The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Andreas Schutze - One of the best experts on this subject based on the ideXlab platform.
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discrimination and quantification of volatile organic compounds in the ppb range with gas sensitive sic fets using Multivariate Statistics
Sensors and Actuators B-chemical, 2015Co-Authors: Andreas Schutze, M Bastuck, Anita Lloyd Spetz, Christian Bur, Donatella Puglisi, Mike AnderssonAbstract:Abstract Gas sensitive field effect transistors based on silicon carbide, SiC-FETs, have been studied for indoor air quality applications. The selectivity of the sensors was increased by temperature cycled operation, TCO, and data evaluation based on Multivariate Statistics. Discrimination of benzene, naphthalene, and formaldehyde independent of the level of background humidity is possible by using shape describing features as input for Linear Discriminant Analysis, LDA, or Partial Least Squares – Discriminant Analysis, PLS-DA. Leave-one-out cross-validation leads to a correct classification rate of 90% for LDA, and for PLS-DA a classification rate of 83% is achieved. Quantification of naphthalene in the relevant concentration range, i.e., 0–40 ppb, was performed by Partial Least Squares Regression and a combination of LDA with a second order polynomial fit function. The resolution of the model based on a calibration with three concentrations was approximately 8 ppb at 40 ppb naphthalene for both algorithms. Hence, the suggested strategy is suitable for on demand ventilation control in indoor air quality application systems.
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condition monitoring of a complex hydraulic system using Multivariate Statistics
Instrumentation and Measurement Technology Conference, 2015Co-Authors: Nikolai Helwig, E Pignanelli, Andreas SchutzeAbstract:In this paper, a systematic approach for the automated training of condition monitoring systems for complex hydraulic systems is developed and evaluated. We analyzed different fault scenarios using a test rig that allows simulating a reversible degradation of component's conditions. By analyzing the correlation of features extracted from raw sensor data and the known fault characteristics of experimental obtained data, the most significant features specific to a fault case can be identified. These feature values are transferred to a lower-dimensional discriminant space using linear discriminant analysis (LDA) which allows the classification of fault condition and grade of severity. We successfully implemented and tested the system for a fixed working cycle of the hydraulic system. Furthermore, the classification rate for random load cycles was enhanced by a distribution analysis of feature trends.
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identification and quantification of hydraulic system faults based on Multivariate Statistics using spectral vibration features
Procedia Engineering, 2015Co-Authors: Nikolai Helwig, Steffen Klein, Andreas SchutzeAbstract:Abstract In this work, an automated statistical analysis of vibration characteristics for the condition monitoring of hydraulic systems is proposed. Features from FFT vibration data of a micromechanical accelerometer measured at the gear pump are extracted, selected by their correlation coefficient to the target fault value, and subsequently reduced to discriminant functions which allow the classification of component conditions using Multivariate Statistics. The approach was successfully evaluated with an experimental hydraulic test bench simulating typical fault scenarios like valve switching degradation or gas leakage of the accumulator. It was shown that the studied fault scenarios can be selectively detected (internal pump leakage) and even quantified concerning the grade of severity in case of valve switching degradation, accumulator gas leakage, and oil aeration. Furthermore, the long-term stability of the statistical model was evaluated over several weeks.
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selectivity enhancement of sic fet gas sensors by combining temperature and gate bias cycled operation using Multivariate Statistics
Sensors and Actuators B-chemical, 2014Co-Authors: M Bastuck, Anita Lloyd Spetz, Mike Andersson, Andreas SchutzeAbstract:In this paper temperature modulation and gate bias modulation of a gas sensitive field effect transistor based on silicon carbide (SiC-FET) are combined in order to increase the selectivity. Data evaluation based on extracted features describing the shape of the sensor response was performed using Multivariate Statistics, here by Linear Discriminant Analysis (LDA). It was found that both temperature cycling and gate bias cycling are suitable for quantification of different concentrations of carbon monoxide. However, combination of both approaches enhances the stability of the quantification, respectively the discrimination of the groups in the LDA scatterplot. Feature selection based on the stepwise LDA algorithm as well as selection based on the loadings plot has shown that features both from the temperature cycle and from the bias cycle are equally important for the identification of carbon monoxide, nitrogen dioxide and ammonia. In addition, the presented method allows discrimination of these gases independent of the gas concentration. Hence, the selectivity of the FET is enhanced considerably.
