Work

Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System

Year: 2025

Type: article

Abstract: With the development of urbanization, building maintenance units (BMUs) have been widely used in super high-rise buildings. As aerial work machinery, condition monitoring plays a vital role in the safety maintenance and management of BMUs. However, BMUs have multi-source heterogeneous data relationships that are difficult for systems to understand. Moreover, at this stage, there is a lack of sufficient samples to support fault diagnosis data. Therefore, this paper proposes a real-time monitoring and fault diagnosis system for BMU operating conditions. This system, based on the Internet of Things (IoT) architecture, acquires and stores data from distributed BMU systems, improving the data collection and sharing rate throughout the entire process. A collaborative fault reasoning chain diagnosis model was established based on heterogeneous knowledge sources and real-time process signals, which increased the accuracy of fault identification to 97%. Finally, through simulation testing and evaluations, the system can stably transmit data within 6–7 days and accurately analyze the operational and fault status of BMU, with an error rate within 5%. It effectively improves the efficiency and accuracy of BMU condition monitoring and fault diagnosis and also provides a new method for the practical application of intelligent BMU operation and maintenance.

Source: Applied Sciences

DOI: 10.3390/app15094829

Authors: Boqian Dong, Kai Liu, Chunli Lei, Ruizhe Song

Institutions: Chang'an University, Lanzhou University of Technology

Language: English

Topic: Elevator Systems and Control

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896