Vibration Sources Identification with Independent Component Analysis

Ye, Hongxian; Yang, Shixi; Yang, Jiangxin; Ji, Huawei

doi:10.1109/wcica.2006.1714191

Cited by 4 publications

References 13 publications

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Using independent component analysis scheme for helicopter main gearbox bearing defect identification

Duan

Corsar

Zhou

et al. 2017

2017 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Abstract-Vibration signal analysis is the most common technique for helicopter health condition monitoring. It has been widely employed to detect helicopter gearbox fault and ensure the safe operation. Through the years, vibration signal analysis has a significant contribution to successfully prevent a number of accidents. However, vibration based bearing identification remains a challenge in bearing defect diagnosis because bearing defects signatures are contaminated by strong background noise. In this paper, the independent component analysis (ICA) scheme was utilized to analyze vibration signal captured from a CS29 Category A helicopter main gearbox, where bearing faults were seeded on the second epicyclic stages planetary gears bearing. The ICA scheme could separate the multichannel signals into the mutually independent components. Hence, it is feasible to reduce noise from sensor signals. The analysis result showed that ICA scheme is a promising method for detecting the bearing fault signatures.Index Terms-Helicopter main gearbox; fault diagnosis; independent component analysis.

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Using independent component analysis scheme for helicopter main gearbox bearing defect identification

Duan

Corsar

Zhou

et al. 2017

2017 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Independent component analysis as applied to vibration source separation and fault diagnosis

Mahvash

Lakis

2014

Journal of Vibration and Control

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In health monitoring of complex mechanical systems such as aircraft engines there are many components whose diagnosis is of great interest for the industry. A conventional way to monitor these components is to collect vibration signals using accelerometers placed in their closest vicinity. However, due to some restrictions such as inaccessibility, it is not always practical to place the accelerometers as such. In many cases, pre-installed instrumentations are used, which are usually inadequate and placed on the carcass of the structure. Nevertheless, even if the accelerometers are positioned very close to the components, they would collect signals not just from one specific component but from other components as well. In this study, we sought to employ frequency-based independent component analysis (ICA) to recover the signals produced by components within a single complex system. In such a case, differences between “blind source separation” and vibration source separation are discussed. A new workaround for the permutation ambiguity encountered in the implication of ICA is proposed. Finally, in order to demonstrate the applicability of the new proposed approach, experimental results carried out on a test bed are presented.

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Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

Jiang

Qin

Zhang

et al. 2011

Noise & Vibration Worldwide

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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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Vibration Sources Identification with Independent Component Analysis

Cited by 4 publications

References 13 publications

Using independent component analysis scheme for helicopter main gearbox bearing defect identification

Using independent component analysis scheme for helicopter main gearbox bearing defect identification

Independent component analysis as applied to vibration source separation and fault diagnosis

Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

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