Shijiazhuang Railway Institute

Shijiazhuang, China

Shijiazhuang Railway Institute

Shijiazhuang, China

Shijiazhuang Tiedao University is a university in Hebei, China under the provincial government. The campus is located at No.17 East Bei'erhuan Road Qiaodong district Shijiazhaung City Hebei Province P.O: 050041Shijiazhuang Tiedao University is a prestigious university of applied engineering. It excels in the fields of humanities, science, economics and management. S.T.D.U. is under the supervision of central and local governments, mainly of Hebei Province. S.T.D.U. was founded in 1950, originally named Shijiazhuang Railway Institute. It was one of the key railway engineering universities in Hebei Province as well as in the whole country. In 2010, the university was renamed as Shijiazhuang Tiedao University.Currently, the university has a faculty staff of 1,414, of which there are more than 844 full-time teachers and scientific researchers, including 193 professors and 414 vice professors of equal qualifications, 300 Doctor and Master tutors, 23792 full-time students, including 10,070 college students from the Si-Fang Campus and 1,067 graduate students. S.T.D.U. has 2 National Teaching Groups, 1 Innovation Group named by the Ministry of Education, 1 Cheung Kong Scholar, 1 winner of National Science Fund for Distinguished Young Scholars, 1 National Outstanding Professional and Technical Personnel winner, 1 Nationally Known Teacher; 2 Yanzhao Scholars; 2 Candidates of Academia of Hebei Province; 2 Candidates of Hundred, Thousand and Ten thousand Talents Program; and more than 10 "National Model Teachers" and "Teachers of Exemplary Moral Conduct", 50 experts who enjoy the Subsidy Award for Experts with Outstanding Contribution to Hebei Province issued by the State Council. In addition, S.T.D.U. has invited more than 120 academic and reputable scholars as part-time professors.S.T.D.U. has been enrolling students from all over the country who are qualified for first-batch enrollment based on their performance in the College Entrance Examination. Now it consists of 46 undergraduate majors, 10 master degree disciplines, 44 postgraduate majors, 5 provincial key disciplines, 4 provincial or ministerial key engineering laboratory centers. Our University has 15 institutional departments, such as the Schools of Civil Engineering, School of Traffic and Transportation Engineering, School of Mechanical Engineering, School of Economics, School of Materials Science and Engineering, School of Computer Technology, School of Electronics and Information, School of Mechanics, Department of Mathematics & Physics, etc. It also possesses 25 research centers, such as Research Centers of Transportation, of Health Monitoring and Control of Building Structural and of Traffic Safety Engineering and others.In 2003, S.T.D.U. started the cooperative PhD program granted by the Ministry of Education of China. In 2009, STDU was selected as an intellectual construction station for cultivating future PhDs by the Academic Degree Commission of the State Council. In 2011 S.T.D.U. was selected to participate in the "National Excellent Engineers" program by the Ministry of Education. Wikipedia.

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Chang X.-Z.,Shijiazhuang Railway Institute
Proceedings - International Conference on Machine Learning and Cybernetics | Year: 2015

Apriori is a classical data mining algorithm. The traditional Apriori algorithm can be optimized to apply to MapReduce model. The MapReduce programming model with the optimized Apriori is built to realize the parallel computing under cloud computing environment. Experimental results show that improved parallel MR-Apriori algorithm greatly shortens the time consumed, and with its strong scalability, it can be better applied to large-scale data analysis, processing and mining. Moreover, the rate of the algorithm is linearly increased with the increase of the node numbers in the mining frequent item sets. © 2015 IEEE.


Li H.,Shijiazhuang Railway Institute | Zhang Y.,Shijiazhuang Railway Institute | Zheng H.,Shijiazhuang Mechanical Engineering College
Mechanical Systems and Signal Processing | Year: 2011

The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in the mechanical system. However, most of the applications of the continuous wavelet transform have widely focused on the use of Morlet wavelet transform. The complex Hermitian wavelet is constructed based on the first and the second derivatives of the Gaussian function to detect signal singularities. The Fourier spectrum of Hermitian wavelet is real; therefore, Hermitian wavelet does not affect the phase of a signal in the complex domain. This gives a desirable ability to extract the singularity characteristic of a signal precisely. In this study, Hermitian wavelet is used to diagnose the gear localized crack fault. The simulative and experimental results show that Hermitian wavelet can extract the transients from strong noise signals and can effectively diagnose the localized gear fault. © 2010 Elsevier Ltd.


Li H.,Shijiazhuang Railway Institute
Key Engineering Materials | Year: 2011

Gearbox vibrations are random cyclostationary signals which are a combination of periodic and random processes due to the machine's rotation cycle and interaction with the real world. The combinations of such components are best considered as cyclostationary. This paper discusses which second order cyclostationary statistics should be used for fault diagnosis of gear crack. The second order cyclostationary statistical methods are firstly introduced and then applied to fault diagnosis of gear crack. This approach is capable of completely extracting the characteristic fault frequencies related to the defect. Experiment results show that the second order cyclostationary statistics is powerful and effective in feature extracting and fault detecting for gearbox. The experimental result shows that the second order cyclostationary statistics can effectively diagnosis gear localized crack fault. © (2011) Trans Tech Publications.


