Signal processing techniques for enhancement of defect detection in ultrasonic guided waves inspection of pipelines

Abstract

pipelines are the main means of transferring oil and gas. In order to avoid failure, they require regular inspection to locate defects and repair them accordingly. Ultrasonic guided waves (UGW) allows long-range inspection of pipelines from one test point. Determining the location of defects is a challenging and manual process. The main aim of this thesis is to develop novel signal processing algorithms for enhancement of data interpretation of UGW tests. The first step of this thesis was to investigate the operation of the device and the existing sources of noise from the literature. Afterward, based on the literature, three different methods were investigated for approaching the inspection problem of UGW. The first one was to utilize the individual signals from the transducer array to develop a defect identification algorithm. Detection of defects was done based on the spatial differences in the received signals from each time sample. The second was to change the current processing algorithm of the device, in order to enhance the SNR of the results; therefore, an adaptive filtering algorithm was implemented in order to remove significant amount of coherent noise from the tests. The third method was to use the power spectrum to develop a defect identification algorithm; Hence, an algorithm was developed that allowed detection of defects based on a comparison between the power spectrum of the reference signal with the one gathered from each time sample of the results. All of the aforementioned algorithms were validated using simulated data generated by a finite element model as well as experimental trials on real pipes. It was demonstrated that not only the algorithms are capable of enhancing the defect detection, but also the current signal processing routine of the device can be modified to produce better results.