| 1. |
- Khodadad, Davood, et al.
(author)
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Full-field 3D deformation measurement : Comparison between speckle phase and displacement evaluation
- 2016
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In: Applied Optics. - : Optical Society of America. - 1559-128X .- 2155-3165 .- 0003-6935 .- 1539-4522. ; 55:27, s. 7735-7743
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Journal article (peer-reviewed)abstract
- The objective of this paper is to describe a full-field deformation measurement method based on 3D speckle displacements. The deformation is evaluated from the slope of the speckle displacement function that connects the different reconstruction planes. For our experiment, a symmetrical arrangement with four illuminations parallel to the planes (x,z) and (y,z) was used. Four sets of speckle patterns were sequentially recorded by illuminating an object from the four directions, respectively. A single camera is used to record the holograms before and after deformations. Digital speckle photography is then used to calculate relative speckle displacements in each direction between two numerically propagated planes. The 3D speckle displacements vector is calculated as a combination of the speckle displacements from the holograms recorded in each illumination direction. Using the speckle displacements, problems associated with rigid body movements and phase wrapping are avoided. In our experiment, the procedure is shown to give the theoretical accuracy of 0.17 pixels yielding the accuracy of 2 × 10-3 in the measurement of deformation gradients
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| 2. |
- Chen, Yousheng, 1985-, et al.
(author)
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Experimental Validation of a Nonlinear Model Calibration Method Based on Multiharmonic Frequency Responses
- 2017
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In: Journal of Computational and Nonlinear Dynamics. - : ASME Press. - 1555-1415 .- 1555-1423. ; 12:4
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Journal article (peer-reviewed)abstract
- Correlation and calibration using test data are natural ingredients in the process of validating computational models. Model calibration for the important subclass of nonlinear systems which consists of structures dominated by linear behavior with the presence of local nonlinear effects is studied in this work. The experimental validation of a nonlinear model calibration method is conducted using a replica of the École Centrale de Lyon (ECL) nonlinear benchmark test setup. The calibration method is based on the selection of uncertain model parameters and the data that form the calibration metric together with an efficient optimization routine. The parameterization is chosen so that the expected covariances of the parameter estimates are made small. To obtain informative data, the excitation force is designed to be multisinusoidal and the resulting steady-state multiharmonic frequency response data are measured. To shorten the optimization time, plausible starting seed candidates are selected using the Latin hypercube sampling method. The candidate parameter set giving the smallest deviation to the test data is used as a starting point for an iterative search for a calibration solution. The model calibration is conducted by minimizing the deviations between the measured steady-state multiharmonic frequency response data and the analytical counterparts that are calculated using the multiharmonic balance method. The resulting calibrated model's output corresponds well with the measured responses.
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| 3. |
- Chen, Yousheng, 1985-, et al.
(author)
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A Pretest Planning Method for Model Calibration for Nonlinear Systems
- 2016
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In: Model Validation and Uncertainty Quantification, Volume 3. - Cham : Springer. - 2191-5644 .- 2191-5652. - 9783319297538 - 9783319297545 ; , s. 371-379
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Conference paper (other academic/artistic)abstract
- With increasing demands on more flexible and lighter engineering structures, it has been more common to take nonlinearity into account. Model calibration is an important procedure for nonlinear analysis in structural dynamics with many industrial applications. Pretest planning plays a key role in the previously proposed calibration method for nonlinear systems, which is based on multi-harmonic excitation and an effective optimization routine. This paper aims to improve the pretest planning strategy for the proposed calibration method. In this study, the Fisher information matrix (FIM), which is calculated from the gradients with respect to the chosen parameters with unknown values, is used for determining the locations, frequency range, and the amplitudes of the excitations as well as the sensor placements. This pretest planning based model calibration method is validated by a structure with clearance nonlinearity. Synthetic test data is used to simulate the test procedure. Model calibration and K-fold cross validation are conducted for the optimum configurations selected from the pretest planning as well as three other configurations. The calibration and cross validation results show that a more accurate estimation of parameters can be obtained by using test data from the optimum configuration.
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| 4. |
- Chen, Yousheng, et al.
