Deep Neural Networks for Elevator Fault Detection

Abstract

The objective of this thesis is to develop novel data extraction, feature extraction and fault detection techniques for the task of elevator fault detection in real-world environments. Aim of the research is to develop systems that can automatically detect the elevator faults commonly present in the systems. In addition, 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. The proposed solutions answer two research questions: how can we detect elevator faults efficiently and how can we detect false alarms in elevator predictive maintenance systems?

Five publications have been developed to address these issues from various perspectives. In this thesis, modern machine learning method called deep learning is applied for elevator fault detection. The relationship between the commonly used time series representations for elevator movement and the target fault event labels are highly complex. Deep learning methods such as deep autoencoder and multilayer perceptron utilize a layered structure of units to extract features from the given vibration input with increased abstraction at each layer. This increases the network’s capacity to efficiently learn the highly complex relationship between the elevator movement and the target fault event labels. This research shows that the proposed deep autoencoder and multilayer perceptron approaches perform significantly better than the established classifying techniques for elevator fault detection such as Random forest algorithm.

An off-line profile extraction algorithm is also developed based on low-pass filtering and peak detection to extract elevator start and stop events from sensor data. These profiles are used to calculate motion and vibration related features, which is called here existing features. Profile extraction algorithm and deep autoencoder model are combined to calculate new deep features fromthe data to improve the results in terms of elevator fault detection. The approaches in this research provided nearly 100% accuracy in fault detection and also in the case of analyzing false positives with new extracted deep features. The results support the goal of this research of developing generic models which can be used in other machine systems for fault detection.