Recursive model-based virtual in-cylinder pressure sensing for internal combustion engines

Das Drucksignal im Zylinder ist ein sehr nützlicher Indikator für moderne Hochleistungs-Verbrennungsmotoren. Allerdings sind direkte Messungen des Zylinderdrucks unpraktisch, da die Bedingungen in den Zylindern von Verbrennungsmotoren ungünstig sind sowie die Installation von Zylinderdrucksensoren schwierig ist. Zahlreiche Methoden (z. B. virtuelle Messmethoden) wurden untersucht, um den Druck im Zylinder aus extern gemessenen Signalen zu rekonstruieren, z. B. aus dem Schwingungssignal des Motorblocks und der Winkelgeschwindigkeit der Kurbelwelle. Viele der vorgeschlagenen Methoden haben vielversprechende Ergebnisse erbracht. Allerdings gibt es immer noch einige Nachteile wie z.B. eine schlecht konditionierte Inversion oder die Notwendigkeit einer großen Datenmenge, um ein inverses Modell durch künstliche neuronale Netze abzuleiten. In dieser Arbeit werden unter Berücksichtigung der aktuellen Zylinderdruck-Rekonstruktionsprobleme lineare modellbasierte, nichtlineare modellbasierte und inverse modellbasierte ZylinderdruckRekonstruktionsmethoden vorgeschlagen, die eine Alternative zu den bestehenden ZylinderdruckRekonstruktionsmethoden darstellen. Alle vorgeschlagenen Methoden basieren auf der rekursiven Zustandsrekonstruktion unter Verwendung des Kalman-Filters oder eines Beobachters, so dass eine direkte Inversion vermieden werden kann. Darüber hinaus werden alle vorgeschlagenen Methoden rekursiv im Zeitbereich durchgeführt, so dass sie für Echtzeit-Implementierungen geeignet sind und auch keine Probleme im Frequenzbereich, wie z. B. Leckeffekte, aufweisen. Darüber hinaus handelt es sich bei allen vorgeschlagenen Methoden um modellbasierte Methoden, und die Modelle werden mit Hilfe von Systemidentifikationstechniken unter Ausschluss künstlicher neuronaler Netze identifiziert, so dass keine großen Datenmengen erforderlich sind. Für die Systemidentifikation und die Validierung der vorgeschlagenen Methoden wurden Datensätze eines Vierzylinder-Dieselmotors unter verschiedenen Motorbetriebsbedingungen erfasst. Die erfassten Daten reichen von der Betriebsbedingung 1200 U/min, 60 Nm bis zur Betriebsbedingung 3000 U/min, 180 Nm. Die rekonstruierten Zylinderdruckkurven und die beiden Verbrennungsmetriken Zylinderdruckspitze und Spitzenort wurden zur Validierung der vorgeschlagenen Zylinderdruckrekonstruktionsmethoden verwendet. Die Ergebnisse der Rekonstruktion des Zylinderdrucks, die mit den in dieser Arbeit vorgeschlagenen Methoden erzielt wurden, zeigen, dass alle vorgeschlagenen Methoden sowohl unter stationären als auch unter nicht-stationären Betriebsbedingungen verwendet werden können und dass die Ergebnisse der Rekonstruktion des Zylinderdrucks mit den Ergebnissen der bestehenden Methoden zur Rekonstruktion des Zylinderdrucks vergleichbar sind. Darüber hinaus kann festgestellt werden, dass es mehrere Faktoren gibt, die die Genauigkeit der Druckrekonstruktion beeinflussen, wie z.B. die Qualität der identifizierten Modelle, des Verzögerungsblocks und der momentanen Motordrehzahl.The in-cylinder pressure signal is a very useful indicator for modern high-performance internal combustion engines. Unfortunately, direct measurements of the in-cylinder pressure are impractical because installing cylinder pressure sensors is difficult and conditions in internal combustion engine cylinders are adverse. Numerous methods (such as virtual sensing methods) have been investigated to reconstruct the incylinder pressure from externally measured signals, such as the engine block structural vibration signal and the engine crank angular speed. Many of the proposed methodologies have shown promising results. However, there still exist some drawbacks, such as ill-conditioned inversion and the need of large number of data to derive an inverse model by artificial neural networks. In this thesis, considering current in-cylinder pressure reconstruction problems, linear model-based, nonlinear model-based, and inverse model-based in-cylinder pressure reconstruction methods, which are alternative to existing cylinder pressure reconstruction methods, are proposed. All the proposed methods are based on the recursive state reconstruction by using the Kalman filter or observer such that a direct inversion can be avoided. Moreover, all the proposed methods are recursively conducted in time domain, so they are suitable for real-time implementations and they also do not have frequency-domain problems such as spectral leakage. Additionally, all the proposed methods are model-based methods, and the models are identified by using system identification techniques excluding artificial neural networks, so the need of a large number of data is not necessary. For system identification and the validation of the proposed methods, the datasets under different engine operating conditions were acquired from a four-cylinder diesel engine. Data acquired is from the operating condition 1200 rpm, 60 Nm to the operating condition 3000 rpm, 180 Nm. The reconstructed cylinder pressure curves and two combustion metrics cylinder pressure peak and peak location were used for validating the proposed cylinder pressure reconstruction methods. According to the cylinder pressure reconstruction results obtained based on using the proposed methods in this thesis, it can be found that all the proposed methods can be used under both stationary and non-stationary operating conditions, and the reconstructed cylinder pressure results are comparable among existing cylinder pressure reconstruction methods. Furthermore, it can also be found that there exist several factors affecting the pressure reconstruction accuracy, such as the quality of the identified models, delay block and instantaneous engine cycle frequency

SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

On-The-Fly Processing of continuous high-dimensional data streams

[EN] A novel method and software system for rational handling of time series of multi-channel measurements is presented. This quantitative learning tool, the On-The-Fly Processing (OTFP), develops reduced-rank bilinear subspace models that summarise massive streams of multivariate responses, capturing the evolving covariation patterns among the many input variables over time and space. Thereby, a considerable data compression can be achieved without significant loss of useful systematic information. The underlying proprietary OTFP methodology is relatively fast and simple it is linear/bilinear and does not require a lot of raw data or huge cross-correlation matrices to be kept in memory. Unlike conventional compression methods, the approach allows the high-dimensional data stream to be graphically interpreted and quantitatively utilised in its compressed state. Unlike adaptive moving-window methods, it allows all past and recent time points to be reconstructed and displayed simultaneously. This new approach is applied to four different case-studies: (i) multi-channel Vis-NIR spectroscopy of the Belousov Zhabotinsky reaction, a complex, ill understood chemical process; (ii) quality control of oranges by hyperspectral imaging; (iii) environmental monitoring by airborne hyperspectral imaging; (iv) multi-sensor process analysis in the petrochemical industry. These examples demonstrate that the OTFP can automatically develop high-fidelity subspace data models, which simplify the storage/transmission and the interpretation of more or less continuous time series of high-dimensional measurements to the extent there are covariations among the measured variables.This research work was partially supported by the Spanish Ministry of Economy and Competitiveness under the project DPI2014-55276-C5-1R, Shell Global Solutions International B.V. (Amsterdam, The Netherlands), Idletechs AS (Trondheim, Norway), the Norwegian Research Council (Grant 223254) through the Centre of Autonomous Marine Operations and Systems (AMOS) at the Norwegian University of Science and Technology (Trondheim, Norway) and the Ministry of Education, Youth and Sports of the Czech Republic (CENAKVA project CZ.1.05/2.1.00/01.0024 and CENAKVA II project L01205 under the NPU I program). The authors want to acknowledge Prof. Bjorn Alsberg for providing the Vis-NIR equipment and the Laboratorio de Sistemas e Tecnologia Subaquatica of the University of Porto, the Hydrographic Institute of the Portuguese Navy and the University of the Azores for carrying out the REP15 exercise, during which the hyperspectral push broom image was collected.Vitale, R.; Zhyrova, A.; Fortuna, JF.; De Noord, OE.; Ferrer, A.; Martens, H. (2017). On-The-Fly Processing of continuous high-dimensional data streams. Chemometrics and Intelligent Laboratory Systems. 161:118-129. doi:10.1016/j.chemolab.2016.11.003S11812916

Challenges of Big Data Analysis

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article give overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasis on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions