Energy Characteristic Parameters Research Articles

Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering

This study based on the 25-year wave hindcast database of the western Pacific and used three unsupervised learning clustering algorithms to classify the wave energy resources in the China East Adjacent Seas. Five wave energy characteristic parameters are comprehensively considered in the calculation process of the clustering algorithm. According to the analysis of the classification results, it can be seen that the Class Ⅳ is the most suitable for wave energy development in the China East Adjacent Seas, followed by the Class Ⅲ and Class Ⅴ. The Class Ⅵ is too far away from the coast to be used as the intended area for wave energy development. The Class Ⅰ is mostly located in inland seas and harbors, which are not suitable for wave energy development. By analyzing the annual average captured power, it can be seen that the optimal capture interval of the existing wave energy converters with mature technology is too large compared with the wave conditions in the China East Adjacent Seas. We should vigorously develop wave energy converters that are more suitable for the wave conditions of Class Ⅲ, Class Ⅳ and Class Ⅴ, and improve the capture efficiency of the wave energy converters.

Analysis of Crack-Characteristic Stress and Energy Characteristics of Sandstone under Triaxial Unloading Confining Pressure

The deformation and failure of underground engineering are usually caused by unloading. In this work, triaxial unloading confining pressure tests are carried out to simulate the failure process of rock mass caused by unloading, analyze the crack-characteristic stress, and study the energy evolution of rock under unloading and the pre-peak and post-peak energy characteristics combined with the energy theory. The results show that, when the confining pressure increases from 5 MPa to 20 MPa, crack closure stress σcc, crack initiation stress σci, dilatancy stress σcd, and peak stress σp are 6.34 times, 2.75 times, 1.93 times, and 1.66 times higher than the original, respectively. By comparing the increase in crack-characteristic stress, it can be found that the confining pressure has a large effect on the crack closure stress and crack initiation stress, while the dilatation stress and peak stress have relatively little influence. From the perspective of energy evolution, the pre-peak axial absorption energy U1 increases exponentially, the elastic energy Ue is similar to U1, and the circumferential consumption energy U3 and dissipation energy Ud are small. After reaching the peak stress, the growth rate of U1 decreases slightly, Ue decreases rapidly, and U3 increases rapidly but only as a small fraction of the total energy, while Ud grows almost exponentially and rapidly becomes the main part of the energy. Under each crack-characteristic stress state, the energy characteristic parameters gradually increase with the increase in confining pressure, which is manifested by the increase in slope in the linear fitting formula of energy characteristic parameters. The release process of the releasable elastic energy after the peak stress can be divided into three stages of “slow–fast–slow”, and the energy release process shows an obvious confining pressure effect.

Research on detection and recognition methods of gas pipelines based on acoustic signal feature analysis

In the process of reconstruction and expansion of gas pipeline, it is easy to destroy in-service gas pipeline and cause safety accidents. In order to realize the detection of in-service pipelines, based on the characteristics of low sound pressure level and easy attenuation of acoustic signals of gas pipelines, the detection and identification method of gas pipelines based on acoustic signal feature analysis was studied by using Hilebert–Huang transform algorithm and optimized Back Propagation (BP) neural network. This method takes the gas pipeline flow noise signals obtained by numerical simulation and experimental verification as the research object, and the underwater acoustic signals are collected for comparative analysis. Empirical Mode Decomposition (EMD) was used to decompose the two signals, and the time-domain waveform of Intrinsic Mode Functions (IMF) component was obtained, and the characteristic parameters of peak value and peak frequency were determined. The energy characteristic parameters of Hilbert marginal spectrum were calculated, and the characteristic database of gas pipeline flow noise signal was obtained. The optimized BP neural network was used for pattern recognition. The results show that the identification rate of gas pipeline acoustic signal can reach 97.5% by using this method, which verifies the effectiveness of the gas pipeline detection and identification method in this paper.

