Predictive maintenance: Maximise uptime and efficiency

Imagine saving thousands of euros and extending the life of your machinery with just a few tweaks to your maintenance strategy, all while contributing to sustainable manufacturing practices.

In an Industry 4.0 landscape where unexpected equipment failure can lead to costly downtime, being able to foresee and prevent these failures is invaluable. But, implementing predictive maintenance isn’t without its challenges. From ensuring data quality to navigating cybersecurity issues, there are several hurdles to overcome. And that’s where our expertise comes in.

Today, we explore the complexity of predictive maintenance, guided by our expert, Toine Vos, senior consultant in Instrumentation and Process Automation. You will learn how to harness the power of Artificial Intelligence (AI) and Machine Learning (ML) to forecast equipment failures, optimise maintenance schedules and improve production efficiency as part of your broader sustainable operations strategy.

What is predictive maintenance?

Predictive maintenance (PDM) is a technique that forecasts when equipment is likely to fail or malfunction. This method relies on advanced technologies like AI and ML, which process extensive historical data. By using these sophisticated tooling, you can perform maintenance exactly when needed, avoiding unexpected failures and extending the life of your equipment.

Benefits of predictive maintenance

With a solid foundation of high-quality data, predictive maintenance offers several key benefits that can greatly improve your production facility’s efficiency and reliability.

Quote: By predicting equipment failures before they happen, it allows production to continue smoothly without unexpected interruptions, minimising downtime and optimising resource allocation.

Additionally, regular monitoring and timely interventions prevent minor issues from escalating into major problems, extending your machinery’s lifespan and preserving its integrity. This protects your investment and supports sustainability by reducing energy consumption and waste, making it an important component of sustainable operations.

Challenges and pitfalls

While the benefits of predictive maintenance are clear, implementing it is a strategic move that comes with its own set of challenges.

One of the primary challenges is the quality of data used by AI and ML algorithms. These technologies rely heavily on high-quality historical data to make accurate predictions. If the data is inaccurate or incomplete, the predictive models will likely produce unreliable results. This can lead to missed failures or false positives, both of which can disrupt your maintenance strategy and affect production efficiency.

Therefore, verifying the data and the algorithm’s results is important to build trust in the system. Regular audits and data verification processes help maintain data integrity, providing a solid foundation for predictive maintenance and contributing to sustainable manufacturing practices.

Scaling and evaluation

It is better to start small and define a pilot project first. Involve more equipment after positive evaluations. An implementation strategy should be set up with a clear vision for the future to ensure long-term success and sustainable operations within a smart factory framework.

Resources and training

The new techniques involved in predictive maintenance are innovative and require a new domain of knowledge and experience. Your maintenance and engineering team must be trained and equipped with the necessary skills to work with these tools. Additionally, new specialists may be needed to strengthen your team and ensure the successful implementation of predictive maintenance.

The role of cybersecurity

Incorporating predictive maintenance into your production facility also involves addressing cybersecurity challenges. Cybersecurity measures must be robust enough to guard against unauthorised access while still allowing seamless usability for authorised personnel. Balancing these needs can be challenging but is vital to protect sensitive information and maintain the reliability of your predictive maintenance system.

Implementing strong encryption, regular security updates, and comprehensive access controls can help mitigate cybersecurity risks and ensure the integrity of your maintenance data.

How we can help

At Royal HaskoningDHV Plant Engineering, we work collaboratively in a multidisciplinary team to address all these challenges and achieve optimal outcomes. Here’s how we can assist you:

• Instrumentation engineers: We follow up on the process engineer’s design to analyse which sensors and measurements can be added to monitor equipment conditions continuously. It’s important to avoid adding too many costly sensors. They also require regular maintenance, testing, and calibration.
• Process automation engineers: We define the control narrative based on the process narrative created by the process engineer. We also specify the required types and quality of data and data storage to create a database for storing valuable historical monitoring data. Additionally, we analyse integration and address gaps between different equipment suppliers. We set up the system automation architecture to support daily monitoring and control of the production facility and collect valuable data for predictive maintenance techniques.
• Data scientists: Our experts design AI and Machine Learning algorithms tailored to your specific needs, ensuring reliable and actionable insights from the data. We ensure that the algorithms are accurate and effective, providing you with the best possible predictions to support your goals in sustainable manufacturing and Industry 4.0.
• Cybersecurity specialists: We ensure that predictive maintenance systems are secure and protected from potential breaches. Our team also makes sure that your data is safe during access, storage, and processing.

Predictive maintenance in fiberglass production industry equipment

Get in touch

Our team can help you design and implement predictive maintenance solutions tailored to your needs. Contact us today to improve your production efficiency and enhance your sustainable operations within the context of Industry 4.0 and the smart factory.