Filtering Of Acoustic Signals Research Articles

Deep Learning-Empowered Digital Twin Using Acoustic Signal for Welding Quality Inspection

Weld site inspection is a research area of interest in the manufacturing industry. In this study, a digital twin system for welding robots to examine various weld flaws that might happen during welding using the acoustics of the weld site is presented. Additionally, a wavelet filtering technique is implemented to remove the acoustic signal originating from machine noise. Then, an SeCNN-LSTM model is applied to recognize and categorize weld acoustic signals according to the traits of strong acoustic signal time sequences. The model verification accuracy was found to be 91%. In addition, using numerous indicators, the model was compared with seven other models, namely, CNN-SVM, CNN-LSTM, CNN-GRU, BiLSTM, GRU, CNN-BiLSTM, and LSTM. A deep learning model, and acoustic signal filtering and preprocessing techniques are integrated into the proposed digital twin system. The goal of this work was to propose a systematic on-site weld flaw detection approach encompassing data processing, system modeling, and identification methods. In addition, our proposed method could serve as a resource for pertinent research.

A pipeline structure for the block QR update in digital signal processing

There exist problems in the field of digital signal processing, such as filtering of acoustic signals that require processing a large amount of data in real time. The beamforming algorithm, for instance, is a process that can be modeled by a rectangular matrix built on the input signals of an acoustic system and, thus, changes in real time. To obtain the output signals, it is required to compute its QR factorization. In this paper, we propose to organize the concurrent computational resources of a given multicore computer in a pipeline structure to perform this factorization as fast as possible. The pipeline has been implemented using both the application programming interface OpenMP and GrPPI, a library interface to design parallel applications based on parallel patterns. We tackle not only the performance challenge but also the programmability of our idea using parallel programming frameworks.

The filtering characteristics of HHT and its application in acoustic log waveform signal processing

Array acoustic logging plays an important role in formation evaluation. Its data is a non-linear and non-stationary signal and array acoustic logging signals have time-varying spectrum characteristics. Traditional filtering methods are inadequate. We introduce a Hilbert-Huang transform (HHT) which makes full preservation of the non-linear and non-stationary characteristics and has great advantages in the acoustic signal filtering. Using the empirical mode decomposition (EMD) method, the acoustic log waveforms can be decomposed into a finite and often small number of intrinsic mode functions (IMF). The results of applying HHT to real array acoustic logging signal filtering and de-noising are presented to illustrate the efficiency and power of this new method.

A Wavelet Analysis of Acoustic Fields and Signals in Ultrasonic Nondestructive Testing

The use of the continuous and discrete wavelet analysis for investigation and filtering of acoustic signals and fields is considered. Special attention is paid to the application of wavelet filtering for improving the quality of the visualization of flaws. Wavelet contour plots, a new method for representing information, are introduced. This technique simplifies the determination of conventional dimensions of flaws.

Method and apparatus for selective acoustic signal filtering

The present invention relates to a method and apparatus for performing selective acoustic signal filtering. The apparatus comprises two acoustic ports, an electrical output port and an acoustic signal processing device, where all three ports have an impedance characteristic. The acoustic signal processing device includes a diaphragm which will manifest vibration in response to an admitted input acoustic signal and generates an output acoustic signal. The signal processing device is characterized by a frequency response such that the spectrum level of the output acoustic signal will have predetermined differences with respect to the input acoustic signal. The frequency response characteristic of the signal processing device may be set and adjusted by the manipulation of the impedance characteristics of either the acoustic output load or the electrical output load.