Shuda Gao
ABSTRACT
The current maintenance of aerospace equipment generally uses regular maintenance, scheduled maintenance, seasonal maintenance, after-the-fact maintenance, and replacement maintenance. These methods are ill-timed, time-consuming, and wasteful of materials. Monitoring the reliability and healthy operating status of each embedded computer electronic component is essential, and maintenance staff will benefit greatly from a data-driven approach to anomaly detection. It can be altered from "repair afterward" to "repair as necessary" and from " repair regularly" to "repair at any time" to solve the practical problems arising from maintenance. The Variational Autoencoder (VAE), which is based on the component storage aging acceleration data, is used in this paper to model the component's normal operating status and perform anomaly detection. The precision and recall of this anomaly detection method are 0.950 and 0.977. This method evaluates the operating status and reliability of each component, improves the reliability and service life of the computer, and establishes the technological framework for the next generation of computer Prognostics and Health Management (PHM) systems.
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Index Terms
- VAE-based anomaly detection for embedded computer electronic components
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