Qualitative Recognition of Typical Loads in Low-Speed Rotor System

Kun Zhang,Zhaojian Yang

doi:10.1155/2017/2798248

Abstract

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.

Highlights

Modernization of machines has resulted in rotating parts that are often subjected to complex and variable loads during operation, which could potentially lead to costly production losses and catastrophic failure [1]
The ensemble empirical mode decomposition (EEMD) method is utilized as the preprocessor for vibration signals and the energy calculations and backpropagation neural network (BPNN) is applied as the load category recognition process
EEMD is a noise-assisted data analysis (NADA) method that works by adding white noise in order to improve the signal analysis and can eliminate mode-mixing phenomena which occur using the traditional EMD method [26]

Summary

Introduction

Modernization of machines has resulted in rotating parts that are often subjected to complex and variable loads during operation, which could potentially lead to costly production losses and catastrophic failure [1]. Other examples include reciprocating rolling plates such as a roller system in a rolling mill under transient load and spindle rotor systems in winding hoists required to withstand linear loads due to weight changes within the hoisting rope During these processes, load changes can directly affect normal operation conditions and by recognizing the load type it is possible to qualitatively determine the running status of the equipment. The methods used for load recognition are based on known characteristics of the system as well as actual measurements of the dynamic response. The paper chooses this as a pointcut, conducting the relevant research and discussion For this purpose, a low-speed rotor system test bench was specially designed and constructed to verify the feasibility of its load identification method. The proposed recognition method can be used online by linking the recognition function with a signal-monitoring interface allowing for real-time monitoring of equipment loads during operation

Theories and Methods

Experiments

Results and Analysis

Conclusions