Artificial Intelligence in Industrial Process Automation System

The purpose of this paper is to mapping and review what has been done on the topic of research on predictive maintenance in SCADA (Supervisory Control and Data Acquisition) based industries. In the research area of predictive maintenance, various methods for predicting damage or time to failure of a machine have been proposed and applied in various industries. This paper systematically categorizes predictive maintenance in SCADA-based industries research based on industry classifications according to ISIC (International Standard Industrial Classification of All Economic Activities). Furthermore, the research scope is explored its connection to the topics of Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), and Supervisory Control and Data Acquisition (SCADA). It is found that 81.5% of the research was conducted on the electricity, gas, steam, and air conditioning supply industries, 11.1% of research was conducted on the mining and quarrying industry, and 7.4% of the research conducted in the manufacturing industry. It is also found that 85.2% of studies used AI and ML, 18.5% of the studies used IoT, and 18.5% of research used AI/ML and IoT technology together.

A stability test chamber simulates specific environmental conditions and tests a product or material in the manufacturing stage. The tested product or material is then analyzed about its shelf-life estimation related to the state and durability of the product for a certain period. The main parameter that must control accurately is temperature. Programmable Logic Controller (PLC) as an industrial computer control system has been widely used. In this paper, the temperature control system was designed based on PLC and supervisory control and data acquisition (SCADA). The PLC and SCADA would indirectly control the heater. The PLC was feeded from the temperature sensor for real-time temperature inside the test chamber. Received temperature value, compared to the temperature set point. The validation and verification of controlled temperature inside the chamber were also investigated. The system was designed on a miniaturized chamber using commercial PLC and SCADA, Stepper motor, slide regulator, heater, and the temperature sensor PT100. The temperature test point for validation and verification was performed at 35 °C, 45 °C, and 55 °C. The result showed that The PT100 successfully had an excellent response to the temperature test point. The correction value for temperature is as high as 0.6 °C with a deviation of 1 %.

The need for predictive maintenance methods has arisen as a key element in improving operational efficiency, reliability, and life expectancy of photovoltaic (PV) systems and the future complex renewable energy infrastructure sets. The Machine learning (ML) technique is sub part of Artificial Intelligence (AI) technology which has widened their adoption in energy analytics, resulting in numerous studies proposing different algorithms for monitoring, prediction, and prevention of system failures. The overview of these approaches is yet to be exhaustive in the existing literature regarding a metric-based evaluation. In addressing this gap, the article undertakes a structured review of the state-of-the-art recent peer-reviewed literature on predictive maintenance in solar PV systems. Each work will, therefore, be appraised against standardized performance metrics models, which include aspects such as accuracy, precision, recall, F1-score, area under the curve (AUC), and model-specific indicators- Root Mean Square Error (RMSE), latency, and execution delays. A numerical analysis table summarizes and compares the predictive capabilities of techniques such as Random Forest, CatBoost, Convolutional Neural Network (CNN) ensembles, Long Short-Term Memory (LSTM) autoencoders, Supervisory Control and Data Acquisition (SCADA) IoT frameworks, and Digital Twins. High-performing models, such as CatBoost and custom CNN architectures, indicate the effectiveness of hybrid deep learning strategies in fault diagnostics. The review establishes a new benchmark for evaluating PdM systems, readying the bar between academic innovation and real-world deployment. It outlines future research directions including model generalization, real-time edge AI deployment, and integration with climate-aware forecasting systems. This work complements an important entry point for other works by researchers and industry stakeholders’ intent on deploying scalable and resilient predictive maintenance solutions in renewable energy networks.

The purpose of this study is to provide students with a 21st century training that can be easily applied to industrial automation training, both online and in person, and that goes beyond traditional learning methods. It involves creating and programming a training package that is compatible with the Ministry of National Education (MEB) curriculum, using the project-based learning (PBL) method and a curriculum that focuses on skills acquisition. Two different brands of programmable logic controllers (PLC) have been integrated into the manufactured training set so that the set can be used to conduct various PLC trainings. In the training set, product separation can be carried out using the electro-pneumatic system with a turntable and two conveyor belts. Motor controls, sensor applications, electro-pneumatics, touch panel and various PLC controls can also be carried out with the training set. The set focuses on the grafcet diagram method and can show how to program the same scenarios in different PLC models. By using PLC software components in the training set, students also get the opportunity to develop and experience their own unique SCADA (supervisory control and Data Acquisition) software with visual programming. This study also includes a sample curriculum prepared for PBL. In this way, all on-set training can be done using the PBL method and students get a 21st century experience. They also get the opportunity to improve their skills. PBL is an innovative and process-oriented learning method and is supported by the Ministry of Education (MoE). The prepared curriculum also includes the learning plan and measurement and assessment scales (rubrics). We anticipate that this package will be effective and useful in providing students with 21st century job skills and abilities in the field of industrial automation engineering, both online and face-to-face.

