A genetic algorithm approach for optimization of machinery noise calculations. (April 2019)

• Record Type: Journal Article
• Authors: Rao, DS; Tripathy, DP
• Is Part Of: Noise & vibration worldwide. Volume 50:Number 4(2019)
• Page Start: 112
• Page End: 123
• Publication Date: 2019-04
• Subjects: Genetic algorithm -- machinery noise -- CPU time -- sound pressure level -- mutation variants -- stochastic optimization; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/0957456519839409
• Languages: English
• ISSNs: 0957-4565

Abstract

Noise produced by various noise sources in the mines is considered as a serious environmental problem. Exposure to such noise levels is considered as hazardous to workers working in such conditions. Noise assessment was exercised in a highly mechanized opencast bauxite mine, located in eastern India according to the Director General of Mines Safety Technical Circular No. 18 of 1975 and No.5 of 1990. There are numerous approaches in the literature on machinery noise prediction based on statistical models, soft computing techniques such as fuzzy inference system, artificial neural networks, support vector machines, adaptive neuro fuzzy inference system and other classification methods. The main drawback of statistical models, fuzzy inference system, artificial neural network, support vector machine and adaptive neuro fuzzy inference system is lack of interpretation for human and optimization issues. An attempt has been made to examine the applicability of a genetic algorithm, to take the advantage of genetic structures to find an optimal sound pressure level of the machinery noise taking into consideration the distance, directivity index, sound power level and other attenuation parameters under several noisy operating conditions according to ISO 9613-2:1996, ISO 6395:2008 and other related standards. Genetic algorithm used in this article has several advantages: it can be applied for low, high and dynamical system and uses a simple procedure to determine the order and the parameters with high accuracy. Genetic algorithm model is trained and tested in MATLAB to find the optimum parameters. Experimental results show that genetic algorithm is able to converge and find the optimum values faster along with acceptable computational time. By comparing the predicted values with the measured values, it proves the effectiveness of the proposed model as a useful and efficient method for machinery noise optimization problems.