Coupling Mechanism of Dissipated Energy-Infrared Radiation Energy of the Deformation and Fracture of Composite Coal-Rock under Load.

The fracture of composite coal-rock under load is the process of energy conversion. As the dissipative energy composition, there is a correlation between the infrared radiation energy and the coal-rock states. Based on theories of theoretical mechanics, modern quantum mechanics, thermodynamics, and other disciplines, first, this paper explained the causes of infrared radiation energy in the process of coal-rock fracture by using the microanalysis method. After that, the mathematical model of dissipation energy–infrared radiation energy coupling was deduced and established, and the experimental analysis was carried out under different loading conditions. The analysis shows that the conversion of mechanical energy and internal energy in the process of loading caused constant collisions between molecules in coal-rock, which led to a temperature rise. After entering the excited state, molecules have to transition to a lower energy level, which generates infrared radiation. The experimental results show that there was a strong correlation between energy characteristic parameters, which is consistent with the established relationship. In addition, the energy conversion and dissipated energy changes in the loading process had stages. Before the elastic–plastic stage, the dissipated energy obtained by coal-rock energy conversion was less, but it increased rapidly in the later stage, which eventually led to the fracture of coal-rock. In the early elastic–plastic period, infrared radiation energy was the main component of the dissipated energy and its variation trend was consistent with the dissipated energy. After that, the infrared radiation energy remained stable, but the dissipation energy still increased. At this time, infrared radiant energy was no longer the main component of dissipated energy. And the infrared radiation energy dropped rapidly before coal-rock fracture, which had certain precursory characteristics. The coupling mechanism of dissipated energy–infrared radiation energy can be used to explain the failure reason of composite coal-rock under different loading conditions from the perspective of energy, which will provide a new idea for assisting the prediction of coal-rock dynamic disasters.

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.

Application of multi‐source information fusion based on D‐S evidence theory in insulation defect identification of DC XLPE cable

During the installation and laying process of cross-linked polyethylene (XLPE) cable, it is difficult to avoid the influence of external harsh environment, which leads to insulation deterioration and failure. The partial discharge and leakage current play an important role in formation mechanism, characteristic law and identification technology of DC XLPE cable defect. However, the defect identification accuracy of single source data is limited. The correlation between partial discharge and leakage current of typical defect is analysed here. Based on the shape characteristic parameters of typical DC partial discharge spectrum and the energy characteristic parameters of leakage current wavelet decomposition node, a weighted Dempster–Shafer (D-S) evidence theory is used to fuse and identify different information sources. The results show that the classification rate of the proposed method can reach 88%, which can effectively identify insulation defects.

Experimental study on acoustic emission (AE) characteristics and crack classification during rock fracture in several basic lab tests

A series of rock tests including Brazilian indirect tension test (BITT), three-point bending test (TPBT), modified shear test (MST) and uniaxial compression test (UCT) were conducted to investigate the acoustic emission (AE) characteristics and crack classification during rock fracture. The test results show that the rock fracture process presents an obvious segmented variation feature and has a dramatic increasing period, according to the change trends of AE hits and AE energy characteristic parameters. The AE characteristics are closely related to the types of micro-cracks produced in the rock fracture process. The elastic strain energy released by shear crack is greater than that released by tensile crack. Most of AE signals generated in compression and shear failures that mainly produce shear cracks have low average frequency (AF) values and low peak frequencies (below 100 kHz). On the contrary, most of AE signals generated in bending and tensile failures that mainly produce tensile cracks have low RA (ratio of rise time to amplitude) and high peak frequencies (above 100 kHz). In addition, the dividing lines were defined to distinguish the tensile cracks and shear cracks in the AF-RA scatter plots for different rocks. For instance, the AE signals above dividing line accounted for more than 62%, which indicated that the tensile cracks were dominant in TPBT. However, the AE signals below dividing line accounted for more than 74%, and the shear cracks were dominant in UCT. Therefore, the AE characteristics can be used to determine the fracture modes of rock, then to shed light on the micro-crack properties.