Uwe Kruger - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis in internal combustion engines using non linear Multivariate Statistics
Proceedings of the Institution of Mechanical Engineers Part I: Journal of Systems and Control Engineering, 2005Co-Authors: David Antory, Uwe Kruger, G W Irwin, Geoff McculloughAbstract:AbstractThis paper presents a statistical-based fault diagnosis scheme for application to internal combustion engines. The scheme relies on an identified model that describes the relationships between a set of recorded engine variables using principal component analysis (PCA). Since combustion cycles are complex in nature and produce non-linear relationships between the recorded engine variables, the paper proposes the use of non-linear PCA (NLPCA). The paper further justifies the use of NLPCA by comparing the model accuracy of the NLPCA model with that of a linear PCA model. A new non-linear variable reconstruction algorithm and bivariate scatter plots are proposed for fault isolation, following the application of NLPCA. The proposed technique allows the diagnosis of different fault types under steady state operating conditions. More precisely, non-linear variable reconstruction can remove the fault signature from the recorded engine data, which allows the identification and isolation of the root cause o...
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detection of incipient tooth defect in helical gears using Multivariate Statistics
Mechanical Systems and Signal Processing, 2001Co-Authors: Naim Baydar, Qian Chen, Andrew Ball, Uwe KrugerAbstract:Multivariate statistical techniques have been successfully used for monitoring process plants and their associated instrumentation. These techniques effectively detect disturbances related to individual measurement sources and consequently provide diagnostic information about the process input. This paper investigates and explores the use of Multivariate statistical techniques in a two-stage industrial helical gearbox, to detect localised faults by using vibration signals. The vibration signals, obtained from a number of sensors, are synchronously averaged and then the Multivariate Statistics, based on principal components analysis, is employed to form a normal (reference) condition model. Fault conditions, which are deviations from a reference model, are detected by monitoring Q - and T2-Statistics. Normal operating regions or confidence bounds, based on kernel density estimation (KDE) is introduced to capture the faulty conditions in the gearbox. It is found that Q - and T2-Statistics based on PCA can detect incipient local faults at an early stage. The confidence regions, based on KDE can also reveal the growing faults in the gearbox.
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detection of incipient tooth defect in helical gears using Multivariate Statistics
Mechanical Systems and Signal Processing, 2001Co-Authors: Naim Baydar, Qian Chen, Andrew Ball, Uwe KrugerAbstract:Multivariate statistical techniques have been successfully used for monitoring process plants and their associated instrumentation. These techniques effectively detect disturbances related to individual measurement sources and consequently provide diagnostic information about the process input. This paper investigates and explores the use of Multivariate statistical techniques in a two-stage industrial helical gearbox, to detect localised faults by using vibration signals. The vibration signals, obtained from a number of sensors, are synchronously averaged and then the Multivariate Statistics, based on principal components analysis, is employed to form a normal (reference) condition model. Fault conditions, which are deviations from a reference model, are detected by monitoring Q - and T2-Statistics. Normal operating regions or confidence bounds, based on kernel density estimation (KDE) is introduced to capture the faulty conditions in the gearbox. It is found that Q - and T2-Statistics based on PCA can detect incipient local faults at an early stage. The confidence regions, based on KDE can also reveal the growing faults in the gearbox.
Barry Lennox - One of the best experts on this subject based on the ideXlab platform.
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model predictive control monitoring using Multivariate Statistics
Journal of Process Control, 2009Co-Authors: Ashraf Alghazzawi, Barry LennoxAbstract:Control loop monitoring has become an important research field over the past decade. Research has primarily targeted single-input single-output (SISO) feedback control systems with limited progress being made on the monitoring of multi-input multi-output (MIMO) control systems and large scale model predictive control (MPC) systems in particular. The size and complexity of MPC systems means that identifying and diagnosing problems with their operation can be challenging. This paper presents an MPC condition monitoring tool based on Multivariate statistical process control (MSPC) techniques. The proposed tool uses intuitive charts to enable casual users of MPC technology to detect abnormal controller operation and to identify possible causes for this behaviour. Through its application to data collected from a large scale MPC system, the proposed technique is shown to be able to identify and diagnose poor control performance resulting from various issues including inappropriate interaction by process operators.
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monitoring a complex refining process using Multivariate Statistics
Control Engineering Practice, 2008Co-Authors: Ashraf Alghazzawi, Barry LennoxAbstract:Over the past decade, Multivariate statistical process control (MSPC) methods have been proven, in the process industries, to be an effective tool for process monitoring, modelling and fault detection. This paper describes the development of a real-time monitoring solution for a complex petroleum refining process with an installed multivariable model predictive controller. The developed solution was designed to track the time-varying and non-stationary dynamics of the process and for improved isolation capabilities, a multiblock approach was applied. The paper highlights the systematic and generic approach that was followed to develop the monitoring solution and stresses the importance of exploiting the knowledge of experienced plant personnel when developing any such system.
