Publicação
ABSTRACT
Changing market demands are dictating the latest
technological evolutions. Digital Transformation,
Maintenance Optimization and Changing Workforce are
only some of the key industry challenges.
Conventionally, plant operation systems aim to improve
production efficiency and product quality while facility
maintenance systems aim to both maximize operational
efficiency and minimize costs. However, when maximizing
production efficiency, maintenance costs are not necessarily
optimized. Although, operation information and
maintenance information must be combined to maximize
profits for the entire plant, this is rarely achieved mainly
because maintenance is not always quantified.
Many digital technologies can be applied to improve
maintenance, monitoring and visualizing the condition of
equipment, utilizing wireless sensors is the first step to make
the plant maintenance more efficient. The combination
with Advanced Analytics such as Artificial Intelligence
and Machine Learning are strong tools for reforming plant
maintenance work. Data Analytics allow you to understand
equipment conditions more deeply by analyzing process
data creating value from process historian Big Data by
classifying, standardizing, organizing and interpreting
process data accumulated in a plant (big data). The Digital
Transformation can be also applied to field activities in a
process plant, such as operator rounds, basic equipment care
and Predictive Maintenance. It is known that by digitalizing
field activities, plant maintenance can reduce maintenance
costs while reducing the use of paper, check worker’s activity
with location data and time, avoid Over-Maintenance and
assure the efficiency and integrity of field work (less mistakes
and data for procedure analysis). New AR technologies are
enabling field operators to improve maintenance efficiency
and the quality of field work by providing communication
solutions through standard web browsers, where specialists
can make video calls to transmit easy-to-visualize image
and text data, helping less-experienced operators anywhere,
reducing human error and improving the safety and
efficiency of field work.
Keywords: Digital Transformation, Advanced Analytics,
Artificial Intelligence, Predictive Maintenance and Over-
Maintenance.

Author: Eduardo Ishikawa1,
Eduardo Ishikawa¹
1 Yokogawa. Brazil

Corresponding author: Eduardo Ishikawa. Yokogawa. Alameda Xingu, 850. Barueri, SP, Brazil - 06455-030. Phone: +55-11-3513-1419.
E-mail eduardo.ishikawa@br.yokogawa.com

O PAPEL vol. 82, num. 1, pp. 70 - 72 - JAN 2021
Anexos

PDF | 630 Kb

 

Esta publicação fala sobre
Para procurar por publicações similares, clique
nos temas acima ou nos textos listados ao lado.
Você também pode realizar uma pesquisa
no campo superior desta página.
Você pode ainda publicar seu comentário logo abaixo, assim como mandar sua sugestão por e-mail.


Participe, deixe abaixo os seus comentários.

Ajax Indicator
Ajax Indicator

Institucional

Conheça aqui a rede de comunicação da Associação Brasileira Técnica de Celulose e Papel.

Contato

A ABTCP espera sua mensagem, seja para anunciar, enviar sugestões ou tirar dúvidas sobre nossas publicações.

O Papel | ABTCP | Todos os direitos reservados 2009