Multi station assembly process and determining the optimal sensor placement using chaos embedded fast simulated annealing
This paper presents a new methodology for allocation of sensors in Multi Station Assembly processes. It resolves two core issues i.e. determining the optimal number of sensors to be used and the best locations for each of sensors. The effect of noise on the sensor placement has been minimized by maximizing the determinant of Fisher information matrix. The paper conceives objective function that is significant over other contributions in respect of adding the effect of noise coupled with the sensor data. To optimize the proposed objective function, a new algorithm is developed that combines Chaotic sequences with traditional Evolutionary Fast Simulated Annealing (EFSA) and therefore termed as chaos embedded fast simulated annealing (CEFSA). The proposed algorithm finds the optimal sensor distribution and allocation with minimum noise term in sensor data. The paper also reports the details of a numerical example, carried out in an industrial context to test the efficacy of proposed algorithm. Further analysis reveals that the proposed approach to obtain optimal number of sensors and selection of best locations offers more generic results compared to previously concluded analysis.
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