Predictive Maintenance in SCADA-Based Industries: A literature review

The convergence of Supervisory Control and Data Acquisition (SCADA) systems with the Internet of Things (IoT) and Machine Learning (ML) is redefining automation, security, and operational efficiency across industries. Traditional SCADA systems, widely used in critical infrastructure such as energy, water management, and industrial processes, are undergoing a transformative shift with the integration of IoT. IoT-enabled sensors and devices provide real-time data streams from diverse operational environments, enabling SCADA systems to achieve enhanced situational awareness and remote monitoring capabilities. Machine Learning further augments SCADA systems by introducing advanced analytics for predictive maintenance and anomaly detection. ML algorithms analyse vast datasets collected through IoT devices to forecast system failures, optimize resource utilization, and ensure uninterrupted operations. This predictive approach minimizes downtime, reduces maintenance costs, and improves overall efficiency. Cloud-driven security frameworks are pivotal in addressing the growing cybersecurity challenges associated with SCADA and IoT integration. These frameworks provide scalable and resilient solutions for protecting data integrity, preventing unauthorized access, and mitigating cyber threats. The deployment of ML-driven cybersecurity solutions enhances the ability to detect and respond to sophisticated attacks targeting SCADA environments. This paper examines the evolution of SCADA systems in the era of IoT, focusing on the transformative role of ML and cloud-driven security. It explores innovative applications, highlights the challenges of integrating these technologies, and provides insights into future advancements for creating robust and secure automated systems.

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.

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.

Проаналізовано особливості застосування систем SCADA (Supervisory Control And Data Acquisition) та технологій Інтернету речей в умовах переходу промисловості до Індустрії 4.0. Показано, що використання веб SCADA забезпечує більшість вимог до таких систем та є масштабованим рішенням на всіх її рівнях. Розроблено та реалізовано веб-SCADA, компоненти якої працюють на одноплатних комп’ютерах типу Raspberry Pi, визначені вимоги до технічних характеристик компонентів системи.

Artificial Intelligence (AI) is revolutionizing industrial process automation by introducing intelligent decision-making and adaptive control to traditionally deterministic systems. In the petrochemical and oil & gas industries where safety, efficiency, and reliability are paramount, AI technologies such as machine learning, deep learning, and digital twins enhance plant operations through predictive maintenance, process optimization, and asset integrity management. Despite challenges in certification, data quality, and cybersecurity, AI continues to evolve as an indispensable enabler of smart and self-optimizing industrial plants. This research examines the integration of AI within programmable logic controllers (PLCs), distributed control systems (DCS) and supervisory control and data acquisition (SCADA) frameworks, the improvements it brings in efficiency, energy management, and maintenance scheduling as well as examines the real-world implementations from major automation vendors such as Honeywell, Emerson, Yokogawa and Siemens.

For monitoring the energy supply from the hybrid small-scale wind turbine generator (WTG) and rooftop solar Photovoltage (PV) systems, this paper presents the design of a management program of the studied system based on the Internet of Things (IoT) technology. The proposed studied system consists of digital power meters that communicate wirelessly to the Programmable Logic Controller (PLC) through the ZigBee communication standard. By using a free cloud platform will greatly facilitate the Supervisory Control and Data Acquisition (SCADA) interface design work for a Human Machine Interface (HMI) or mobile phone. This system configuration may be easy to be fitted for collecting electrical information such as voltage, current, power, frequency of the system to be monitored. This is one of the cheap solutions deployed in small-scale hybrid power systems (HPS) or factories because wireless communication is very convenient in construction and installation.

This paper deals with the design and implementation of an internet of things (IoT) based supervisory control and data acquisition (SCADA) system for a multi microgrid system. The individual microgrid in this work is suggested to be consisting of solar photovoltaic (PV) array, battery bank, and diesel generator, in addition to national grid. IoT is used to provide a suitable wireless means to transfer the monitoring and control data between the generation subsystems and the microgrid controller. SCADA architecture that make use of IoT infrastructure enables efficient and reliable power sharing and also give a clear image about the system status and subsystem failure and power shortage. This system gets benefit of various sensors, including current, voltage, power, and temperature sensors, that are used to collect data from of the PV array and battery bank, and also gives clear image about power demand in each microgrid. The microcontroller ESP32 is used as remote terminal unit (RTU) controller in SCADA system in order to get data from sensors as well as to communicate with the master terminal unit (MTU). By using Cayenne remote server IoT platform deploy the message queuing telemetry transport (MQTT) protocol to store, monitor, and manage data from a MTU. The designed system is low-cost and effective system to monitor and control microgrids that contain many distributed generators.

