Shanghai Institute of Radio Equipment

Shanghai, China

Shanghai Institute of Radio Equipment

Shanghai, China
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Pan J.,Xi'an Jiaotong University | Chen J.,Xi'an Jiaotong University | Zi Y.,Xi'an 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.,Xi'an Jiaotong University | Zi Y.,Xi'an Jiaotong University | He Z.,Xi'an 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.


Chen J.,Xi'an Jiaotong University | Chen J.,University of Alberta | Zuo M.J.,University of Alberta | Zi Y.,Xi'an 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.


Sun H.,Xi'an Jiaotong University | Zi Y.,Xi'an Jiaotong University | Yuan J.,Xi'an 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.


He S.,Xi'an Jiaotong University | Zi Y.,Xi'an Jiaotong University | Chen J.,Xi'an Jiaotong University | Zhao C.,Georgia Institute of Technology | And 3 more authors.
Measurement Science and Technology | Year: 2014

The instrumented tracking and telemetry ship with a ship-borne satellite antenna (SSA) is the critical device to ensure high quality of space exploration work. To effectively detect mechanical anomalies that can lead to unexpected downtime of the SSA, an ensemble multiwavelet (EM) is presented for identifying the anomaly related incipient-signatures within the measured dynamic signals. Rather than using a predetermined basis as in a conventional multiwavelet, an EM optimizes the matching basis which satisfactorily adapts to the anomaly related incipient-signatures. The construction technique of an EM is based on the conjunction of a two-scale similarity transform (TST) and lifting scheme (LS). For the technique above, the TST improves the regularity by increasing the approximation order of multiscaling functions, while subsequently the LS enhances the smoothness and localizability via utilizing the vanishing moment of multiwavelet functions. Moreover, combining the Hilbert transform with EM decomposition, we identify the incipient-signatures induced by the mechanical anomalies from the measured dynamic signals. A numerical simulation and two successful applications of diagnosis cases (a planetary gearbox and a roller bearing) demonstrate that the proposed technique is capable of dealing with the challenging incipient-signature identification task even though spectral complexity, as well as the strong amplitude/frequency modulation effect, is present in the dynamic signals. © 2014 IOP Publishing Ltd.


Chen J.,Xi'an Jiaotong University | Zi Y.,Xi'an Jiaotong University | He Z.,Xi'an Jiaotong University | Yuan J.,Shanghai Institute of Radio Equipment
Mechanical Systems and Signal Processing | Year: 2013

Due to the character of diversity and complexity, the compound faults detection of rotating machinery under non-stationary operation turns into a challenging task. Multiwavelet with two or more base functions and many excellent properties provides a possibility to detect and extract all the features of compound faults at one time. However, the fixed basis functions independent of the vibration signal may decrease the accuracy of fault detection. Moreover, the decomposition result of discrete multiwavelet transform does not possess time invariance, which is harmful to extract the feature of periodical impulses. To overcome these deficiencies, based on the Hermite splines interpolation, taking the minimum envelope spectrum entropy as the optimization objective, adaptive redundant lifting multiwavelet is developed. Additionally, in order to eliminate error propagation of decomposition results, adaptive redundant lifting multiwavelet is improved by adding the normalization factors. As an effective method, Hilbert transform demodulation analysis is used to extract the fault feature from the high frequency modulation signal. The proposed method incorporating improved adaptive redundant lifting multiwavelet (IARLM) with Hilbert transform demodulation analysis is applied to compound faults detection for the simulation experiment, rolling element bearing test bench and traveling unit of electric locomotive. Compared with some other fault detection methods, the results show the superior effectiveness and reliability on the compound faults detection. © 2012 Elsevier Ltd.


Chen J.,Xi'an Jiaotong University | Zi Y.,Xi'an Jiaotong University | He Z.,Xi'an Jiaotong University | Yuan J.,Shanghai Institute of Radio Equipment
Measurement Science and Technology | Year: 2012

Rotating machinery fault detection is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of non-stationarity and nonlinearity, the detection and extraction of the fault feature turn into a challenging task. Therefore, a novel method called improved spectral kurtosis (ISK) with adaptive redundant multiwavelet packet (ARMP) is proposed for this task. Spectral kurtosis (SK) has been proved to be a powerful tool to detect and characterize the non-stationary signal. To improve the SK in filter limitation and enhance the resolution of spectral analysis as well as match fault feature optimally, the ARMP is introduced into the SK. Moreover, since kurtosis does not reflect the actual trend of periodic impulses, the SK is improved by incorporating an evaluation index called envelope spectrum entropy as supplement. The proposed method is applied to the rolling element bearing and gear fault detection to validate its reliability and effectiveness. Compared with the conventional frequency spectrum, envelope spectrum, original SK and some single wavelet methods, the results indicate that it could improve the accuracy of frequency-band selection and enhance the ability of rotating machinery fault detection. © 2012 IOP Publishing Ltd.


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.


Zhang L.,Jiangsu University | Gao L.-L.,Shanghai Institute of Radio Equipment
Journal of Alloys and Compounds | Year: 2015

SnAgCu alloys are promising as the lead-free solders for replacing traditional SnPb solders, 0.1 wt.% La2O3 nano-particles are selected as the additives into these solders for further improve the property. The interface reaction and the growth kinetics of intermetallic compounds (IMC) at SnAgCu/Cu and SnAgCu-nano La2O3/Cu interface were investigated during thermal cyclic loading. The growth rate of IMC in SnAgCu-nano La2O3/Cu is lower than that of SnAgCu/Cu, the La2O3 nanoparticles can reduce the diffusion coefficient and activation energies of IMC layers. And the finite element simulation demonstrates that von Mises stress concentrates at the Cu3Sn layer near Cu6Sn5 layer, the crack can be found in the stress concentrated area in the experiment, and SnAgCu-nano La2O3/Cu shows small length of crack comparing with SnAgCu/Cu, which demonstrates the La2O3 nanoparticles can enhance the reliability of SnAgCu/Cu solder joints. © 2015 Elsevier B.V. All rights reserved.


Huang F.,Nanjing University of Science and Technology | Huang F.,Shanghai Institute of Radio Equipment | Sheng W.,Nanjing University of Science and Technology | Lu C.,Nanjing University of Science and Technology | Ma X.,Nanjing University of Science and Technology
Signal Processing | Year: 2012

In conventional reduced rank minimum variance beamformer (RRMVB), a rank-reducing transformation is usually obtained from eigenvectors of the estimated sample covariance matrix, while the eigenvectors are usually obtained via eigen-decomposition. To alleviate the computational burden caused by eigen-decomposition, a fast reduced rank minimum variance beamformer (FRRMVB) is proposed in this paper. In the estimated covariance matrix case, a set of receive data vectors are taken as a rough and fast estimate of the true interference subspace, and the rank-reducing transformation is chosen as the augmentation of the estimated interference subspace with the steering vector of the desired signal. As FRRMVB performs without eigen-decomposition, it requires less computational load and is easier to be executed in practical applications compared with the conventional RRMVB. Moreover, it has good performance even with small sample size. Simulation results demonstrate the efficiency of the proposed method. The proposed method can be used for real-time adaptive array processing. © 2012 Elsevier B.V.

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