through use of monitoring and service related data2017In: Through-life Engineering Services / [ed] Redding, Louis, Roy, Rajkumar, Shaw, Andy, Springer International Publishing , 2017, p. 113-132Chapter in book (Refereed)
    Abstract [en]

    The book chapter addresses which measures five manufacturing companies have taken, or plan to take, regarding use of data originating from monitoring, service, support, maintenance, repairs as well as other sources, in order to improve the knowledge on availability in the context of providing Functional Products. Commonly, the objective of Functional Products is to provide a function to customers with a specified level of availability (or improvement of productivity or efficiency). The results indicate that systematic planning and collection of relevant data, which is either pre-processed on-board (i.e., locally) or sent as is to central or cloud-based storage and processing, in combination with additional necessary data from other sources, is crucial to build knowledge in order to uphold and improve the level of availability agreed upon with customers. As the use of software in Functional Products increases, the knowledge on availability related to software must be augmented—which can be a challenge for many companies whose operations have been rooted in hardware. Further, the results reveal that getting high-quality input is key in order to use the collected data for analytics and to find root causes. The latter may change how the current value-chain operates and secures the quality of necessary data when providing functions to customers if partners are involved in the provider consortium.

  • 10. Olsson, Tomas
    et al.
    Källström, Elisabeth
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development. Volvo Construction Equipment.
    Gillblad, Daniel
    Funk, Peter
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Lundin, Joakim
    Svensson, Magnus
    Larsson, Jonas
    Fault Diagnosis of Heavy Duty Machines: Automatic Transmission Clutches2014Conference paper (Refereed)
  • 11.
    Olsson, Tomas
    et al.
    School of Innovation, Design and Engineering, Mälardalen University, Västerås , SICS Swedish ICT, Isafjordsgatan 22, Box 1263, SE-164 29 Kista, Sweden.
    Xiong, Ning
    School of Innovation, Design and Engineering, Mälardalen University, Västerås.
    Källström, Elisabeth
    Volvo Construction Equipment, Eskilstuna.
    Holst, Anders
    SICS Swedish ICT, Isafjordsgatan 22, Box 1263, SE-164 29 Kista, Sweden.
    Funk, Peter
    School of Innovation, Design and Engineering, Mälardalen University, Västerås.
    Fault Diagnosis via Fusion of Information from a Case Stream2015In: Case-Based Reasoning Research and Development: 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28–30, 2015, Proceedings, Encyclopedia of Global Archaeology/Springer Verlag, 2015, p. 275-289Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel approach to fault diagnosis applied to a stream of cases. The approach uses a combination of casebased reasoning and information fusion to do classification. The approach consists of two steps. First, we perform local anomaly detection on-board a machine to identify anomalous individual cases. Then, we monitor the stream of anomalous cases using a stream anomaly detector based on a sliding window approach. When the stream anomaly detector identifies an anomalous window, the anomalous cases in the window are classified using a CBR classifier. Thereafter, the individual classifications are aggregated into a composite case with a single prediction using a information fusion method. We compare three information fusion approaches: simple majority vote, weighted majority vote and Dempster-Shafer fusion. As baseline for comparison, we use the classification of the last identified anomalous case in the window as the aggregated prediction

  • 12.
    Xu, Cheng
    et al.
    Department of Information Technology, Uppsala University.
    Källström, Elisabeth
    Luleå University of Technology, Department of Engineering Sciences and Mathematics, Product and Production Development.
    Risch, Tore
    Department of Information Technology, Uppsala University.
    Lindström, John
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Håkansson, Lars
    Department of Applied Signal Processing, Blekinge Institute of Technology.
    Larsson, Jonas
    Volvo Construction Equipment, Eskilstuna.
    Scalable Validation of Industrial Equipment using a Functional DSMS2017In: Journal of Intelligent Information Systems, ISSN 0925-9902, E-ISSN 1573-7675, Vol. 48, no 3, p. 553-577Article in journal (Refereed)
    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.

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