(author)
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Bias errors of different simulation methods for linear and nonlinear systems
- 2016
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In: Nonlinear Dynamics, Volume 1. - Cham : Springer. - 9783319152202 ; , s. 505-520
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Conference paper (other academic/artistic)abstract
- Responses of mechanical systems are often studied using numerical time-domain methods. Discrete excitation forces require a transformation of the dynamic system from continuous time into discrete time. Such a transformation introduces an aliasing error. To reduce the aliasing error, different discretization techniques are used. The bias errors introduced by some discretization techniques are studied in this paper.Algebraic expressions of the bias error obtained for some discretization methods are presented. The bias error depends on the assumption of the characteristics of the load between two subsequent time steps; here the zero-order, first-order and Lagrange second-order assumptions are studied. Different simulation methods are also studied for numerical evaluation of the derived theoretical bias errors. The discretization techniques are implemented for Runge-Kutta, the Digital Filter method and for the Pseudo Force in State Space method.The study is carried out for both a linear and a nonlinear system; two numerical examples assist in evaluating the theory. Perfect matches between the numerically estimated bias errors and the theoretical ones are shown. The results also show that, for the nonlinear example, the fourth order Runge-Kutta method is less accurate than the Digital Filter and the used single step Pseudo Force in State Space method.
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| 5. |
- Chen, Yousheng, et al.
(author)
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Validation of a model calibration method through vibrational testing of a mechanical system with local clearance
- 2016
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In: Proceedings of ISMA2016 International conference on noise and vibration engineering and USD2016 International conference on uncertainty in structural dynamics. - Leuven, Belgium : Katholieke University Leuven. - 9789073802940 ; , s. 2581-2595
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Conference paper (peer-reviewed)abstract
- Nonlinear finite element models are often validated using experimental data. A previously proposed calibration method, which concerns pre-test planning, multi-sinusoidal excitation and an effective optimization routine, is improved with an extended version of the pre-test planning. The improved method is validated using a test structure with a clearance type nonlinearity. From the pretest planning, an optimal configuration for the data acquisition is determined. The multi-harmonic nonlinear frequency response functions (FRFs) of the structure under test are then generated by a multi-sinusoidal excitation. Model calibration is conducted by minimizing the difference between the experimental multi-harmonic nonlinear FRFs and their analytical counterparts. The uncertainties of the estimated parameters are assessed by a k-fold cross validation, which confirm that the uncertainties of the estimated parameters are small when the optimal configuration is applied.
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| 6. |
- Källström, Elisabeth, et al.
(author)
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Identification of vibration properties of heavy duty machine driveline parts as a base for adequate condition monitoring: Axle
- 2016
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In: ICSV 2016 - 23rd International Congress on Sound and Vibration. - : INT INST ACOUSTICS & VIBRATION. - 9789609922623
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Conference paper (peer-reviewed)abstract
- 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.
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| 7. |
- Källström, Elisabeth, et al.
(author)
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Identification of vibration properties of heavy duty machine driveline parts as a base for adequate condition monitoring: Torque converter
- 2016
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In: ICSV 2016 - 23rd International Congress on Sound and Vibration. - : INT INST ACOUSTICS & VIBRATION. - 9789609922623
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Conference paper (peer-reviewed)abstract
- 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.
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| 8. |
- Källström, Elisabeth, et al.
(author)
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On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
- 2018
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In: International Journal of Prognostics and Health Management. - : PHM Society. - 2153-2648. ; 9:1
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Journal article (peer-reviewed)abstract
- 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.
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| 9. |
- Xu, Cheng, et al.
(author)
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Scalable Validation of Industrial Equipment using a Functional DSMS
- 2017
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In: Journal of Intelligent Information Systems. - : Springer. - 0925-9902 .- 1573-7675. ; 48:3, s. 553-577
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Journal article (peer-reviewed)abstract
- 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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| 10. |
- Kans, Mirka, 1971-, et al.
(author)
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Data Driven Maintenance : A Promising Way of Action for Future Industrial Services Management
- 2022
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In: International Congress and Workshop on Industrial AI 2021. IAI 2021. - Cham : Springer. - 9783030936389 - 9783030936396 ; , s. 212-223, s. 212-223
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Conference paper (peer-reviewed)abstract
- Maintenance and services of products as well as processes are pivotal for achieving high availability and avoiding catastrophic and costly failures. At the same time, maintenance is routinely performed more frequently than necessary, replacing possibly functional components, which has negative economic impact on the maintenance. New processes and products need to fulfil increased environmental demands, while customers put increasing demands on customization and coordination. Hence, improved maintenance processes possess very high potentials, economically as well as environmentally. The shifting demands on product development and production processes have led to the emergency of new digital solutions as well as new business models, such as integrated product-service offerings. Still, the general maintenance problem of how to perform the right service at the right time, taking available information and given limitations is valid.The project Future Industrial Services Management (FUSE) project was a step in a long-term effort for catalysing the evolution of maintenance and production in the current digital era. In this paper, several aspects of the general maintenance problem are discussed from a data driven perspective, spanning from technology solutions and organizational requirements to new business opportunities and how to create optimal maintenance plans. One of the main results of the project, in the form of a simulation tool for strategy selection, is also described.
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