Study On Offshore Pipeline Sensor Array Defect Information Extraction And Fusion

Currently, offshore pipeline detection technology and offline signal analysis are research hotspots in the international non-destructive testing (NDT) field. The research on technologies, methods, and approaches for pipeline detection makes great theoretical and realistic sense. Based on"863"of the high technology research and development program"Offshore pipeline detection device and inspection technology", this dissertation extracted the echo arrival time feature from ultrasonic signal provided by large amounts of ultrasonic sensors built in the detection device, obtained defect dimension, type, and level information by feature level and decision level fusion approaches. In addition, this dissertation attempted to apply a new technology, pulse eddy current (PEC), to the area of pipeline nondestructive testing by designing the structure of a PEC probe and probe array for pipeline detection preliminarily, by extracting a lot of new signal features, and by combining different features for defect classification. All these provide key technologies for the offline data analysis system of the program.Considering the project demand and the harsh and complicated offshore environment, in order to eliminate the uncertainty as much as possible, and to achieve reasonable and effective result, an offline data processing strategy with multiple fusion levels was built. This strategy analyzes data from different types of sensors separately by using different approaches and then combines the extracted signal and/or defect features on feature and decision levels. Thus, the negative interplay between different types of sensors and technologies can be removed, the prerequisites of fusion such as normalization and association can be reduced, and the overall status of a defect can be mastered from multiple points of view.In the section of defect information extraction based on ultrasonic signal, the nonlinear and non-stable ultrasonic signal was decomposed, selected, reconstructed, and transformed by using time-frequency analysis approaches such as Empirical Mode Decomposition (EMD) and Hilbert transform. The arrival time of each echo was extracted from noisy and overlapped ultrasonic signal for getting accurate pipeline wall thickness. This method can not only improve the measurement precision of pipeline wall thickness and the dimension and location of inner crack distinctively, but can also diminish the blind area of ultrasonic time-of flight technology.In the research on PEC technology, the idea of special PEC probe and probe array for pipeline detection was proposed preliminarily. Then, a descending point of PEC differential signal was extracted in time domain and so the signal can be divided into several segments. Three pairs of shape features from each segment respectively and two spectrum features were extracted. Thereafter, the robustness and generalization of new features were analyzed, compared, and summarized and some valuable conclusions were obtained. These conclusions enriched the useable resources for PEC signal explanation and provided theoretic basis for fast and effective detection. Furthermore, by plenty of experiments, a"dual peak"signal phenomenon was discovered which has not been reported at present. Finally, as a result of large numbers of and various types of features, defects can be classified in 2-D and 3-D spaces quickly and accurately by combining different features properly. The best 2-D and 3-D combinations were recommended and the stability and generalization of the two recommended combinations were validated.Besides PEC signal features, ultrasonic signal can be used for defect classification as well. After the introduction of the cause and category of pipeline defects, a mutual information matrix was built by using all the components decomposed from ultrasonic signal (by using the independent component analysis approach). Then, the mutual information features of ultrasonic signal can be extracted. Based on these features, a defect can be classified correctly into certain category by using neural network fusion.In the section of defect dimension fusion, two approaches were proposed for feature level fusion based on the information from same or different types of sensors. Their accuracy, speed, and computation burden were compared and analyzed. The weighted addition approach referring the hierarchy analysis idea is based on real measurement data. It does not need any prior information and so has less computation burden but less accuracy as well. It is wise to select an appropriate method or combine two methods together for the improvement of fusion efficiency.In the section of defect level assessment, some research was conducted on the fusion methods based on uncertainty reasoning. Then, the invalidation problem of evidence theory was analyzed and some typical modification methods were compared. Two weighted addition approaches based on basic probability assignment (BPA) similarity and evidence reliability respectively were proposed and the invalidation problem was solved by weighted averaging all the evidences before combining them. On the basis of revised evidence theory, a strategy for pipeline defect level assessment was built. A more reasonable and more reliable result can be deduced by integrating information from several assessment rules. A 3-D simulation display and level assessment software for pipeline defects was made.All the approaches regarding ultrasonic signal feature extraction and information fusion can be used in other similar signal processing cases.