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Mathematical Problems in Engineering
Volume 2017, Article ID 2798248, 10 pages
Research Article

Qualitative Recognition of Typical Loads in Low-Speed Rotor System

1College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, Taiyuan 030024, China

Correspondence should be addressed to Zhaojian Yang; nc.ude.tuyt@naijoahzgnay

Received 15 May 2017; Revised 11 August 2017; Accepted 1 October 2017; Published 26 October 2017

Academic Editor: Yuri Vladimirovich Mikhlin

Copyright © 2017 Kun Zhang and Zhaojian Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


While the load variations within the low speed rotor systems affect the operating conditions and mechanical properties, they may also provide information on machine faults. Therefore, load recognition is of great significance in operational monitoring for detecting early warning signs of failure and diagnosing faults. In this paper, five types of typical loads in a low-speed rotor system are qualitatively analyzed. Moreover, a method is presented based on the vibration signals from a low-speed rotor system using the ensemble empirical mode decomposition (EEMD), energy feature extraction, and backpropagation neural network (BPNN). A low-speed rotor test bench was designed and manufactured for load recognition and an experiment was set up based on certain load characteristics. Loading tests for five representative categories were conducted and various vibration signals were collected simultaneously. The EEMD was shown to eliminate the mode mixing seen in traditional EMD, which resulted in a clear decomposition of the signal. Finally, the characteristics were imported into a BPNN after energy feature extraction, and the different types of load were accurately recognized. Comparing the experimental results to existing data, a total recognition rate of 92.38% was achieved, demonstrating that the proposed method is both reliable and efficient.