Current Issue : October - December Volume : 2019 Issue Number : 4 Articles : 5 Articles
According to the performance degradation problem of feature extraction from higher-order statistics in the context of alphastable\nnoise, a new feature extraction method is proposed. Firstly, the nonstationary vibration signal of rolling bearings is\ndecomposed into several product functions by LMD to realize signal stability. ,en, the distribution properties of product\nfunctions in the time domain are discussed by the comparison of heavy tails and characteristic exponent estimation. Fractional\nlower-order p-function optimization is obtained by the calculation of the distance ratio based on K-means algorithms. Finally, a\nfault feature dataset is established by the optimal FLOS and lower-dimensional mapping matrix of covariation to accurately and\nintuitively describe various bearing faults. Since the alpha-stable noise is effectively suppressed and state described precisely, the\npresented method has shown better performance than the traditional methods in bearing experiments via fractional lower-order\nfeature extraction....
As the amount of data generated by monitoring the condition of rolling bearings is increasing, it has become a research hotspot in\nrecent years to dig valuable information from massive data and identify unknown bearing states. In Internet technology, the\ncollaborative filtering recommendation technology provides users with an intelligent means of filtering information. Aiming at\nthe difficulty in designing the recommendation system scoring matrix in the field of fault diagnosis, we first obtain the bearing\nfeature matrix based on the wavelet frequency band energy and then design a scoring matrix that accurately describes the bearing\nstate; finally, we design a joint scoring matrix for bearing state identification by combining the matrix of these two different\ncharacteristics. After that, a collaborative filtering recommendation system for bearing state identification is proposed based on\nmatrix factorization-based collaborative filtering and gradient descent algorithm. *is method is used to identify and verify two\ntypes of fault data of rolling bearing: different position faults and different types of faults on the outer ring. *e results show that\nthe accuracy of the two identifications has reached more than 90%....
Line 2 of the Guanjingkou Pipe Jacking Project in Chongqing encountered a pipe sticking problem, whose occurrence was\ninevitably attributed to the higher total frictional resistance of pipe strings rather than the maximum jacking force. Line 1, which is\nabout to start construction, has basically the same construction environment as Line 2 using the same microshield and pipe string\nsizes. To avoid repeating the pipe sticking problem of Line 2, the mutual friction characteristics between the surrounding rocks\nand jacked pipe strings are studied for Line 1 by adopting the same test method under seven complex contact conditions (the\npresence or various combinations of three substances, i.e., extrapipe string field debris, bentonite slurry, and sand-laden waste\nslurry, on the jacked pipe string-surrounding rock contact surface are mainly considered). The results show that sufficient\nbentonite slurry can effectively reduce the frictional resistance, and when the amount of bentonite is insufficient, the average\nfriction coefficient (AFC) of the later contact surface increases by 50%mm70%. The comparison of the monitored versus predicted\njacking forces indicates that the value predicted by the test is slightly higher than the monitored force and the variation trends of\nthe two match well, thus proving the correctness of the test results. It is possible to continue predicting the variation trends of the\njacking force and frictional resistance based on the contact situation outside the pipe string wall, which greatly lowers the\nprobability of re-encountering pipe sticking.The test results not only explain the important role of bentonite slurry in reducing the\npipe string wall frictional resistance but also suggest that an increase in the pipe string wall frictional resistance resulting from the\ncomplex contact inflow into the overexcavation gap is the root cause of pipe sticking; moreover, the number of jacked pipe strings\nmatching a single IJS is the second cause of pipe string sticking. The methodology of this study can provide a reference for other\nstudies concerning the jacking force of long-distance rock microshield tunnelling....
The results of the tests for a friction pair â??a cylindrical specimen made of\n0.45% carbon steel-a counter specimen-liner made of polytetrafluoroethylene F4-\nBâ? during sliding friction are presented. The test results at different levels\nof contact load are analyzed using the Archardâ??s equation and are presented\nas a friction fatigue curve. The concept of the frictional stress intensity\nfactor during sliding friction is introduced, and an expression that relates the\nwear rate to this factor and is close in shape to the Paris equation in fracture\nmechanics is proposed....
A frozen soil is a multiphase medium composed of solid particles, ice, and water, and the cementation between the solid particles\nand ice strengthens with a decrease in temperature. Based on the theory of a composite solid-state saturated porous medium, a\nuniform solution for the vertical vibration of an end-bearing pile in a frozen soil is derived analytically. (e axial displacements\nunder impact loading in the time domain are calculated by using the numerical inverse transformation technique. (e solution\ncan degenerate into an end-bearing pile in a saturated soil layer as the temperature approaches the freezing point. If the cementation\nbetween the solid particles and ice is ignored, the amplitude of the displacement will be overestimated. (e numerical\nresults show that temperature has a significant impact on the dynamic responses of the pile due to variation of the ice content and,\nconsequently, of the cementation between solid particles and ice....
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