Vibration Sensor Dataset for Estimating Fan Coil Motor Health
Heitor Lifsitch, Gabriel Rocha, Hendrio Bragan\c{c}a, Cl\'audio Filho,, Leandro Okimoto, Allan Amorin, F\'abio Cardoso

TL;DR
This paper introduces FAN-COIL-I, a real-world vibration dataset from Fan Coil motors, designed to improve motor health monitoring and predictive maintenance through authentic operational data.
Contribution
The paper presents a novel, high-resolution, real-world vibration dataset for Fan Coil motors, enabling more accurate diagnosis and benchmarking in motor health research.
Findings
Provides a comprehensive real-world vibration dataset
Enables improved motor health prediction models
Serves as a benchmark for motor diagnosis research
Abstract
To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these…
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