Sue Greenfield, Barbara Whelan, Elizabeth Cohn
Use of dimensional analysis to reduce medication errors
The Journal of nursing education
problem energy depletion brought wind energy consideration replace oil chemicalbased energy however breakdown wind turbines major concern accordingly unsupervised learning performed using vibration signal wind power generator achieve outlier detection performance 97 analyzed vibration data wavelet packet conversion identified specific frequency band showed large difference normal abnormal data emphasize specific frequency bands highpass filters applied maximize difference subsequently dimensions data reduced principal component analysis giving unique characteristics data preprocessing process normal data collected wind farm located northern sweden first preprocessed trained using long shortterm memory lstm autoencoder perform outlier detection lstm autoencoder model specialized timeseries data learns patterns normal data detects data outliers therefore propose method outlier detection data preprocessing unsupervised learning utilizing vibration signals wind generators facilitate quick accurate detection wind power generator failures provide alternatives problem energy depletion