Citation: Wang, L.; Lu, S.; Liu, X.;
Liu, J. Two-Stage Ultrasound Signal
Recognition Method Based on
Envelope and Local Similarity
Features. Machines 2022, 10, 1111.
https://doi.org/10.3390/
machines10121111
Academic Editor: Dimitrios
Chronopoulos
Received: 19 October 2022
Accepted: 18 November 2022
Published: 23 November 2022
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Article
Two-Stage Ultrasound Signal Recognition Method Based on
Envelope and Local Similarity Features
Liwei Wang, Senxiang Lu , Xiaoyuan Liu and Jinhai Liu *
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
* Correspondence: liujinhai@mail.neu.edu.cn
Abstract:
Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect
detection and inversion. Current methods, based on machine learning and deep learning have been
able to classify signals with significant differences. However, the ultrasonic internal detection signal
is interspersed with a large number of anomalous signals of an unknown origin and is affected by
the time shift of echo features and noise interference, which leads to the low recognition accuracy
of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper
proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity
features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature
extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification
accuracy under the conditions of the echo feature time shift and noise interference. In the second
stage, an abnormal signal detection method, based on the local similarity feature extraction and
enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal
detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal
recognition method, based on the envelope and local similarity features (TS-ES) in this paper is
97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%.
Keywords:
ultrasonic testing; signal classification; anomaly detection; envelope curve; dynamic
time warping
1. Introduction
Ultrasonic testing technology (UT) is the most successful nondestructive testing tech-
nique (NDT) for quality assessment and the defect detection of engineering materials [
1
,
2
].
Among them, the phased-array ultrasound detection (PAUT) is far more efficient and accu-
rate than the conventional single-probe intra-ultrasound detection techniques by virtue
of the multiple sensors in the array [
3
]. Due to the complexity of the pipeline environ-
ment, the phased-array ultrasound detector collects ultrasonic internal detection data as
an array of data, containing different numbers of echoes and interspersed with abnormal
signals. In industrial applications, the high-precision identification of ultrasonic internal
detection data can effectively improve the identification and inversion accuracy of defect
detection [
4
]. At present, many researchers have carried out a lot of research work around
signal classification, It mainly includes machine learning based and deep learning based
approaches.
The machine learning based approach first extracts features from the signal, according
to the designed rules and then classifies the extracted features using machine learning
methods. An unsupervised classification method was proposed in the literature [
5
], which
uses the natural breakpoint method and ultrasound signal mechanism, by setting an energy
threshold to obtain the peak position of the ultrasound signal echo for classification. The
method has a high accuracy for signals with distinct echo characteristics. In the literature [
6
],
the ultrasound signal is processed and its time domain, frequency and wavelet domain
features are extracted using the fast Fourier transform and wavelet packet variation, and
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