Li H.,Shijiazhuang Railway Institute
Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011 | Year: 2011

A new approach to bearing localized fault diagnosis based on time scale spectrum of continuous Morlet wavelet transform technique is presented. The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in mechanical system. The theoretical background of complex Morlet continuous wavelet is introduced in detail. The experimental results show that time scale spectrum of continuous Morlet wavelet transform can effectively detect and diagnose the bearing fault. © 2011 IEEE.


Li H.,Shijiazhuang Railway Institute
Advanced Materials Research | Year: 2011

A new approach to gear localized fault diagnosis under run-up based on angle domain average and Teager Kaiser energy operator demodulation technique is presented. The non-stationary vibration signals are transformed from the time domain transient signal to angle domain stationary one using order tracking technique. Teager Kaiser energy operator is a nonlinear operator that can track the signal energy and identify the instantaneous frequency and instantaneous amplitude of mono-component signal. The instantaneous frequency and instantaneous amplitude both exhibit a characteristic signature in the presence of a cracked tooth. The experimental result shows that angle domain average and Teager Kaiser energy operator demodulation technique can effectively diagnose gear localized crack fault.


Li H.,Shijiazhuang Railway Institute
Journal of Computers | Year: 2011

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. This paper deals with the detection of bearing faults in gearbox under non-stationary run-up of gear drives. In order to process the non-stationary vibration signals such as run-up or rundown vibration signals effectively, the order bi-spectrum technique is presented. This new method combines computed order tracking technique with bi-spectrum analysis. First, the vibration signal is sampled at constant time increments during run-up of gearbox and then uses numerical techniques to resample the data at constant angle increments. Therefore, the vibration signals are transformed from the time domain transient signal to angle domain stationary one. Second, the re-sample signal is processed by bi-spectrum analysis method. The procedure is illustrated with the experimental vibration data of a gearbox. The experimental results show that order bi-spectrum technique can effectively diagnosis and diagnosis the faults of bearing. © 2011 ACADEMY PUBLISHER.


Liu Q.-K.,Shijiazhuang Railway Institute
Gongcheng Lixue/Engineering Mechanics | Year: 2010

Our country has a vast territory, and wind circumstance is very complicated. With the development of High Speed Passenger Lines and speed up of trains, the security of train operation in strong wind is becoming serious and remarkable, and it is very important and necessary to establish an effective system to prevent wind induced accidents and ensure safety of train operation under strong winds. In this paper, it is indicate that the system consists of software measures and hardware measures. For software measures (operation regulation), the methods to get a regulation wind velocity were analyzed, the measurement and forecast procedures of the wind velocity along railway were indicated, the necessary wind tunnel experiments and field observation were analyzed. For hardware measures, experiments and setting procedures of a wind barrier were indicated; the optimum design procedure of a train aerodynamic shape and route selection of railway were pointed out. At last, the whole procedure to establish the system and necessary experiment work were shown by a flowchart.


Li H.,Shijiazhuang Railway Institute
Advanced Materials Research | Year: 2012

A new approach to fault diagnosis of gear wear based on Local mean decomposition (LMD) is proposed. Local mean decomposition can adaptively decomposes the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. LMD is capable of revealing interesting feature embedded in the signal. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the approach based on local mean decomposition is better to extract the fault characteristics of the faulty gear and can effectively diagnose the gear wear fault. © (2012) Trans Tech Publications, Switzerland.


Li H.,Shijiazhuang Railway Institute
Advanced Materials Research | Year: 2012

A novel method of bearing fault diagnosis based on local mean decomposition (LMD) is proposed. LMD method is self-adaptive to non-stationary and non-linear signal. LMD can adaptively decompose the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. Then the envelope spectrum is applied to the selected product function that stands for the bearing faults. Therefore, the character of the bearing fault can be recognized according to the envelope spectrum of product function. The experimental results show that local mean decomposition based envelope spectrum can effectively detect and diagnose bearing inner and outer race fault under strong background noise condition. © (2012) Trans Tech Publications, Switzerland.


Li H.,Shijiazhuang Railway Institute
Proceedings of the World Congress on Intelligent Control and Automation (WCICA) | Year: 2010

The continuous wavelet transform enables one to look at the evolution in the time scale joint representation plane. This advantage makes it very suitable for the detection of singularity generated by localized defects in mechanical system. The Fourier spectrum of complex Morlet wavelet is real, which the Fourier spectrum has no complex phase, the complex Morlet wavelet does not affect the phase of a signal in complex domain. This gives a desirable ability to detect the singularity characteristic of a signal precisely. In this study, the complex Morlet wavelet amplitude and phase map are used in conjunction to detect and diagnose the bearing fault. The complex Morlet wavelet amplitude and phase map are found to show distinctive signatures in the presence of bearing inner race or outer race damage. The experimental results show that the Morlet wavelet amplitude and phase map can extract the transients from strong noise signals and can effectively diagnose the faults of bearing. © 2010 IEEE.

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