Condition monitoring of elevator using deep learning and frequency analysis approach

In this research, we propose automated deep learning feature extraction technique to calculate new features from fast fourier transform (FFT) of data from a accelerometer sensor attached to an elevator car. Data labelling is performed with the information provided by maintenance data. Calculated features attached with class variables are classified using random forest algorithm. We have achieved 100% accuracy in fault detection along with avoiding false alarms based on new extracted deep features, which outperforms results using existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.