Journal of Vibroengineering
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Experimental study on cavity pressure of carbon dioxide fracturing tube
Full text linkThe peak pressure and duration of high-pressure gas in the process of rock breaking by carbon dioxide fracturing will have a great impact on the fracturing effect of rock mass. For the sake of obtaining dynamic pressure response characteristic in the fracturing tube, the pressure-time curve of liquid CO2 blasting system was measured through several cavity pressure test of carbon dioxide fracturing tube. The cavity pressure test under different parameters of carbon dioxide quantity, activator quantity and thickness of energy release plate is carried out, in which TST6200 transient signal acquisition instrument, 109c12 type blasting shock wave sensor and special signal processing software system are used. The relation curve between pressure and time in the liquid CO2 fracturing tube was effectively measured and the variation law of pressure curve was analyzed. The results show that after igniting the liquid carbon dioxide fracturing tube, the cavity pressure rise slowly, then increases sharply and decays rapidly, when the pressure reaches the yield pressure limit of energy release plate, the energy release plate will be destroyed and high pressure gas will be released rapidly; the peak pressure in the CO2 fracturing system ranges from 169.1 MPa to 260.2 MPa, the duration of positive pressure is from 21.1 ms to 74.8 ms, and the peak pressure arrival time is between 13.2 ms and 67.7 ms. This paper attempts to establish the formula relationship between peak cavity pressure and thickness of energy release plate for carbon dioxide fracturing tube; the welded flat head formula, tensile failure formula and shear failure formula are used to calculate peak cavity pressure of carbon dioxide fracturing tube, in which the calculated results are compared with the measured values, the welded flat head formula severely underestimates the peak cavity pressure; conversely, the tensile failure formula slightly overestimated the peak cavity pressure; the shear failure formula can accurately reflect the peak cavity pressure of fracturing tube. The conclusions can be directly used for the design and optimization of fracturing tube by using different thickness energy release plate to control the cavity pressure of carbon dioxide fracturing tube
An adaptive multi band-pass filter algorithm and its application in fault diagnosis of rolling bearing
Full text linkConstruction of an optimal band-pass filter for effective envelope demodulation spectral (EDS) analysis of rolling element bearing has been studied widely and amount of methods have been arising. However, most of these methods only get the envelope demodulation analysis result of a specific frequency band. In fact, multiple resonance bands might be caused when rolling bearing fails, especially when compound fault arises, and some key components buried in the original signal are often neglected by the above methods such as fast Kurtogram and Autogram algorithms. Therefore, it is particularly necessary to establish a multi-band pass filter algorithm for EDS. In the paper, an adaptive multi-band pass filter method based on signal energy is proposed, and then the square wave envelope analysis method is used for multi-band pass filtered signal to extract the fault characteristic frequency of rolling bearing. In addition, since the phase of the signal retains a lot of useful information of the original signal, the phase information of the multi-band filtered signal is retained and used for signal reconstruction. Not only the early weak fault feature could be extracted, but also the compound fault feature of rolling bearing could also be extracted by the proposed method, which is verified thorough simulation and experiments
Fault diagnosis of rolling bearings based on improved enhanced envelope spectrum
Full text linkAiming at the problem that traditional fast spectral correlation (Fast-SC) cannot effectively extract the fault feature of rolling bearings under strong background noise, we propose to select a band with abundant fault information on the spectral frequency axis by Spectral Gini Index (SGI) and integrate over them to obtain the Improved Enhanced Envelope Spectrum (IEES). The proposed method doesn’t rely on the selection of precise fault characteristic frequency, and has a good industrial application prospect. The simulated and experimental results of rolling bearings prove the effectiveness of the algorithm
Design of low-cost wireless noise monitoring sensor unit based on IoT concept
Full text linkThe recent expansion of wireless sensor networks (WSN) in urban cities has led to possibility of generation of large amounts of information data from the environmental monitoring systems among which one of the most concerning is the excessive urban noise pollution. As opposed to the conventional noise mapping procedures that involve costly and time-consuming measurement process with a traditional high-priced noise level meter, the low-cost wireless sensor networks provide a method for achieving data collection and analysis with a higher level of granularity. Тhis paper presents a wireless noise sensor unit design for continuous environmental noise level monitoring as a framework for the realization of the Internet of Things (IoT) and „Smart city” concept. The concept involves the complete noise data information system, from sensor structure to data visualization and data analysis. The overall design, characteristics and performance of the sensing system for continuous measuring urban noise pollution is discussed
Periodic non-sinusoidal time-delay stochastic resonance weak fault diagnosis method and its application
Full text linkStochastic resonance is the use of nonlinear systems to synchronize an original signal with noise. This method is commonly used to extract useful signals by reducing noise and has been widely used for mechanical weak fault diagnosis. This paper analyzes the characteristics of a periodic non-sinusoidal potential function, considers the shape of the model, and introduces a time-delay. The steady-state probability density function, effective potential function, and signal-to-noise ratio are then analyzed. As a result, a signal detection method for periodic non-sinusoidal time-delay stochastic resonance (PNTSR) is proposed. Experimental and engineering data are used to explain the PNTSR through the simulation. It is found that the PNTSR method has better fault detection results when compared to the classic bi-stable stochastic resonance method
