Sensor Health Monitoring based on Ontologies. A Systematic Literature Review

Flight research relies on precise and reliable measurement data for validation, collected from highly modified research aircraft. These aircraft integrate data from standard onboard systems and experimental sensor networks. Conventionally, the quality of this data has been systematically examined at the user stage when the data is processed for further work. The validation of sensor data is a highly customized and aircraft-specific process, often requiring manual post-processing before the data can be used for aerophysical, structural, or aeroelastic analysis by domain-specific researchers. As this process requires the domain-specific knowledge of flight test engineers, it can take several weeks before the validation is finalized. Often, the flight test engineers also face obstacles in parameter monitoring in-flight. Some of the extensively equipped aircraft of the German Aerospace Center record up to 3000 parameters. Therefore, the manual detection of sensor faults immediately during or after an experiment is hardly possible. In addition, the specific characteristics of the different research aircraft limit the ability to transfer fault detection methods across aircraft. This paper presents the first systematic literature review of ontology-based sensor health monitoring (OSHM), conducted according to the PRISMA statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses). We synthesize 70 publications across prognostics and health management (PHM), modeling and simulation (M), and related domains. Contributions are classified by expressiveness, reasoning mechanisms, and application scope. Results show two dominant research streams: rule-based ontologies in PHM and more expressive ontology-based methods in M Strengths, limitations, and challenges of OSHM are analyzed, highlighting its potential for predictive aircraft maintenance and its integration with digital twin developments.