Investigating the Efficiencies of Fusion Algorithms for Accurate Equipment Monitoring and Prognostics

Ugochukwu Ejike Akpudo and Jang-Wook Hur
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Ugochukwu Ejike Akpudo: Department of Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, 61 Daehak-ro (Yangho-dong), Gumi 39177, Gyeongbuk, Korea
Jang-Wook Hur: Department of Mechanical Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, 61 Daehak-ro (Yangho-dong), Gumi 39177, Gyeongbuk, Korea

Energies, 2022, vol. 15, issue 6, 1-18

Abstract: Recent findings suggest the need for optimal condition monitoring due to increasing counter-productive issues ranging from threats to life, malware, and hardware failures. Several prognostic schemes have been reported across many disciplines; however, the issues of sensor data discrepancy emanating from varying loading and operating conditions of cyber-physical system (CPS) components still remain a challenging factor. Nonetheless, a significant part of these prognostic schemes comprises a sensor/feature fusion module for comprehensive health indicator (HI) construction. This study investigates the prowess of unsupervised fusion algorithms for constructing optimal HI construction on two publicly available datasets—a simulated turbofan engine degradation experiment and an actual production plant condition monitoring dataset. The fusion efficiencies of the algorithms were evaluated using standard metrics for prognostic parameter assessments. The results show that the autoencoder is more reliable for real-life applications, including cases with uniform degradation patterns and the more complex scenarios with irregular degradation paths in the sensor measurements/features, and is expected to direct continued research for improved multi-sensor-based prognostics and health management of industrial equipment.

Keywords: autoencoder; sensor fusion; condition monitoring; health indicator; prognostics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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