선형 시간-주파수 표현에서 모터와 기어박스의 고장 특성 감지를 위한 가중 잔차 레니 정보에 관한 연구

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Many studies have been conducted for fault detection of rotating machinery under varying speed conditions using time-frequency representation (TFR). However, the parameters of TFR have been selected by researchers empirically in most previous studies. Also, the previously proposed TFR measures do not suggest the optimal parameter for fault diagnosis. This paper thus proposed a TFR measure to select the parameter from the perspective of detecting fault features. The proposed measure, Weighted Residual Rényi Information (WRRI), is based on Rényi Information, selected through a comparative study among previously suggested measures. WRRI, defined as a modified form of the input atom of Rényi Information, consists of two terms. The first term is the residual term that extracts the fault feature, and the second term is the weighting term that reduces the effect of noise. The validation process consists of the two steps; 1) analytic signal, 2) motor, and gearbox signal. In the validation using an analytic signal, it confirmed that WRRI suggested a better parameter for detecting fault features than the Rényi Information. Also, in the validation using a motor testbed signal and gearbox testbed signal, it confirmed that WRRI was possible to select more suitable parameters for fault diagnosis than the Rényi Information.변속 조건에서 운전되는 회전기기 고장진단을 위해 시간-주파수 표현을 사용한 많은 연구들이 수행되어왔다. 하지만 대부분의 연구에서 시간-주파수 표현의 파라미터는 연구자들에 의해 경험적으로 선택되었다. 또한 이전에 제안된 시간-주파수 표현 측정방법도 고장 진단을 위한 최적의 파라미터를 제안해주지 못한다. 본 연구에서는 고장 특징 검출을 목적으로 시간-주파수 표현의 파라미터를 제안해주는 측정방법을 제안한다. 제안 측정방법 가중 잔차 레니 정보(WRRI)는 이전 연구들에서 제안된 측정밥법들에 대한 비교연구를 통해 선정된 레니 정보에 기반한다. WRRI는 레니 정보의 입력 형태를 2가지 성분으로 구성된 변형 형태를 통해 정의된다. 첫 번째 성분은 고장 특징 추출을 위한 잔차성분이고, 두 번째 성분은 노이즈의 영향성을 줄이기 위한 가중성분이다. 검증 과정은 산술적 신호와 모터, 기어 박스로 이루어진 신호를 통해 2 단계로 진행된다. 산술적 신호를 사용한 검증과정에서 WRRI는 기존 측정 방법인 레니 정보보다 고장 특징 검출에 더 적합한 시간-주파수 표현 파라미터를 제안했다. 또한 모터와 기어박스 테스트베드 신호를 사용한 검증과정에서 WRRI는 레니 정보보다 고장 특징 추출과 진단에 더 적합한 시간-주파수 파라미터를 제안했다.Chapter 1 . Introduction 1 1.1 Introduction 1 Chapter 2 . TFR Measure for Readability 4 2.1 Linear TFR 4 2.2 TFR Measures 11 2.3 Comparative Study of Previous Measure 13 Chapter 3 . TFR Measure for Detectability 16 3.1 Fault Feature Detectability 16 3.2 Weighted Residual Rnyi Information 22 Chapter 4 . Validation of the Proposed Measure 29 4.1 Analytic Signals Having Fault Feature 29 4.2 Experiment Signal 33 Chapter 5 . Conclusion 57 Bibliography 58 국문 초록 64Maste

Recursive Parametric Frequency/Spectrum Estimation for Nonstationary Signals With Impulsive Components Using Variable Forgetting Factor

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Wavelet Analysis and Denoising: New Tools for Economists

This paper surveys the techniques of wavelets analysis and the associated methods of denoising. The Discrete Wavelet Transform and its undecimated version, the Maximum Overlapping Discrete Wavelet Transform, are described. The methods of wavelets analysis can be used to show how the frequency content of the data varies with time. This allows us to pinpoint in time such events as major structural breaks. The sparse nature of the wavelets representation also facilitates the process of noise reduction by nonlinear wavelet shrinkage , which can be used to reveal the underlying trends in economic data. An application of these techniques to the UK real GDP (1873-2001) is described. The purpose of the analysis is to reveal the true structure of the data - including its local irregularities and abrupt changes - and the results are surprising.Wavelets, Denoising, Structural breaks, Trend estimation

Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

K

A Wavelet Visible Difference Predictor

In this paper, we describe a model of the human visual system (HVS) based on the wavelet transform. This model is largely based on a previously proposed model, but has a number of modifications that make it more amenable to potential integration into a wavelet based image compression scheme. These modifications include the use of a separable wavelet transform instead of the cortex transform, the application of a wavelet contrast sensitivity function (CSF), and a simplified definition of subband contrast that allows us to predict noise visibility directly from wavelet coefficients. Initially, we outline the luminance, frequency, and masking sensitivities of the HVS and discuss how these can be incorporated into the wavelet transform. We then outline a number of limitations of the wavelet transform as a model of the HVS, namely the lack of translational invariance and poor orientation sensitivity. In order to investigate the efficacy of this wavelet based model, a wavelet visible difference predictor (WVDP) is described. The WVDP is then used to predict visible differences between an original and compressed (or noisy) image. Results are presented to emphasize the limitations of commonly used measures of image quality and to demonstrate the performance of the WVDP. The paper concludes with suggestions on how the WVDP can be used to determine a visually optimal quantization strategy for wavelet coefficients and produce a quantitative measure of image quality

CNN AND LSTM FOR THE CLASSIFICATION OF PARKINSON'S DISEASE BASED ON THE GTCC AND MFCC

Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 percent of Parkinson's disease sufferers have some form of early speech impairment, recent studies on tele diagnosis of Parkinson's disease have focused on the recognition of voice impairments from vowel phonations or the subjects' discourse. In this paper, we present a new approach for Parkinson's disease detection from speech sounds that are based on CNN and LSTM and uses two categories of characteristics Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Cepstral Coefficients (GTCC) obtained from noise-removed speech signals with comparative EMD-DWT and DWT-EMD analysis. The proposed model is divided into three stages. In the first step, noise is removed from the signals using the EMD-DWT and DWT-EMD methods. In the second step, the GTCC and MFCC are extracted from the enhanced audio signals. The classification process is carried out in the third step by feeding these features into the LSTM and CNN models, which are designed to define sequential information from the extracted features. The experiments are performed using PC-GITA and Sakar datasets and 10-fold cross validation method, the highest classification accuracy for the Sakar dataset reached 100% for both EMD-DWT-GTCC-CNN and DWT-EMD-GTCC-CNN, and for the PC-GITA dataset, the accuracy is reached 100% for EMD-DWT-GTCC-CNN and 96.55% for DWT-EMD-GTCC-CNN. The results of this study indicate that the characteristics of GTCC are more appropriate and accurate for the assessment of PD than MFCC

Analysis of the structure of time-frequency information in electromagnetic brain signals

This thesis encompasses methodological developments and experimental work aimed at revealing information contained in time, frequency, and time–frequency representations of electromagnetic, specifically magnetoencephalographic, brain signals. The work can be divided into six endeavors. First, it was shown that sound slopes increasing in intensity from undetectable to audible elicit event-related responses (ERRs) that predict behavioral sound detection. This provides an opportunity to use non-invasive brain measures in hearing assessment. Second, the actively debated generation mechanism of ERRs was examined using novel analysis techniques, which showed that auditory stimulation did not result in phase reorganization of ongoing neural oscillations, and that processes additive to the oscillations accounted for the generation of ERRs. Third, the prerequisites for the use of continuous wavelet transform in the interrogation of event-related brain processes were established. Subsequently, it was found that auditory stimulation resulted in an intermittent dampening of ongoing oscillations. Fourth, information on the time–frequency structure of ERRs was used to reveal that, depending on measurement condition, amplitude differences in averaged ERRs were due to changes in temporal alignment or in amplitudes of the single-trial ERRs. Fifth, a method that exploits mutual information of spectral estimates obtained with several window lengths was introduced. It allows the removal of frequency-dependent noise slopes and the accentuation of spectral peaks. Finally, a two-dimensional statistical data representation was developed, wherein all frequency components of a signal are made directly comparable according to spectral distribution of their envelope modulations by using the fractal property of the wavelet transform. This representation reveals noise buried processes and describes their envelope behavior. These examinations provide for two general conjectures. The stability of structures, or the level of stationarity, in a signal determines the appropriate analysis method and can be used as a measure to reveal processes that may not be observable with other available analysis approaches. The results also indicate that transient neural activity, reflected in ERRs, is a viable means of representing information in the human brain.reviewe