KEYWORDS: Education and training, Data modeling, Windows, Feature extraction, Safety, Data processing, Matrices, Process modeling, Instrument modeling, Engineering
The safety hazards caused by elevator faults are increasing day by day, and it is urgent to study the problem of elevator fault prediction. In this paper, a combined model elevator fault prediction method based on CNN, LSTM and self-attentive mechanism is proposed. The operating parameters and high frequency faults of elevators are statistically determined, and a large amount of real-time elevator data is collected to build a data set. The advanced features are extracted by CNN and fed to LSTM for training, and then fed to softmax classifier for classification prediction after further feature extraction by the self-attention mechanism. The experimental results show that the method can effectively predict elevator faults.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.