Comparison between commercial and open-source SCADA packages—A case study

This paper presents an implementation of automation scheme for control of three phase Permanent Magnet Synchronous Motor (PMSM) drive. Performance evaluation and monitoring of drive is described using Supervisory Control and Data Acquisition (SCADA) and Programmable Logic Controller (PLC) system. The control, operation and monitoring of PMSM drive in three basic modes, i.e. position, speed and torque modes is achieved through PLC programming and SCADA. A Human Machine Interface (HMI) control is also developed for operation of drive system at field level controls. Ladder logic programs according to desired control mode of operations are developed to control and monitor the status of PMSM drive. SCADA screens have been developed to control and monitor the status of the motor and for field level control HMI touch screens are designed. The real time data related to PMSM drive such as output of position encoder, torque developed by motor, power input to motor, speed etc. are accessed for supervisory control of drive.

Chain tensioner is one of the components of timing chain which applies force on timing chain to reduce excess slack or to maintain optimum tension in chain for transmitting drive torque uniformly. Timing chain plays vital role in proper and timely working of internal combustion engines. Chain tensioner is one of the components of timing chain. If timing chain has excess slack, the engine may goes out of valve time causing piston to collide with valves in cylinder head. Hence, it is very important to keep precise chain tension and to check the life of chain tensioner. In this work, a test rig is designed and developed to estimate the dynamic life of chain tensioner. Using this test rig, chain tensioners of various sizes can be tested. To conduct this test, initially direct drive system was proposed to operate the test machine. However, a belt drive system was used to reduce noise and vibration levels in machine during operation. Programmable Logic Control (PLC) and Supervisory Control and Data Acquisition (SCADA) are used to plot the results of experimental analysis on computer.Chain tensioner is one of the components of timing chain which applies force on timing chain to reduce excess slack or to maintain optimum tension in chain for transmitting drive torque uniformly. Timing chain plays vital role in proper and timely working of internal combustion engines. Chain tensioner is one of the components of timing chain. If timing chain has excess slack, the engine may goes out of valve time causing piston to collide with valves in cylinder head. Hence, it is very important to keep precise chain tension and to check the life of chain tensioner. In this work, a test rig is designed and developed to estimate the dynamic life of chain tensioner. Using this test rig, chain tensioners of various sizes can be tested. To conduct this test, initially direct drive system was proposed to operate the test machine. However, a belt drive system was used to reduce noise and vibration levels in machine during operation. Programmable Logic Control (PLC) and Supervisory Control and Data Acquisition ...

Abstract: In the oil and gas sector, supervisory control and data acquisition (SCADA) systems have become a revolutionary force that are transforming operations throughout the whole value chain. This in-depth analysis looks at the development, design, and various uses of SCADA systems in the upstream, middle, and downstream domains. It projects that the global SCADA market for oil and gas will grow to $4.52 billion by 2026. Real-time well monitoring, production optimization, and remote operations are made possible by SCADA systems in upstream operations, which greatly increase output and efficiency. The goal of midstream applications is to increase pipeline efficiency and safety by using predictive maintenance, enhanced leak detection, and flow optimization. By streamlining quality assurance, energy management, and process control, SCADA systems can completely transform refinery operations. SCADA deployments are not without difficulties, though, including data management complications, cybersecurity threats, and legacy system interoperability. In addition to addressing these problems, the study looks at new developments that could improve SCADA capabilities, such as edge computing, digital twins, AI and machine learning integration, and 5G technology. According to the research, SCADA is essential for promoting efficiency, safety, and innovation in the oil and gas industry. Businesses that successfully integrate SCADA technologies while overcoming implementation obstacles will be best positioned to prosper in the complicated and fiercely competitive energy market.

Developments and upgrades in the field of industrial information technology, particularly those relating to information systems’ technologies for the collection and processing of real-time data, have introduced a large number of new threats. These threats are primarily related to the specific tasks these applications perform, such as their distinct design specifications, the specialized communication protocols they use and the heterogeneous devices they are required to interconnect. In particular, specialized attacks can undertake mechanical control, dynamic rearrangement of centrifugation or reprogramming of devices in order to accelerate or slow down their operations. This may result in total industrial equipment being destroyed or permanently damaged. Cyber-attacks against Industrial Control Systems which mainly use Supervisory Control and Data Acquisition (SCADA) combined with Distributed Control Systems are implemented with Programmable Logic Controllers. They are characterized as Advanced Persistent Threats. This paper presents an advanced Spiking One-Class Anomaly Detection Framework (SOCCADF) based on the evolving Spiking Neural Network algorithm. This algorithm implements an innovative application of the One-class classification methodology since it is trained exclusively with data that characterize the normal operation of ICS and it is able to detect divergent behaviors and abnormalities associated with APT attacks.

