Prediction of shallow bit position based on vibration signal monitoring of bit broken rock

The speed of drilling has a great relationship with the rock breaking efficiency of the bit. Based on the above background, the purpose of this article is to predict the position of shallow bit based on the vibration signal monitoring of bit broken rock. In this article, first, the mechanical research of drill string is carried out; the basic changes of the main mechanical parameters such as the axial force, torque, and bending moment of drill string are clarified; and the dynamic equilibrium equation theory of drill string system is analyzed. According to the similarity criterion, the corresponding relationship between drilling process parameters and laboratory test conditions is determined. Then, the position monitoring test system of the vibration bit is established. The acoustic emission signal and the drilling force signal of the different positions of the bit in the process of vibration rock breaking are collected synchronously by the acoustic emission sensor and the piezoelectric force sensor. Then, the denoised acoustic emission signal and drilling force signal are analyzed and processed. The mean value, variance, and mean square value of the signal are calculated in the time domain. The power spectrum of the signal is analyzed in the frequency domain. The signal is decomposed by wavelet in the time and frequency domains, and the wavelet energy coefficients of each frequency band are extracted. Through the wavelet energy coefficient calculated by the model, combined with the mean, variance, and mean square error of time-domain signal, the position of shallow buried bit can be analyzed and predicted. Finally, by fitting the results of indoor experiment and simulation experiment, it can be seen that the stress–strain curve of rock failure is basically the same, and the error is about 3.5%, which verifies the accuracy of the model.

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Acoustic Emission Monitoring of Tool Wear in End-Milling Using Time-Domain Averaging

Journal of Manufacturing Science and Engineering ◽

10.1115/1.2830581 ◽

1999 ◽

Vol 121 (1) ◽

pp. 8-12 ◽

Cited By ~ 20

Author(s):

D. V. Hutton ◽

F. Hu

Keyword(s):

Acoustic Emission ◽

Tool Wear ◽

Time Domain ◽

End Milling ◽

Mean Deviation ◽

Processing Scheme ◽

Wear Process ◽

Emission Signal ◽

The Mean ◽

Time Domain Averaging

The characteristics of the acoustic emission signal during the tool wear process in end milling are analyzed, and a signal processing scheme for abstracting the mean time domain averaging deviation of the signal to monitor tool wear is proposed. Experiments indicate that the mean deviation value is sensitive to flank wear and its normalized value is not as dependent on milling parameters as the acoustic emission root mean square signal.

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Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

Expert Systems with Applications ◽

10.1016/j.eswa.2020.114391 ◽

2021 ◽

Vol 168 ◽

pp. 114391

Author(s):

Mohsen Motahari-Nezhad ◽

Seyed Mohammad Jafari

Keyword(s):

Signal Processing ◽

Acoustic Emission ◽

Time Domain ◽

Acoustic Emission Signal ◽

Life Prediction ◽

Remaining Useful Life ◽

Emission Signal ◽

Useful Life

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Acoustic Information Acquisition and Time Domain Analysis on Alternating Current Rail Flash Butt Welding

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.16 ◽

2012 ◽

Vol 239-240 ◽

pp. 16-20

Author(s):

Qi Bing Lv ◽

Ke Li Tan ◽

Xi Zhang ◽

Jian Chen ◽

Guo Qing Liu

Keyword(s):

Time Domain ◽

Acoustic Signal ◽

Information Acquisition ◽

Welding Process ◽

Welding Current ◽

Time Domain Analysis ◽

Mean Square ◽

Butt Welding ◽

The Mean ◽

The Stability

Based on the mobile rail flash butt welding machine UN5-150ZB, the synchronous data acquisition hardware system was designed to collect welding current, welding voltage and flash acoustic signal in welding process, and the software platform with the functions of signal collecting, waveform display and data operation was developed by higher-level programming language LabVIEW. After the welding current, welding voltage and flash acoustic signal in welding process had been collected, the mean, variance and mean square value of flash acoustic signal in time-domain were analyzed. Through comparison, the relationship between these characteristics and the stability of flash was analyzed. The result shows that the changes of mean and variance of flash acoustic signal are not obvious, and do not correlate with stability of flash, but the mean square value in time domain is closely associated with the stability of flash, and the stability of flash can be indicated by the mean square value.

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Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405420949127 ◽

2020 ◽

pp. 095440542094912

Author(s):

Mohamad Javad Anahid ◽

Hoda Heydarnia ◽

Seyed Ali Niknam ◽

Hedayeh Mehmanparast

Keyword(s):

Acoustic Emission ◽

Frequency Domain ◽

Time Domain ◽

Acoustic Emission Signal ◽

Cutting Tool ◽

Cutting Speed ◽

Cutting Parameters ◽

Emission Signal ◽

Signal Parameters ◽

Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

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Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406220915232 ◽

2020 ◽

Vol 234 (17) ◽

pp. 3521-3540

Author(s):

Hoi Yin Sim ◽

Rahizar Ramli ◽

Ahmad Saifizul

Keyword(s):

Acoustic Emission ◽

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Acoustic Emission Signal ◽

Discrete Wavelet ◽

Mean Square ◽

Crest Factor ◽

Emission Signal ◽

Best Parameter ◽

All Speeds

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

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Performance dynamics of pdc cutters used in breaking ductile brittle rock

