Characterizing the time-frequency image by morphological pattern spectrum for evaluating electromagnetic environment complexity

Time–frequency representations (TFR) have been intensively employed for analysing vibration signals in gear fault diagnosis. However, in many applications, TFR are simply utilized as a visual aid to detect gear defects. An attractive issue is to utilize the TFR for automatic classification of faults. A key step for this study is to extract discriminative features from TFR as input feature vector for classifiers. This article contributes to this ongoing investigation by applying morphological pattern spectrum (MPS) to characterize the TFR for gear fault diagnosis. The S transform, which combines the separate strengths of the short-time Fourier transform and wavelet transforms, is chosen to perform the time–frequency analysis of vibration signals from gear. Then, the MPS scheme is applied to extract the discriminative features from the TFR. The promise of MPS is illustrated by performing our procedure on vibration signals measured from a gearbox with five operating states. Experiment results demonstrate the MPS to be a satisfactory scheme for characterizing TFRs for an accurate classification of gear faults.

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Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis

Journal of Sound and Vibration ◽

10.1016/j.jsv.2013.01.017 ◽

2013 ◽

Vol 332 (13) ◽

pp. 3329-3337 ◽

Cited By ~ 10

Author(s):

Bing Li ◽

Shuang-shan Mi ◽

Peng-yuan Liu ◽

Zheng-jun Wang

Keyword(s):

Fault Diagnosis ◽

Morphological Pattern ◽

Pattern Spectrum ◽

Time Frequency

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Hot Strip Tail Flick Recognition Based on Morphological Pattern Spectrum

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.572.25 ◽

2012 ◽

Vol 572 ◽

pp. 25-30

Author(s):

Li Jing Han ◽

Jian Hong Yang ◽

Min Lin ◽

Jin Wu Xu

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Steel Strip ◽

Wavelet Packet ◽

Support Vector ◽

Tail Flick ◽

Morphological Pattern ◽

Feature Extraction Method ◽

Pattern Spectrum ◽

Hot Strip

Hot strip tail flick is an abnormal production phenomenon, which brings many damages. To recognize the tail flick signals from all throwing steel strip signals, a feature extraction method based on morphological pattern spectrum is proposed in this paper. The area between signal curves after multiscale opening operation and the horizontal axis is computed as the pattern spectrum value and it reflects the geometric information differences. Then, support vector machine is used as the classifier. Experimental results show that the total correct rate based on pattern spectrum feature reached 96.5%. Compared with wavelet packet energy feature, the total correct rate is 92.1%. So, the feasibility and availability of this new feature extraction method are verified.

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Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite

10.5194/egusphere-egu21-11024 ◽

2021 ◽

Author(s):

Jing Yuan ◽

Zijie Wang ◽

Dehe Yang ◽

Qiao Wang ◽

Zeren Zima ◽

...

Keyword(s):

Short Term Memory ◽

Satellite Observation ◽

Original Data ◽

Electromagnetic Environment ◽

Mel Frequency Cepstral Coefficients ◽

Frequency Profile ◽

Time Frequency ◽

Wave Data ◽

Audio Clip ◽

Long Short Term Memory

Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients&#160;(MFCCs) of the patch data as a type of&#160;cepstral&#160;representation of the&#160;audio&#160;clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile.&#160;Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM

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