A Fast Fourier Transform for High-Speed Signal Processing

Corinthios, M.J.

doi:10.1109/t-c.1971.223359

Cited by 36 publications

(16 citation statements)

References 5 publications

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“…Corinthios [20,21,22] has used the formalism of Pease to implement an algorithm (previously noted by Cochran, Cooley, et. ale [16]) which does not produce the output in bit reversed order.…”

Section: Previous Workmentioning

confidence: 99%

Ultrafast Fourier-transform parallel processor

Greenberg¹

1980

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Section: Previous Workmentioning

confidence: 99%

Ultrafast Fourier-transform parallel processor

Greenberg¹

1980

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“…The signal analysis has been set up as one of the useful methods for fault diagnosis. Many useful techniques for signal analysis have been proposed such as fast Fourier transform (FFT) (Corinthios, 1971), higher-order statistics (HOS) (Swami, Giannakis, & Zhou, 1997), and adaptive order-tracking (Bai, Huang, Hong, & Su, 2005;Wu, Huang, & Huang, 2004). Nonetheless, the fault diagnosis system of sound emission or vibration signals analysis is stressed in the time-frequency domain information.…”

Section: Introductionmentioning

confidence: 99%

An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network

Liu

2009

Expert Systems with Applications

254

118

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“…Many useful techniques for signal analysis have been proposed such as Fast Fourier Transform (FFT) [8], [9] but this technique has a limit in time resolution because of using fixed time windows. Other techniques that have been developed such as the Short Time Fourier Transform (STFT) [10], WignerVille distribution (WVD) [11], [12], time-average analysis [13], cumulants [14], resonance demodulation technique [15], Continuous Wavelet Transform (CWT) [16], [17], [18], Discrete Wavelet Transform (DWT) [19], etc.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of rotary machine by mean of self adaptive growing neural network

Barakat

Druaux

Lefebvre

et al. 2011

2011 19th Mediterranean Conference on Control &Amp; Automation (MED)

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For the improvement of reliability, safety and efficiency advanced methods of supervision, fault detection and fault diagnosis become increasingly important for many technical processes. To determine the condition of an inaccessible fault in an operating mechanical system, the vibration signal of the machine is continuously monitored by placing sensors close to the source of the vibrations. These signals are further processed to extract the features and identify the status of the machine. This paper deals with a new scheme for the detection and diagnosis of localized defects in machine rotating components based on Self Adaptive Growing Neural Network (SAGNN) classifier which is a novel approach to pattern classification propounded for tackling fault diagnosis tasks. Feature vector is extracted from signal in time domain and SAGNN is trained and used as a diagnosis classifier. In order to evaluate SAGNN, the data sets obtained from vibration signals are classified with both SAGNN and Radial Basis Function (RBF) classifiers. The proposed algorithm is applied to the fault diagnosis of mechanical rotary machine, and the test results show that classification by mean of SAGNN identify the faults of rotary elements (Gears, bearing and belts) more accurately and has better diagnosis performance compared to the RBF.

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A Fast Fourier Transform for High-Speed Signal Processing

Cited by 36 publications

References 5 publications

Ultrafast Fourier-transform parallel processor

Ultrafast Fourier-transform parallel processor

An expert system for fault diagnosis in internal combustion engines using wavelet packet transform and neural network

Monitoring of rotary machine by mean of self adaptive growing neural network

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