Feature Extraction Method for Predicting Depression by Frequency Domain Analysis

Aiming at the local fault diagnosis of planetary gearbox gears, a feature extraction method based on improved dynamic time warping (IDTW) is proposed. As a calibration matching algorithm, the dynamic time warping method can detect the differences between a set of time-domain signals. This paper applies the method to fault diagnosis. The method is simpler and more intuitive than feature extraction methods in the frequency domain and the time-frequency domain, avoiding their limitations and disadvantages. Due to the shortcomings of complex calculation, singularity and poor robustness, the paper proposes an improved method. Finally, the method is verified by envelope spectral feature analysis and the local fault diagnosis of gears is realised.

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Discriminant DCT Feature Extraction

Computational Intelligence and its Applications - Biometric Image Discrimination Technologies ◽

10.4018/978-1-59140-830-7.ch008 ◽

2006 ◽

pp. 205-221

Author(s):

David Zhang ◽

Xiao-Yuan Jing ◽

Jian Yang

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Discrete Cosine Transform ◽

Domain Analysis ◽

Frequency Domain Analysis ◽

Experimental Results ◽

Cosine Transform ◽

Analysis Technique

This chapter provides a feature extraction approach that combines the discrete cosine transform (DCT) with LDA. The DCT-based frequency-domain analysis technique is introduced first. Then, we describe the presented discriminant DCT approach and analyze its theoretical properties. Finally, we offer detailed experimental results and a chapter summary.

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A Novel Local Time-Frequency Domain Feature Extraction Method for Tool Condition Monitoring Using S-Transform and Genetic Algorithm

IFAC Proceedings Volumes ◽

10.3182/20140824-6-za-1003.02515 ◽

2014 ◽

Vol 47 (3) ◽

pp. 3516-3521 ◽

Cited By ~ 4

Author(s):

Javad Soltani Rad ◽

Youmin Zhang ◽

Chevy Chen

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Frequency Domain ◽

Condition Monitoring ◽

Extraction Method ◽

Tool Condition Monitoring ◽

Feature Extraction Method ◽

Time Frequency ◽

Tool Condition ◽

S Transform

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Effective and extensible feature extraction method using genetic algorithm-based frequency-domain feature search for epileptic EEG multiclassification

Medicine ◽

10.1097/md.0000000000006879 ◽

2017 ◽

Vol 96 (19) ◽

pp. e6879 ◽

Cited By ~ 15

Author(s):

Tingxi Wen ◽

Zhongnan Zhang

Keyword(s):

Genetic Algorithm ◽

Feature Extraction ◽

Frequency Domain ◽

Extraction Method ◽

Feature Extraction Method ◽

Feature Search

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A Vibration Feature Extraction Method Based on Time-Domain Dimensional Parameters and Mahalanobis Distance

Mathematical Problems in Engineering ◽

10.1155/2021/2498178 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Junjun Chen ◽

Bing Xu ◽

Xin Zhang

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Time Domain ◽

Mahalanobis Distance ◽

Extraction Method ◽

Vibration Signal ◽

Feature Extraction Method ◽

Feature Parameters ◽

Dimensional Parameters ◽

Failure Types

To accurately describe the characteristics of a signal, the feature parameters in time domain and frequency domain are usually extracted for characterization. However, the total number of feature parameters in time domain and frequency domain exceeds twenty, and all of the feature parameters are used for feature extraction, which will result in a large amount of data processing. For the purpose of using fewer feature parameters to accurately reflect the characteristics of the vibration signal, a simple but effective vibration feature extraction method combining time-domain dimensional parameters (TDDP) and Mahalanobis distance (MD) is proposed, i.e., TDDP-MD. In this method, ten time-domain dimensional parameters are selected to extract fault features, and the distance evaluation technique based on Mahalanobis distance criterion function is also introduced to calculate the feature vector, which can be used to classify different failure types. Finally, the proposed method is applied to fault diagnosis of rolling element bearings, and experimental analysis results show that the proposed method can recognize different failure types accurately and effectively with only ten time-domain dimensional parameters and a small quantity of training samples.

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An Improved Feature Extraction Method for Rolling Bearing Fault Diagnosis Based on MEMD and PE

Polish Maritime Research ◽

10.2478/pomr-2018-0080 ◽

2018 ◽

Vol 25 (s2) ◽

pp. 98-106 ◽

Cited By ~ 1

Author(s):

Hu Zhang ◽

Lei Zhao ◽

Quan Liu ◽

Jingjing Luo ◽

Qin Wei ◽

...

