Processing ultrasonic inspection data from multiple scan patterns for turbine rotor weld build-up evaluations

Guan, Xuefei; Rasselkorde, El Mahjoub; Abbasi, Waheed A.; Zhou, S. Kevin

doi:10.1063/1.4914710

Cited by 1 publication

(1 citation statement)

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“…Aiming at the noise problem of ultrasonic signals, Harvey et al used a sparse Bayes learning algorithm and an over-complete dictionary to decompose ultrasonic signals into sparse representations, reducing noise components in the signals [21]. Abbasi et al proposed a weld imaging data processing method based on a multi-scanning mode for defects in different directions of pipeline welds and realized the processing of detection data using weight-building technology [22]. Song et al developed an ultrasonic transducer array that integrates three incidence modes of acoustic beams, established a spatial model of the array, and completed the ultrasonic imaging of pipeline defects [23].…”

Section: Introductionmentioning

confidence: 99%

An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure

Wu,

Huang,

Zhang

et al. 2023

Meas. Sci. Technol.

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Ultrasonic in-line inspection is one of the most widely adopted nondestructive testing methods for defect detection in pipelines. In practical industrial scenes, pipeline specification varies, sensor failure occurs frequently, and the number of pipeline defect samples is scarce. How to detect defects without false detection caused by invalid data and with limited labeled samples is a challenging problem in this area. An ultrasonic in-line inspection data processing method considering invalid data caused by sensor failure is proposed in this paper to enhance the accuracy of the defect and its profile detection. Firstly, the multi-channel data is aggregated according to sampling time. The data dimension is reduced, and the accuracy of the invalid waveform detection adopting isolation forest arithmetic is improved. The methods are adopted for the invalid waveforms replacement with two-dimensional cubic spline interpolation using adjacent sensors. Secondly, a natural breakpoint method is adopted to locate the echo peaks. Residual wall thickness is evaluated by calculating the time difference between the echo peaks. A fast pseudo-colorization method based on sliding windows and a morphological image processing method are proposed to detect defects and their profiles efficiently utilizing the residual wall thickness. Finally, practical in-line inspection data is utilized to evaluate the performance. The experiment results illustrate that the detection accuracy is enhanced on different sizes of pipelines without requiring labeled samples.

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Section: Introductionmentioning

confidence: 99%