On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

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 machine in combination with feature extraction and classification methods may be utilized. This paper 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 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 anomaly is detected, the Case-Based diagnosis module is activated for fault severity estimation

On-board Feature Extraction for Clutch Slippage Deviation Detection

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.Godkänd; 2015; 20151118 (eliibe); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Elisabeth Källström Ämne: Datorstödd maskinkonstruktion/Computer Aided Design Uppsats: On-board Feature Extraction for Clutch Slippage Deviation Detection Examinator: Docent John Lindström Institutionen för teknikvetenskap och matematik, Luleå tekniska universitet Diskutant: Dr. Ulf Bodin, Institutionen för system- och rymdteknik, Luleå tekniska universitet Tid: Torsdag 17 december 2015 kl 10.00 Plats: E632, Luleå tekniska universitetFastelaboratoriet - VINNEX

Datadriven övervakning för transmission och axlar

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

Datadriven övervakning för transmission och axlar

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

Olweus vs Värdegrundsarbete : - En dokumentstudie

Syftet är att utifrån komponenterna i Olweusprogrammet hitta gemensamma komponenter i de nationella värdegrundsriktlinjerna och värdegrundsarbetet i Strömsund, samt diskutera och analysera detta utifrån mobbningsforskning. Vi har tittat på hur många komponenter som är jämförbara och hur dessa kan kopplas till utvärderingar som är gjorda av dels värdegrundsarbete och dels Olweusprogrammet. Studien är en dokumentstudie i rapportform. Studien baseras på utvald litteratur samt utvalda artiklar och forskning som gjorts av programmen och de allmänna värdegrundsriktlinjerna, samt Strömsunds värdegrundsarbete som är en kombination av de nationella riktlinjerna och Olweus programkomponenter. Man kan konstatera att Strömsunds värdegrundsarbete har mest gemensamt med Olweus orginalprogram. De har hela 17 komponenter gemensamt med Olweus orginalprogram. De har mer gemensamt med Olweus orginalprogram än den version av Olweusprogrammet som används i Sverige idag. Värdegrunden har 9 gemensamma komponenter med Strömsunds värdegrundsarbete vilket gör det ungefärligt likvärdigt med den version av Olweusprogrammet som används idag i Sverige.

On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

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.Validerad;2018;Nivå 1;2018-03-19 (rokbeg)</p

On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine

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.Validerad;2018;Nivå 1;2018-03-19 (rokbeg)</p

Identification of Vibration Properties of Wheel Loader Driveline Parts as a Base for Adequate Condition Monitoring : Bearings

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

Scalable validation of industrial equipment using a functional DSMS

A stream validation system called SVALI is developed in order to continuously validate correct behavior of industrial equipment. A functional data model allows the user to define meta-data, analyses, and queries about the monitored equipment in terms of types and functions. Two different approaches to validate that sensor readings in a data stream indicate correct equipment behavior are supported: with the model-and-validate approach anomalies are detected based on a physical model, while with learn-and-validate anomalies are detected by comparing streaming data with a model of normal behavior learnt during a training period. Both models are expressed on a high level using the functional data model and query language. The experiments show that parallel stream processing enables SVALI to scale very well with respect to system throughput and response time. The paper is based on a real world application for wheel loader slippage detection at Volvo Construction Equipment implemented in SVALI