Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Information System Exploration and Research
Mochamad Rizal Fauzan
Department of Electrical Engineering, National Taipei University of Technology, Taiwan

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Unsupervised Anomaly Detection for Water Pump Fault Identification Under Extreme Class Imbalance Mochamad Rizal Fauzan; Ghayrie Habsy Alghifary; Muhamad Husen
Journal of Information System Exploration and Research Vol. 4 No. 4: October 2026
Publisher : shmpublisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joiser.v4i4.114

Abstract

Water pump fault detection in industrial systems is challenged by extreme class imbalance, where anomalous states account for less than 7% of operational data and labeled fault examples are scarce. This paper presents a comparative evaluation of three unsupervised anomaly detection approaches Isolation Forest (IF), Long Short-Term Memory Autoencoder (LSTM-AE), and One-Dimensional Convolutional Neural Network Autoencoder (1D-CNN-AE) applied to a real-world multivariate water pump sensor dataset comprising 220,320 one-minute observations across 51 sensors. All models are trained exclusively on normal operational data, eliminating dependence on fault labels. A sliding-window preprocessing pipeline (W = 30 timesteps) with a scaler fitted only on normal training data ensures no temporal data leakage. Anomaly thresholds are derived statistically (mean + 3σ) from reconstruction errors on the normal test partition. Using a chronological 60/40 train-test split and threshold-free metrics, Isolation Forest achieves AUROC = 0.9973 and Recall = 0.9850, while LSTM-AE yields F1 = 0.9218 and Precision = 0.8892. The results reveal a fundamental sensitivity-selectivity trade-off across unsupervised paradigms and provide actionable guidance for deploying anomaly detection in water infrastructure monitoring systems.
Co-Authors Ghayrie Habsy Alghifary Muhamad Husen