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Sensors 2017, 17(7), 1676; https://doi.org/10.3390/s17071676

Identification of Load Categories in Rotor System Based on Vibration Analysis

College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
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Received: 21 April 2017 / Revised: 2 July 2017 / Accepted: 17 July 2017 / Published: 20 July 2017
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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Abstract

Rotating machinery is often subjected to variable loads during operation. Thus, monitoring and identifying different load types is important. Here, five typical load types have been qualitatively studied for a rotor system. A novel load category identification method for rotor system based on vibration signals is proposed. This method is a combination of ensemble empirical mode decomposition (EEMD), energy feature extraction, and back propagation (BP) neural network. A dedicated load identification test bench for rotor system was developed. According to loads characteristics and test conditions, an experimental plan was formulated, and loading tests for five loads were conducted. Corresponding vibration signals of the rotor system were collected for each load condition via eddy current displacement sensor. Signals were reconstructed using EEMD, and then features were extracted followed by energy calculations. Finally, characteristics were input to the BP neural network, to identify different load types. Comparison and analysis of identifying data and test data revealed a general identification rate of 94.54%, achieving high identification accuracy and good robustness. This shows that the proposed method is feasible. Due to reliable and experimentally validated theoretical results, this method can be applied to load identification and fault diagnosis for rotor equipment used in engineering applications. View Full-Text
Keywords: rotor system; identification of load categories; back propagation neural network; ensemble empirical mode decomposition rotor system; identification of load categories; back propagation neural network; ensemble empirical mode decomposition
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Zhang, K.; Yang, Z. Identification of Load Categories in Rotor System Based on Vibration Analysis. Sensors 2017, 17, 1676.

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