A Normalized Frequency-Domain Energy Operator for Broken Rotor Bar Fault Diagnosis

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

Download Full-text

An incipient fault diagnosis method for axial piston pump based on the Teager energy operator

CSAA/IET International Conference on Aircraft Utility Systems (AUS 2018) ◽

10.1049/cp.2018.0300 ◽

2018 ◽

Author(s):

Qiang Gao ◽

Jiawei Xiang ◽

Hesheng Tang

Keyword(s):

Fault Diagnosis ◽

Energy Operator ◽

Piston Pump ◽

Axial Piston Pump ◽

Teager Energy Operator ◽

Diagnosis Method ◽

Incipient Fault ◽

Teager Energy ◽

Axial Piston ◽

Incipient Fault Diagnosis

Download Full-text

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.470.683 ◽

2013 ◽

Vol 470 ◽

pp. 683-688

Author(s):

Hai Yang Jiang ◽

Hua Qing Wang ◽

Peng Chen

Keyword(s):

Fault Diagnosis ◽

Fault Detection ◽

Bayesian Network ◽

Frequency Domain ◽

Evaluation Method ◽

Roller Bearing ◽

Rotating Machinery ◽

Discrimination Index ◽

Vibration Signals ◽

Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Download Full-text

Mine Ventilator Fault Diagnosis Based on Harmonic Wavelet Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.613 ◽

2011 ◽

Vol 143-144 ◽

pp. 613-617

Author(s):

Shuang Xi Jing ◽

Yong Chang ◽

Jun Fa Leng

Keyword(s):

Fault Diagnosis ◽

Wavelet Analysis ◽

Frequency Domain ◽

Vibration Signal ◽

Wavelet Function ◽

Frequency Region ◽

Analysis Method ◽

Vibration Signals ◽

Harmonic Wavelet ◽

Frequency Components

Harmonic wavelet function, with the strict box-shaped characteristic of spectrum, has strong ability of identifying signal in frequency domain, and can extract weak components form vibration signals in frequency domain. Using harmonic wavelet analysis method, the selected frequency region and other frequency components of vibration signal of mine ventilator were decomposed into independent frequency bands without any over-lapping or leaking. Simulation and diagnosis example show that this method has good fault diagnosis effect, and the ventilator fault is diagnosed successfully.

Download Full-text