This paper discusses the new auspicious trends of internet of things (IoT) and its advanced developments in various sectors of today’s IT arena, including for industrial sectors. Each object acts as a communication node (or entity) that is able to communicate with other nodes, and corresponding information could be accessed and controlled, via several of today’s commonly used electronic devices such as laptops and cellular devices, etc., through an IoT-designed platform. Moreover, in industrial automation sectors, the employment of IoT is very advantageous, in terms of remote communication support; low-cost operations and maintenance; and autonomous collaborations among remote networked field devices, etc. The facility of autonomous collaborations provisioned by IoT, in the context of a supervisory control and data acquisition (SCADA) system as part of an industrial control system, somehow exists in a distributed network protocol (DNP3) during automation and controls. Therefore, the current study takes the step to model a new IoT framework for a SCADA system, which will be efficient at facilitating industrial automation through the collaborative DNP3-Modbus acquisitions and automation, called the SCADA-IoT system. An IoT gateway is employed and configured that supports for both SCADA protocols, such as DNP3 and Modbus and is efficient at communicating, from networked field devices, with inter-processing from both. In the overall SCADA-IoT design, the transmission is carried from an enormous amount of sensors and/or field devices, employing proprietary and nonproprietary protocols; further, sent information is analyzed via big data, stored in a cloud center, monitored and controlled over a SCADA-IoT supportive platform. At the same time, information security (IS) is a big associated challenge and one of the main contributions of this study. Thus, this study also analyzed the potential security mechanisms for securing SCADA-IoT and found cryptography to be a noteworthy security solution, based on the proposed system requirements and its communication demands.

The traditional industrial factory automation, the so-called SCADA (Supervisory control and data acquisition), based on OPC (OLE for Process Control) technology. In recent years, under the impact of information technology, OPC technology is now actively updated to the new standard OPC UA (Unified Architecture). SCADA and the Internet of things, in essence, are the data capture and display, there are many overlapping features. Based on the JustIoT Internet of Things system, this paper designs the bridge hardware and software to bring the operation data of old factory automation equipment into the JustIoT with OPC client or the TCP Modbus protocol. The main feature of HMI SCADA is real-time map monitoring and control. This research adds that feature to JustIoT. Tools are added to JustIoT to let users create HMI page, add graphics and animation gif, and set up the association between pictures and data points. Then, remote monitoring and control can be performed. The cloud supervisory control system has been successfully applied in the offshore wind power monitoring.

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.

SCADA (Supervisory Control and Data Acquisition), these are systems based on real-time processing, control and design for real time infrastructure such as water pumping stations, oil purification plants and rail control centers etc. In the initial structure of SCADA there is no security mechanism that prevents or detects the attacks in SCADA communication network. SCADA technology is not new but was deployed in limited networks; currently these systems have been deployed and accessible by many networks such as LAN/WAN, Mobile networks, Web Browsers and GPRS through internet technology. Using Modern communication facilities SCADA systems are vulnerable from different types of internet attacks that create major security problems for SCADA communication. The current paper proposes cryptography solutions for securing SCADA communication. In first part, we propose Cryptography solution using Advance Encryption Standard (AES) and SHA-1 algorithms to secure each end of SCADA communication by implementing security services such as authentication, integrity, and confidentiality In Second part, we propose hybrid cryptography solution for SCADA and give solution to prevent the major attacks that in- Securing the SCADA communication. Hybrid Cryptography Secure Simulation Model is used to discuss and give solutions for putting security services such as authentication, integrity, confidentiality, and non-repudiation in APDU (Application Protocol Data Unit) and LPDU (Link Protocol Data Unit) within SCADA DNP3 protocol, and Rest of paper provide solutions to overcome/prevent attacks such as Guessing Shared Key, Brute force from Authentication; Eavesdropping, Key Cracking, Man in the Middle from Confidentiality; Frame Injection, Data Replay, Data Deletion from Integrity. We use DNP3Sec as framework. Current paper gives research Directions and methods to more secure real time systems.

