Fundamentals of modern spectral analysis

Hunter, Matthew T.; Kourtellis, Achilleas; Ziomek, C.; Mikhael, W.B.

doi:10.1109/autest.2010.5613592

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…erefore, in this case, the application of frequency-domain analysis is born, and the frequency-domain analysis of the signal can reflect the frequency-domain components and distribution of the signal, which has a good engineering and scientific research application prospect. e frequency-domain analysis of the signal mainly includes two kinds of spectrum analysis: classical spectrum and modern spectrum [10].…”

Section: Related Workmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Wang¹,

Li²,

Qin

2021

Scientific Programming

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Error diagnosis and detection have become important in modern production due to the importance of spinning equipment. Artificial neural network pattern recognition methods are widely utilized in rotating equipment fault detection. These methods often need a large quantity of sample data to train the model; however, sample data (especially fault samples) are uncommon in engineering. Preliminary work focuses on dimensionality reduction for big data sets using semisupervised methods. The rotary machine’s polar coordinate signal is used to build a GAN network structure. ANN and tiny samples are utilized to identify DCGAN model flaws. The time-conditional generative adversarial network is proposed for one-dimensional vibration signal defect identification under data imbalance. Finally, auxiliary samples are gathered under similar conditions, and CCNs learn about target sample characteristics. Convolutional neural networks handle the problem of defect identification with small samples in different ways. In high-dimensional data sets with nonlinearities, low fault type recognition rates and fewer marked fault samples may be addressed using kernel semisupervised local Fisher discriminant analysis. The SELF method is used to build the optimum projection transformation matrix from the data set. The KNN classifier then learns low-dimensional features and detects an error kind. Because DCGAN training is unstable and the results are incorrect, an improved deep convolutional generative adversarial network (IDCGAN) is proposed. The tests indicate that the IDCGAN generates more real samples and solves the problem of defect identification in small samples. Time-conditional generation adversarial network data improvement lowers fault diagnosis effort and deep learning model complexity. The TCGAN and CNN are combined to provide superior fault detection under data imbalance. Modeling and experiments demonstrate TCGAN’s use and superiority.

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Section: Related Workmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Wang¹,

Li²,

Qin

2021

Scientific Programming

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“…A spectrum analyzer [5] is a device that is used technically for assessing a frequency source with respect to its strength. It is used in getting a visual display to show the different bands.…”

Section: Spectrum Analyzermentioning

confidence: 99%

Wireless/Wired Automatic Switched Plasma Tweeter & Speaker System

Singh¹,

Srivastava²,

Khare³

et al. 2017

IOSR JECE

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“…Figure 49 shows the geographic mask concept applied in a satellite mission simulation on a specific mission day from the GS located at UPM University, where satellite entry and exit points indicate the available satellite FOV from the GS location in this specific satellite pass (see Figure 49( There are various types of equipment, from the swept spectrum analyzer developed in the 1960s as the first test equipment (Deery, 2007), through the FFT (Fast Fourier Transform) spectrum analyzer based on the transformation of discrete or digitized signals into the frequency domain using the discrete Fourier transform, to the latest high performance equipment with analog to digital converters implemented since 1980s. In this sense, there are different processes and techniques for measuring and locating interference sources according to the type of analyzer selected (Brown, 1997;Nagano et al 2008;Hunter et al 2011;Gorin, 2003): Width) filtering, can now be done digitally, improving performance and reducing calibration requirements. The DSP integration has improved the performance of the swept spectrum analyzer, and has led to the development and prevalence of the FFT based spectrum analyzer.…”

Section: Geographic Maskmentioning

confidence: 99%

“…Basically, once the local oscillator module provides a sinusoidal signal to the down-converter module it is mixed with the input signal producing the desired frequency; in a swept type, the sinusoidal signal is swept linearly over the frequency band or span to be measured; in an FFT type, the local oscillator module provides a frequency stepped sinusoidal signal whose step size is determined by the frequency coverage of the FFT. Figure 51 shows the simplified block diagram of these types of spectrum analyzers (Hunter et al 2011). Considering the purposes are to locate signal interfering sources surrounding the GS location, to capture signal spectrum data relative to the frequency versus level, and to in-situ preliminary analysis, spectrum analyzer specifications such as the frequency coverage and accuracy, the amplitude accuracy and the resolution bandwidth must be taken into account when selecting the equipment.…”

Section: Geographic Maskmentioning

confidence: 99%

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Analysis Model to Optimize Ground Stations in Built-up Areas

Chinchilla¹

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Fundamentals of modern spectral analysis

Cited by 7 publications

References 7 publications

Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Rotating Machinery Fault Diagnosis Method Based on Improved Semisupervised Generative Confrontation Network

Wireless/Wired Automatic Switched Plasma Tweeter & Speaker System

Analysis Model to Optimize Ground Stations in Built-up Areas

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