Abstract: Initiation of blade cracks in Francis turbine runners endangers the safety operation of power stations, so it is crucial to detect the cracks before emergencies happen. This article is a preliminary study of applying acoustic emission (AE) technique to detecting the large-scale turbine runners. A series of experiments had been carried out on an HLA286a-LJ-800 Francis turbine runner. The attenuation characteristics due to propagation distance were studied. From the tests, it is concluded that AE signals are detectable after propagating at a distance of 6 m. The propagation distance is the major factor of attenuation. As a result, although attenuation is incurred, it is feasible to apply AE technique to monitoring crack signals in runners. However, it depends on the understanding of background noise and extraction of right signals.
Abstract: The flow characteristics of water in filleted microchannels were simulated based on CFD method. The flow pressure drop at different aspect ratioand Re number were rearranged on the simulating results with laminar flow model. The results indicated that the pressure drop enlarges with the increase of in the case of the constant width of the microchannel. Within the range of Re number of interest, Poiseuille number of the flow is constant for different Re, but decreases with increasing aspect ratio. An equation was fitted to describe the relationship between Po number and aspect ratio, i.e. .
Abstract: This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, one-dimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method
Abstract: This paper described the theory which based on the spread spectrum communication system, a simulation model was designed based on MATLAB, and each module of the model was briefly introduced. In a particular simulation condition, ran the simulation program, and an expected result was obtained. Meanwhile, by design a spread spectrum communication system of two users, detect the bit error rate corresponding with the two users in different bit error rate (SNR), directly verified the validity of the model system.
Abstract: The method to use planar array to identify the noise sources in high speed train is described. Through investigating the noise sources in high speed train, it is shown that the important noise sources are situated at windshield, door, air outlet, joints between headwall and sidewall. The spectra characteristics and sound pressure contour of these noise sources are obtained. The sound energies of these noise sources are concentrated in the frequency range of 0~1000 Hz mainly. Some effective countermeasures against interior noise of high speed train are suggested.Interior train noise decreases the comfort for the passengers inside the vehicle. As speeds increase, noise inevitably also increases. It has been known that rolling noise levels increase at a rate of 30log10v (with v the speed). The noise from aerodynamic sources increases more rapidly with speed, increasing at typically around 60log10v, and above 300 km/h aerodynamic noise becomes significant [1-2]. In order to carry out effective countermeasures against high speed noise, a detailed knowledge of the distribution and the properties of the sound sources is necessary. One of the methods to identify the different train sources is to carry out noise measurements with devices such as antennae. An acoustic antenna is a series of microphones, whose outputs are processed in order to focus on acoustic sources and to enable an acoustic map to be drawn. Accurate measurements on high-speed train usually require the development of specific tools [4-6]. The main sources identified from different studies on various high-speed trains are the pantograph, the recess of the pantograph, the inter-coach spacing, the bogie, the nose of the power car, the surfaces, the rear power car, louvres, ventilators[7-11]. However, it has not been reported how to locate interior train noise sources, which is critical for interior noise abatement and reduction design. In this paper, a planar microphone array is introduced and used to study the noise sources and spectra characteristics in high speed train. Measurement principle of planar microphone arrayAccording to the distance between the sound source and microphone array, the propagation model of sound signal can be divided as near-field and far-field model. Near-field model is suitable for source location and measurement of interior train noise. At near field, the sound wave near microphone array can be taken as spherical wave. According to spherical wave propagation theory, sound waves from source q at tf will reach the i-th microphone in the microphone array at the moment of tf +tiq. Where, tiq is the time for sound waves to get to the microphone position (xi, yi, zi) from sound source q at (xf, yf, zf). The microphone will take t to receive sound emitted from sound source, where (1)For homogeneous medium with the sound speed c, propagation time can be obtained as (2)Where，riq is the distance between sound source and i-th microphone: (3)According to delay-sum beamforming principle, the sound radiated from sound source focused by planar microphone array is decided by the microphone array’s output pq(tf) : (4)Where, Where, pi(tf+ti) is the signal recorded by the i-th microphone at tf +ti , T is transpose operation, rref is reference distance of sound radiation, wi is the window function of array, which can reduce the sidelobe level after beamforming.After focusing, the output power spectrum of the sound source is： (5)Based on sound field information obtained by microphone array, sound