# Classifying X-Ray Tube Malfunctions: AI-Powered CT Predictive Maintenance System
## 1 Introduction  
Medical CT devices play a crucial role in modern healthcare by providing accurate and detailed images for diagnostic purposes. According to the UNSCEAR report [1], an estimated 403 million CT exams/procedures are performed in the world annually. However, like all medical diagnostic machines, they can suffer from failures affecting their performance and reliability. These failures cause numerous problems for both medical providers and patients. For patients, they usually increase the time to diagnose and cause inconvenience related to longer waiting times or rescheduling. For medical providers, they mostly cause financial losses. In some cases, the malfunctions can lead to decreased diagnostic accuracy, and in the most extreme and rare cases, even to injuries or deaths. Traditional approaches to diagnosing and predicting failures in CT devices often rely on manual inspection combined with expert knowledge.  
Maintenance can be generally grouped into three groups [2, 3]:  
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* Corrective maintenance--Existing errors are corrected when they occur;
* Preventive maintenance--Maintenance is performed periodically, on predetermined intervals;
* Predictive maintenance (PdM)--Predicting a malfunction before it happens. PdM is the newest of the approaches, and it is deeply tied to data [4]. Compared to preventive maintenance, it does not rely on predetermined service intervals but tries to intelligently determine when the maintenance is needed/will be needed. This is possiblethanks to the ever-increasing utilisation of sensors and the increasing amount of big data. There are two essential parts of PdM [4]--anomaly detection and remaining useful life (RUL) prediction. In this work, we will focus on the former, which is concerned with detecting undesired behaviour in the machines. As it utilises detection and prediction and is tied to data, PdM is strongly tied to machine learning. In the last several years, machine learning has become an increasingly effective way to automate the fault detection process in different domains [5, 6], including healthcare [3], as it can uncover hidden patterns and relationships in complex data. From the perspective of the review [4], our work would fall under the umbrella of \"behavioural/collective anomalies\". In these anomalies, a data point and a data pattern can differ from the normal/expected state. In our case, this would be a behaviour and data pattern of the sensors of a CT machine.  
This work investigates the application of ML algorithms in a practical problem--PdM of medical CT devices. Our PdM system is based on the classification of the current state of the X-ray tube, which is based on the sensor data from sensors present in a CT machine. The problem is rather specific and under-investigated; our review identified only one directly related work within a still rather sparse field--PdM in healthcare devices, where we identified 10 works. We leverage historical data to learn patterns and relationships that can help identify potential failures and provide early warnings, enabling proactive maintenance and minimising equipment downtime. To do so, we utilise both hand-crafted and learned/extracted features in combination with several machine learning models.