Recognition Algorithm of Acoustic Emission Signals Based on Conditional Random Field Model in Storage Tank Floor Inspection Using Inner Detector

Abstract

Acoustic emission (AE) technique is often used to detect inaccessible area of large storage tank floor with AE sensors placed outside the tank. For tanks with fixed roofs, the drop-back signals caused by condensation mix with corrosion signals from the tank floor and interfere with the online AE inspection. The drop-back signals are very difficult to filter out using conventional methods. To solve this problem, a novel AE inner detector, which works inside the storage tank, is adopted and a pattern recognition algorithm based on CRF (Conditional Random Field) model is presented. The algorithm is applied to differentiate the corrosion signals from interference signals, especially drop-back signals caused by condensation. Q235 steel corrosion signals and drop-signals were collected both in laboratory and in field site, and seven typical AE features based on hits and frequency are extracted and selected by mRMR (Minimum Redundancy Maximum Relevance) for pattern recognition. To validate the effectiveness of the proposed algorithm, the recognition result of CRF model was compared with BP (Back Propagation), SVM (Support Vector Machine), and HMM (Hidden Markov Model). The results show that training speed, accuracy, and ROC (Receiver Operating Characteristic) results of the CRF model outperform other methods.

Pitting Judgment Model Based on Machine Learning and Feature Optimization Methods

“…Datasets of different proportions were input into different classification models for testing. During the training process, we used receiver operating characteristic (ROC) curve and area under curve (AUC) to evaluate the training effect of the model (Li et al, 2015). Each group of tests was repeated for 100 times, and the best-performing dataset division ratio and classification model were selected according to the average score.…”