The extensive deployment of escalators has greatly improved travel convenience; however, significant concerns have been raised due to the increasing frequency of safety incidents in recent years. Ensuring the safe operation of escalators and detecting faults in a timely manner have become critical concerns for both manufacturers and maintenance personnel. Traditional periodic inspections are resource-intensive and increasingly deemed inadequate due to the growing diversity and number of escalators. In this article, a data acquisition and transmission system for the main drive shaft bearing of the escalator, based on the Internet of Things (IoT), is designed using the main drive shaft bearing as an example. Additionally, a fault classification model combining a deep autoencoder (DAE) and Bidirectional Long Short-Term Memory Network (BiLSTM) is proposed. The experimental results of this study demonstrate that the DAE-BiLSTM-based fault diagnosis model provides accurate fault detection and early warnings, achieving an accuracy rate exceeding 99%, while significantly reducing the computational costs and training time.

Current status of orchard mechanization technologies and key development priorities in the 15th Five-Year Plan period

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