Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation

Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.

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Blind Source Separation of Single-channel Mechanical Signal Based on Empirical Mode Decomposition

Journal of Mechanical Engineering ◽

10.3901/jme.2011.04.012 ◽

2011 ◽

Vol 47 (04) ◽

pp. 12 ◽

Cited By ~ 10

Author(s):

Wenfeng WU

Keyword(s):

Empirical Mode Decomposition ◽

Blind Source Separation ◽

Single Channel ◽

Source Separation ◽

Mode Decomposition ◽

Mechanical Signal

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Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x17500048 ◽

2017 ◽

Vol 09 (02) ◽

pp. 1750004 ◽

Cited By ~ 1

Author(s):

Pawel Rzeszucinski ◽

Michal Juraszek ◽

James R. Ottewill

Keyword(s):

Empirical Mode Decomposition ◽

Ensemble Empirical Mode Decomposition ◽

Diagnostic Information ◽

Optimal Parameters ◽

Vibration Data ◽

Mode Decomposition ◽

Ultrasound Signal ◽

Diagnostic Capabilities ◽

Mixing Problem ◽

Mode Mixing

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

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