In the modern industrial control landscape, Supervisory Control and Data Acquisition (SCADA) systems serve as critical intermediaries in automating and managing complex processes across many industries, including energy, manufacturing, and utilities. While maintaining the availability and efficiency of these systems is essential, their increased integration into networked environments has exposed them to a growing array of cyber threats. As SCADA systems continue to grow and be deployed globally, malicious actors are increasingly target critical infrastructure, making security management progressively more challenging. Our research explores the benefits of conducting forensic investigations focused on SCADA Human-Machine Interface (HMI) systems, emphasizing how centralized equipment analysis can help overcome the unique challenges these systems present. To assist investigators in efficiently collecting system-wide data from SCADA HMI environments, we developed a forensic analysis framework that enables the evaluation of system discrepancies and evidence of potential cyberattacks. Additionally, we present a comprehensive forensic analysis of a SCADA testbed featuring Ignition, a popular SCADA software platform developed by Inductive Automation. Our investigation reveals that SCADA HMI forensics can provide valuable insight into the system's state, allowing for the identification and cross-analysis of critical equipment and operational data across memory, network, and disk. This framework is further supported by the creation of the Ignition Forensic Artifact Carving Tool (IFACT), a SCADA HMI Forensic Analysis Tool designed to assist in extracting artifacts and parsing system information from forensic data sourced from Ignition HMIs. Using IFACT, we achieve accurate insights into device and equipment data in SCADA systems, investigating data persistence in volatile memory and the variability of its lifetime based on the system state.

In supervisory control and data acquisition (SCADA) systems or the Internet of Things (IoT), human machine interface (HMI) performs the function of data acquisition and control, providing the operators with a view of the whole plant and access to monitoring and interacting with the system. The compromise of HMI will result in lost of view (LoV), which means the state of the whole system is invisible to operators. The worst case is that adversaries can manipulate control commands through HMI to damage the physical plant. HMI often relies on poorly understood proprietary protocols, which are time-sensitive, and usually keeps a persistent connection for hours even days. All these factors together make the vulnerability mining of HMI a tough job. In this paper, we present EUFuzzer, a novel fuzzing tool to assist testers in HMI vulnerability discovery. EUFuzzer first identifies packet fields of the specific protocol and classifies all fields into four types, then using a relatively high efficiency fuzzing method to test HMI. The experimental results show that EUFuzzer is capable of identifying packet fields and revealing bugs. EUFuzzer also successfully triggers flaws of actual proprietary SCADA protocol implementation on HMI, which the SCADA software vendor has confirmed that four were zero-day vulnerabilities and has taken measures to patch up.

Supervisory Control and Data Acquisition (SCADA) is used in modern power system for monitoring, controlling, gathering the information, transfer to information back to a central station, carry out necessary analysis and control carry out necessary analysis and control. A SCADA system involves the integrated technologies of computer, communication, electric devices etc. Here the communication media provides the physical medium for transferring, controlling and acquisition of data within SCADA from remote locations. Although wireless communications media become an attractive option as communications networks for SCADA system, compared with wired communications, wireless communications in power system environments presents a number of security and reliability issues. As in SCADA system the communication media plays an important role, to meet objectives of the SCADA systems, different wired and wireless communications media have been applied in modern power system environment. This paper presents a concept about SCADA system using Cognitive Radio based IEEE 802.22 standard for overcoming all existing limitations of SCADA's communication media for future power system where details design, analyses of feasibility and benefits over wired and other wireless systems are also described.

The need for renewable energy has led to a fast increase of number of the wind turbines constructed each year. To monitor wind turbines farms, operating and maintenance managers need new effective and automatic tools compatible with a large number of wind turbines. This monitoring task is usually completed by Condition Monitoring System, but researches have been conducted on the utilization of SCADA (Supervisory Control And Data Acquisition) data for condition and predictive maintenance. This paper explains the difficulty of using this new source of information, and introduces the different techniques presented in the literature form 2001 up to 2014. Two classes of approaches can be identified: “internal” approaches using only data from one turbine, and “external” approaches relying on the comparison of one turbine to the other within the same farm. Both approaches have different pros and cons: internal approaches make use of the link between the components in the same turbine and so reduce the influence of operating conditions on fault indicators; external approaches make use of the correlation between SCADA variables measured on different turbines and can thus reduce the influence of wind conditions on the fault indicators. This paper sums up the latest available techniques and it shows that new areas of research can be explored with SCADA data. The obtained fault indicators still remain sensitive to the operating conditions and stochastic variations of the wind load. Combining advantages of the two approaches could reduce both influences.