Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis

Amarnath, M.; Krishna, I. R. Praveen

doi:10.1016/j.measurement.2014.08.015

Cited by 84 publications

(41 citation statements)

References 26 publications

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“…There, a second step to determine failure-sensitive features is usually needed and applied. In vibration based fault diagnosis, the most commonlyused features have been generated from temporal, spectral, wavelet [10][11][12], and other signal representations. Such different representations can be regarded as different observations of the vibration signal, and from a specific point of view each observation is a modality of the vibration signal [13].…”

Section: Introductionmentioning

confidence: 99%

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

et al. 2015

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“…In addition, the signal-to-noise ratio (SNR) is very low. In contrast to conventional methods [3][4][5], stochastic resonance (SR) is a popular method in weak signal detection. SR can transfer the energy from noise to the signal, which will amplify the weak signal and improve the output SNR [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Underwater Location Beacon Signal Detection Method Based on Mixing and Normalizing Stochastic Resonance

Liang

Wan

Wang

et al. 2020

Sensors

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A flight data recorder (FDR) is an electronic recording device placed in an aircraft for the purpose of facilitating the investigation of aviation accidents. If an aircraft crashes into water, an underwater locator beacon (ULB), which is installed on the FDR, is triggered by water immersion, and emits an ultrasonic 10 ms pulse signal once per second at 37.5 kHz. This pulse signal can be detected by sonar equipment. However, the ULB signal only can be detectable 1-2 kilometers from the surface in normal conditions. Stochastic resonance (SR) is a rising theory in the field of weak signal detection. The classical stochastic resonance limits state that the input must be small-parameter and the sampling frequency must be 50 times higher than the signal frequency. It cannot be applied to the ULB signal detection. To resolve this problem, this paper presents a novel approach named mixing and normalizing stochastic resonance (MNSR). By mixing the ULB signal and normalizing SR system parameters, MNSR provides a new way to detect weak ULB signal. Meanwhile, we propose the parameters adjustment method of MNSR. We prove the effectiveness through numerical simulation. An experiment in a tank is employed to verify the practicability of this method.Sensors 2020, 20, 1292 2 of 15 classical stochastic resonance cannot detect the large-parameter (large-amplitude or high-frequency) signal directly. To make SR more practical, researchers have proposed several large-parameter stochastic resonance (LPSR) methods, such as twice sampling stochastic resonance [11], re-scaling frequency stochastic resonance (RFSR) [13], system parameters normalization transform stochastic resonance, or parameters normalized stochastic resonance (PNSR) [14]. These methods transform the large-parameter signal into a small-parameter signal which satisfies the small-parameter conditions numerically. The disadvantage is the requirement of a high sampling frequency (at least 50 times to the signal frequency, and generally 200 times or more). It is hard to achieve in engineering application scenarios when the weak signal frequency is high. Another LPSR method, modulated stochastic resonance (MSR) [15], modulates the high-frequency weak signal immersed with noise to a low difference frequency signal to implement LPSR. MSR requires a long signal duration to ensure that the duration contains at least two complete periods of the low difference frequency signal. To realize the detection of ULB signal which is high frequency and short pulse by SR method, mixing and normalizing stochastic resonance (MNSR) is proposed in this paper. Through mixing the ULB signal with a carrier signal, the ULB signal detection can be replaced by the "low" frequency difference signal detection. Here, the "low" means the difference signal frequency is lower than the ULB signal frequency and the sampling frequency. Under the restriction that the sampling frequency must be 50 times higher than the signal frequency, MNSR allows the sampling frequency to be much lower than RFSR, which detects ...

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“…Most often, unwanted noise is produced by the measuring system which can be removed using median filter. Amaranth and Praveen Krishna [12,13,17] investigated the surface wear failure in spur as well as helical gears using EMD technique. Ensemble empirical mode decomposition (EEMD) technique is used to extract feature related to faults for the vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

Sagar

Vivekkumar

Reddy

et al. 2017

IOP Conf. Ser.: Mater. Sci. Eng.

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Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis

Cited by 84 publications

References 26 publications

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis

A Novel Underwater Location Beacon Signal Detection Method Based on Mixing and Normalizing Stochastic Resonance

Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

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