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Sensors and Materials, Volume 30, Number 4(2) (2018)
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pp. 821-832
S&M1541 Research Paper of Special Issue
https://doi.org/10.18494/SAM.2018.1783
Published: April 27, 2018

Mechanical Vibration Fault Detection for Turbine Generator Using Frequency Spectral Data and Machine Learning Model: Feasibility Study of Big Data Analysis [PDF]

Long-Yi Chang, Yi-Nung Chung, Chia-Hung Lin, Jian-Liung Chen, Chao-Lin Kuo, and Shi-Jaw Chen

(Received October 23, 2017; Accepted December 26, 2017)

Keywords: vibration signal, mechanical vibration fault, frequency spectral data, radial-based color relation analysis

The frequency spectra of vibration signals can be used to monitor the mechanical conditions of a turbine generator. Frequency-based features are extracted by fast Fourier transformation (FFT). The changes in frequency spectral data and amplitude are used to separate the normal condition from the fault conditions. These features indicate that the characteristic frequencies are 1 × f, 2 × f, 3 × f, and two other frequency bands, < 0.4 × f and > 3 × f, where the frequency f is the rotor frequency. The power spectral data shows the mechanical vibration fault at particular characteristic frequencies. Then, radial-based color relation analysis (CRA) is applied to identify mechanical faults, including normal condition, oil-membrane oscillation, imbalance, and no orderliness. Using practical records, the experimental results will show that the proposed method has a higher accuracy in mechanical vibration fault detection.

Corresponding author: Long-Yi Chang

Cite this article
Long-Yi Chang, Yi-Nung Chung, Chia-Hung Lin, Jian-Liung Chen, Chao-Lin Kuo, and Shi-Jaw Chen, Mechanical Vibration Fault Detection for Turbine Generator Using Frequency Spectral Data and Machine Learning Model: Feasibility Study of Big Data Analysis, Sens. Mater., Vol. 30, No. 4, 2018, p. 821-832.