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Related Experiment Video

Updated: Jul 13, 2026

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography (CT) and Light Microscopy (LM) Correlated with Scanning Electron Microscopy (SEM)
10:42

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography (CT) and Light Microscopy (LM) Correlated with Scanning Electron Microscopy (SEM)

Published on: June 16, 2016

State-based prognostics with similarity enhancement for CT X-ray tubes using operational logs.

Changjun Chen1, Xiangjie Wang1, Linze Qian2

  • 1Department of Clinical Engineering, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.

Biomedizinische Technik. Biomedical Engineering
|July 12, 2026
PubMed
Summary

This study introduces a novel method for estimating the remaining useful life (RUL) of CT X-ray tubes. The approach accurately predicts tube degradation without needing predefined failure points, enhancing medical equipment maintenance.

Keywords:
CT X-ray tubeoperational logsremaining useful life estimationsimilarity analysisstate-based prognostics

Related Experiment Videos

Last Updated: Jul 13, 2026

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography (CT) and Light Microscopy (LM) Correlated with Scanning Electron Microscopy (SEM)
10:42

In Depth Analyses of LEDs by a Combination of X-ray Computed Tomography (CT) and Light Microscopy (LM) Correlated with Scanning Electron Microscopy (SEM)

Published on: June 16, 2016

Area of Science:

  • Medical Imaging Technology
  • Prognostics and Health Management

Background:

  • Computed Tomography (CT) X-ray tubes are critical, high-cost components with limited operational lifespans.
  • Accurate estimation of remaining useful life (RUL) is essential for effective maintenance and operational planning.

Purpose of the Study:

  • To develop a method for estimating CT X-ray tube RUL.
  • To enable RUL prediction without requiring a preset failure threshold or prior knowledge of tube life.

Main Methods:

  • Selected CT scan types with stable tube current distribution.
  • Developed a composite metric for a candidate health indicator (HI).
  • Normalized HI to a percentage health indicator (PHI) and applied state-based prognostics with similarity enhancement for RUL estimation.

Main Results:

  • Validated the method using real-world CT tube log data with leave-one-out cross-validation.
  • Achieved optimal prediction performance and enabled cross-tube RUL estimation.
  • Demonstrated that predictions reflect actual tube degradation without a failure threshold.

Conclusions:

  • The proposed approach offers an effective and practical framework for CT X-ray tube RUL estimation.
  • Shows significant potential for practical application in medical equipment maintenance strategies.