Synthesising the sound of a car engine based on envelope decomposition and overlap smoothing
Full text linkThe synthesised sound of a car engine is used to alert people to the approach of an electric vehicle, to personalise the sound of an engine and for virtual reality. A methodology for synthesising engine sound based on concatenating samples is proposed. First, using filtering, the engine sound is decomposed into a combination of low-frequency harmonics that depend on the engine speed and high-frequency narrowband amplitude-modulated signals. The high-frequency signals are modulated by the harmonics that depend on the engine speed. The carrier and envelope of the amplitude-modulated signal are extracted with a Hilbert transform. The decomposed segments are concatenated by overlap smoothing. All the concatenated segments are assembled to form a synthesised sound. Finally, the synthesised sound is evaluated using the cepstrum distance and subjective auditory experiment, and it is compared with the raw engine sound and other synthesised sound
Array optimization of sparse regularization equivalent source acoustic holography algorithm
Full text linkIn order to improve the accuracy of the sparse regularization equivalent source acoustic holography algorithm, based on the analysis of the holographic algorithm theory, an optimized array arrangement is proposed. The sensing matrix constructed by the array parameters directly affects the accuracy of the acoustic imaging algorithm. By analyzing the influence of the sensing matrix on the imaging algorithm, the Restricted Isometry Constant (RIC) is chosen to evaluate the sensing matrix. Using genetic algorithm (GA), the RIC is taken as the fitness value, and the optimal pseudo-random array is selected and compared with the conventional array arrangement for acoustic imaging. Experiments show that the optimized pseudo-random array has better imaging effect under the same number of sensor measurements, and provides an optimization method for the design of acoustic array
Ensembled mechanical fault recognition system based on deep learning algorithm
Full text linkPrimary detection and removal of mechanical fault is vital for the recovery of mechanical and electrical equipment. The conventional mechanical fault recognition modules are not able obtain highly sensitive feature attributes for mechanical fault classification in the absence of prior knowledge. The fault diagnosis via data-driven methods have become a point of expansion with recent development in smart manufacturing and fault recognition techniques using the concept of deep learning. In this work, a combination of feature selection with Artificial Intelligence (AI) algorithm is presented for the mechanical fault recognition to deal with smart machine tools. This article proposes a CNN based fault recognition and classification framework that uses the combination of feature extraction, feature vector decomposition using Empirical Mode Decomposition (EMD) and deep neural network (DNN) for recognising the different fault states of the rotating machinery. The experimental outcomes obtained by the combination of EMD, feature selection module and Convolutional Neural Network (CNN) provides the detailed fault information by selecting the sensitive features from large number of faulty feature attributes. The proposed fault recognition and classification method performs better in terms of all the parameters yielding 99.01 % accuracy with respective cross-entropy loss of 0.325 and time complexity of 18 mins and 31 seconds. The comparative analysis is also done with other mainstream models and other state of the art methods, which reveals that the maximum improvement of 12.29 % is attained in terms of accuracy for the proposed fault recognition method. The presented method is robust in terms of reduction of network size, improvement of mechanical fault recognition, providing classification accuracy along with high fault diagnostic solution
A study on the extraction of characteristics of compound faults of rolling bearings based on ITD-AF-CAF
Full text linkIn view of the cyclostationary characteristics of vibration signals from aero-engine, the combination of cyclic autocorrelation function and intrinsic timescale decomposition (ITD) has been proposed. According to the proposed method, vibration signals are decomposed by ITD algorithm to obtain the autocorrelation function of proper rotation components (PRC), based on which characteristic extraction and identification of compound faults of rolling bearings is made possible. To validate the effectiveness of method, an analysis has been given to the vibration signals of rolling bearings collected by sensors of different positions in different compound fault modes. As shown by results, the method combining ITD and cyclostationary theory can precisely and effectively extract the characteristic frequency relative to the type of faults and identify the compound faults
Nonlinear vibration characteristics and time-delayed displacement control of rolling mill under dynamic rolling force
Full text linkAn expression for the dynamic rolling force of a rolling mill is derived in terms of the vibration and process parameters by analyzing the dynamic rolling process. A nonlinear vibration model of the rolling mill rolls is established. The amplitude-frequency and bifurcation equations are obtained using a multi-scale approximation method, to solve the dynamic equation with time-delayed displacement control. With a 1780 rolling mill as an example, it is found that the primary and cubic stiffness due to the dynamic rolling force and external excitation lead to a jump phenomenon in the vibration system, making it unstable. When the gain coefficient and delay time are taken reasonably, the amplitude of the vibration system is reduced, the resonance region shrinks, and the jump is eliminated. Finally, the bifurcation topological curve corresponding to the transition set of the vibration system is studied using the singularity theory, with and without time-delayed displacement control. The results show that the vibration of the rolling mill rolls can be restrained by varying the initial parameters and through the time-delayed displacement control. Thus, the established vibration model of the rolling mill is verified, and the effectiveness of the time-delayed displacement control in reducing the rolling mill vibration is confirmed