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Research Papers

Nonlinear Squeezing Time-Frequency Transform and Application in Rotor Rub-Impact Fault Diagnosis

[+] Author and Article Information
Shibin Wang, Laihao Yang, Chaowei Tong, Baoqing Ding

State Key Laboratory for Manufacturing
Systems Engineering,
Xi'an Jiaotong University,
Xi'an 710049, China

Xuefeng Chen

State Key Laboratory for Manufacturing
Systems Engineering,
Xi'an Jiaotong University,
Xi'an 710049, China
e-mail: chenxf@mail.xjtu.edu.cn

Jiawei Xiang

Zhejiang Provincial Key Laboratory
of Laser Processing Robot,
Key Laboratory of Laser Precision
Processing and Detection,
Wenzhou 325035, Zhejiang, China

1Corresponding author.

Manuscript received January 12, 2017; final manuscript received May 7, 2017; published online August 24, 2017. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 139(10), 101005 (Aug 24, 2017) (13 pages) Paper No: MANU-17-1018; doi: 10.1115/1.4036993 History: Received January 12, 2017; Revised May 07, 2017

Vibration signal analysis has been proved as an effective tool for condition monitoring and fault diagnosis for rotating machines in the manufacturing process. The presence of the rub-impact fault in rotor systems results in vibration signals with fast-oscillating periodic instantaneous frequency (IF). In this paper, a novel method for rotor rub-impact fault diagnosis based on nonlinear squeezing time-frequency (TF) transform (NSquTFT) is proposed. First, a dynamic model of rub-impact rotor system is investigated to quantitatively reveal the periodic oscillation behavior of the IF of vibration signals. Second, the theoretical analysis for the NSquTFT is conducted to prove that the NSquTFT is suitable for signals with fast-varying IF, and the method for rotor rub-impact fault diagnosis based on the NSquTFT is presented. Through a dynamic simulation signal, the effectiveness of the NSquTFT in extracting the fast-oscillating periodic IF is verified. The proposed method is then applied to analyze an experimental vibration signal collected from a test rig and a practical vibration signal collected from a dual-rotor turbofan engine for rotor rub-impact fault diagnosis. Comparisons are conducted throughout to evaluate the effectiveness of the proposed method by using Hilbert–Huang transform, wavelet-based synchrosqueezing transform (SST), and other methods. The application and comparison results show that the fast-oscillating periodic IF of the vibration signals caused by rotor rub-impact faults can be better extracted by the proposed method.

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Figures

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Fig. 1

Schematic diagram of the rub-impact rotor system

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Fig. 2

Schematic diagram of the rub and impact forces

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Fig. 3

The vibration responses of the rotor system with rub-impact: vibration response in the x-direction (a) and in the y-direction (b) and the spectrum of the vibration in the x-direction (c) and in the y-direction (d)

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Fig. 4

The IF of the vibration response of the rub-impact rotor system: the IF of the vibration in the x-direction (a) and in the y-direction (b) and the spectrum of the oscillatory IF in the x-direction (c) and in the y-direction (d)

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Fig. 5

The flowchart of the proposed method for rotor rub-impact fault diagnosis

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Fig. 6

The TFRs obtained by (a) NSquTFT, (b) STFT, (c) S-method, (d) RSTFT, (e) WTSST, and (f) HHT

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Fig. 7

(a) The IF estimated by NSquTFT and HT and (b) the spectrum of the oscillated part of the estimated IF

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Fig. 8

The logarithmic average MSE values of the IF estimation by HT, WTSST, EMD, EEMD, and NSquTFT for different noise levels

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Fig. 9

The rotor-bearing test rig: (a) overall view and (b) the part of the mechanical system

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Fig. 10

Evolution of the vibration and the speed of the test rig

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Fig. 11

(a) The waveform of the vibration signal measured 3 min before the significant increase of vibration, (b) the spectrum of the vibration signal, and (c) the zoomed-in version of the spectrum for the low-frequency band

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Fig. 12

The NSquTFT representation of the vibration signal

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Fig. 13

(a) The IF extracted by the ridge search method for the rotating frequency component of the vibration signal and (b) the spectrum of the oscillation part of the extracted IF

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Fig. 14

The rub-impact location on the lock nut and the inwall of the loading end cover

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Fig. 15

The comparison results by (a) STFT, (b) RSTFT, (c) WTSST, and (d) HHT

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Fig. 16

Evolution of the vibration and the speed of the test rig in the normal condition

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Fig. 17

(a) The waveform of the vibration signal measured when the speed is 2000 r/min, (b) the spectrum of the vibration signal, and (c) the zoomed-in version of the spectrum for the low-frequency band

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Fig. 18

The analysis result for the normal condition: (a) the NSquTFT representation of the vibration signal, (b) the extracted IF, and (c) the spectrum of the oscillation part of the extracted IF

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Fig. 19

Evolution of the vibration and the speed of the engine during the ground-run testing

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Fig. 20

The vibration signal analyzed by the NSquTFT: (a) its waveform and (b) its spectrum

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Fig. 21

The NSquTFT representation of the vibration signal

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Fig. 22

(a) The IF extracted by the ridge search method for the rotating frequency component of the vibration signal and (b) the spectrum of the oscillation part of the extracted IF

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Fig. 23

The comparison results by (a) WTSST and (b) HHT

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