The manufacturing industry is undergoing a paradigm shift. Because of increasing connectivity, we can gather a lot of data from manufacturing systems for the first time in history. The increasing connectivity also enables the linking, analysis, and performance optimization of supply chain components, even if they are geographically dispersed. The AI-enabled Manufacturing and Maintenance Lab (AIMM) aims to accelerate developments in this field using Artificial Intelligence. In this interview with Geert-Jan van Houtum, we will take a surface dive into some complex challenges in predictive maintenance.
Prof. Geert-Jan van Houtum holds a position as a professor of maintenance and reliability at the Industrial Engineering and Innovation Sciences (IE & IS) department at Eindhoven University. His expertise includes maintenance optimization, inventory theory, and operations research, focusing on system availability and Total Cost of Ownership (TCO).
EAISI AIMM Lab is a collaboration between Eindhoven University Technology, KMWE, Lely, Marel, and Nexperia.
What is predictive maintenance, and what is its purpose?
Traditionally, businesses either replace components when they fail, so-called ”reactive” maintenance, or use lifetime estimations to determine the best moment for maintenance, called age-based maintenance. Usually, reactive maintenance leads to machine downtime, while age-based maintenance is accompanied by the risk of replacing expensive components too soon. Predictive maintenance aims to be more proactive. Using data and AI, we can start actively monitoring the condition of components in real-time; it allows us to predict more accurately when a component is on the verge of failure and needs replacing.
What is the role of data analysis and AI in predictive maintenance?
For many components, you know why they deteriorate over time. You know the failure mechanism, and how to measure the component’s condition. For instance, when you drive a car, you know that the profile on the tire wears down. You can regularly check to see if the amount of profile is still within safety limits and replace the tire if deemed necessary.
There are also components where the failure mechanism is known, but the best way to measure the component’s state is unknown. Before predictive maintenance can be used in these situations, it is required to find a way to measure its state. Artificial Intelligence may be used as part of an inspection solution, such as visual inspection using computer vision, but this is not always necessary or desirable.
Finally, there are cases where the failure mechanism is unknown or has not yet been accurately mapped. Here the first step is to conduct a root-cause analysis. By collecting large amounts of data on all possible root causes, you can try to match patterns in the data to failure cases. Here, data analysis and artificial intelligence play an important role because they provide critical insights into the data that can be interpreted to create knowledge. This process drives innovation.
What is the most challenging aspect of determining the root cause using data?
Many failure mechanisms either occur infrequently or only under specific conditions. In these cases, there is simply insufficient data to perform data analysis or train a neural network, making it incredibly difficult to identify the root cause. Honestly, those situations are real head-scratchers.
Nonetheless, some businesses have found great success using anomaly detection algorithms. Such algorithms identify perturbations of normal behavior, which indicate the presence of a defect or fault in the equipment. Before Artificial Intelligence gained relevance, statistical process control was the gold standard for measuring anomalies. Through the integration of AI-based techniques, anomaly detection has become more refined and gives more intricate insights into the nature of anomalies.
What does AI research in manufacturing and maintenance mean to the world?
When equipment and manufacturing lines do not function properly, it leads to disruptions throughout service and manufacturing supply chains. This runs back all the way to the consumer. It is accompanied by pressure on the environment, increased cost of serving the customers in an alternative way, and in some cases unavailability of life-saving equipment or medicine. AI technologies allow us to do more with less. For instance, predictive maintenance allows us to avoid possibly catastrophic equipment failures while preventing unnecessary maintenance. It is the perfect combination of the financial incentive of businesses, societal values, and the Sustainable Development Goals (SDGs).
Is applying predictive maintenance techniques always more beneficial than more traditional forms of maintenance?
Initial investments, as well as the running costs for predictive maintenance solutions, are significant. Therefore, right now, predictive maintenance is most valuable to businesses that suffer large losses when their equipment fails or when equipment failures cause safety concerns. By working together with industry partners in our Lab, we ensure that our solutions are not only technically feasible and novel but also adhere to societal, industrial, and financial requirements. Predictive maintenance will play a large role in the manufacturing industry, but developments go slowly and it will not replace all traditional maintenance.
On the 15th of September, 2022, Geert-Jan will speak about a predictive concept for geographically dispersed technical systems during our Labs Meetup on AI for Autonomous Systems in the Netherlands. Want to join? Sign up here