Predicting ASTI automated weather station (AWS) failure based on data-forwarding behavior

Authors

Aira Villapando ⋅ PH Advanced Science and Technology Institute, Department of Science and Technology
Jay Samuel Combinido ⋅ PH Advanced Science and Technology Institute, Department of Science and Technology
Rene Mendoza ⋅ PH Advanced Science and Technology Institute, Department of Science and Technology

Abstract

The exposure of the DOST-ASTI automated weather stations (AWS) to extreme weather events makes conventional condition-based maintenance methods insufficient in terms of preventive maintenance for proactive or corrective actions. The integration of machine learning to early failure detection, meanwhile, has become part of continuous developments on predictive maintenance (PdM). This study shows that by employing machine learning algorithms, failure of an AWS in one to eight weeks of time based on its data-forwarding behavior is predictable. There is some relation between the 7-day mean and variance of an AWS data-forwarding frequency and the condition of an AWS weeks in advance. Results also show that applying synthetic minority oversampling technique (SMOTE) in preprocessing is an effective method to balancing the dataset.

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Issue

2018: Proceedings of the 36th Samahang Pisika ng Pilipinas Physics Conference

Article ID

SPP-2018-1B-02

Section

Complex Systems and Data Analytics

Published

2018-05-16

How to Cite

[1]

A Villapando, JS Combinido, and R Mendoza, Predicting ASTI automated weather station (AWS) failure based on data-forwarding behavior, Proceedings of the Samahang Pisika ng Pilipinas 36, SPP-2018-1B-02 (2018). URL: https://proceedings.spp-online.org/article/view/SPP-2018-1B-02.