Automated inspection systems are important for maintenance and rehabilitation of pipeline systems in North America given their budgetary constraints, demand on providing quality service, and the need for preserving their pipeline infrastructure. Automated ultrasonic signal classification systems are finding increasing use in many applications for the recognition of large volumes of inspection signals. This paper presents an automated signal classification system to process A-scan signals acquired with the Ultrasound transducer from a pipe region of interest (ROI). The overall approach consists of three major steps, preprocessing of the signal, multi-resolution analysis for feature extraction, and neural network classification. Finally, a post processing scheme to interpret the classifier outputs and classify the ROI into an appropriate defect class taking into consideration some a priori knowledge of the problem is developed. The proposed post processing scheme is composed of several steps that combine the statistics from the classification matrix as well as a two-step procedure based on k-nearest neighbor and non-linear regression. The feature extraction, classification and post processing schemes proposed in this paper provide a working proof-of-concept for developing this inspection system into an automated field applicable tool.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction