The purpose of this study is to find out how Fast Fourier Transform (FFT) based features could be used in fault detection and prediction in elevators. The overall main objective is to improve on the existing maintenance systems.
The data we analyzed in this work were obtained through applying FFT on vertical vibration signal of each elevator movement. The goal was to study the trend of data over time and detect any significant change that can indicate potential faulty behaviors inside elevators. Due to the challenges in analyzing a stream of high-dimensional FFT data, we decided to utilize two dimensionality reduction techniques, namely Feature selection with Dominant frequencies analysis and Feature extraction with Autoencoder. After that, we used change point detection on the newly acquired features to detect significant changes.
For validation, we first observed the FFT spectrum of each elevator and singled out the ones that contain clear visual changes. For these elevators, we estimated the visual change points and had them as the target outputs for our algorithms. The goal was to see if our implementation of feature selection and feature extraction, combined with change point detection could correctly the target change points.
Final results showed that all significant visual changes in the original spectrums could be detected through the use of feature selection and feature extraction, together with change point detection. Furthermore, we were able to calculate the percentage change in mean vibration amplitude of elevators to determine the most problematic cases with high increases in vibration level. These findings indicated that FFT based features can be used in identifying potential faulty behaviors in elevator systems and the techniques used in this work have shown promising results
