Abstract
The article proposes a model with an artificial intelligence approach that integrates risks through the Grey Clustering method applying the "Triangulation of center-point based on Whitening functions -CTWF", for this, the data established is standard data (minimum standards that the four workshops of a company in the industrial sector must meet) and sampled data (real data obtained in the field) to test the grey classes. In this study, the different types of risks (lighting, noise and hand-arm vibration) were globally evaluated and analyzed in the four workshops of a heavy machinery maintenance services company in the industrial sector (welding shop, hydraulic shop, machine shop 1 and machine shop 2), located in Lima, Peru. According to the results obtained from the level of hygienic quality in each workshop, the welding workshop is at a very poor-quality level, while the others are at a good and very good level; regarding the four workshops, it was determined that the noise level is not recommended as they do not meet the minimum required standards. Therefore, control measures were proposed in the four workshops where the level of irrigation is bad and very bad. This study will benefit companies in the industrial sector that need to analyze the level of hygienic quality in their work areas with a global approach in order to apply control measures with prevention, protection of health and physical integrity of workers.
Highlights
In the companies of the industrial sector, there can be found diverse types of risks that can affect the zone of comfort and the health of the workers such as excessive level of noise, inadequate illumination, high levels of vibration, etc. [1]; where generally does not exist a method that simultaneously covers these types of risks that will be necessary to obtain indicators of management of safety and occupational health
The present study proposes a model based on artificial intelligence that integrates these risks through the method of Grey Clustering [2] applied in an industrial sector company located in Lima – Peru, where it is detected that in some of its workshops there are high levels of exposure to hygiene risks such as noise, lighting and vibration; being necessary to have an objective assessment and propose control measures that is where this study aims
Risk levels will be evaluated in four workshops of a heavy machinery maintenance service company of the industrial sector, located in the province of Lima, Peru, where the results obtained in the occupational hygiene monitoring are observed as high levels of noise, lighting and hand-arm vibration, which will be evaluated globally in each workshop through the Grey Clustering method [3]
Summary
In the companies of the industrial sector, there can be found diverse types of risks that can affect the zone of comfort and the health of the workers such as excessive level of noise, inadequate illumination, high levels of vibration, etc. [1]; where generally does not exist a method that simultaneously covers these types of risks that will be necessary to obtain indicators of management of safety and occupational health. It is proposed to use the Grey Clustering methodology, which is an artificial intelligence approach [4], to be applied by means of "Centrepoint Triangulation based on Whitenization Functions - CTWF" [5], since these are mainly applied to test if the objects of observation belong to predetermined classes, known as grey classes [6], as it is evidenced in the studies of selection of innovative strategies [7], in the evaluation of air quality by grey incidence [8], as well as in the management of occupational safety and health [9]; and as this study will be based on a small group of criteria with limited information, its application will be the most appropriate as grey clustering Method considers this in its analysis For this reason, the specific objective of this study is to simultaneously analyze the different types of risks (lighting, noise and hand-arm vibration) present in each workshop, where a global evaluation of the risks will be obtained according to the methodology of Grey Clustering [10] in the heavy machinery maintenance service company of the industrial sector in Peru, and based on the application of the method, control measures can be proposed.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
