Software platform prototyping for management and control of industrial plant asset maintenance integrated with predictive maintenance models

• Grant number: 20/13127-2
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: June 01, 2021
• End date: November 30, 2022
• Field of knowledge: Engineering - Electrical Engineering - Electrical, Magnetic and Electronic Measurements, Instrumentation
• Principal Investigator: Nicolas Goulart Silva
• Grantee: Nicolas Goulart Silva
• Company: Dipsie Engenharia e Software S/A
• CNAE: Desenvolvimento de programas de computador sob encomenda ; Desenvolvimento e licenciamento de programas de computador customizáveis ; Serviços de engenharia
• City: Vinhedo
• Pesquisadores principais: Douglas Castilho Mariani ; Julia Reinaldi Finassi Pinto ; Tomaz Marques Gonçalves da Silva
• Associated researchers: Felipe Augusto Passarini
• Associated research grant: 19/09086-1 - Predictive model for detection of imminent failures in industrial electric machines, AP.PIPE
• Associated scholarship(s): 21/05705-9 - Software platform prototyping for management and control of industrial plant asset maintenance integrated with predictive maintenance models, BP.PIPE

Abstract

With the increasing need to be more competitive, industries are forced to incorporate technologies to optimize costs, increase productivity and efficiency in their processes. Currently, companies are connecting their assets in order to make the best use of their data to create a new and powerful business value. This great movement in the industry is known as the digital transformation, Industry 4.0 and the industrial Internet of Things (IIoT). Connecting the devices is just the first step. The real value is in the information generated through these data that is transmitted from these devices, such as the business insights that data can enable. Maintenance costs correspond to the majority of the total operating expenses of industrial manufacturing or process plants. Depending on the type of industry, maintenance costs can represent 15% to 30% of the cost of goods produced. The proposal of predictive models for the development of predictive maintenance solutions, has as great innovation the use of computational intelligence techniques that enable the ability to analyze in real time, parameters and real variables of machines operation, as well as learn patterns reading and make a comparison between the collected data with a database that contemplates several states operation of the machine. These models need to be combined with software solutions that meet the needs of the industry, thus allowing the predictive model developed in Phase 1 of the project to generate real value combined with a management and control solution, capable of genuinely integrating the factory floor and the various existing devices and technologies. (AU)