• Title/Summary/Keyword: features of time and frequency domain

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Fault Diagnosis of Bearing Based on Convolutional Neural Network Using Multi-Domain Features

  • Shao, Xiaorui;Wang, Lijiang;Kim, Chang Soo;Ra, Ilkyeun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.5
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    • pp.1610-1629
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    • 2021
  • Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

Comparison of HRV Time and Frequency Domain Features for Myocardial Ischemia Detection (심근허혈검출을 위한 심박변이도의 시간과 주파수 영역에서의 특징 비교)

  • Tian, Xue-Wei;Zhang, Zhen-Xing;Lee, Sang-Hong;Lim, Joon-S.
    • The Journal of the Korea Contents Association
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    • v.11 no.3
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    • pp.271-280
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    • 2011
  • Heart Rate Variability (HRV) analysis is a convenient tool to assess Myocardial Ischemia (MI). The analysis methods of HRV can be divided into time domain and frequency domain analysis. This paper uses wavelet transform as frequency domain analysis in contrast to time domain analysis in short term HRV analysis. ST-T and normal episodes are collected from the European ST-T database and the MIT-BIH Normal Sinus Rhythm database, respectively. An episode can be divided into several segments, each of which is formed by 32 successive RR intervals. Eighteen HRV features are extracted from each segment by the time and frequency domain analysis. To diagnose MI, the Neural Network with Weighted Fuzzy Membership functions (NEWFM) is used with the extracted 18 features. The results show that the average accuracy from time and frequency domain features is 75.29% and 80.93%, respectively.

Time-Frequency Domain Analysis of Acoustic Signatures Using Pseudo Wigner-Ville Distribution

  • Jeon, Jae-Jin
    • Proceedings of the Acoustical Society of Korea Conference
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    • 1994.06a
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    • pp.674-679
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    • 1994
  • Acoustic signal such as speech and scattered sound, are generally a nonstationary process whose frequency contents vary at any instant of time. For time-varying signal, whether a nonstationary or a deterministic transient signal, a traditional frequency domain representation does not reveal the contents of signal characteristics and may lead to erroneous results such as the loss of desired characteristics features or the mis-interpretation for a wrong conclusion. A time-frequency domain representation is needed to characterize such signatures. Pseudo Wigner-Ville distribution (PWVD) is ideally suited for portraying nonstationary signal time-frequency domain and carried out by adapting the fast Fourier transform algorithm. In this paper, the important properties of PWVD were investigated using both stationary and nonstationry signatures by numerical examples PWVD was applied to acoustic sigtnatures to demonstrate its application for time-ferquency domain analysis.

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Defect evaluations of weld zone in rails considering phase space-frequency demain (위상공간-주파수 영역을 고려한 레일 용접부의 결함 평가)

  • 윤인식;권성태;장영권;정우현;이찬석
    • Journal of the Korean Society for Railway
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    • v.2 no.2
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    • pp.21-30
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    • 1999
  • This study proposes the analysis and evaluation method of time series ultrasonic signal using the phase space-frequency domain. Features extracted from time series signal analyze quantitatively characteristics of weld defects. For this purpose, analysis objectives in this study are features of time domain and frequency domain. Trajectory changes in the attractor indicated a substantial difference in fractal characteristics resulting from distance shifts such as parts of head and flange even though the types of defects are identified. These differences in characteristics of weld defects enables the evaluation of unique characteristics of defects in the weld zone. In quantitative fractal feature extraction, feature values of 3.848 in the case of part of head(crack) and 4.102 in the case of part of web(side hole) and 3.711 in the case of part of flange(crack) were proposed on the basis of fractal dimension. Proposed phase space-frequency domain method in this study can integrity evaluation for defect signals of rail weld zone such as side hole and crack.

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Classification of Emotional States of Interest and Neutral Using Features from Pulse Wave Signal

  • Phongsuphap, Sukanya;Sopharak, Akara
    • 제어로봇시스템학회:학술대회논문집
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    • 2004.08a
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    • pp.682-685
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    • 2004
  • This paper investigated a method for classifying emotional states by using pulse wave signal. It focused on finding effective features for emotional state classification. The emptional states considered here consisted of interest and neutral. Classification experiments utilized 65 and 60 samples of interest and neutral states respectively. We have investigated 19 features derived from pulse wave signals by using both time domain and frequency domain analysis methods with 2 classifiers of minimum distance (normalized Euclidean distanece) and ${\kappa}$-Nearest Neighbour. The Leave-one-out cross validation was used as an evaluation mehtod. Based on experimental results, the most efficient features were a combination of 4 features consisting of (i) the mean of the first differences of the smoothed pulse rate time series signal, (ii) the mean of absolute values of the second differences of thel normalized interbeat intervals, (iii) the root mean square successive difference, and (iv) the power in high frequency range in normalized unit, which provided 80.8% average accuracy with ${\kappa}$-Nearest Neighbour classifier.

