Are unexpected equipment failures causing downtime and costing you money? It’s time to take your facility to the next level with the latest approach to Industry 4.0, the Fourth Industrial Revolution, which emphasizes automated processes and data-driven solutions.
As an industrial operator, it’s crucial to stay ahead of the game and make informed decisions for your plant or facility. That’s where Industrial Service Solutions (ISS) comes in with our Spectare® Intelligent Platform and a total systems approach. We’ve identified three major digital solutions trends that are transforming facility operations worldwide–let’s dive in and see how you can avoid equipment failures and save costs.
1. Digital twin technology
Mirror, mirror on the wall, who’s the most informed of them all? With digital twin technology, an integral part of Industry 4.0, industrial operators can create a virtual reflection of their physical objects for simulations and testing processes, allowing them to make better decisions and mitigate risk without having to disrupt their processes. The use of digital twins is revolutionizing the game with its ability to provide accurate simulations and real-time status updates.
What is a digital twin?
A digital twin may sound like a complicated concept, it is simply a copy of a physical object that is simulated with life-like characteristics. This twin will operate as a digital copy that can be used in simulations or in real-time status updates. The applications of digital twin technology is broad, as it can be used to simulate many different devices.
For example, a copper mine recently used digital twin tech to create a digital duplicate of the mine’s grinder circuit to allow for optimal use control. By creating a more efficient monitoring process for this circuit, the mine’s operations can move forward with less interruptions in their workflow. This is just one example of how a digital twin can save time, energy, and money for facilities.
Key considerations for implementing a digital twin:
- Effective implementation requires a significant amount of data.
- Proper data collection through sensors is necessary for mapping and real-time updates.
- Quality of data must be carefully managed.
- Machine learning is crucial for mapping the effects of stressors and energy usage on a system.
- Digital twin technology is particularly effective in real-time simulation and monitoring of processes.
Digital twin technology is a game-changer for any industrial facility that needs real-time simulation and monitoring of complex processes. Implementing the Spectare® Intelligent Platform to simulate specific parts of a system can save time, energy, and money.
2. Artificial intelligence (AI) and machine learning
Looking for a way to optimize cost savings, create sophisticated prediction models, and take a total systems approach to monitoring your industrial facility? Look no further than the increasing number of use cases for artificial intelligence and machine learning algorithms in industrial systems.
As AI technology advances, its industrial applications are becoming more effective. Machine learning, a subcategory of AI, involves algorithms that learn from data to make predictions and decisions. With machine learning, these algorithms become better at solving problems as more data is processed. It’s no surprise that many companies are integrating AI-driven systems into their facilities.
What are AI and machine learning?
But what exactly is the difference between AI and machine learning? While AI is a broad category of technology, machine learning is a specific application that involves algorithms that can learn from data. For industrial systems, machine learning is especially effective at cost-saving and decision-making processes.
Prestige Metal Recycling in Houston, Texas, is applying ISS machine learning technology to reduce downtime and improve the safety of its automobile shredding facility. Multiple cameras monitor the shredder infeed belts, enabling the system to automatically identify objects that could jam the system or cause an explosion. By implementing machine learning, Prestige is increasing operational efficiency while allowing site crews to focus on other tasks.
Key considerations for implementing AI/machine learning:
- High-quality data is necessary for effective machine learning processes.
- Choosing the right algorithms for each specific use case is crucial for providing necessary information.
- Consulting with integration teams can help make informed choices about which processes to apply machine learning.
- Integration of machine learning algorithms is part of a total systems approach to automation and system monitoring.
- Machine learning algorithms learn from the constant stream of sensor data to execute automated processes and make recommendations.
The increasing use cases for AI and machine learning algorithms in industrial systems offer operators a powerful tool for optimizing cost savings, creating sophisticated prediction models, and taking a total systems approach to monitoring their facilities. As AI technology continues to advance, the potential benefits for industrial operations are only set to grow.
3. Internet of Things (IoT) and Industrial Internet of Things (IIoT)
Looking to optimize your facility operations with cost savings and error reduction? The Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT), may be just what you need. While IoT is often associated with household appliances, its applications for industrial systems are extensive and revolutionizing facilities across the globe.
What is IoT/IIoT?
IoT is a network of interconnected sensors, software, and technology that constantly share data to enhance system performance. Meanwhile, IIoT is an application of IoT that is specifically designed for cost-savings and error reduction in industrial settings.
Applications are well underway for IIoT in plant systems. A manufacturing plant in Suzhou, China, has already implemented IIoT technology, resulting in significant cost-cutting effects and efficiency improvements. As more manufacturers adopt IIoT measures, facilities worldwide are being optimized and their operations are being improved.
Key considerations for implementing IoT/IIoT:
- Establishing infrastructure to ensure effective data transfer between sensors and devices.
- Choosing the right types of sensors for collecting and transmitting data effectively.
- Setting up network infrastructure with connectivity parameters that allow for high bandwidth and consistent connection.
- Implementing security protocols and cybersecurity measures to protect data privacy in a fully shared system.
- Ensuring the integrity of the network housing an IIoT to achieve efficient automation.
By establishing the right infrastructure, selecting the right sensors, setting up proper network connectivity and security, and ensuring network integrity, manufacturers can fully leverage the benefits of IIoT technology. As more and more facilities adopt IIoT measures, the potential for increased efficiency and cost savings in industrial settings continues to grow.
Conclusion
As the industrial landscape undergoes a digital transformation, it’s essential for operators to keep up with the trends that can save them costs and prevent equipment failure or downtime. Digital twin technology, machine learning, and the industrial internet of things are quickly becoming vital components of Industry 4.0.
To integrate these trends into operations, a total systems approach that uses data holistically is essential. ISS’ Spectare® Intelligent Platform offers effective condition monitoring and process optimization to achieve this.
Industrial Service Solutions works with manufacturers to help them navigate the many changes involved with automation for optimal results. Contact the team at ISS today and let us help your facility embrace a digital transformation and thrive in the Industry 4.0 era.