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

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.