Transient Characteristics of Water-Jet Propulsion with a Screw Mixed Pump during the Startup Process

In order to investigate the transient hydrodynamic characteristics of water-jet propulsion with a screw mixed pump during the startup period and to overcome the difficulties in measuring the transient process parameters over a very short period of time, a method based on a flume experiment combined with computational fluid dynamics (CFD) is proposed. The thrust and torque of the pump-jet propulsion according to mooring status at different rotational speeds were measured by the test, which provided a group of data for the boundary and initial conditions of the numerical calculation of the user-defined function (UDF). Consecutive changes in the parameters of the water-jet propulsion dynamics can be captured from the numerical simulation of the startup process with the UDF. Thus, the transient hydrodynamic characteristics of water-jet propulsion according to time or rotation speed were obtained. The results show that the relationship between the thrust or torque of the water-jet propeller and pump rotational speed is close to the quadratic functions. The energy characteristic parameters of screw mixed water-jet pump, such as the flow rate, head, shaft power, and efficiency, rapidly increase and decrease and then remain relatively stable.

Gear fault feature extraction and diagnosis method under different load excitation based on EMD, PSO-SVM and fractal box dimension

Aiming at the problem of gear fault feature extraction and fault classification under different load excitation, we present a new fault diagnosis method that combines three methods, including empirical mode decomposition (EMD), particle swarm optimization support vector machine (PSO-SVM) and fractal box dimension. First, the non-stationary original vibration signal of gear fault is decomposed into several intrinsic mode functions (IMF) by EMD method. Then, the time, frequency, energy characteristic parameters and box dimension are calculated separately from the time domain, frequency domain, energy domain and fractal domain. And then the gear fault characteristics under different load excitation are obtained. Finally, the extracted feature parameters are input into the PSO-SVM model for gear fault classification. The experimental results show that the proposed method can effectively identify gear failure types under different load excitation.

Application of variational mode decomposition energy distribution to bearing fault diagnosis in a wind turbine

For the unsteady characteristics of a fault vibration signal of a wind turbine’s rolling bearing, a bearing fault diagnosis method based on variational mode decomposition of the energy distribution is proposed. Firstly, variational mode decomposition is used to decompose the original vibration signal into a finite number of stationary components. Then, some components which comprise the major fault information are selected for further analysis. When a rolling bearing fault occurs, the energy in different frequency bands of the vibration acceleration signals will change. Energy characteristic parameters can then be extracted from each component as the input parameters of the classifier, based on the K nearest neighbour algorithm. This can identify the type of fault in the rolling bearing. The vibration signals from a spherical roller bearing in its normal state, with an outer race fault, with an inner race fault and with a roller fault were analyzed. The results showed that the proposed method (variational mode decomposition is used as a pre-processor to extract the energy of each frequency band as the characteristic parameter) can identify the working state and fault type of rolling bearings in a wind turbine.

Fault Diagnosis Method of Gear Based on Local Mean Decomposition and Least Squares Support Vector Machine

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

Fault Diagnosis Method of Rolling Bearing Based on Ensemble Local Mean Decomposition and Neural Network

For a problem of mode mixing occurs in implementation process of local mean decomposition (LMD) method, an analytical method based on ensemble local mean decomposition (ELMD) and neural network is proposed to apply to fault diagnosis of rolling bearing, the vibrational signal of rolling bearing is decomposed into a series of product functions(PF) by ELMD method. The PF components which contain main fault information are selected to perform a further analysis. The kurtosis coefficient and energy characteristic parameters extracted from these PF components can be used as the input parameters of the neural network to identify the working status and fault types of rolling bearing. Through the analysis of rolling bearing with fault-free, inner-race fault and outer-race fault, the results indicate that the method based on ELMD and neural network has a higher failure recognition rate than the method based on wavelet packet analysis and neural network, and the working status and fault types of rolling bearing can be identified accurately and effectively.

