A novel health evaluation strategy for multifunctional self-validating sensors

Area of Science:

• Sensor Technology
• Reliability Engineering
• Data Fusion

Background:

• Sensor performance evaluation is critical for reliable operation.
• Existing fault diagnosis methods are often qualitative.
• Multifunctional sensors require comprehensive health assessment.

Purpose of the Study:

• To develop a quantitative method for evaluating multifunctional sensor health.
• To introduce the concept of health reliability degree (HRD).
• To assess sensor health from both local and global perspectives.

Main Methods:

• Utilized multi-variable information fusion and grey comprehensive evaluation.
• Defined HRD for single components over time and overall sensors at a single time point.
• Employed information entropy and analytic hierarchy process for weighting sensitive units and time points.

Main Results:

• A health evaluating experimental system for multifunctional self-validating sensors was designed and tested.
• The proposed HRD method demonstrated feasibility across five different health level situations.
• Results confirmed HRD's ability to quantitatively indicate health levels and respond quickly to performance changes.

Conclusions:

• The novel HRD methodology provides a quantitative measure of multifunctional sensor health.
• The approach effectively integrates multi-variable data for comprehensive sensor evaluation.
• HRD offers a fast and reliable tool for monitoring sensor performance and detecting changes.