Nexo Revista Científica ◽

10.5377/nexo.v34i04.12694 ◽

2021 ◽

Vol 34 (04) ◽

pp. 1490-1498

Author(s):

Oleg B. Trushkin ◽

Hamzja I. Akchurin

Keyword(s):

Dynamic Response ◽

Axial Force ◽

Rock Breaking ◽

Brittle Rock ◽

Mean Square ◽

Rock Fracturing ◽

Response Factors ◽

Widespread Application ◽

Cycle Amplitude ◽

The Mean

The widespread application of cutting - chipping action bits with PDC cutters is held back due to the intense chipping and breakage of the latter. This article presents the results of bench-scale tests conducted to determine the values of three mutually perpendicular components of the load on sharp-edged and beveled rock-breaking cutters of 13.5 mm in diameter as well as the dynamic-response factors and mean square deviations (MSD) of these components. The forces change in time by leaps, which reflects the rock fracturing under the cutter. The MSD accepted as per-cycle amplitude is four times as low on average as the mean axial force; when a sharp-edged and a beveled cutter is used, the MSD is by 150 to 300 and by 300 to 500 % lower than the mean circumferential force.

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Enhanced CORDIC Based Rotator Design for Sinusoidal Transforms

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.c4725.029320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1001-1004

Keyword(s):

Fourier Transform ◽

Frequency Domain ◽

Discrete Fourier Transform ◽

Mean Square Error ◽

Time Domain ◽

Basis Function ◽

The Other ◽

Mean Square ◽

Circular Rotation ◽

The Mean

Transforms play an important role in conversion of information from one domain to the other. To be more specific transforms like Discrete Fourier transform (DFT) and Discrete Cosine transform (DCT) helps us to migrate from one time domain to frequency domain based on the basis function selected. The basis function of the every sinusoidal transform carries out a circular rotation to convert information from one domain to the other. There are applications related to communication which requires this rotation into the hyperbolic trajectory as well. Multiplierless algorithm like CORDIC improves the latency of the transforms by eliminating the number of multipliers in the basis function. In this paper we have designed and implemented enhanced version of CORDIC based Rotator design. The Enhanced version is simulated for order 1 to order 36 to emphasize on the results of the proposed algorithm. Results shows that the enhanced CORDIC rotator design surpasses the Mean square error after the order 18 compared to standard CORDIC. Unified CORDIC also can be implemented using the said algorithm to implement different three trajectories.

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Oscillating Velocity and Enhanced Diffusivity of Nanosystems from a New Quantum Transport Model

Journal of Nano Research ◽

10.4028/www.scientific.net/jnanor.16.49 ◽

2012 ◽

Vol 16 ◽

pp. 49-54 ◽

Cited By ~ 2

Author(s):

Paolo Di Sia

Keyword(s):

Quantum Transport ◽

Time Domain ◽

Mechanical Model ◽

Transport Model ◽

Quantum Mechanical ◽

Mean Square ◽

Velocity Correlation ◽

Velocity Correlation Functions ◽

The Mean ◽

Quantum Treatment

The generalizations of the Drude model describe classically the most important quantities referred to transport phenomena, i.e the velocity correlation functions, the mean square deviation of position and the diffusion coefficient. The quantum effects, appearing in systems at nano-dimensions, require a quantum treatment of the previous models. I have presented a new ‘time-domain’ quantum mechanical model for transport in nanosystems, which comprehends the oscillator strength weights; this model demonstrates high generality and good application perspectives also in the study of ions, solutions, and in nano-bio-systems. This quantum mechanical extension allows to test the diffusion in nanostructured materials for biological, medical and nanopiezotronic devices. The theoretical elaboration of experimental data shows the interesting presence of an initial oscillating behaviour in velocity and an enhanced initial diffusivity, offering considerable informations for the experimental researchers.

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Online Monitoring Technology of Power Transformer based on Vibration Analysis

Journal of Intelligent Systems ◽

10.1515/jisys-2020-0112 ◽

2021 ◽

Vol 30 (1) ◽

pp. 554-563

Author(s):

Junhong Meng ◽

Maninder Singh ◽

Manish Sharma ◽

Daljeet Singh ◽

Preet Kaur ◽

...

Keyword(s):

Mean Square Error ◽

Online Monitoring ◽

Power Transformer ◽

Vibration Signal ◽

Reconstruction Error ◽

Mean Square ◽

Reconstructed Signal ◽

The Mean ◽

Online Monitoring System ◽

Better Than

Abstract This paper presents a method for the study of the influence of stability of a power transformer on the power system based on the vibration principle. Traditionally, the EMD and EEMD algorithms are employed to test the box vibration signal data of the power transformer under three working conditions. The proposed method utilizes a partial EMD screening along with MPEEMD method for the online monitoring of power transformer. A complete online monitoring system is designed by using the STM32 processor and LabVIEW system. The proposed system is compared with EMD and EEMD algorithms in terms of the number of IMFs obtained by decomposition, maximum correlation coefficient, and mean square error. The inherent mode correlation, when compared with the mean square error of the reconstructed signal, shows that the reconstruction error of MPEEMD algorithm is 4.762×10−15 which is better than the traditional EMD algorithm. It is observed from the results that the proposed method outperforms both EMD and EEMD algorithms.

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