Cyber security risk assessment for SCADA and DCS networks

The paper presents the proportional, integral and derivative (PID) digital controller based cascade control system and its experimental validation through three different testing scenarios namely, control action through Supervisory Control and Data Acquisition (SCADA), control action using Programmable Logic Controller (PLC) and interfacing of PLC with NI-OPC server. In this study, Micrologix-1200 PLC and RSVIEW-32 SCADA have been used with RSLinx communication Software. The PLC-SCADA control loop has been implemented with the functionalities such as, real time data analysis, set point modifications, automatic report generation and integration of data with MS-Excel and MS-Access. The enhancement in project data analysis is effectively done through the integration of PLC with Labview. The obtained result proved that the conventional control system can be enhanced further more with PLC as well as NI-OPC server significantly.

<span>The purpose of this article is to describe the development of a prototype of a monitoring and control system for a fire-fighting system, implemented through SCADA (supervisory control and data acquisition) for which the OPC (OLE for process control) is known. Known KepServerEX6 (single data source connectivity platform), with which the integration of the PLC (programmable logic controller), the human-machine interface (HMI) and the computer will be controlled, with the application of monitoring the operating parameters of a fire fighting system. These parameters are the pressure and flow of the electric pump, the values of the parameters are regularly controlled automatically by means of a frequency variator, according to the sprinkler actuation, the variator varies its operating frequency until achieving the optimal pressure and flow values. The level of correlation between the indicators to be monitored by the SCADA system is highly significant, equal to 0.977, 0.983 and 0.997 respectively. In addition, the dispersion model of the indicators monitored by the SCADA system will be determined, is the quadratic, with a determination factor of 0.999.</span>

Electricity consumers, in an effort to reduce electricity bills and become more environmentally friendly, are often installing solar power plants. Therefore, the amount of additional power electronics increases and affects the energy quality due to increasing current harmonics. Due to the increase in equipment such as frequency converters and soft-start drives, the quality of the supplied electricity deteriorates, thus causing additional losses in the transmission equipment. The purpose of this work is to develop a calculations algorithm in a programmable logic controller that would help to estimate what losses are incurred in a transformer due to current harmonics. Programmable logic controller will be able to exchange data with power analyzers and energy management (EMS) or Supervisory control and data acquisition (SCADA) systems using the Internet of Things.

In this research study, an automated design is introduced for the Photovoltaic (PV) Maximum Power Point Tracking (MPPT) Technique using a Programmable Logic Controller (PLC) S7-1200 type. The perturb and Observe (P&O) algorithm was employed to track the maximum power point continuously and reduce output fluctuations. This innovative design seeks to address the usual constraints associated with standard P&O-MPPT algorithms by harnessing the power of PLC automation. The system was meticulously modelled and simulated using the Siemens Automation program (TIA Portal) with the use of a ladder and Structured Control Language (SCL) languages, depicted as a function block. Furthermore, an experimental prototype was developed employing a PLC as the control unit to authenticate the accuracy and effectiveness of the P&O algorithm’s outcomes with a DC-DC Boost converter. The system’s performance was compared under varying meteorological conditions. Additionally, a Supervisory Control and Data Acquisition (SCADA) screen was developed for periodic monitoring and displaying results on the PC screen. The system has proven to be highly efficient in tracking maximum power, periodic monitoring, and displaying results on the computer screen.

The supervisory control and data acquisition (SCADA) systems monitor and control industrial control systems in many industrial and economic sectors such as water treatment, power plants, railroads, and gas pipelines. The integration of SCADA systems with the internet and corporate enterprise networks for various economical reasons exposes SCADA systems to attacks by hackers who could remotely exploit and gain access to SCADA systems to damage the infrastructure and thereby harming people’s lives. The simplicity of datasets and possible overfitting of models to training data are some of the issues in the previous research. In this paper, we present detecting and classifying malicious command and response packets in a SCADA network by analyzing attribute differences and history of packets using k-means clustering. This study presents a solution to classify SCADA cyber attacks to detect and classify SCADA attacks with high accuracy using a big data framework that comprises of Apache Hadoop and Apache Mahout. Apache Mahout’s random forest classification algorithm is applied on SCADA’s gas pipeline dataset to categorize attacks. When 70% of the data is used for training the classifier, our approach resulted in 5–17% improvement in accuracy for the classification of read response attacks and 2–8% improvement in accuracy for write command attacks with respect to using the original dataset.