Fast-varying AM–FM components extraction based on an adaptive STFT

Xie, Huimin; Lin, Jing; Lei, Yaguo; Liao, Yuhe

doi:10.1016/j.dsp.2012.02.007

Cited by 35 publications

(16 citation statements)

References 13 publications

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“…20 Therefore, it is very difficult to obtain the accurate reoccurrence period of transient impulses directly from TFD image. It is necessary to transform the fault periodic impulse in noise-suppressed time-frequency domain back to time domain for further demodulation analysis.…”

Section: Review Of Stft and Inverse Stft (Istft)mentioning

confidence: 99%

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

Wang

Liú

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by timefrequency distribution image dimensionality reduction. Since time-frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time-frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.

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Section: Review Of Stft and Inverse Stft (Istft)mentioning

confidence: 99%

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

Wang

Liú

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…If these components highly overlap in the time domain or the frequency domain, signal synthesis cannot be accomplished by conventional methods, such as frequency windowing, filtering. Due to their superiority in analyzing non-stationary signals over traditional tools (like the Fourier transform), various time-frequency representations (TFRs) have raised a variety of vital applications in the representation and synthesis of the non-stationary multicomponent signals [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Robust multicomponent LFM signals synthesis algorithm based on masked ambiguity function

Tao

Rao

et al. 2015

Digital Signal Processing

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“…Time-frequency analyses were developed to process non-stationary signals using a frequency transformation process divided based on windows across the time axis to capture informative events. The basic time-frequency analysis method is a short-time Fourier transform (STHT) or a spectrogram, such as a limited time window-width Fourier spectral analysis [16,17]. The challenges of the STHT method, such as a failure of the assumption that the pieces of a non-stationary signal are stationary, difficulty adapting the observation window size to the size of a real stationary piece of signal, and the conflict between frequency resolution and time resolution (which is related to the Heisenberg uncertainty principle) limit its usability.…”

Section: Introductionmentioning

confidence: 99%

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

Nguyen

Prosvirin

Kim

2020

Sensors

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The vibration signals of gearbox gear fault signatures are informative components that can be used for gearbox fault diagnosis and early fault detection. However, the vibration signals are normally non-linear and non-stationary, and they contain background noise caused by data acquisition systems and the interference of other machine elements. Especially in conditions with varying rotational speeds, the informative components are blended with complex, unwanted components inside the vibration signal. Thus, to use the informative components from a vibration signal for gearbox fault diagnosis, the noise needs to be properly distilled from the informational signal as much as possible before analysis. This paper proposes a novel gearbox fault diagnosis method based on an adaptive noise reducer–based Gaussian reference signal (ANR-GRS) technique that can significantly reduce noise and improve classification from a one-against-one, multiclass support vector machine (OAOMCSVM) for the fault types of a gearbox. The ANR-GRS processes the shaft rotation speed to access and remove noise components in the narrowbands between two consecutive sideband frequencies along the frequency spectrum of a vibration signal, enabling the removal of enormous noise components with minimal distortion to the informative signal. The optimal output signal from the ANR-GRS is then extracted into many signal feature vectors to generate a qualified classification dataset. Finally, the OAOMCSVM classifies the health states of an experimental gearbox using the dataset of extracted features. The signal processing and classification paths are generated using the experimental testbed. The results indicate that the proposed method is reliable for fault diagnosis in a varying rotational speed gearbox system.

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Fast-varying AM–FM components extraction based on an adaptive STFT

Cited by 35 publications

References 13 publications

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

Robust multicomponent LFM signals synthesis algorithm based on masked ambiguity function

A Reliable Fault Diagnosis Method for a Gearbox System with Varying Rotational Speeds

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