2.10 THE STRENGTHS OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO DIAGNOSTIC AND MONITORING SYSTEMS

Abstract

In this article, the development of technologies and progress in the field of artificial intelligence (AI) have led to significant changes in equipment diagnostics and monitoring systems. The introduction of AI into these systems makes it possible to more accurately and timely detect possible failures and malfunctions in the operation of equipment, as well as anticipate possible problems, deficiencies and damages.

Key words: analytical and statistical modeling, diagnostic and monitoring systems, quality and reliability of input data for analysis.

Introduction. The role of AI in the development of diagnostic and monitoring systems for equipment is to process a large amount of data and analyze it in real time. The use of AI allows you to automate the data processing process, which significantly reduces the time and resources spent on equipment diagnostics and monitoring. The modernization of diagnostic and monitoring systems of equipment using AI also contributes to improving the accuracy and reliability of the results obtained. Thanks to machine learning algorithms, AI is able to detect hidden and complex patterns in data, which allows it to recognize even minor changes indicating deviations from the norm. Another important role of AI in equipment diagnostics and monitoring systems is the ability to predict possible failures and malfunctions. By analyzing historical data and learning based on certain patterns, AI is able to predict the likelihood of a problem in the future. This allows you to take the necessary measures to prevent and eliminate possible breakdowns, which in turn saves time and resources of enterprises. Thus, AI plays a key role in the progress of equipment diagnostics and monitoring systems, ensuring more efficient and reliable operation of equipment. The introduction of AI into these systems allows you to prevent problems and deficiencies before they actually occur, reduce maintenance and repair costs, and increase the duration of equipment operation.

In recent years, the integration of artificial intelligence (AI) into diagnostic and monitoring systems of equipment has become a widespread practice. The use of machine learning, one of the varieties of AI, plays a significant role in improving the accuracy of equipment diagnostics. Machine learning allows diagnostic systems to process and analyze large amounts of data quickly and efficiently. Machine learning algorithms can detect hidden patterns and patterns in data that may not be visible to the human eye. One of the advantages of using machine learning in diagnostics is the ability to detect malfunctions early and predict equipment failures. Machine learning systems can analyze data obtained from various sensors and diagnostic devices, and based on them predict possible problems and recommend measures to prevent them.

The development of AI (artificial intelligence) in recent decades has led to the emergence of new opportunities in the field of monitoring and diagnostics of equipment. In particular, the use of neural networks in equipment condition monitoring systems has become a widespread approach. The integration of artificial intelligence (AI) into equipment diagnostics and monitoring systems has a significant impact on the development of automated failure forecasting. This technology provides an opportunity to significantly improve the accuracy and reliability of forecasts, as well as optimize maintenance and repair processes. One of the important aspects of the development of automated AI-based equipment failure forecasting is the analysis of large amounts of data. Artificial intelligence provides the ability to process and analyze huge amounts of information collected from various sensors and sensors, which allows you to identify hidden dependencies and trends that are inaccessible to human analysis. As a result of the use of automated forecasting based on AI, enterprises are able to quickly respond to possible equipment failures, prevent them, and plan preventive maintenance.