Audio Feature-based Anomaly Detection of Working Status of Turbine Room
The turbine room of hydropower station contains mechanical equipment such as bearings and top cover.Due to the influence of hydraulic factors,the abnormal operation of the turbine room causes a large safety risk.Accurate maintenance based on big data analysis is essential for the reliable operation of turbine room.This paper takes the abnor-mal working condition of the turbine room as the research object and establishes an abnormality detection model based on audio data.The process of building the detection model includes model training,feature engineering and development of classification model.The feature engineering uses methods such as STFT,Log-Mel and MFCC.The experimental results prove the effectiveness of the Log-Mel method through a case study conducted at the Xiluodu hydropower station.This study not only achieves the low-cost purpose of anomaly detection,but also provides a valuable reference for the health monitoring of hydropower units.
audio datawaterwheel chamberSTFTLog-MelMel-scale frequency cepstrum coefficients(MFCC)time-domain featuressupport vector machine
NETL
NSTL
万方数据
