Author: "Dong, Jianfei" / Topic: estimation theory - Searchworks@Jio Institute Digital Library Search Results

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Start Over Author "Dong, Jianfei" Topic estimation theory

Author: Dong, Jianfei, Pandharipande, Ashish, van Driel, Willem, and Zhang, Guoqi
Subjects: *LIGHT emitting diodes, *OPTOELECTRONICS, *LUMINOUS efficiency function, *ESTIMATION theory, *LIGHTING, *QUANTUM perturbations
Abstract: Lumen depreciation, the condition that the light output falls below a fraction of its peak output, is known to be the main failure mode in light emitting diodes (LEDs). In lighting systems comprising multiple LEDs, lumen depreciation leads to reduced average illuminance and a distorted illumination rendering. Diagnosing lumen depreciation in LED lighting systems is thus an important problem. We propose an estimation approach to diagnosing individual LED failures using a photosensor system. We specifically study the impact of perturbations in the photosensor positions on diagnosability by analyzing the sensor output matrix—the matrix relating the luminous flux values of the multiple LEDs to the measurements at the photosensor system. The conditions on diagnosability are first derived in terms of bounds on the smallest singular value of the sensor output matrix. Subsequently, sufficient conditions are derived on the photosensor positions to ensure that individual LED lumen depreciations are diagnosable. Numerical results are provided to validate our analytical results. [ABSTRACT FROM PUBLISHER]
Published: 2012
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Author: Dong, Jianfei and Verhaegen, Michel
Subjects: *ESTIMATION theory, *FAULT location (Engineering), *LEAST squares, *MATHEMATICAL models, *ALGORITHMS, *ANALYSIS of covariance, *DATA extraction, *INVARIANT subspaces
Abstract: Classical methods for estimating additive faults are based on state-space models, e.g., moving horizon estimation (MHE) and unknown input observers (UIOs). This paper contributes new direct design methods from closed-loop I/O data for systems with stable inversion, which do not require building a state-space model by first principles, nor require identifying it. Inspired by subspace identification, we use the input and output (I/O) relationship of a plant in a Vector ARX (VARX) form to parameterize least-squares (LS) problems for estimating faults. We prove that with the order of the VARX descriptions tending to infinity, the fault estimates are unbiased. Under lower relative degrees, we prove that our new methods are equivalent to system-inversion-based estimation for both LTI and LTV systems. We will show more general unbiased estimation conditions for higher relative degrees. These require that the underlying inverted system from faults to outputs is stable. Algorithms of identifying unbiased fault estimation filters from data will be developed in this paper based on single LS. Moreover, covariance of the fault estimates can also be extracted from data. [ABSTRACT FROM AUTHOR]
Published: 2012
Full Text: View/download PDF

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