An integrated approach to evaluate the measurement capability and acceptability of acoustic emission sensors

Abstract

Abstract Acoustic emission (AE) is a pivotal technique in condition-based maintenance (CBM) and recent years have witnessed a significant surge in the deployment of AE sensors in industrial applications. With this increase in availability, there comes a substantial challenge: evaluating the measurement capability of sensors within specific applications. As such, this study identifies a critical need for a structured approach to evaluate the measurement capabilities of AE sensors and subsequently judge their acceptability against guideline criteria. To address this need, we present an integrated approach that systematically guides the capability evaluation of AE sensors, incorporating both qualitative and quantitative analyses. The qualitative analysis aims to scrutinize the diagnostic accuracy of sensors by assessing the detectability of features critical for diagnostics. The quantitative analysis leverages the Gage repeatability and reproducibility (Gage R&R) to statistically evaluate sensor characteristics. A comprehensive experimental study further investigates the impact of measurement sources on the sensors’ repeatability, and reproducibility. This study illustrates the qualitative findings regarding sensor’s diagnostic accuracy in both time and frequency domains, revealing promising performance in diagnostic-based evaluations. In quantitative analysis, we demonstrate the results of sensor capability in terms of repeatability and reproducibility, providing the variations of different sources in statistics-based evaluations. We thoroughly investigate the influence of significant factors, quantifying their contributions to the sensor’s measurement capability. Furthermore, we introduce metrics designed to assess sensor’s acceptability, according to explicit acceptance and rejection criteria. Our experimental results affirm that root mean square measurements are within acceptable ranges for both sensors, while spectral entropy (SE) measurements for PK15I sensor satisfy the acceptable level. For HZ136I sensor, however, SE measurements are deemed conditionally acceptable. Ultimately, the proposed approach provides a robust framework for the comprehensive evaluation of AE sensor measurement capabilities, offering invaluable guidance for sensor selection and enhancement in industrial applications.