PLC Control Logic Error Monitoring and Prediction Using Neural Network

Jung, Insung; Mulman, BM; Thapa, Devinder; Koo, Lock-Jo; Bae, Jinhee; Hong, Sanghyun; Yeo, Sungjoo; Park, C.M.; Park, S.C.; Wang, Gi-Nam

doi:10.1109/icnc.2008.776

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…Since processes are becoming more complex [11], and the functionality of machines are growing, the operator needs a powerful tool to control and monitor production systems. A Human Machine Interface (HMI) system represents the interface between the human being (operator) and the process (machine) [12].…”

Section: E3mentioning

confidence: 99%

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Al-Radhi¹,

Al-Kamil

Szakács

2020

Journal of Petroleum Research and Studies

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Degassing station breakdowns can be dangerous to the operator health and the environment. Programmable logic controllers (PLCs) are key modules of manufacturing control systems that are applied in the complex oil and gas units to reduce manpower and unnecessary faults. However, feeding a PLC with data is a difficult part due to the need of system log files which records all events that occur in the oil fields and provide visibility to a given environment. Moreover, most critical chemical processing plants and oil distributions are visualized and inspected by Supervisory Control and Data Acquisition Systems (SCADA). These systems have been focused on safety, and there are issues that they could be the target of worldwide terrorists. Along with the frequently rising internet-related attacks, there is indication that our degassing stations may similarly be susceptible; for that reason, it is essential to secure PLC and SCADA from undesired incidents. Recently, machine learning (ML) has been increasing interest in industrial systems to detect, identify, and store information. Therefore, we propose to apply an advance ML based on deep neural networks to the PLC system with the purpose of: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systems and its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4) Making predictive maintenance possible. The SIMATIC S7-1214 CPU universal TIA platform was used as the main decision-making module. Experimental results demonstrate the effectiveness and utility of the proposed approach to process large amounts of data analytics and sensor measurements, allows it to spot potential problems and provide possible solutions.

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Section: E3mentioning

confidence: 99%

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Al-Radhi¹,

Al-Kamil

Szakács

2020

Journal of Petroleum Research and Studies

View full text Add to dashboard Cite

show abstract

“…Reference [ 15 ] suggested integrating PLCs into engines to prevent hardware and software failures through monitoring production speed and efficiency. Reference [ 16 ] applied neural networks to monitor PLC programs and predict PLC errors, which improves the fault detection capabilities, maintenance, and reduces downtime of the production line. Reference [ 17 ] applied a PLC-based approach to monitoring renewable energy generation by collecting information on energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

Cheng

Pourhejazy

Hung

et al. 2021

Sensors

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Smart monitoring plays a principal role in the intelligent automation of manufacturing systems. Advanced data collection technologies, like sensors, have been widely used to facilitate real-time data collection. Computationally efficient analysis of the operating systems, however, remains relatively underdeveloped and requires more attention. Inspired by the capabilities of signal analysis and information visualization, this study proposes a multi-method framework for the smart monitoring of manufacturing systems and intelligent decision-making. The proposed framework uses the machine signals collected by noninvasive sensors for processing. For this purpose, the signals are filtered and classified to facilitate the realization of the operational status and performance measures to advise the appropriate course of managerial actions considering the detected anomalies. Numerical experiments based on real data are used to show the practicability of the developed monitoring framework. Results are supportive of the accuracy of the method. Applications of the developed approach are worthwhile research topics to research in other manufacturing environments.

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“…After the obtained data are processed, the training process is completed with the Matlab Neural Net fitting tool and using the Levenberg-Marquardt method [9] [10]. Programmable Logical Controller (PLC) is often used as a real-time controllers of automation systems and applications due to its flexibility, reliability, and relatively low-cost advantage in controlling complex systems [11], [12]. They are primarily used in the control of both machinery and industrial processes as an essential component of industrial automation systems.…”

mentioning

confidence: 99%

An Intelligent Machine Condition Monitoring Model for Servo Systems

MUTLU

Aküner

Akgün

2022

Balkan Journal of Electrical and Computer Engineering

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The installation of industrial servo systems and the determination of control parameters are limited to the skills and knowledge of the commissioner. In addition, commissioned systems are often not re-optimized if environmental influences or loads change. The goal of this research is to create an artificial neural network (ANN) model for servo systems that will keep the servo system's proportional, integral, and derivative (PID) parameters working optimally. For this process, a machine condition monitoring algorithm developed with the ANN technique, which uses the data such as actual current, torque, power, position to be obtained from the servo system on an industrial controller, for the control and rearrangement of the parameters.

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PLC Control Logic Error Monitoring and Prediction Using Neural Network

Cited by 7 publications

References 4 publications

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

A model-based machine learning to develop a PLC control system for Rumaila degassing stations

Smart Monitoring of Manufacturing Systems for Automated Decision-Making: A Multi-Method Framework

An Intelligent Machine Condition Monitoring Model for Servo Systems

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