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A New Endpoint Detection Method Based on Chaotic System Features for Digital Isolated Word Recognition System

  • Zang, Xian;Chong, Kil-To
    • Proceedings of the IEEK Conference
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    • 2009.05a
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    • pp.37-39
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    • 2009
  • In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. Since the background noise presenting to record will introduce disturbance while we just want to get the stationary parameters to represent the corresponding speech section, in particular, a major source of error in automatic recognition system of isolated words is the inaccurate detection of beginning and ending boundaries of test and reference templates, thus we must find potent method to remove the unnecessary regions of a speech signal. The conventional methods for speech endpoint detection are based on two simple time-domain measurements - short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Comparing with the conventional methods based on short-time energy and short-time zero-crossing rate, the novel approach based on time-domain Lyapunov Exponents(LEs) is low complexity and suitable for Digital Isolated Word Recognition System.

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Variations of Autocovariances of Speech and its related Signals in time, frequency and quefrency domains (음성 및 음성 관련 신호의 주파수 및 Quefrency 영역에서의 자기공분산 변화)

  • Kim, Seon-Il
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2011.05a
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    • pp.340-343
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    • 2011
  • To distinguish between a group of speech signals and nonspeech signals, you can use several features in domains like frequency, quefrency and time. It is very important to use features that differentiate two signal groups. As a feature to separate two signal groups, autocorrelation method was proposed and the variances between groups were studied. Autocovariances were just calculated for the time domain signal. Signals were divided into segments which consist of 128 data to be transformed to the frequency and quefrency domains. Autocovariances between each coefficient of segments in FFTs and quefrencies were found and they were averaged over wide spectrum. It is clear that the autocovariances in frequency domain show great differences between groups of signals.

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Power Quality Disturbances Detection and Classification using Fast Fourier Transform and Deep Neural Network (고속 푸리에 변환 및 심층 신경망을 사용한 전력 품질 외란 감지 및 분류)

  • Senfeng Cen;Chang-Gyoon Lim
    • The Journal of the Korea institute of electronic communication sciences
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    • v.18 no.1
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    • pp.115-126
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    • 2023
  • Due to the fluctuating random and periodical nature of renewable energy generation power quality disturbances occurred more frequently in power generation transformation transmission and distribution. Various power quality disturbances may lead to equipment damage or even power outages. Therefore it is essential to detect and classify different power quality disturbances in real time automatically. The traditional PQD identification method consists of three steps: feature extraction feature selection and classification. However, the handcrafted features are imprecise in the feature selection stage, resulting in low classification accuracy. This paper proposes a deep neural architecture based on Convolution Neural Network and Long Short Term Memory combining the time and frequency domain features to recognize 16 types of Power Quality signals. The frequency-domain data were obtained from the Fast Fourier Transform which could efficiently extract the frequency-domain features. The performance in synthetic data and real 6kV power system data indicate that our proposed method generalizes well compared with other deep learning methods.

Rotor Fault Detection of Induction Motors Using Stator Current Signals and Wavelet Analysis

  • Hyeon Bae;Kim, Youn-Tae;Lee, Sang-Hyuk;Kim, Sungshin;Wang, Bo-Hyeun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09a
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    • pp.539-542
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    • 2003
  • A motor is the workhorse of our industry. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. Different internal motor faults (e.g., inter-turn short circuits, broken bearings, broken rotor bars) along with external motor faults (e.g., phase failure, mechanical overload, blocked rotor) are expected to happen sooner or later. This paper introduces the fault detection technique of induction motors based upon the stator current. The fault motors have rotor bar broken or rotor unbalance defect, respectively. The stator currents are measured by the current meters and stored by the time domain. The time domain is not suitable to represent the current signals, so the frequency domain is applied to display the signals. The Fourier Transformer is used for the conversion of the signal. After the conversion of the signals, the features of the signals have to be extracted by the signal processing methods like a wavelet analysis, a spectrum analysis, etc. The discovered features are entered to the pattern classification model such as a neural network model, a polynomial neural network, a fuzzy inference model, etc. This paper describes the fault detection results that use wavelet decomposition. The wavelet analysis is very useful method for the time and frequency domain each. Also it is powerful method to detect the features in the signals.

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Hearing aid application of feedback cancellation algorithm in frequency domain (주파수 대역에서의 피드백 제거 알고리즘의 보청기 응용)

  • Jarng, Soon-Suck
    • The Journal of the Acoustical Society of Korea
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    • v.35 no.4
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    • pp.272-279
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    • 2016
  • In this paper, the realization of a hearing aid adaptively cancelling feedback noise was considered. Conventional least mean square method in time domain was transformed into frequency domain in order to minimize computational burden. The adaptive filter algorithm was evaluated by Matlab (Matrix laboratory), and it was confirmed by CSR 8675 Bluetooth DSP IC (Digital Signal Processor Integrated Circuit) chip firmware realization. Some remote control features by a smart phone was added to the smart hearing aid for user interface easiness.