The cost of electric power has become one of the most representative expenditures in the industrial environment and other organizations, only after human resources. Currently, seeking the reduction of expenses in processes and operations has become a necessity for survival in the market. Therefore, the optimization of any process is the measurement, only what is measured can be managed and therefore improved. The reduction and good use of energy is not possible without having a reliable source of information; that is why the implementation of a useful monitoring system will indicate the expenses and in what way to proceed in order to establish real opportunities for savings, as well as strategies for their maintenance in different networks. Thus, the following paper is the result of the implementation of an electrical monitoring system for an industry in the region.

The implementation of new technologies within the areas of design and automation, play a relevant role in manufacturing processes, especially when seeking to increase efficiency and volume within production lines, improving the quality of products, reducing waste, increasing safety, as well as creating ergonomic work spaces. The present work was carried out with the purpose of improving the levels of quality and productivity in the process of dispensing and gluing in the assembly of a sensor, due to the demands of the clients, it was decided to design and automate a cell to carry out this operation with greater efficiency. Through the use of mechanical design tools and software, such as Solid Works, which helped establish the design of the manufacturing cell, PLC’S, Driver’s and Servomotors, which together achieve the integration of both branches.

This article investigates the use of automatic learning classification techniques applied to the task of recognizing the correct shape and color of pieces in a connector using neural networks. The system presented here shows that you can use a set of features extracted from the side view of the piece to recognize the shape of the piece and the color. The proposed model is based on two stages, one performs detection and the other is for recognition. In the first stage, color segmentation algorithms have been tested. In the second stage, a method of extracting personalized features in a color recognition approach is used. Finally, the use of a multilayer artificial neural network (ANN) is proposed to recognize and interpret the different possible shapes and colors with which the pieces can come.