Mika Ruusunen

Fuzzy modelling of carbon dioxide in a burning process

Abstract Model based observation of carbon dioxide (CO2) in a burning process is discussed. The described model structure is a combination of fuzzy Takagi-Sugeno (TS) models and operation regime based modelling approach. The selection of local modelling regions and input variables is based on general combustion theory. Recursive stochastic gradient method is employed to model training. Simulations with experimental data are analysed to verify the validity of the discussed combustion observation approach. Performance is further compared to neural and linear models. Results indicate that the presented model has good generalisation properties and it is capable to capture systems behaviour.

Comparison of three change detection algorithms for an electronics manufacturing process

In a sequential manufacturing process, a product proceeds through different manufacturing stages. At these stages, sensors monitor the features of the product. In this paper, the information produced by the sensors is employed to detect abrupt changes in process variables. The developed algorithms contribute to an on-line application to a manufacturing system. A literature survey revealed the most common methods utilized in change detection. On-line applicability and transferability to new manufacturing lines are the most important features for real applications. During both on-line and off-line tests, some of the presented methods showed satisfactory results. Real-time, on-line manufacturing environment sets also its requirements for the applications. In the future, the possibility of combining expert knowledge with the aforementioned methods is the crucial point to study. The information thus received has usage in the preventive maintenance and quality control.

Real-time moisture content monitoring of solid biomass in grate combustion

A novel method for real-time moisture level monitoring of the solid biomass fuel in grate combustion is presented. The measurement principle is based on temperature sensor information from both flame and a fuel bed. Based on the combustion theory and data analysis, selected features have been extracted from the fused sensor information and estimate of the fuel quality is then made continuously with calculated features. The monitoring approach has been tested in a 300 kW stoker combustion unit intended for decentralized heat production, over a wide range of different process conditions and wood fuel moistures, giving satisfactory accuracy for control purposes. The availability of moisture information made possible to adjust primary/secondary air ratio, leading to reduction of emissions and excess air. Based on the results, the method has capability to give new possibilities for cost effective control and more energy efficient use of solid biomass as a fuel in small-scale energy production.

An Attempt to Find an Empirical Model between Barkhausen Noise and Stress

A nonlinear empirical model between stress and Barkhausen noise is identified in this study. The identification procedure uses a genetic algorithm followed by a Nelder-Mead optimization procedure. The model is identified with the data set where an external load is applied to RAEX400 low alloyed hot-rolled steel samples. The results of the study show that the identified model performs well in stress predictions. The identified model includes three terms which are in accordance with the literature.

Monitoring of Automated Screw Insertion Processes-A Soft Computing Approach

Abstract A soft computing monitoring approach for automated screw insertions is presented. A model based monitoring method is developed with systematically collected experimental data and fundamental process knowledge to verify the quality of assemblies. The model for quality monitoring is based on Linguistic Equations (LE)-a non-linear scaling framework for model variables. Fuzzy reasoning and basic statistical methods are combined to interpret the model residuals and faults. Preliminary tests indicate that the proposed method could successfully cope with changes in manufacturing parameters. Based on the results, the method seems to provide valuable information for quality control of the screw insertion task.

Model-Based Method for Combustion Power Stabilisation in Grate-Fired Boilers

Abstract A model-based control strategy for the compensation of combustion power fluctuations in small-scale biomass fired boilers is presented. Delay-free heat output model is obtained by utilising combustion temperature measurements. Local principal component regression models are constructed to cope with changing combustion conditions and redundant input variables found by data analysis. The modelling approach was validated using data from 30 kW and 300 kW boilers. Usability of the discussed monitoring method for compensating fuel power disturbances by the primary air control was tested in practice. The results imply that modelling information can be utilised as a part of the control structure, enabling the cost-effective way to reduce emissions and maintain optimal combustion conditions in the distributed energy production with solid biomass fuels.

Some factors affecting performance of a wireless sensor network — entropy-based analysis

Electromagnetic interferences and other disturbances in industrial environment may decrease the performance of wireless sensor networks. However, the lack of empirical results providing proof for these arguments is evident. In this paper, analysis results of systematic experiments carried out for potential, disturbing factors in industrial environment are presented. Moreover, a novel entropy-based approach to measure changes in jitter variation is introduced. The selected approach performed well, being able to point out statistically significant sources of disturbances.

Model selection in large-scale databases

Abstract A procedure for surveying process data sets is presented. For this, linear models constructed in varying length, sliding data windows to determine the usefulness of data segments for process identification are utilised. The discussed approach has been applied to an industrial wood debarking plant and a biomass boiler analysis, enabling the preliminary study of process variables and conditions affecting the non-optimal process conditions. In addition, main process interactions and delays were easily discovered from the structures of the interpretable linear model candidates. It is concluded that the analysis can provide valuable information also for modelling and control of continuous processes.

Information Theoretic Approach to Improve Performance of Networked Control Systems

Networked control systems (NCS) could be utilised in several industrial applications. However, the variable time delays introduced by the network impair the NCS performance, resulting even in the instability of the controlled process. To mitigate the delay problems, the advantage is taken from model-based, adaptive controllers. This calls for an efficient approach for on-line analysis of measurements applied to update the controller state in NCS. The paper introduces a new adaptive Model Predictive Controller (MPC) capable of compensating for variations in measurement and actuating delays. Weighting factors for delayed measurements and actuators are adjusted based on normalised version of mutual information that is calculated using a procedure described in the paper. The method is superior compared with other, more usual, metrics.