Identification of noise sources in factory’s sound field by using genetic algorithm

Abstract

Noise control is important and essential in factory, where the noise level is restricted by the Occupational Safety and Health Act. Before noise abatement being performed, the identification work in searching for the location and sound power level (SWL) of noisy sound sources is absolutely prerequisite. Several researches on new techniques of single noise control have been well addressed and developed; however, the research work on sound identification for the existing multi-noise plant is hardly found sufficient. Under the circumstance of unrecognized noises, the noise control work will expectedly be extravagant and fruitless. Therefore, the numerical approach in distinguishing noises from a multi-noise plant becomes crucial and obligatory. In this paper, the novel technique of genetic algorithm (GA) in conjunction with the method of minimized variation square will be adopted and used in the following numerical optimization. In addition, various sound monitoring systems in detecting the noise condition within the plant area will also be introduced. Before noises identification, the accuracy of mathematical model has then been proved to be in good agreements comparing to the simulated data of SoundPlan, a commercialized simulation package in sound field. Moreover, three kinds of multi-noise plants have been fully discussed and acknowledged by GA optimization. The results reveal that the relevant locations and sound power levels (SWLs) of noises can be precisely recognized. This paper surely provides a rapid methodology in the noise identification work for a multi-noise plant.