A new rotating machinery fault diagnosis method based on local oscillatory-characteristic decomposition

Zhang, Kang; Chen, Xiangmin; Liao, Lida; Tang, Mingzhu; Wu, Jiateng

doi:10.1016/j.dsp.2018.02.018

Cited by 20 publications

(9 citation statements)

References 25 publications

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“…But with the development of research, some defects have been found, such as lack of adaptability of WT [11], the problems of end effects [12], over-and undershoot [13], mode mixing [14] in EMD, and the problems of large amount of calculation, or local amplitude abrupt changes [15] in LMD. Recently, the authors proposed another new adaptive signal decomposition method --local oscillatory characteristic decomposition (LOD) [16]. This method is based on the local time-scale characteristic of the signal itself, and by mathematical operations including differential, coordinate domain transform, and piecewise linear transform, enabling the self-adaptive decomposition of a multi-component signal into a series of mono-oscillation components (MOCs) whose instantaneous frequencies have physical meanings.…”

Section: Introductionmentioning

confidence: 99%

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

Zhang

Niu²,

et al. 2021

Measurement Science Review

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The rolling bearing and gear fault features are generally shown as modulation characteristics of their vibration signals. The empirical envelope (EE) method is an accordingly common demodulation method. However, the EE method has the defects of over- and undershoot, which may lead to demodulation error. According to this, an envelope optimization algorithm -- empirical optimal envelope (EOE) is introduced into the EE method, and an improved empirical envelope (IEE) method is obtained to calculate the instantaneous amplitude and instantaneous frequency of mono-component modulation signal. Furthermore, aiming at the actual measured mechanical vibration signal has multi-component modulation feature, the IEE method is combined with an adaptive signal decomposition method -- local oscillatory characteristic decomposition (LOD) proposed by the author, thereby a new multi-component signal demodulation method based on LOD and IEE is proposed. The proposed method is compared with Hilbert transform (HT) and Teager energy operator (TEO) demodulation methods by the simulation signal and actual measured mechanical vibration signal. The results show that the demodulation effects including edge effects, negative frequency, over- and undershoot of the proposed method are significantly improved and can extract the rolling bearing and gear fault feature information clearly.

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Section: Introductionmentioning

confidence: 99%

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

Zhang

Niu²,

et al. 2021

Measurement Science Review

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“…The method is highly efficient in the calculation and more suitable for real-time online processing and analysis of data. The LOD method has been applied to fault analysis of gears and rolling bearings since its proposal and has achieved a good analysis outcome [22,23], but some limitations have been encountered in further applications. For example, although the piecewise linear transformation operation improves the calculation efficiency, the linear connection may lead to the unsmoothness of the decomposed MOC components.…”

Section: Introductionmentioning

confidence: 99%

The Rational Spline Interpolation Based-LOD Method and Its Application to Rotating Machinery Fault Diagnosis

et al. 2020

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Local oscillatory-characteristic decomposition (LOD) is a relatively new self-adaptive time-frequency analysis methodology. The method, based on local oscillatory characteristics of the signal itself uses three mathematical operations such as differential, coordinate domain transform, and piecewise linear transform to decompose the multi-component signal into a series of mono-oscillation components (MOCs), which is very suitable for processing multi-component signals. However, in the LOD method, the computational efficiency and real-time processing performance of the algorithm can be significantly improved by the use of piecewise linear transformation, but the MOC component lacks smoothness, resulting in distortion. In order to overcome the disadvantages mentioned above, the rational spline function that spline shape can be adjusted and controlled is introduced into the LOD method instead of the piecewise linear transformation, and the rational spline-local oscillatory-characteristic decomposition (RS-LOD) method is proposed in this paper. Based on the detailed illustration of the principle of RS-LOD method, the RS-LOD, LOD, and empirical mode decomposition (EMD) are compared and analyzed by simulation signals. The results show that the RS-LOD method can significantly improve the problem of poor smoothness of the MOC component in the original LOD method. Moreover, the RS-LOD method is applied to the fault feature extraction of rotating machinery for the multi-component modulation characteristics of rotating machinery fault vibration signals. The analysis results of the rolling bearing and fan gearbox fault vibration signals show that the RS-LOD method can effectively extract the fault feature of the rotating mechanical vibration signals.

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“…Some scholars tried to modify EMD by improving the extreme point fitting [ 22 ]. Moreover, some variants of EMD, such as intrinsic time-scale decomposition (ITD) [ 23 ], local mean decomposition (LMD) [ 24 ], and local oscillatory-characteristic decomposition (LOD) [ 25 ] were developed by modifying the envelope definition. However, they are also sensitive to noise.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

Tang

Pang

et al. 2019

Entropy

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The accurate fault diagnosis of gearboxes is of great significance for ensuring safe and efficient operation of rotating machinery. This paper develops a novel fault diagnosis method based on hierarchical instantaneous energy density dispersion entropy (HIEDDE) and dynamic time warping (DTW). Specifically, the instantaneous energy density (IED) analysis based on singular spectrum decomposition (SSD) and Hilbert transform (HT) is first applied to the vibration signal of gearbox to acquire the IED signal, which is designed to reinforce the fault feature of the signal. Then, the hierarchical dispersion entropy (HDE) algorithm developed in this paper is used to quantify the complexity of the IED signal to obtain the HIEDDE as fault features. Finally, the DTW algorithm is employed to recognize the fault types automatically. The validity of the two parts that make up the HIEDDE algorithm, i.e., the IED analysis for fault features enhancement and the HDE algorithm for quantifying the information of signals, is numerically verified. The proposed method recognizes the fault patterns of the experimental data of gearbox accurately and exhibits advantages over the existing methods such as multi-scale dispersion entropy (MDE) and refined composite MDE (RCMDE).

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A new rotating machinery fault diagnosis method based on local oscillatory-characteristic decomposition

Cited by 20 publications

References 25 publications

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

A New Demodulation Method for Mechanical Fault Feature Extraction based on LOD and IEE

The Rational Spline Interpolation Based-LOD Method and Its Application to Rotating Machinery Fault Diagnosis

Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping

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