Author(s)

Jinshuo Zhang ¹

Affiliation(s)

¹ Washington University of St. Louis, Saint Louis, Missouri, 63105, USA

Corresponding Author

Jinshuo Zhang

ABSTRACT

This study focuses on the application of multi-source heterogeneous data fusion in real-time status monitoring and fault warning systems for construction machinery, and conducts in-depth analysis of the latest developments in status monitoring and fault diagnosis technology for construction machinery. A monitoring scheme combining data-driven and machine learning is proposed to address the problem of frequent failures in construction machinery in complex operating environments. This solution utilizes efficient data collection and processing from multiple sensors, and applies deep learning models to achieve fault prediction and diagnosis. It can effectively identify potential faults, prevent risks in advance, and improve equipment reliability and operational safety. This article starts with the overall design architecture and core technologies, and provides a detailed introduction to the construction process of data preprocessing, feature extraction, and fault diagnosis models. It also explores the challenges of outdoor operating conditions in monitoring the status of construction machinery. Research has shown that the application of automated state monitoring and early warning systems can significantly reduce the incidence of failures, minimize economic losses, and improve operational efficiency and safety.

KEYWORDS

Multi source heterogeneous data fusion, fault warning system, engineering machinery status monitoring, deep learning model

CITE THIS PAPER

Jinshuo Zhang, Research on Real Time Condition Monitoring and Fault Warning System for Construction Machinery under Multi Source Heterogeneous Data Fusion. Journal of Engineering Mechanics and Machinery (2024) Vol. 9: 139-144. DOI: http://dx.doi.org/10.23977/jemm.2024.090217.

REFERENCES

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[2] Olcese Umberto. Equipment Detection and Maintenance in Mechanical Workshop Based on Anomaly Detection Algorithm[J]. Kinetic Mechanical Engineering, 2022, 3(2): 1-9.
[3] Huang L, Pan X, Liu Y G L .An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction[J].sensors, 2023, 23(16). 
[4] Liu Y, Liu J, Zhang F, et al. Data-model Interactive Health Condition Assessment for Hydropower Unit[J].IOP Publishing Ltd, 2024.DOI:10.1088/1742-6596/2752/1/012020.

Sponsors, Associates, and Links

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• Ultra-Precision Machining Process
  
  
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• Micro-Electro-Mechanical Systems
  
  
• Journal of Precision Instrument and Machinery
  
  
• Engineering and Solid Mechanics
  
  
• Fracture and Damage Mechanics
  
  
• Frontiers in Tribology
  
  
• Fluid and Power Machinery
  
  
• Chemical Process Equipment
  
  
• Journal of Assembly and Manufacturing
  
  
• Mechanical Vibration and Noise