source can be effectively identified after acoustic signal processing by beamforming or power spectrum estimation theory.Sound power spectrum analysis of measured areasAccording to the main noise sources and the weak parts of train structure, 8 measurement areas are arranged as Table 1. In test, high speed train runs at 300km/h. 8×8 planar microphone array and self-developed acquisition and analysis system of train noise are used. A-weighted sound pressure level (SPL) of measured areas are shown in Table 1.Table 1 A-weighted SPL of the measured areasAs seen from Table 1, the SPL are higher at windshield, door, air outlet, joints between headwall and side walls. To reduce interior noise of high speed train, these areas should be treated heavily.The sound power spectrum of the measured area are shown in Fig. 1-Fig. 9. As seen from the figures, the noise energy is concentrated in the frequency range of 0~1000 Hz. To reduce the interior noise of high-speed train, noise reduction strategies must be adopted at low frequency parts.Fig. 1 Sound power spectrum of measured areaFig. 2 Sound power spectrum of measured area 2Fig.3 Sound power spectrum of measured area 3Fig. 4 Sound power spectrum of measured area 4Fig. 5 Sound power spectrum of measured area 5Fig. 6 Sound power spectrum of measured area 6Fig. 7 Sound power spectrum of measured area 7Fig. 8 Sound power spectrum of measured area 8Fig. 9 Sound power spectrum of measured area 9Noise distribution of the measured areasNoise distribution at the door. Fig.10 and Fig.11 are the sound pressure contours at the measured area 1 and 2 respectively. It can be shown that the SPL are higher at the upper right corner and at the bottom of the door. So it can be sure that the junction between the door and train wall is not sealed well, which results in acoustic leak. The sound pressure at lower right corner is significantly greater than that of upper right corner. The maximum sound pressure at lower right corner is 93.8 dB. Lower right corner is near to the compartment floor, so compartment floor vibration is one of the major noise sources.It is beneficial for reducing noise at the door to improve the seal, to reduce the floor vibration and to use good soundproof deck.Fig. 10 Sound pressure contours of measured area 1Fig. 11 Sound pressure contours of measured area 2Noise distribution at air outlet. Figures 12 and 13 are the sound pressure at air supply outlet and air return outlet respectively. It can be shown that the SPL are higher at these two measured places, and they will become the main sources of interior noise. So noise elimination measures should be adopted at air duct to reduce the compartment noise. Fig. 12 Sound pressure contours of measured area 3 Fig. 13 Sound pressure contours of measured area 4Noise distribution at center in carriage and floor above the bogie. Fig. 14 and Fig. 15 are the sound pressure contours at center in carriage and floor above the bogie respectively. It is shown that the SPL at the center in carriage are 5.21dB(A) greater than that of floor above the bogie, so the vibration from bogie is greater. By optimizing the bogie structure to reduce the panels vibration, interior train noise can be reduced greatly.Fig. 14 Sound pressure contours of measured area 5Fig. 15 Sound pressure contours of measured area 6Noise distribution at joints of the sidewall and window. Fig. 16 is the sound pressure contours at joints of sidewall and window. It is shown that the noise level is higher at the left joints, so the seals is not tight at the left joints, measures should be taken to improve the seals at left joints.Fig. 16 sound pressure contours of measured area 7Noise distribution at joints of the sidewall and headwall. Fig. 17 is the sound pressure contours at joints of the sidewall and headwall. It is shown that the noise level is higher at the right joints between sidewall and headwall. So the seals is not tight at the right joints, and there are acoustic leak. It is beneficial for reducing interior noise to optimize the coupled type between sidewall and headwall, which will improve the seal effect.Fig. 17 sound pressure contours of measured area 8Noise distribution at windshield. Fig. 18 is the sound pressure contours at windshield. It is shown that the SPL level at the windshield is 1-3dB(A) higher than that of other areas. It is helpful for reducing noise at the windshield to use tight lock windshield with good flexibility, which will enhance connection strength and connection tightness between coaches. Fig. 18 Sound pressure contours of measured area 9ConclusionsA good understanding of the sources and their characterizations are necessary to find a good solution. In order to locate noise sources in high speed train, a technique is introduced based on a spectrum analysis of those noise sources by using the data measured with a microphone array. Through a series of experiments and analysis, the conclusions can be obtained as follows:The SPL are higher at low frequency range of 0~1000 Hz and at the areas of windshield, door, windshield, the joints between sidewall and headwall. To reduce noise in high speed train, the low frequency noise at these areas should be treated seriously. Some effective countermeasures against interior noise of high speed train are suggested. AcknowledgementsThe authors wish to thank China Natural Science Foundation(50975289), China Postdoctoral Science Foundation(20100471229) for funding this work.REFERENCES Talotte C. Aerodynamic noise: a critical survey. Journal of Sound and Vibration. 