Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation

Peng, Z.K.; Meng, Guang; Chu, Fulei; Lang, Zi–Qiang; Zhang, W. M.; Yang, Yixin

doi:10.1109/tim.2011.2124770

Cited by 232 publications

(93 citation statements)

References 20 publications

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“…In the past decades, a number of mathematical methods have been successfully developed for detecting the instantaneous frequencies of electrical, mechanical and medical signals, such as [8][9][10][11][12][13][14][15], but the simplest of which is through detecting the zero-crossings contained in the signal [8]. However, the practical application of such a simple method is often constrained due to the concern of its poor robust performance against noise.…”

Section: B Transient Period Detection Methodsmentioning

confidence: 99%

Wind turbine condition monitoring by detecting the transient period of a single generator stator current signal

Yang

Tian

Peng

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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A wind farm often consists of a number of wind turbines (WTs) operating simultaneously, which not only challenges the operation of the wind farm but makes the condition monitoring (CM) more costly than ever before if each turbine is equipped with a standard commercially available WT CM system (e.g. SKF's WindCon system). With the continual increase of wind farm size, the operator is under increasing pressure to lower the CM cost so that the energy cost can be accordingly reduced in the end. The research presented in this paper is for meeting such a requirement by developing a new cost-effective CM technique based on detecting the transient period of a single WT generator stator current signal. Instead of collecting multiple numbers of CM signals from WT key components, the new technique relies on the use of only a single electric current signal collected from WT generator stator. Moreover, the new technique directly deals with the time waveform of the signal without using any artificial transforms (e.g. Fourier transform). Therefore, it is potentially able to provide more reliable CM result. Experiments have shown that the proposed technique is valid for detecting both the electrical and mechanical faults emulated on a specially designed WT drive train test rig.

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Section: B Transient Period Detection Methodsmentioning

confidence: 99%

Wind turbine condition monitoring by detecting the transient period of a single generator stator current signal

Yang

Tian

Peng

2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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“…Peng et al [166,167] systematically studied the parametric TFA, and first proposed polynomial chirplet transform and splinekernelled chirplet transform [168]. They then generalized warblet transform [169], and later parameterized TFA [170,171].…”

Section: Sparse Decomposition Analysismentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…For the purpose of comparison, Fig. 2(b)-(g) depict the TFDs of the Doppler signal, which are generated by the STFT, WVD, pseudo WVD, polynomial chirplet transform (PCT [23]) and proposed MWT with the kernel characteristic parameters equal to the coefficients of the polynomial in Fig. 2(a).…”

Section: Matched Wigner Transformmentioning

confidence: 99%

“…This procedure keeps iterating until no evident modification is observed in the concentration of the TFD by the MWT or in the estimated IF. When the concentration of the TFD is used as the criterion, the Renyi entropy can be used to measure the concentration [23,24], which is defined as follows:…”

Section: If Estimate Based On the Mwtmentioning

confidence: 99%

Parameter estimation of underwater moving sources by using matched Wigner transform

Yang

2016

Applied Acoustics

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Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation

Cited by 232 publications

References 20 publications

Wind turbine condition monitoring by detecting the transient period of a single generator stator current signal

Wind turbine condition monitoring by detecting the transient period of a single generator stator current signal

Basic research on machinery fault diagnostics: Past, present, and future trends

Parameter estimation of underwater moving sources by using matched Wigner transform

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