Papers by Keyword: Rotating Machinery

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Authors: Ling Li Jiang, Zong Qun Deng, Si Wen Tang
Abstract: This research aims at defining a character of rotating machinery——KIC, that the typical rolling bearing and gear failure modes can be effectively identified by using the character. Firstly, A correlation coefficient matrix is composed by the correlation coefficient between two-two inde-pendent component derived from kernel independent component (KICA). Then the KIC is defined by the correlation coefficient matrix. Experimental results show that the KIC has a good effect for identifying the bearing and gear fault modes, so it can be used as sensitive character for rotating machinery fault diagnosis.
Authors: Zheng Yao, Qing Xin Zhao
Abstract: The on-line fault diagnostics technology for machines is fast emerging for the detection of incipient faults as to avoid the unexpected failure. On the basis of fault diagnosis theory and method, this paper presents a applications of techniques for fault detection and classification in rotating machinery based on fuzzy theory and neural network theory, the basic structure and working principle of the fault intelligent diagnosis system are introduced, the knowledge stored in the neuron-fuzzy system has been extracted by a fuzzy rule set with an acceptable degree of interpretability, the model of fuzzy fault diagnosis and the self-study principle are described. The practice proves that this is an effective method of large-scale and complicated electronic equipment, and it can also be applied to other fault diagnosis of complex systems and has certain portability.
Authors: Wen Bin Zhang, Yan Ping Su, Jie Min, Yan Jie Zhou
Abstract: In this paper, a novel method to recognize rotor fault pattern was proposed based on rank-order morphological filter, harmonic window decomposition, sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Then, the six feature frequency bands which contain the typical fault information were extracted by harmonic window decomposition that needs not decomposition; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different rotor vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in fault diagnosis of rotating machinery effectively.
Authors: Xiao Yan Yang, Xiong Zhou, Yi Ke Tang
Abstract: In fault diagnosis of large rotating machinery, the number of fault sources may be subject to dynamic changes, which often lead to the failure in accurate estimation of the number of sources and the effective isolation of the fault source. This paper introduced the expansion of the fourth-order cumulant matrices in estimating the dynamic fault source number, plus the relationship between the source signal number and the number of sensors being utilized in the selection of the blind source separation algorithm to achieve adaptive blind source separation. Experiments showed that the source number estimation algorithm could be quite effective in estimating the dynamic number of fault sources, even in the underdetermined condition. This adaptive blind source separation algorithm could then effectively achieve fault diagnosis in respect to the positive-determined, overdetermined and underdetermined blind source separation.
Authors: Gang Yu, Jian Kang
Abstract: As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.
Authors: Wen Bin Zhang, Jia Xing Zhu, Ya Song Pu, Yan Ping Su
Abstract: . Aiming at the purification of rotor center’s orbit, a new approach was presented by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a series of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose some interested IMFs and reconstructed the needed signal. By doing this the noises would be eliminated successfully. At last the purification of rotor center’s orbit was obtained by extracting the useful signal component. Simulation and practical results show the advantage of EEMD in noise de-noising and purification of rotor center’s orbit. This method also has simple algorithm and high calculating speed; it provides a new way for purification of rotor center’s orbit of rotating machinery.
Authors: Denis Shutin, Leonid Savin, Alexander Babin
Abstract: The paper examines the issues of improving the rotor units by means of using support units with actively changeable characteristics. An overview of the known solutions related to the use of active bearings in various types of turbomachinery is provided. A closer look is given at the design and features of active radial bearings, the main elements of which are fluid film bearings. The results of mathematical modeling of active hybrid bearings are presented. The prospects of the use of this type of supports to improve the dynamic characteristics of rotating machinery, including reducing vibrations caused by various factors, are analyzed. Promising directions of development of active bearings are considered, which primarily involves the modification of system components and rotor motion control system algorithms, including intelligent technologies and artificial intelligence methods.
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