Control systems are crucial to many industries, allowing for automated processes, more efficient operations, and reducing human error. As manufacturing evolves, new technologies are becoming available to improve such control systems. Here are three examples of advanced control systems technologies in use today.
Industrial IoT and control systems
One of the advanced control systems technologies is industrial IoT. IoT stands for the Internet of Things, a collective network of devices. Devices in an IoT network include anything that connects and exchanges data with other devices and systems.
The devices within the network do not need to be connected publicly to the internet, but they must be connected to a network, able to transmit data, and individually addressable. Devices in an IoT network will have sensors, software, and other technology capable of data exchange.
Industrial IoT is an area of IoT that focuses specifically on industrial industries, such as automotive or aerospace. It is similar to IoT and includes interconnected devices within a network. However, industrial IoT is networked with industrial applications.
Connecting industrial devices within a single network for data transmission improves operations’ efficiency and efficacy. Data collection, analysis, and exchange are much more straightforward. As a result, clients can benefit economically and with improved productivity.
Implementing industrial IoT
For industrial IoT to work effectively, multiple technologies must be combined. Some necessary technologies include big data, robotics, cloud platforms, machine learning, and M2M communication.
The goals, processes, and principles of Industrial IoT are different from standard IoTs. As a result, control systems recruitment requires specialist skills and experience in such technical systems. Implementing industrial IoT provides many benefits to businesses, including:
- Increased efficiency and line optimisation.
- Reduced errors.
- Increased work safety.
- Predictive maintenance.
- Greater field service efficiency.
- Increased customer satisfaction.
- Improved facility management
While industrial IoT has existed for some time, it is becoming more widespread due to the growth of real-world adoption. The rise of 5G networks will also increase adoption rates for such advanced control systems technologies.
However, security is one of the biggest concerns with industrial IoT since early IoT device manufacturers paid little attention to it. Many big manufacturers, including Cisco, are part of the IoT Consortium, which prioritises security in developing IoT devices.
Machine learning in control systems
Machine learning is another advanced control systems technology. It involves developing systems that can adapt and learn without operation.
Traditional control systems require pre-defined algorithms and models. However, machine learning uses a data-driven approach to enhance performance. This makes machine learning control systems ideal for complex and dynamic environments.
Machine learning can be implemented into various control systems in various industries to ensure efficiency, adaptability, and optimisation. The main application of machine learning is to help improve the reliability and efficiency of control systems.
The first of these key applications is adaptative control. Using real-time data, control systems can adapt and learn via machine learning. This means systems can adjust behaviour in response to changing conditions.
Fault detection and diagnosis are another key application of machine learning in control systems. It enables analysis of vast quantities of data, allowing control systems to identify fault patterns or anomalies.
Identifying such occurrences allows for the early detection of faults and issues. Suitable preventative maintenance steps can then be taken to reduce downtime and improve system reliability. Industries, such as power and manufacturing, can benefit most from reduced costs and improved safety.
The other key application of machine learning in control systems is optimisation. Through analysing data machine learning algorithms can learn strategies for optimal performance. This is most useful for a constant, balanced supply in the power industry.
However, three factors are required for machine learning in control systems to be effective: large quantities of data, model interpretability, and robust real-world scenarios. With ongoing research and development, machine learning is quickly becoming integral to many industries.
Cybersecurity in control systems
Traditionally, control systems placed little importance on security. However, as cyber-attacks are becoming increasingly complex, there is a growing need for cybersecurity measures in control systems.
Control systems crucial to infrastructure in prime industries, such as industrial, power, and transportation, have become prime targets for cyber threats.
Given the interconnected nature of devices in control systems and the reliance on digital components, such networks are now a prime target. Gaining unauthorised access and manipulating control systems is a top concern due to their damaging effects.
Hackers and criminal organisations seek vulnerabilities in control systems that they can exploit. Once they’ve gained access to a control system, they risk disrupting operations, compromising data, or damaging it.
Such occurrences can lead to significant financial losses and operational disruptions to organisations. In some cases, this can also result in a serious threat to public safety. To ensure the safety of control systems, there are six key steps:
- Network security parameters.
- Workstation hardening.
- User account management.
- Security updates.
- Backup and recovery.
- Security monitoring and risk assessment.
Implementing secure control systems
Fundamentally, the security of control systems relies heavily on implementing robust access control and authentication mechanics. This ensures that only authorised people can access control systems and their critical components.
Implementing encryption protocols can also help reduce the risk of manipulation and compromised data. Data encryption ensures that transmitted data is secure. Additionally, control systems should receive regular system and security updates.
Cybersecurity efforts need to adapt to the changing landscape to ensure the future safety of control systems. Continuous monitoring, anomaly detection, and threat intelligence will help to reduce the risk.
Such efforts must focus on current and emerging threats to ensure control systems’ safety and prevent loss. Both manufacturers and users of control systems need to identify and address vulnerabilities promptly to avoid such threats.
Advances in control systems technology provide many benefits to businesses. From streamlining operations to improving efficiency, there are many reasons why adopting such advanced technology is beneficial. However, staying alert to current cybersecurity threats is essential. Given the highly skilled nature of advanced control systems technology, technical recruitment seeks to find experienced and qualified engineers in the control systems job market to design, implement, and manage such systems effectively.
