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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Tony Darmanto
Universitas Widya Dharma
Computer Science & IT

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Distance Correlation-Based Regression Tree Algorithm For Structural Damage Detection Jimmy Tjen; Genrawan Hoendarto; Tony Darmanto; Thommy Willay
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 2 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i2.5402

Abstract

This paper proposes a novel idea of a fault detection algorithm based on the Regression Tree (RT) algorithm from the decision tree learning and the distance correlation, which is the nonlinear version of Pearson’s correlation, to reduce the number of sensors without significantly decreasing the model predictive accuracy and the fault diagnosis capability. A numerical validation on an experimental dataset provided by the Los Alamos National Laboratory (LANL) with MATLAB software shows that the proposed algorithm has a comparable model predictive accuracy to the classical RT while requiring a smaller number of sensors (5 instead of 24) and more robust in detecting faults with false negative and positive rates < 15%. Furthermore, we demonstrate that our proposed algorithm runs about 4 times faster than the classical RT on an experimental dataset with 4096 samples on an 8-core, 16 GB RAM machine. In a real-life setup, the proposed algorithm can be used to provide a sensor installment plan on a structure. Such that, the user can still monitor the presence of a fault inside a building precisely, but with a cheaper maintenance cost.
Co-Authors Genrawan Hoendarto Jimmy Tjen Willay, Thommy