scholarly journals Analysis of Vibration and Acoustic Signals for Noncontact Measurement of Engine Rotation Speed

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 683 ◽  
Author(s):  
Xuansheng Shan ◽  
Lu Tang ◽  
He Wen ◽  
Radek Martinek ◽  
Janusz Smulko

The non-contact measurement of engine speed can be realized by analyzing engine vibration frequency. However, the vibration signal is distorted by harmonics and noise in the measurement. This paper presents a novel method for the measurement of engine rotation speed by using the cross-correlation of vibration and acoustic signals. This method can enhance the same frequency components in engine vibration and acoustic signal. After cross-correlation processing, the energy centrobaric correction method is applied to estimate the accurate frequency of the engine’s vibration. This method can be implemented with a low-cost embedded system estimating the cross-correlation. Test results showed that this method outperformed the traditional vibration-based measurement method.

Author(s):  
С.И. Герасимов ◽  
В.Д. Глушнев

Корреляционная обработка сигналов как частный случай использования цифровой обработки данных, получаемых с акустических датчиков, находит широкое применение в современных ультразвуковых расходомерах жидкости и газа. К ним можно отнести как непосредственно корреляционные меточные расходомеры, так и расходомеры преимущественно время-импульсного или время-пролетного типов, где корреляционная обработка акустических сигналов является дополнением к общему методу измерения объемного расхода жидкости и газа. Применение корреляционной обработки позволяет повысить разрешающую способность расходомера в целом и обеспечить выделение полезного сигнала на фоне присутствия шумов с высокой степенью достоверности. В статье описан способ вычисления дискретных корреляционных функций на основе обобщенного определения дискретной корреляционной функции через свертку дискретизированных сигналов с выходов датчиков потока. Суть данного метода сводится к вычислению набора значений кумулятивных произведений отсчетов зондирующих сигналов, взятых с разным шагом в зависимости от общего количества отсчетов сигналов и предполагаемого числа значений корреляционной функции. Полученный набор значений оформляется в виде двумерного массива или матрицы, однако для большего понимания его можно представить как таблицу. Результаты суммы отдельных элементов этой таблицы или матрицы, выбранных согласно установленному правилу, и будут являться конечными значениями взаимной корреляционной функции акустических сигналов. В рамках работы составлены непосредственно алгоритм вычисления дискретной корреляционной функции в соответствии с рассмотренным методом расчета корреляционной функции, приведены примеры вычисления программным способом взаимной и автокорреляционной функций акустических сигналов, приближенных по своим свойствам к сигналам реальных ультразвуковых расходомеров. Предложенный вариант расчета дискретных корреляционных функций может быть применен в энергоэффективных вычислительных модулях расходомеров, предназначенных для длительной эксплуатации от источника автономного питания, обладающих низкой производительностью. Correlation signal processing as a particular case of using a digital data processing obtained from acoustic sensors is widely used in modern ultrasonic liquid and gas flowmeters. These include both direct correlation flowmeters and predominantly a time-pulse or time-of-flight type’s flowmeters, where the correlation processing of acoustic signals is an addition to the general method for measuring the volumetric flow rate of liquid and gas. The use of correlation processing makes it possible to increase the resolution of the flowmeter as a whole and to ensure the useful signal extraction against the background of the noise presence with a high degree of reliability. The article describes a method for calculating discrete correlation functions based on the generalized definition of a discrete correlation function through the convolution of sampled signals from the flow sensors outputs. The essence of this method comes down to calculating a values set ​​of the cumulative products of the probing signal’s samples taken with different steps depending on the total number of signal samples and the assumed number of the correlation function samples. The resulting values sequence ​​is formatted as a two-dimensional array or matrix, but for better understanding it can be represented as a table. The results of the sum of the individual elements of this table or matrix, selected according to the established rule, will be the final values ​​of the cross-correlation function of acoustic signals. Within the framework, an algorithm for calculating the discrete correlation function is directly compiled in accordance with the considered method for calculating the correlation function, examples of software calculation of the cross- and autocorrelation functions of acoustic signals, which are close in their properties to the real signals of ultrasonic flowmeters, are given. The proposed option for calculating discrete correlation functions can be applied in energy-efficient computational modules of flowmeters designed for long-term operation from an autonomous power source with low performance.


2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Patrick Fleischmann ◽  
Heinz Mathis ◽  
Jakub Kucera ◽  
Stefan Dahinden

The cross-correlation method allows phase-noise measurements of high-quality devices with very low noise levels, using reference sources with higher noise levels than the device under test. To implement this method, a phase-noise analyzer needs to compute the cross-spectral density, that is, the Fourier transform of the cross-correlation, of two time series over a wide frequency range, from fractions of Hz to tens of MHz. Furthermore, the analyzer requires a high dynamic range to accommodate the phase noise of high-quality oscillators that may fall off by more than 100 dB from close-in noise to the noise floor at large frequency offsets. This paper describes the efficient implementation of a cross-spectrum analyzer in a low-cost FPGA, as part of a modern phase-noise analyzer with very fast measurement time.


2012 ◽  
Vol 588-589 ◽  
pp. 948-952
Author(s):  
Wei Zhang ◽  
Jin Fang Cheng ◽  
Jie Xu

At present the cross-correlation processing can only suppress the isotropic noise by vector hydrophone sound pressure and vibration velocity combined. The coherent composition of the actual ambient noise makes the detection ability of cross-correlation spectrum reduced. Use XWVD theory, proposed a cross symmetry-correlation function (Cross-SCF). Analysis of simulation data under different SNR and Different nature noise combination proving that the noise suppression Performance of suggested Cross-SCF has nothing to do with noise properties, and compared with the cross-correlation processing have indeed better than coherent noise suppression ability.


2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.


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