A contribution to the applicability of complex wavelet analysis of ultrasonic signals

Yang, Wenxian; Hull, J.B.; Seymour, M.D.

doi:10.1016/j.ndteint.2003.12.003

Cited by 22 publications

(6 citation statements)

References 11 publications

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“…The shaft torque and rotational speed data measured in the different cases are plotted in Fig.2. The polynomial equations fitting to these data are also derived with the aid of the polynomial curve fitting technique [7]. In Fig.2, the fitting curves and the corresponding polynomial equations are given for facilitating analysis.…”

Section: Condition Monitoring Techniquementioning

confidence: 99%

Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Yang¹,

Tavner²,

Wilkinson³

2008

4th IET International Conference on Power Electronics, Machines and Drives (PEMD 2008)

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Some large wind turbines use a low speed synchronous generator, directly-coupled to the turbine, and a fully rated converter to transform power from the turbine to mains electricity. This paper considers the condition monitoring and diagnosis of mechanical and electrical faults in such a variable speed machine. The application of wavelet transforms is investigated because of the disadvantages of conventional spectral techniques in processing instantaneous information in turbine signals derived from the wind, which is variable and noisy. A new condition monitoring technique is proposed which removes the negative influence of variable wind in machine condition monitoring. The technique has a versatile function to detect mechanical and electrical faults in the wind turbine. Its effectiveness is validated by experiments on a wind turbine condition monitoring test rig using a permanent-magnet synchronous generator, which can be driven by aerodynamic forces from a drive motor controlled by an external model, representing wind and turbine rotor behaviour. Within the technique wavelet transforms are employed for noise cancellation and are extended to diagnose faults by taking advantage of their powerful capabilities in analysing non-stationary signals. The diagnosis of wind turbine rotor imbalance in the will be used as an illustrative example, heralding the possibility of detecting a wind turbine mechanical faults by power signal analysis.

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Section: Condition Monitoring Techniquementioning

confidence: 99%

Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Yang¹,

Tavner²,

Wilkinson³

2008

4th IET International Conference on Power Electronics, Machines and Drives (PEMD 2008)

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“…The slot in the plate was generated to simulate damage to a metallic material. Because shear wave transducers are suitable for measuring the relative distance of the object from the mounting position of the transducers [26] and a shear vertical (SV) wave at 45 • incidence is optimal for the inspection of crack-like damage [27], they are adopted in this experiment. The velocity of the plane shear wave is 3100 m s −1 .…”

Section: Experimental Set-upmentioning

confidence: 99%

Identification of weak ultrasonic signals in testing of metallic materials using wavelet transform

Fan¹,

Zuo²,

Wang³

2006

Smart Mater. Struct.

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Non-destructive testing using ultrasonic signals has been widely employed to detect material damage and prevent accidents. A collected ultrasonic signal may be noisy and weak because of the grains in materials, incomplete contact between transducers and the mounting surface, and the long transmission path. Stationary wavelet transform has been applied together with kurtosis and universal de-noising to analyze ultrasonic signals in an attempt to identify the weak signals encountered in testing of metallic materials. The time-of-flight of signal in a metallic material is estimated by cross-correlation analysis. Application of the method is demonstrated through the ultrasonic testing of a thin steel plate with a slot.

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“…Nevertheless, the phase information reveals more accurately, isolated singularities in a signal than in the modulus. [2][3][4] Also, using the phase information, different kinds of transition points of the analyzed signal, i.e., local maxima and inflection points, can be distinguished.…”

Section: Introductionmentioning

confidence: 99%

An Application of Complex Wavelets for High Frequency Switching Noise Detection

Chen

Yang

Wang

2009

J CIRCUIT SYST COMP

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Switching signal is always composed of a series of fast voltage and current transient impulsive waves, which are created during turn-on and turn-off operation of the switching elements. This paper presents a scalogram and phase spectrum analyzing approach for the high-frequency switching noise detection. A complex Gaussian wavelet is used to extract the oscillation feature of the noise signal. The proposed scheme is more suitable in discriminating the type of switching noise than conventional wavelet transform approaches. Simulation results show the effectiveness and advantages of the method.

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A contribution to the applicability of complex wavelet analysis of ultrasonic signals

Cited by 22 publications

References 11 publications

Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms

Identification of weak ultrasonic signals in testing of metallic materials using wavelet transform

An Application of Complex Wavelets for High Frequency Switching Noise Detection

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