Artificial Intelligence Model based on Grey Clustering for Integral Analysis of Industrial Hygiene Risk

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.

@article{Delgado2021,
  title     = {Artificial Intelligence Model based on Grey Clustering for Integral Analysis of Industrial Hygiene Risk},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {4},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Alexi Delgado and Diana Aliaga and Cristian Carlos and Lisseth Vergaray and Chiara Carbajal},
  doi       = {10.14569/IJACSA.2021.0120449},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120449}
}