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Asokan, A.
- Fault Detection and Diagnosis for Three-tank System Using Robust Residual Generator
Abstract Views :360 |
PDF Views:107
Authors
A. Asokan
1,
D. Sivakumar
1
Affiliations
1 Dept of E&I, Annamalai University, Annamalai Nagar-608 002, Chidambaram, IN
1 Dept of E&I, Annamalai University, Annamalai Nagar-608 002, Chidambaram, IN
Source
Indian Journal of Science and Technology, Vol 2, No 7 (2009), Pagination: 23-29Abstract
Fault detection and diagnosis (FDD) is a task to deduce from observed variable of the system if any component is faulty, to locate the faults and also to estimate the fault magnitude present in the system. The main goal when synthesizing robust residual generators, for diagnosis and supervision, is to attenuate influence from model uncertainty on the residuals while keeping fault detection performance. In this paper, a design procedure for robust residual generators is developed with two key elements. One is the use of a reference model that represents desired performance. The other is an optimization criterion, based on robust H∞ filtering, used to synthesize the residual generator.Keywords
Fault Detection, Robust Residual Generation, Structured Residual Approach, H∞ FilteringFull Text
References
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- Gertler J (1998) Fault Detection of Dynamical Systems, Marcel Dekker, Inc. USA.
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- Gertler J (1993) Residual generation in model based fault diagnosis. Control- Theory & Adv. Technol. 9, 259-285.
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- Gertler J, Staroswiecki M and Shen M (2002) Direct design of structured residuals for fault diagnosis in linear systems. American Control Conf. Anchorage, Alaska.
- Wu J, Biswas G, Abdelwahed S and Manders E (2005) A hybrid control system design and implementation for a three tank test bed In Proc. of the 2005 IEEE Conf. on Control Appl. pp: 645–650.
- KÄoppen-Seliger B, Alcorta-Garca E and Frank PM (1999) Fault detection: different strategies for modelling applied to the three tank benchmark - a case study. Eur. Control Conf. Karlsruhe, Germany.
- Comparative Analysis of Flame Image Features for Combustion Analysis
Abstract Views :157 |
PDF Views:0
Authors
Affiliations
1 St. Joseph’s College of Engineering and Technology, Palai, Bharananganam Pravithanam Road, Choondacherry-686579, Kerala, IN
2 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai-600008, Tamil Nadu, IN
3 PSNA College of Engineering and Technology, Kothandaraman Nagar, National Highway 209, Dindigul – 624622, Tamil Nadu, IN
4 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai – 600008, Tamil Nadu, IN
1 St. Joseph’s College of Engineering and Technology, Palai, Bharananganam Pravithanam Road, Choondacherry-686579, Kerala, IN
2 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai-600008, Tamil Nadu, IN
3 PSNA College of Engineering and Technology, Kothandaraman Nagar, National Highway 209, Dindigul – 624622, Tamil Nadu, IN
4 Department of Instrumentation, Annamalai University, Rukmani Lakshmipathy Road, Egmore, Chennai – 600008, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 6 (2016), Pagination:Abstract
Background/ Objective: This article identifies the best feature of the flame video, captured with a camera with frequency response in visible spectrum, from which the flame temperature can be estimated. Methods/Statistical analysis: The flame videos at different air and fuel inlets with different boiler temperatures were recorded from a diesel fired boiler prototype. In the video frames, the flame region was localised by intensity based adaptive thresholding. The correlation between boiler temperature and measures of central tendency and dispersion of different colour channels of the video frames were investigated. Findings: Among the features of the flame video, Standard deviation of blue channel grey levels above 32.95, variance greater than 1293 and mean absolute deviation (MAD) above 30.38 could efficiently represent the region of optimum combustion air supply at which boiler temperature is maximum above 684 degree Celsius. Range of green channel grey levels, interquartile mean, variance and mean absolute deviation of blue channel grey levels are the video features exhibiting maximum correlation (ρ>-0.96) with boiler temperature. Applications/Improvements: The features of the flame video which are correlated with its temperature can be utilised to develop non-intrusive methods of temperature measurement. This will enable efficient control of combustion process.Keywords
Combustion, Flame Image Processing, Flame Temperature Measurement, Image Features, Video ProcessingFull Text