Mike Andersson - One of the best experts on this subject based on the ideXlab platform.
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discrimination and quantification of volatile organic compounds in the ppb range with gas sensitive sic fets using Multivariate Statistics
Sensors and Actuators B-chemical, 2015Co-Authors: Andreas Schutze, M Bastuck, Anita Lloyd Spetz, Christian Bur, Donatella Puglisi, Mike AnderssonAbstract:Abstract Gas sensitive field effect transistors based on silicon carbide, SiC-FETs, have been studied for indoor air quality applications. The selectivity of the sensors was increased by temperature cycled operation, TCO, and data evaluation based on Multivariate Statistics. Discrimination of benzene, naphthalene, and formaldehyde independent of the level of background humidity is possible by using shape describing features as input for Linear Discriminant Analysis, LDA, or Partial Least Squares – Discriminant Analysis, PLS-DA. Leave-one-out cross-validation leads to a correct classification rate of 90% for LDA, and for PLS-DA a classification rate of 83% is achieved. Quantification of naphthalene in the relevant concentration range, i.e., 0–40 ppb, was performed by Partial Least Squares Regression and a combination of LDA with a second order polynomial fit function. The resolution of the model based on a calibration with three concentrations was approximately 8 ppb at 40 ppb naphthalene for both algorithms. Hence, the suggested strategy is suitable for on demand ventilation control in indoor air quality application systems.
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selectivity enhancement of sic fet gas sensors by combining temperature and gate bias cycled operation using Multivariate Statistics
Sensors and Actuators B-chemical, 2014Co-Authors: M Bastuck, Anita Lloyd Spetz, Mike Andersson, Andreas SchutzeAbstract:In this paper temperature modulation and gate bias modulation of a gas sensitive field effect transistor based on silicon carbide (SiC-FET) are combined in order to increase the selectivity. Data evaluation based on extracted features describing the shape of the sensor response was performed using Multivariate Statistics, here by Linear Discriminant Analysis (LDA). It was found that both temperature cycling and gate bias cycling are suitable for quantification of different concentrations of carbon monoxide. However, combination of both approaches enhances the stability of the quantification, respectively the discrimination of the groups in the LDA scatterplot. Feature selection based on the stepwise LDA algorithm as well as selection based on the loadings plot has shown that features both from the temperature cycle and from the bias cycle are equally important for the identification of carbon monoxide, nitrogen dioxide and ammonia. In addition, the presented method allows discrimination of these gases independent of the gas concentration. Hence, the selectivity of the FET is enhanced considerably.
Henrique Llacer Roig - One of the best experts on this subject based on the ideXlab platform.
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trace metal dynamics in an industrialized brazilian river a combined application of zn isotopes geochemical partitioning and Multivariate Statistics
Journal of Environmental Sciences-china, 2021Co-Authors: Myller S Tonha, Daniel Araujo, Rafael Pereira De Araujo, Bruno C A Cunha, Wilson Machado, Joelma Ferreira Portela, Joao Pr Souza, Hikari K Carvalho, Elton Luiz Dantas, Henrique Llacer RoigAbstract:Abstract The Paraiba do Sul (PSR) and Guandu Rivers (GR) water diversion system (120 km long) is located in the main industrial pole of Brazil and supplies drinking water for 9.4 million people in the metropolitan region of Rio de Janeiro. This study aims to discern the trace metals dynamics in this complex aquatic system. We used a combined approach of geochemical tools such as geochemical partitioning, Zn isotopes signatures, and Multivariate Statistics. Zinc and Pb concentrations in Suspended Particulate Matter (SPM) and sediments were considerably higher in some sites. The sediment partition of As, Cr, and Cu revealed the residual fraction (F4) as the main fraction for these elements, indicating low mobility. Zinc and Pb were mostly associated with the exchangeable/carbonate (F1) and the reducible (F2) fractions, respectively, implying a higher susceptibility of these elements to being released from sediments. Zinc isotopic compositions of sediments and SPM fell in a binary mixing source process between lithogenic (δ66/64ZnJMC ≈ + 0.30‰) and anthropogenic (δ66/64ZnJMC ≈ + 0.15‰) end members. The lighter δ66/64ZnJMC values accompanied by high Zn concentrations in exchangeable/carbonate fraction (ZnF1) enable the tracking of Zn anthropogenic sources in the studied rivers. Overall, the results indicated that Hg, Pb, and Zn had a dominant anthropogenic origin linked to the industrial activities, while As, Cr, and Cu were mainly associated with lithogenic sources. This work demonstrates how integrating geochemical tools is valuable for assessing geochemical processes and mixing source effects in anthropized river watersheds.
