Band-Stop Filtering Method of Combining Functions of Butterworth and Hann Windows to Ultrasonic Guided Wave

Zheng, Guolei; Tian, Yimei; Zhao, Weigao; Jia, Shichao; He, Nan

doi:10.1061/(asce)ps.1949-1204.0000621

Cited by 3 publications

(2 citation statements)

References 36 publications

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“…The ultrasonic method detects the damage state by accurately analyzing changes in the ultrasonic signal characteristics [ 11 , 12 ]. Due to the anisotropy of refractory materials, ultrasonic testing methods have a low signal-to-noise ratio in applications, and it is difficult to obtain useful damage information from the signals [ 13 ]. The radiographic method adopts a negative as a recording medium, which can directly obtain a direct visual image of the damage [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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Refractory materials are basic materials widely used in industrial furnaces and thermal equipment. Their microstructure is similar to that of many heterogeneous high-performance materials used in micro/nanodevices. The presence of damage can reduce the mechanical properties and service life of refractory materials and even cause serious safety accidents. In this paper, a novel percussion and particle swarm optimization-support vector machine (PSO-SVM)-based method is proposed to detect damage in refractory materials. An impact is applied to the material and the generated sound is recorded. The percussion-induced sound signals are fed into a mel filter bank to generate time–frequency representations in the form of mel spectrograms. Then, two image descriptors—the local binary pattern (LBP) and histogram of oriented gradient (HOG)—are used to extract the texture information of the mel spectrogram. Finally, combining both HOG and LBP features, the fused features are input to the PSO-SVM algorithm to realize damage detection in refractory materials. The results demonstrated that the proposed method could identify five different degrees of damage of refractory materials, with an accuracy rate greater than 97%. Therefore, the percussion and PSO-SVM-based method proposed in this paper has high potential for field applications in damage detection in refractory material, and also has the potential to be extended to research on damage detection methods for other materials used in micro/nanodevices.

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Section: Introductionmentioning

confidence: 99%

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Yang

Peng

Zhou³

et al. 2023

Micromachines

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“…It is sensitive to changes in structural state and can be related to several structural characteristics [12]. However, the signals collected by the ultrasonic method are usually accompanied with noise, and effective noise reduction methods are required to obtain useful information [13,14]. The radiography method detects the pipeline by evaluating the attenuation of the rays [15] which pass through the pipeline.…”

Section: Introductionmentioning

confidence: 99%

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

et al. 2022

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Pipeline transportation is the main method for long-distance gas transportation; however, ponding in the pipeline can affect transportation efficiency and even cause corrosion to the pipeline in some cases. A non-destructive method to detect pipeline ponding using percussion acoustic signals and a convolution neural network (CNN) is proposed in this paper. During the process of detection, a constant energy spring impact hammer is used to apply an impact on the pipeline, and the percussive acoustic signals are collected. A Mel spectrogram is used to extract the acoustic feature of the percussive acoustic signal with different ponding volumes in the pipeline. The Mel spectrogram is transferred to the input layer of the CNN and the convolutional kernel matrix of the CNN realizes the recognition of pipeline ponding volume. The recognition results show that the CNN can identify the amount of pipeline ponding with the percussive acoustic signals, which use the Mel spectrogram as the acoustic feature. Compared with the support vector machine (SVM) model and the decision tree model, the CNN model has better recognition performance. Therefore, the percussion-based pipeline ponding detection using the convolutional neural network method proposed in this paper has high application potential.

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Research on the localization of pipe burst based on transient flow in a water supply network

Zheng,

Tian,

et al. 2023

Structural Health Monitoring

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Methods for determining pipe burst (PB) locations based on supervisory control and data acquisition systems (SCADAs) have been widely studied. Existing location methods either only detect PB areas or are only applied to relatively simple pipe networks. Thus, it is necessary to develop a PB localization method that can both accurately locate the PB and be used for complex pipe networks. Accordingly, in this study, a complex experimental pipe network with a high-frequency SCADA was built, and PB events were constructed under different conditions. After they were analyzed, the water pressure and flow anomalous features of the PBs were summarized. According to the propagation laws and damping theories of transient flow (TF), a PB localization method, that is, the TF simulation (TFS) method, was first proposed and developed after a TF model of the experimental pipe network was established. Using this method, two PB events were recognized, the burst pipes were identified, and the PBs were accurately located. Compared with the negative pressure wave method, the TFS method exhibits a higher location accuracy and a wider applicability to pipe networks. Through error analyses, it is shown that the TFS method has small errors. To reduce cost, the sampling frequency was decreased to 50 Hz, that is, the minimum required sampling frequency for this study, and under this condition, PB localization was successfully realized. An additional three PB localizations illustrate that they have acceptable errors and that the TFS method has good repeatability.

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Band-Stop Filtering Method of Combining Functions of Butterworth and Hann Windows to Ultrasonic Guided Wave

Cited by 3 publications

References 36 publications

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Percussion and PSO-SVM-Based Damage Detection for Refractory Materials

Percussion-Based Pipeline Ponding Detection Using a Convolutional Neural Network

Research on the localization of pipe burst based on transient flow in a water supply network

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