Fast Averaged Cyclic Periodogram method to compute spectral correlation and coherence

Alsalaet, Jaafar

doi:10.1016/j.isatra.2022.01.029

Cited by 8 publications

(6 citation statements)

References 35 publications

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“…Taking into account noise due to other mechanical/electrical sources, bearing vibration can be represented by the following equation [1,34]:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…where S 2X (f, α) is the spectral correlation function that represents the power distribution of the signal with respect to its spectral frequency f and cyclic frequency α and X( f ) represents the Fourier transform of the signal blocks. The SC function can be calculated by using a number of techniques such as FFT accumulation method [41], averaged cyclic periodogram (ACP) [3], fast spectral correlation [32], or Fast ACP [34]. For efficiency, the Fourier transform of the signal X( f ) is calculated using the FFT.…”

Section: Cs Vibration Signalsmentioning

confidence: 99%

“…Each block is windowed and transformed using the FFT and then correlated with the transforms of the other blocks. Details are provided in [34]. In this work, block size of 256 is used for all cases since it provides a compromise between accuracy and processing time.…”

Section: Cs Vibration Signalsmentioning

confidence: 99%

“…However, the computation time is the major disadvantage of CS analysis techniques [32]. With the development of efficient algorithms to calculate the spectral correlation (SC) and spectral coherence (SCoh) [32][33][34], the computation time limitation is no more of concern. The SCoh map is a bi-frequency color-coded map with one axis representing the cyclic frequency and the other axis representing the spectral frequency.…”

Section: Introductionmentioning

confidence: 99%

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Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network

Alsalaet¹,

Hajnayeb²,

Baheth³

2023

Meas. Sci. Technol.

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Accurate fault diagnosis is vital for modern maintenance strategies to improve machinery reliability and efficiency. Automated predictive tools, such as deep learning, are gaining more attention as the need for more general and robust diagnosis algorithms is crucial. In this work, a rotational-speed-independent diagnosis algorithm based on using a novel 2D color-coded map as the input to a deep artificial neural network (DNN) is proposed. The 2D map is named normalized diagnostic feature-gram (NDFgram). The proposed algorithm is applied for bearing fault diagnosis to investigate its effectiveness. For that purpose, the bearing vibration signals are processed first to obtain the bi-frequency spectral coherence data. Secondly, diagnostic features (DF) are calculated at specific cyclic frequencies owing to bearing faults by integrating the obtained SCoh data over the spectral frequency domain using a center frequency and frequency range. The calculated DFs are represented by a 2D map against the center frequency and frequency resolution. The maps from different fault features are stacked together to form the diagnostic patterns. Thirdly, a pretrained convolutional neural network (CNN) is applied to learn the feature pattern and diagnose the bearing faults. The CNN is trained using fixed-speed data and then it is applied to diagnose faults in the test data recorded at the same speed. Then, it is also tested using variable-speed data and data of another ball bearing type in order to show the independency on the rotational speed and ball bearing type in practice. The results show a 100% success rate for the constant-speed tests and 98.16% accuracy for the variable-speed testing dataset. The accuracy of diagnosing the faults of the second type of ball bearing is 98.56%. The diagnosis accuracy of the proposed method is still high even when a white noise is artificially added to the signals in the noise insusceptibility test.

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“…Taking into account noise due to other mechanical/electrical sources, bearing vibration can be represented by the following equation [1,34]:…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Cs Vibration Signalsmentioning

confidence: 99%

Section: Cs Vibration Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network

Alsalaet¹,

Hajnayeb²,

Baheth³

2023

Meas. Sci. Technol.

Self Cite

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show abstract

“…The spectral coherence calculation extracts the phase and spectral magnitude correlation between the quantities being computed based on temporal correlation [ 24 , 25 , 26 ]. By calculating the cosine of the angle of the input vector in the high–dimensional space, each frequency point’s spectral value is treated as a random variable, and the correlation between the two random variables is calculated (the cosine of the angle of the vector).…”

Section: Msc–sgmd Algorithmmentioning

confidence: 99%

Train Axlebox Bearing Fault Diagnosis Based on MSC–SGMD

Bai,

Xue,

Chen

2023

Sensors

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Train axlebox bearings are subject to harsh service conditions, and the difficulty of diagnosing compound faults has brought greater challenges to the maintenance of high–quality train performance. In this paper, based on the traditional symplectic geometry mode decomposition (SGMD) algorithm, a maximum spectral coherence signal reconstruction algorithm is proposed to extract the intrinsic connection between the SGMD components with the help of the frequency domain coherence idea and reconstruct the key signal components so as to effectively improve the extraction of composite fault features of axlebox bearings under different speed conditions. Firstly, based on the traditional SGMD algorithm, the vibration signal of the axle box is decomposed to extract its symplectic geometry components (SGCs). Secondly, the spectral coherence coefficient between the SGCs is calculated, and the signal in which the maximum value is located is taken as the key component for the additive reconstruction Finally, the envelope spectrum is used to extract the reconstructed signal fault features. The inner race, outer race, and compound bearing failure vibration signal acquisition under different speed conditions were carried out on the equal scale axlebox bearing failure simulation test bench, and the effectiveness of the proposed algorithm was verified based on the axlebox vertical acceleration signal.

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Estimation of Transplanting and Harvest Dates of Rice Crops in the Philippines Using Sentinel-1 Data

Cauba,

Darvishzadeh,

Schlund

et al. 2024

Remote Sensing Applications: Society and Environment

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Fast Averaged Cyclic Periodogram method to compute spectral correlation and coherence

Cited by 8 publications

References 35 publications

Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network

Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network

Train Axlebox Bearing Fault Diagnosis Based on MSC–SGMD

Estimation of Transplanting and Harvest Dates of Rice Crops in the Philippines Using Sentinel-1 Data

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