Keyword(s):

Feature Extraction ◽

Frequency Domain ◽

Frequency Band ◽

Extraction Method ◽

Rolling Bearing ◽

Extraction Methods ◽

Permutation Entropy ◽

Vibration Signals ◽

Feature Extraction Method ◽

Bearing Vibration

Abstract The health condition of rolling bearing can directly influence to the efficiency and lifecycle of rotating machinery, thus monitoring and diagnosing the faults of rolling bearing is of great importance. Unfortunately, vibration signals of rolling bearing are usually overwhelmed by external noise, so the fault frequencies of rolling bearing cannot be readily obtained. In this paper, an improved feature extraction method called IMFs_PE, which combines the multivariate empirical mode decomposition with the permutation entropy, is proposed to extract fault frequencies from the noisy bearing vibration signals. First, the raw bearing vibration signals are filtered by an optimal band-pass filter determined by SK to remove the irrelative noise which is not in the same frequency band of fault frequencies. Then the filtered signals are processed by the IMFs_PE to get rid of the relative noise which is in the same frequency band of fault frequencies. Finally, a frequency domain condition indicator FFR(Fault Frequency Ratio), which measures the magnitude of fault frequencies in frequency domain, is calculated to compare the effectiveness of the feature extraction methods. The feature extraction method proposed in this paper has advantages of removing both irrelative noise and relative noise over other feature extraction methods. The effectiveness of the proposed method is validated by simulated and experimental bearing signals. And the results are shown that the proposed method outperforms other state of the art algorithms with regards to fault feature extraction of rolling bearing.

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Roll Eccentricity Extraction Method Based on Comprehensive Signal Processing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.184-185.732 ◽

2012 ◽

Vol 184-185 ◽

pp. 732-737

Author(s):

Yang Yang ◽

Yi Qun Wang ◽

Gang Chen

Keyword(s):

Signal Processing ◽

Frequency Domain ◽

Extraction Method ◽

Rolling Force ◽

Domain Analysis ◽

Frequency Domain Analysis ◽

Total Output ◽

Engineering Practice ◽

Thickness Gauge ◽

Backup Roll

In modern steel production, strip products account for more than half of the total output. Roll eccentricity is ubiquitous in strip mills, which has a periodic impact on the strip thickness difference. Roll eccentricity compensation is an important part of the automatic gauge control system. Most roll eccentricity compensations extract the roll eccentricity based on the frequency domain analysis of the rolling force. It is difficult to ensure measurement precision of roll eccentricity due to the complexity of interference of the rolling force, therefore the compensation accuracy is difficult to ensure. The roll eccentricity extraction method, based on comprehensive signal processing, was introduced in this paper combined with engineering practice. The roll eccentricity was comprehensively calculated by thickness gauge and other signals that have been delayed; the roll phases were calculated by the roll rotational displacement; and finally, the roll eccentric fluctuation of the upper backup roll and lower backup roll were extracted. The roll eccentricity could be predicted by calculating roll eccentric fluctuations and phases, in order to provide the basis for accurate compensation. According to the comparison with roll eccentricity extracted by the frequency domain analysis of the rolling force, the roll eccentricity obtained by the roll eccentricity extraction method, based on comprehensive signal processing, is more accurate. It provides a reliable basis for roll eccentricity compensation in order to improve the accuracy of the thickness of the strip products.

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PALMPRINT IDENTIFICATION BY FOURIER TRANSFORM

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001402001757 ◽

2002 ◽

Vol 16 (04) ◽

pp. 417-432 ◽

Cited By ~ 215

Author(s):

WENXIN LI ◽

DAVID ZHANG ◽

ZHUOQUN XU

Keyword(s):

Decision Making ◽

Feature Extraction ◽

Fourier Transform ◽

Frequency Domain ◽

Extraction Method ◽

Feature Matching ◽

Identification Process ◽

Feature Extraction Method ◽

Palmprint Identification ◽

New Feature

Palmprint identification refers to searching in a database for the palmprint template, which is from the same palm as a given palmprint input. The identification process involves preprocessing, feature extraction, feature matching and decision-making. As a key step in the process, in this paper, we propose a new feature extraction method by converting a palmprint image from a spatial domain to a frequency domain using Fourier Transform. The features extracted in the frequency domain are used as indexes to the palmprint templates in the database and the searching process for the best match is conducted by a layered fashion. The experimental results show that palmprint identification based on feature extraction in the frequency domain is effective in terms of accuracy and efficiency.

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