2000, 231(3):549-562 Mellet C, Letourneaux F, Poisson F, Talotte C. High speed train noise emission: Latest investigation of the aerodynamic/rolling noise contribution. Journal of Sound and Vibration. 2006, 293(3): 535-546 Kitagawa T, Nagakura K. Aerodynamic noise generated by shinkansen cars. Journal of Sound and Vibration 2000, 231(5):913-924. BARSIKOW B. Experiences with Various Configurations of Microphone Arrays Used to Locate Sound Sources on Railway Trains Operated by the DB AG [J]， Journal of Sound and Vibration， 1996， 193 (1)： 283-293 BARSIKOW B, KING W F and PFIZENMAIER E. Wheel/rail noise generated by a high speed train investigated with a line array of microphones Journal of Sound and Vibration 1987,118(1):99~112. Nagakura K. Localization of aerodynamic noise sources of Shinkansen trains. Journal of Sound and Vibration, 2006, 293(3):547-556 Iwamoto K, Higashi A. Some consideration toward reducing aerodynamic noise on pantograph. Japanese Railway Engineering, 1993, 122 (2):1-4 Ikeda M, Morikawa T, Manabe K. Development of low aerodynamic noise pantograph for high speed train. Proc 1994 Int Congr Noise Control Eng, 1994, 1, 169-178 Ikeda M, Suzuki M, Yoshida K. Study on optimization of panhead shape possessing low noise and stable aerodynamic characteristics. Quarterly Report of Railway Technical Research Institute, 2006, 47(2):72-77 Fremion N, Vincent N, Jacob M. Skirts and barriers for reduction of wayside noise from railway vehicles—an experimental investigation with application to the BR185 locomotive. Journal of Sound and Vibration. 2003, 267(3):709-719  Frid A. Aerodynamic noise radiated by the intercoach spacing and the bogie of a high-speed train. Journal of Sound and Vibration. 2000, 231(3):577-593
Abstract: The current method of micro-force generated for sensor calibration is scarcity and complexity. In the paper we propose a mechanism based on flexible hinges for micro-force, illustrate its working principle and establish the mechanical model. By theoretical analysis the analytical expression of the force reduction multiplier is derived. After initializing some parameters, the result shows that this mechanism can produce the large range and high resolution force of µN. The force can be applied in calibration of micro-force sensor.
Abstract: In order to judge and control applied force of Chinese massage robot’s end-effector on human body accurately, multi-dimensional interactive forces between massage robot’s end-effector and human should be measured. In this paper, a novel two-axis force sensor suitable for massage robot’s end-effector is presented, which is much smaller than existing sensors but in the same range measurement. Mechanical structure is introduced, theoretical analysis of elastic body is made, and finite element analysis is used to analyze its static characteristic. Then, the distribution of strain gauges and design of Hilton Bridge Circuit are described in detail. Finally, a prototype is fabricated. Decoupling algorithm is designed to reduce the interference error. The result of static calibration experimental data shows that the sensor has features of high precision and sensitivity.
Abstract: A moisture measurement method based on microwave attenuation has been proposed. The principles of the method were analyzed theoretically and the structure of the moisture measurement instrument was designed. The microwave source, the isolator, the adjustable attenuator, the receiving antenna, the temperature sensor and the single chip microcomputer (SCM) system are all selected and designed. It can be concluded that the moisture measurement instrument with microwave attenuation method can be used in the online measurement system with high speed.
Abstract: A displacement sensor with controlled measuring force and its characteristics analysis are discussed in this paper. The displacement sensor consists of an electric induction transducer with high resolution and a voice coil motor (VCM). The measuring principles, structure of the sensor are discussed. Theory model, dynamic and static characteristics of VCM are analysed. A measuring system for surface topography with large measuring range is constructed based on the displacement sensor and 2D moving platform. Measuring force of the sensor in measurement process of surface topography can be controlled at μN level and hardly changes. It has been used in measurement of bearing ball，bullet mark, etc.
Abstract: The insulators leakage current in transmission line can be used to characterize the filth degree of insulator surface and its accurate measurement is very important. An on-line monitor platform for insulators leakage current is introduced. Then a new multiscale self-correcting method for insulators leakage current is put forward. In this method, the leakage current information is decomposed based on multilayer and multiscale theory to gain each scale detailed signal and smooth the information in the coarsest scale at first. Furthermore, each scale detailed signal is decomposed once again and processed at different thresholds. Afterwards, the signal is reconstructed. The reconstructed detailed signal and the coarsest scale smoothed information are filtered using Kalman filter. In the end, the optimal detailed signal and the smoothed detailed signal are reconstructed and the signal is optimally estimated in the finest scale. Finally, the method was been applied to safety monitoring for a real transmission line. The results show that, compared with traditional Kalman filter method, the precision of new method raises 27%. This method has the characteristics of strong denoising ability and good robustness. It can be applied to the similar complex situation.