A conversion tool for translating Python']Python-based machine learning models to structured text codes

被引：0

作者：

Campos, Yasmin Adriane de Paula ^{[1
]}

Pereira, Paulo Haron da Silva ^{[2
]}

Duarte, Robson Aparecdo ^{[3
]}

Perez, Jose Manuel Gonzalez Tubio ^{[4
]}

Pessin, Gustavo ^{[4
,5
]}

Pinto, Thomas Vargas Barsante ^{[3
,4
,6
]}

机构：

[1] Univ Fed Ouro Preto, Dept Engn Controle & Automacao, Ouro Preto, Brazil

[2] Vale Base Met, Canaa Dos Carajas, Brazil

[3] Vale SA, Canaa Dos Carajas, Brazil

[4] Inst Tecnol Vale, Lab Robot Controle & Instrumentacao, Ouro Preto, Brazil

[5] Univ Fed Ouro Preto, Dept Computacao, Ouro Preto, Brazil

[6] Univ Fed Minas Gerais, Dept Engn Elect, Belo Horizonte, Brazil

来源：

SOFTWAREX | 2025年 / 29卷

关键词：

Machine learning; !text type='Python']Python[!/text; Structured text; Industry; PLC;

D O I：

10.1016/j.softx.2024.102005

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We present a converter software program that automatically translates Python-based machine learning algorithms into Structured Text codes. This tool empowers engineers to efficiently generate machine learning models in a programming language widely used in industrial controllers. It supports the conversion of decision tree and multilayer perceptron models built using scikit-learn library. Moreover, the generated Structure Text code is compatible with ABB's Industrial IT 800xA DCS syntax. A practical example demonstrates the effectiveness of this converter software program and its potential to enhance the integration of machine learning models into industrial automation systems.

引用

页数：6

共 14 条

[1] Azarmipour M, 2019, IEEE IND ELEC, P5513, DOI 10.1109/IECON.2019.8927026
[2] On the Implementation of Industrial Automation Systems Based on PLC
Basile, Francesco
Chiacchio, Pasquale
Gerbasio, Diego
[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2013, 10 (04) : 990 - 1003
[3] Boudribila Abderrahmane, 2023, 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT), P650, DOI 10.1109/CoDIT58514.2023.10284185
[4] Machine Learning for Fluid Mechanics
Brunton, Steven L.
Noack, Bernd R.
Koumoutsakos, Petros
[J]. ANNUAL REVIEW OF FLUID MECHANICS, VOL 52, 2020, 52 : 477 - 508
[5] Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges
Dalzochio, Jovani
Kunst, Rafael
Pignaton, Edison
Binotto, Alecio
Sanyal, Srijnan
Favilla, Jose
Barbosa, Jorge
[J]. COMPUTERS IN INDUSTRY, 2020, 123
[6] A critical review of artificial intelligence in mineral concentration
Gomez-Flores, Allan
Ilyas, Sadia
Heyes, Graeme W.
Kim, Hyunjung
[J]. MINERALS ENGINEERING, 2022, 189
[7] Ready or Not, AI Comes- An Interview Study of Organizational AI Readiness Factors
Joehnk, Jan
Weissert, Malte
Wyrtki, Katrin
[J]. BUSINESS & INFORMATION SYSTEMS ENGINEERING, 2021, 63 (01) : 5 - 20
[8] Using Structured Text Source Code Metrics and Artificial Neural Networks to Predict Change Proneness at Code Tab and Program Organization Level
Kumar, Lov
Sureka, Ashish
[J]. PROCEEDINGS OF THE 10TH INNOVATIONS IN SOFTWARE ENGINEERING CONFERENCE, 2017, : 172 - 180
[9] Langmann R, 2016, INT CONF REMOT ENGIN, P10, DOI 10.1109/REV.2016.7444433
[10] Machine learning applications in minerals processing: A review
McCoy, J. T.
Auret, L.
[J]. MINERALS ENGINEERING, 2019, 132 : 95 - 109

← 1 2 →