In this blog post, we explore artificial intelligence in maintenance: what it can do and practical examples that help understand the potential of artificial intelligence in maintenance.
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Maintenance management is the predictive and reactive maintenance of equipment and systems. While the goal of maintenance is to ensure the equipment reliability and efficiency, there are several challenges that can disrupt the flow of activities.
One of the central challenges is the complexity of data management. Reliable and up-to-date maintenance data is the foundation of the activities of a maintenance organization, and as technology advances, the amount of data generated by the production process is constantly growing. For example, an increase in the number of different measurement points provides information that, in the best case, can be used effectively in maintenance planning. Often, the increase in data is followed by a conflict, as the analysis and efficient use of rich data can be difficult. Misinterpretations can lead to wrong conclusions and actions that do not solve the actual problem.
Manufacturing processes and equipment are also constantly evolving, and their diversity increases the maintenance burden. For example, manufacturing plants use equipment from multiple manufacturers, and each piece of equipment may have its unique maintenance requirements. This complicates the standardized maintenance process and challenges the staff.
In addition, resource optimization challenges maintenance management. Effective use of resources and materials is important, but there are usually not enough resources to meet all needs. For example, if the staff lacks resources, they cannot respond quickly enough to sudden failures. This is especially problematic in industries where equipment utilization is critical, and even short production stoppages can result in significant financial losses.
Artificial intelligence in maintenance offers innovative solutions to maintenance management challenges and has real potential to improve efficiency and reduce costs.
One of the central areas where artificial intelligence can help is data analysis. Maintenance generates a large amount of data from various devices and processes. AI can be used to efficiently analyze this data. For example, machine learning models can identify trends and exceptions in real time, helping maintenance staff make quick, data-driven decisions. When data analysis is automated, the organization can respond to problems earlier and prevent equipment failures.
Artificial intelligence in maintenance also makes daily tasks easier. For example, language models can help maintenance staff find the right data quickly and reduce human error. This can be especially useful when workers are dealing with complex devices or processes. With artificial intelligence, maintenance personnel can focus on what they’re good at instead of searching for information.
Artificial intelligence also improves the efficiency of preventive maintenance, which is one of the main objectives of maintenance. Preventive maintenance uses analytics and historical data to predict when equipment needs maintenance. Artificial intelligence can review past failures and maintenance activities and assess the maintenance schedule for a specific piece of equipment. This reduces unexpected failures and downtime. In addition, organizations can optimize their resources when maintenance tasks can be scheduled in advance.
Here are three practical examples of applying AI in maintenance.
This use case focuses on the use of language models (ChatGPT, Copilot) to retrieve and summarize data. They provide immediate answers to staff questions, guide maintenance activities, share data on equipment operation, and help manage documents.
The main goal is to facilitate the daily tasks of maintenance system users, such as installers. Language models understand questions well, and can retrieve and combine data from large volumes of documents and transform them into an easy-to-understand format
Another practical use case focuses on maintenance scheduling. AI-based maintenance planning provides an intelligent solution for resource and schedule management. It helps you optimize schedules and reduce the possibility of human error.
The AI engine helps monitor and combine maintenance and repairs, considering various variables such as resources, spare part availability, and production schedules. As a work planner’s tool, the system automatically schedules maintenance tasks and suggests optimization alternatives. It reduces the need to manually collect data manually from different sources and helps streamline the interaction between maintenance and production.
Our third use case covers the challenges of data quantity and quality in predictive maintenance, especially in complex factory environments.
At the core of the solution is machine learning and deep learning, which can be used to analyze and predict equipment behavior. The process can use statistical methods, such as averaging vibration sensors, and set rules to flag potential problems. More sophisticated machine learning methods help detect more subtle changes in the data and improve problem identification.
We recommend that you start using artificial intelligence in maintenance today. We have a clear reason for this: for the first time ever, low-threshold solutions are available to everyone, and out-of-the-box solutions and custom tools enable rapid deployment.
The Novi maintenance management system already comes with AI. Ask our experts how you can implement intelligent solutions as part of your daily maintenance activities and improve the efficiency of your operations. Our team of experts will help you
You can also download our expert material: Using artificial intelligence in maintenance: practical examples and versatile opportunities