ITBM-RBM news

A series of rock tests including Brazilian indirect tension test (BITT), three-point bending test (TPBT), modified shear test (MST) and uniaxial compression test (UCT) were conducted to investigate the acoustic emission (AE) characteristics and crack classification during rock fracture. The test results show that the rock fracture process presents an obvious segmented variation feature and has a dramatic increasing period, according to the change trends of AE hits and AE energy characteristic parameters. The AE characteristics are closely related to the types of micro-cracks produced in the rock fracture process. The elastic strain energy released by shear crack is greater than that released by tensile crack. Most of AE signals generated in compression and shear failures that mainly produce shear cracks have low average frequency (AF) values and low peak frequencies (below 100 kHz). On the contrary, most of AE signals generated in bending and tensile failures that mainly produce tensile cracks have low RA (ratio of rise time to amplitude) and high peak frequencies (above 100 kHz). In addition, the dividing lines were defined to distinguish the tensile cracks and shear cracks in the AF-RA scatter plots for different rocks. For instance, the AE signals above dividing line accounted for more than 62%, which indicated that the tensile cracks were dominant in TPBT. However, the AE signals below dividing line accounted for more than 74%, and the shear cracks were dominant in UCT. Therefore, the AE characteristics can be used to determine the fracture modes of rock, then to shed light on the micro-crack properties.

L'Agenda

A series of rock tests including Brazilian indirect tension test (BITT), three-point bending test (TPBT), modified shear test (MST) and uniaxial compression test (UCT) were conducted to investigate the acoustic emission (AE) characteristics and crack classification during rock fracture. The test results show that the rock fracture process presents an obvious segmented variation feature and has a dramatic increasing period, according to the change trends of AE hits and AE energy characteristic parameters. The AE characteristics are closely related to the types of micro-cracks produced in the rock fracture process. The elastic strain energy released by shear crack is greater than that released by tensile crack. Most of AE signals generated in compression and shear failures that mainly produce shear cracks have low average frequency (AF) values and low peak frequencies (below 100 kHz). On the contrary, most of AE signals generated in bending and tensile failures that mainly produce tensile cracks have low RA (ratio of rise time to amplitude) and high peak frequencies (above 100 kHz). In addition, the dividing lines were defined to distinguish the tensile cracks and shear cracks in the AF-RA scatter plots for different rocks. For instance, the AE signals above dividing line accounted for more than 62%, which indicated that the tensile cracks were dominant in TPBT. However, the AE signals below dividing line accounted for more than 74%, and the shear cracks were dominant in UCT. Therefore, the AE characteristics can be used to determine the fracture modes of rock, then to shed light on the micro-crack properties.

En direct de l'ecri

A series of rock tests including Brazilian indirect tension test (BITT), three-point bending test (TPBT), modified shear test (MST) and uniaxial compression test (UCT) were conducted to investigate the acoustic emission (AE) characteristics and crack classification during rock fracture. The test results show that the rock fracture process presents an obvious segmented variation feature and has a dramatic increasing period, according to the change trends of AE hits and AE energy characteristic parameters. The AE characteristics are closely related to the types of micro-cracks produced in the rock fracture process. The elastic strain energy released by shear crack is greater than that released by tensile crack. Most of AE signals generated in compression and shear failures that mainly produce shear cracks have low average frequency (AF) values and low peak frequencies (below 100 kHz). On the contrary, most of AE signals generated in bending and tensile failures that mainly produce tensile cracks have low RA (ratio of rise time to amplitude) and high peak frequencies (above 100 kHz). In addition, the dividing lines were defined to distinguish the tensile cracks and shear cracks in the AF-RA scatter plots for different rocks. For instance, the AE signals above dividing line accounted for more than 62%, which indicated that the tensile cracks were dominant in TPBT. However, the AE signals below dividing line accounted for more than 74%, and the shear cracks were dominant in UCT. Therefore, the AE characteristics can be used to determine the fracture modes of rock, then to shed light on the micro-crack properties.