Abstract
In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extracted from sensor data including both acceleration and magnetic signals. In addition, a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved above 90% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset when compared to 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.
Highlights
In recent years, elevator systems have been used increasingly extensively in apartments, commercial facilities, and office buildings
The results show that the new deep features provide better accuracy in terms of fault detection and avoiding false positives from the data, which is helpful in detecting false alarms for elevator predictive maintenance strategies
This research focuses on the health monitoring of elevator systems using a novel fault detection technique
Summary
Elevator systems have been used increasingly extensively in apartments, commercial facilities, and office buildings. Elevator systems need proper maintenance and safety. The step for improving the safety of elevator systems is the development of predictive and pre-emptive maintenance strategies, which will reduce repair costs and increase the lifetime while maximizing the uptime of the system [2,3]. Elevator production and service companies are opting for a predictive maintenance policy to provide better service to customers. They are remotely monitoring faults in elevators and estimating the remaining lifetime of the components responsible for faults. Elevator systems require fault detection and diagnosis for healthy operation [4]
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