Time filter

Source Type

Zhang J.-K.,McMaster University | Yuen C.,Singapore University of Technology and Design | Huang F.,Shanghai Institute of Radio Equipment
IEEE Transactions on Vehicular Technology | Year: 2012

In this paper, we develop two kinds of closed-form decompositions on phase-shift-keying (PSK) constellations by exploiting linear congruence equation theory: the one for factorizing a pq -PSK constellation into a product of p- and q-PSK constellations and the other for decomposing a specific complex number into a difference of a point in p-PSK constellation and a point in q-PSK constellation. With this, we present a novel and simple signal design technique to blindly and uniquely identify frequency selective channels with zero-padded block transmission by only processing the first two block received signals. In a noise-free case, a closed-form solution to determine the transmitted signals and the channel coefficients is obtained. In a Gaussian noise and Rayleigh fading environment, we prove that our scheme enables full diversity for the generalized likelihood ratio test (GLRT) receiver. When only finite received data are given, the linearity of our signal design allows us to use iterative sphere decoders to approximate GLRT detection so that the joint estimation of the channel and symbols can be efficiently implemented. © 2012 IEEE.

Pan J.,Xian Jiaotong University | Chen J.,Xian Jiaotong University | Zi Y.,Xian Jiaotong University | Yuan J.,Shanghai Institute of Radio Equipment | And 2 more authors.
Mechanical Systems and Signal Processing | Year: 2016

It is significant to perform condition monitoring and fault diagnosis on rolling mills in steel-making plant to ensure economic benefit. However, timely fault identification of key parts in a complicated industrial system under operating condition is still a challenging task since acquired condition signals are usually multi-modulated and inevitably mixed with strong noise. Therefore, a new data-driven mono-component identification method is proposed in this paper for diagnostic purpose. First, the modified nonlocal means algorithm (NLmeans) is proposed to reduce noise in vibration signals without destroying its original Fourier spectrum structure. During the modified NLmeans, two modifications are investigated and performed to improve denoising effect. Then, the modified empirical wavelet transform (MEWT) is applied on the de-noised signal to adaptively extract empirical mono-component modes. Finally, the modes are analyzed for mechanical fault identification based on Hilbert transform. The results show that the proposed data-driven method owns superior performance during system operation compared with the MEWT method. © 2016 Elsevier Ltd.

Chen J.,Xian Jiaotong University | Zi Y.,Xian Jiaotong University | He Z.,Xian Jiaotong University | Yuan J.,Shanghai Institute of Radio Equipment
Hsi-An Chiao Tung Ta Hsueh/Journal of Xi'an Jiaotong University | Year: 2012

Signals of mechanical equipment faults in operation with obscure symptoms and weak features are always contaminated by stronger background noise. To solve the difficulty, a new method called adaptive redundant multiwavelet is proposed. Following Chui-Lian multiwavelet and two-scale similarity transforms, and taking the minimum envelope spectrum entropy as the optimization objective and genetic algorithms as the optimization tool, the redundant multiwavelet is adaptively constructed. Compared with the Fourier transform, Db6 scalar wavelet transform and CL3 multiwavelet transform, the applications to fault diagnosis rub-impact for a rolling element bearing of outer-race and a flue gas turbine unit of show the improved effectiveness of the proposed method.

Sun H.,Xian Jiaotong University | Zi Y.,Xian Jiaotong University | Yuan J.,Xian Jiaotong University | Yuan J.,Shanghai Institute of Radio Equipment | And 3 more authors.
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | Year: 2013

A denoising method of the improved neighboring coefficients and the undecimated multiwavelet transform is proposed, Hilbert-Huang time-frequency analysis is applied as the post-processing method. The proposed method is applied to the incipient fault diagnosis of planetary gearboxes. In the planetary gearbox, the fault response is quite weak; the vibration is obviously non-stationary and evidently nonlinear; low-frequency characteristics are easily immersed in heavy noise. Therefore, the existing fault diagnosis theory and technology for traditional fixed-shaft gearboxes fail to solve the difficulty in the planetary gearbox fault diagnosis. The undecimated multiwavelet transform has the shift-invariant property in time domain, which can effectively weaken the Gibbs phenomena in the neighborhood of the discontinuities. The improved neighboring coefficients can select variant sizes of neighboring window and flexible thresholds at different decomposition levels, which can correctly extract the incipient fault features in the non-stationary signals. Hilbert-Huang time-frequency analysis can intuitively represent the non-stationary and nonlinear features of the collected signals. Experiments indicate that the proposed method can correctly extract the weak fault features caused by the incipient pitting defects in the planetary gearbox. © 2013 Journal of Mechanical Engineering.

Chen J.,Xian Jiaotong University | Chen J.,University of Alberta | Zuo M.J.,University of Alberta | Zi Y.,Xian Jiaotong University | And 3 more authors.
Smart Materials and Structures | Year: 2013

Condition identification of mechanical equipment from vibration measurement data is significant to avoid economic loss caused by unscheduled breakdowns and catastrophic accidents. However, this task still faces challenges due to the complexity of equipment and the harsh environment. This paper provides a possibility for equipment condition identification by proposing a method called customized lifting multiwavelet packet information entropy. Benefiting from the properties of multi-resolution analysis and multiple wavelet basis functions, the multiwavelet method has advantages in characterizing non-stationary vibration signals. In order to realize the accurate detection and identification of the condition features, a customized lifting multiwavelet packet is constructed via a multiwavelet lifting scheme. Then the vibration signal from the mechanical equipment is processed by the customized lifting multiwavelet packet transform. The relative energy in each frequency band of the multiwavelet packet transform coefficients that equals a percentage of the whole signal energy is taken as the probability. The normalized information entropy is obtained based on the relative energy to describe the condition of a mechanical system. The proposed method is applied to the condition identification of a rolling mill and a demountable disk-drum aero-engine. The results support the feasibility of the proposed method in equipment condition identification. © 2013 IOP Publishing Ltd.

Discover hidden collaborations