Fault diagnosis for uncertain systems in closed loop: applied to semiconductor equipment
In high-tech industries, the productivity and economic value of complex machines is directly tied to its reliability and uptime. Unfortunately, without proper maintenance, machines are bound to break down at some point due to defects, aging parts, or general wear and tear.
When these failures happen unexpectedly, they lead to costly downtime and a significant loss of productivity. To avoid this, it is essential to detect faults early and address them before they lead to serious disruptions.
Preventive maintenance
Traditionally, industry has used preventive maintenance - performing repairs at regular intervals - or reactive maintenance, fixing equipment only after failures.
However, these approaches are both costly and inefficient. Predictive maintenance, which involves detecting potential faults in advance, offers a more proactive way to minimize equipment downtime.
As machines become more advanced, they are equipped with a growing number of sensors, actuators, and components. This results in an enormous volume of available data.
While Artificial Intelligence (AI) and Machine Learning are powerful tools for processing this data, deep engineering insight and a solid understanding of physical principles are crucial to truly grasp the underlying root cause of faulty behavior and respond effectively.
Physical models, which are typically developed or estimated during the design and integration phases, are available even before machines are commissioned, but are generally left unused.
For his PhD research has shown that leveraging these models with data generated by the machines presents a significant opportunity for more effective monitoring and optimized maintenance strategies.
Mechatronic production equipment
At the heart of predictive maintenance for mechatronic production equipment are model-based fault detection and isolation (FDI) systems.
These systems rely upon mathematical models and simulate normal equipment behavior, enabling real-time predictions of machine health. By continuously comparing expected behavior versus actual behavior, FDI systems can detect small deviations that indicate potential upcoming failures.
The ability to process data in real time allows for nearly instantaneous fault detection, which is crucial for high-performance machines, where even brief downtime can lead to significant financial losses.
These diagnostics systems are integral to the broader digital twin concept, providing not only predictive capabilities but also deeper insights into normal machine behavior. This allows engineers to quickly diagnose issues and guide maintenance teams to the precise source of problems.
Continuous monitoring
In summary, Classens鈥� research presents an approach on leveraging available data and models for continuous monitoring of mechatronic production equipment.
The developed algorithms are implemented on industrial semiconductor applications which illustrates the effectiveness for complex high-tech systems.
By detecting faults early and scheduling maintenance before breakdowns occur, businesses can reduce costly downtime, increase productivity, and ensure the longevity of their equipment.
As machines become more complex and the financial stakes increase, these diagnostics systems will play an even more critical role in maximizing productivity and keeping production equipment running smoothly.
Title of PhD thesis: . Supervisors: Tom Oomen and Maurice Heemels.