# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 3 Diagnosing Failures in Medical Equipment
### X-Ray Tube Failures  
X-ray tubes have always been somewhat problematic parts [20] of the medical products for X-ray-based imaging techniques. In over 130 years of their continual evolution, there have been many milestones improving their performance, reliability or both. For a more detailed breakdown of the history and development of X-ray tubes, see [21].  
Ref. [21] lists possible failures of the X-ray tube as related to either ageing or manufacturing defects. For ageing, they list eight different reasons, \"cathode filament burnout\", \"target micro-cracking\", or \"X-ray tube arcing\", among others. Some of them are related to tungsten evaporation caused by different effects (listed as \"cathode filament burnout\", \"target micro-cracking\", \"glass crazing or etching\"), others to reduction of vacuum levels (listed as \"X-ray tube arcing\", \"slow leaks in X-ray tube\", \"inactivity\"), failure of ball bearings (listed as \"ball bearings of rotating anode\") and others fall within the category of \"accidental damage\". The work showcases that there are diverse reasons for X-ray tube failure or degradation that need to be taken into consideration when creating a PdM system for the X-ray tube.  
There are two possible ways to address the creation of the X-ray tube PdM system.  
The first approach consists of creating a classification system for classifying possible X-ray tube failures into several classes. The approach would require pinpointing and monitoring variables related to those specific failures. Causes of both \"cathode filament burnout\" and \"target micro-cracking\" are visible only on a microscopic level; \"slow leaks in X-ray tube\" would, on the other hand, require non-stop granular monitoring of the gas environment within the X-ray tube. While the continuous adoption of the IoT in the MedTech industry could lead to the development of required monitoring systems, current X-ray tubes may lack such sensors with granular data. On the other hand, some metrics would already be available, for example, the \"X-ray tube arcing\", as it is part of our dataset. However, this approach has another major obstacle. To create such a specific system, one would need to ask the X-ray tube manufacturers to conduct a post-failure inspection and examination of every X-ray tube to pinpoint the specific failure category and obtain the necessary labels for AI systems. The primary benefit of such a system is that such an approach could allow for easy refurbishing of some of the X-ray tubes, as only specific problematic parts could be repaired.  
The second approach is based on a binary classification of the current X-ray tube state into a working state and a faulty state. We will not further investigate specific reasons for this failure, as, anyway, once deemed faulty, the X-ray tube is replaced with a new one. This approach could lead to some minor label noise, for example, when the X-ray tube is replaced due to external mechanical damage. In such a case, the parameters of the X-ray tube could correspond to the working class while still being replaced. Another possible case could be if maintenance were performed preemptively. Such a case would be when a technician was already present for other reasons, but an X-ray tube was also close to the end of its useful life. We do not expect such occurrences to be frequent. Also, these preemptive changes should not create a significant noise as the lifespan of X-ray tubes is usually in years, while such preemptive changes would be in days or a few weeks early. In such a time, X-ray tubes should be classified as faulty anyway. This approach was selected based on the available data explained below.