###### 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. Keywords:elevator system; deep autoencoder; fault detection; feature extraction; random forest; profile extraction +
Footnote †: journal:  
Profile extraction for health monitoring is a major issue in automated industrial applications such as elevator systems, computer numerical control, machinery, and robotics [12]. Although rotating machine have been running for decades, but profile extraction and processing methods are not widely available [13]. Profile extraction methods have applied in electric vehicles [14], computer numerical control systems [15] and horizontal planes [16]. Kalman filter [17] is one of the methods being used for profile extraction. However, we have developed an off-line profile extraction algorithm based on low-pass filtering and peak detection to extract elevator start and stop events from sensor data including both acceleration and magnetic signals. In the last decade, neural networks [18] have extracted highly meaningful statistical patterns from large-scale and high-dimensional datasets. Neural networks [19] has also been used to improve elevator ride comfort via speed profile design. Neural networks [20] has been applied successfully to nonlinear time-series modeling. A deep learning network can self-learn the relevant features from multiple signals [21]. Deep learning algorithms are frequently used in areas such as bearing fault diagnosis [22], machine defect detection [23], vibration signal analysis [24], computer vision [25] and image classification [26]. Autoencoding is a process for nonlinear dimension reduction with natural transformation architecture using feedforward neural network [27]. Autoencoders have proven powerful as nonlinear feature extractors [28]. Autoencoders can increase the generalization ability of machine learning models by extracting features of high interest as well as making possible its application to sensor data [29]. Autoencoders were first introduced by [PERSON] [30], and have been studied for decades. Traditionally, feature learning and dimensionality reduction are the two main features of autoencoders. Recently, autoencoders have been considered one of the most compelling subspace analysis techniques because of the existing theoretical relations between autoencoders and latent variable models [31]. Autoencoders have been used for feature extraction from the data in systems such as induction motors [32] and wind turbines [33] for fault detection, different from elevator systems as in our research. In our previous research, raw sensor data, mainly acceleration signals, were used to calculate elevator key performance and ride quality features, which we call here existing features.