International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

In the modern era, it is a challenge to develop a high-quality business product that adequately serves its real purpose, by implementing Machine Learning (ML) and Large Language Models (LLM), both generally defined as AI. For this reason, Danlex 78 proposes a multilayer approach for developing an augmented analytics platform and Service 4.0 solution to accomplish service excellence in x-ray inspection systems. In this approach, AI is important, but still just a tool for achieving targeted business goals. The complex ecosystem of business knowledge combined with science and technology leads to our working solution. This article presents our approach and marks the most important KPIs for achieving service excellence in X-ray inspection systems maintenance.

Many industries, like the aviation industry and border control, rely on X-ray inspection systems for their security operations. These systems are state of the art complex electromechanical machines that need to be maintained by highly qualified service specialists. The most critical part of each x-ray inspection system is the x-ray generator. In this article, we propose a technique for remote diagnostics based on electrical and thermal telemetry monitoring. We have studied the most important electrical and thermal parameters of various conventional X-ray generators equipped with stationary anode X-ray tubes, up to 300kV acceleration voltage, and their correlation to the operational condition of the generator. The experimental results, obtained in a real operational environment, show that a limited number of monitoring parameters are enough to properly estimate the condition and diagnose some failures in X-ray generators. A simplified physical model, based on electrical and thermal parameters, has been proposed and compared with the results.