AI in maintenance: Data as the Key, AI as the Door Opener

Author: Hatun-Nur Karaca, Consultant at Orianda Solutions AG – a valantic company

AI concepts offer not only a way out of the limitations of conventional methods, but also a wealth of innovative solutions to elevate maintenance to a new level.

Conventional maintenance methods, both proactive and reactive, are reaching their limits when it comes to effectively managing the increasing complexity and dynamics of modern machines and assets. It is becoming increasingly clear that traditional proactive and reactive approaches alone are no longer sufficient to meet the requirements: More precise, data-driven and predictive maintenance strategies are required. In this changing landscape, new opportunities are opening up through the use of artificial intelligence (AI).

With precise data collection and analysis, AI enables real-time monitoring of assets that goes far beyond what was previously possible. It offers the ability to not only detect current conditions, but also predict potential performance issues before they occur. This predictive maintenance enables companies to minimize downtime and maximize productivity.

But how are AI-based approaches revolutionizing maintenance practices? And how can they help companies meet the ever-increasing demands on the efficiency and reliability of their assets?

Effective data management is crucial

Data is the foundation of AI applications, as artificial intelligence serves as a tool or technology to gain new insights or solve complex problems based on existing data. AI models require large amounts of high-quality data in order to learn patterns, make predictions or automate tasks. The implementation of effective data management is therefore crucial to ensure that the potential of AI can be optimally exploited. It is also crucial that AI models are regularly retrained – otherwise performance can deteriorate over time.

Data quality through AI

A high-quality data basis is essential in maintenance. Artificial intelligence (AI) can help to raise quality to a new level. The ability to gain valuable insights from large and often confusing amounts of data is a key feature of AI applications in maintenance. In addition, improving data quality is crucial to increasing efficiency, effectiveness and competitiveness.

Firstly, AI algorithms enable automated data cleansing by identifying and correcting outliers, incorrect values and incomplete data sets. This helps to bring data to a consistent and reliable level.

Furthermore, AI can be used in anomaly detection to identify potential problems in the data at an early stage. By continuously monitoring data quality, AI systems can also detect inconsistencies or quality issues and trigger notifications to enable an immediate response. 

In addition, AI supports data integration by harmonizing data from different sources and correcting inconsistent information. This integration enables a holistic view of the data, which has a positive impact on the effectiveness of maintenance decisions. 

From sensor data to pattern recognition

Sensors in industrial plants continuously collect data that provides a comprehensive picture of the operating status of machines and assets. However, these data volumes are so extensive and complex that manual evaluation is very inefficient. Detailed sensor data forms the basis for any type of analysis and thus, in the next step, the basis for pattern recognition by AI systems. With its ability to process large amounts of data efficiently, it can recognize patterns and anomalies that remain hidden to the human eye. This pattern recognition is crucial for predictive maintenance, as potential problems can be identified at an early stage and failures can be prevented through timely intervention.

To get from sensor data to pattern recognition, comprehensive data preparation is carried out first, including the removal of outliers and incorrect values. From this, suitable AI models can be selected and trained, using both supervised and unsupervised learning.

In supervised learning , known data sets are presented to the model in order to recognize patterns and make predictions or identify anomalies. In unsupervised learning, on the other hand, the model attempts to identify patterns in the data independently, without predefined target variables. The digital twin can serve as a point of comparison for real-time data in order to detect deviations and initiate preventative measures.

Automated fault diagnosis

Systems use the patterns they have recognized from sensor data to create precise and automated diagnoses of malfunctions in machines and assets. The AI analyzes the data in real time, identifies deviations and potential sources of error and thus provides detailed insights into the causes of problems. This enables a faster response to faults, reduces downtimes and optimizes maintenance planning. Automated diagnostic systems significantly increase efficiency in maintenance by making preventive measures feasible and extending the service life of assets. 

The development of fault detection techniques focuses on research areas of artificial intelligence, such as expert systems, neural networks and fuzzy logic. Expert systems use rule-based approaches to simulate expertise in a specific area and solve problems. Neural networks specialize in learning from data so that complex patterns can be identified and accurate predictions can be made.

The fuzzy logic model can also work with noisy, incomplete or fragmented sensor data due to its intrinsic generalization ability. When new sensors are added by smart devices, the model often does not need to be retrained. In this context, the digital twin (a virtual representation of a real machine or asset) is also becoming increasingly important. AI systems use the digital image to make precise diagnoses and proactively address potential faults. They not only analyze current conditions, but also simulate future operating conditions. This broadens the horizons of predictive maintenance and enables preventive and demand-oriented planning of maintenance and repair work. 

Conclusion

The integration of artificial intelligence into maintenance is a significant step forward compared to traditional methods. It enables a more precise, data-driven and predictive approach, which is essential to manage the complexity and dynamics of modern machines and assets. The core of this progress lies in the combination of high-quality data and advanced AI, which together form a robust system for real-time monitoring, fault diagnosis and optimization of maintenance processes. AI is an indispensable tool that enables a more efficient, effective and competitive maintenance strategy through automated data cleansing, anomaly detection and pattern recognition.

Thanks to AI-supported improvements in data quality and precise fault diagnosis, downtimes are significantly reduced, maintenance planning is optimized and the service life of assets is extended. Better data quality also enables companies to better understand and respond to the needs and expectations of their customers, with each data set being not only a picture of the machine, but also a reflection of customer needs.

Outlook

The future of AI in maintenance promises further refinement and integration of technologies that will push the boundaries of what is possible today. 

As new areas of research emerge and algorithms continue to improve, the ability of AI systems to analyze complex data, make predictions and provide accurate diagnoses will grow exponentially – especially the development of self-learning systems.

In addition, the integration of the digital twin into AI-based maintenance systems will enable even more detailed monitoring and simulation of plant conditions, leading to even more proactive and demand-oriented maintenance. The challenge is to continuously improve data quality while ensuring interoperability between different systems and technologies. In light of these developments, AI-based maintenance solutions are expected to become increasingly autonomous and make an even greater contribution to optimizing operational efficiency and reducing operating costs.