Road Machinery Fault Prediction Based on Big Data and Machine Learning

• Published in: 2019 5th International Conference on Control, Automation and Robotics (ICCAR) pp. 536 - 540
• Main Authors: Lige, Xue, Hua, Song Zong, Feng, Shao Zhu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2019
• Subjects: big data; Fault prediction; Internet of things; Machine learning; Neural networks
• DOI: 10.1109/ICCAR.2019.8813333

Road machinery is a significant essential equipment for earthwork, base course, surface layer and maintenance in road construction, such as paver, roller and so on. Machinery fault is an important factor influencing the construction efficiency. This is a time of abundant informatization infrastructure facilities, based on this background, massive data regarding multi-dimensional operation of the construction machinery industry can be acquired by the Internet of Things (hereafter referred to as IOT) or cloud processing technology, at the same time, the data of fault service module belong to CRM system is used as sample data to establish neural network model for machine learning. In so doing, we can get the rule on fault prediction of each subsystem of the road machinery, and establishing a diagnose system to synchronize with the cloud server; this diagnose system will conduct real-time fault prediction on the road machinery served by the IOT cloud server, which can dramatically decreasing the idle loss caused by machinery fault. It is an efficient way to transfer from fault dealing to fault prediction and prevention of road construction machinery.