Papers by Keyword: Cyclostationary

Abstract: In the signal combining system of deep space network, the estimation error of time-delay between signals will reduce the effectiveness. The time-delay alignment technique based on combined output signal as the reference (CC-SUMPLE algorithm) makes use of the mutual information offered by multi-antenna and improves the alignment performance. However, it only takes ordinary cross-correlation into consideration rather than the cyclostationary of digital communication signal during calculating time-delay in the iterative process. As to this problem, this paper proposes multi-antenna signal time-delay alignment algorithm based on cyclostationary of communication signal (MCCC-SUMPLE algorithm) which reconstructs the combined reference signal and takes advantage of multi-cycle frequencies. The simulation results show that the proposed algorithm will improve the estimation accuracy and time-delay alignment performance compared with CC-SUMPLE algorithm.

Abstract: This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

Abstract: The characteristic of cyclical impact is reflected on the signal of rolling bearing in fault condition. The carrier frequency is modulated by times of the failure frequencies. When the traditional cyclical spectrum density (CSD) method is used to analyze the signal, all the modulation frequencies will be demodulated in the cyclic frequency spectrum. In this case, it is difficult to recognize the fault type of the bearing. Therefore, a new cyclical spectrum density method based on the kurtosis energy (CSDK) is proposed. The kurtosis of every cyclic frequency’s slice is used as the weight coefficient of the cyclic frequency’s energy accumulation to extract fault feature effectively. The proposed method has greatly reduced times of the harmonic frequencies’ effect in traditional CSD method. The analysis of the signal gathered from the outer rolling bearing of blast furnace belt cylinder shows that the fault feature extracted by the new method is more clear and accurate than CSD method.

Abstract: Vibration signal of rolling-element bearing is random cyclostationarity when a fault develops, the proper analysis of which can be used for condition monitor. Cyclic spectrum is a common cyclostationary analysis method and has a great many algorithms which have distinct efficiency in different application circumstance, two common algorithms (SSCA and FAM) are compared in the paper. The FAM is recommended to be used in diagnosing rolling-element bearing fault via calculation of simulation signal in different signal to noise ratio. The cyclic spectrum of practice signal of rolling-element bearing with inner-race point defect is analyzed and a new characteristic extraction method is put forward. The preferable result is acquired verify the correctness of the analysis and indicate that the cyclic spectrum is a robust method in diagnosing rolling-element bearing fault.

Abstract: The cyclic autocorrelation function is used with regard to the cyclostationarity of gear vibrations in order to extract the modulation features of gearbox vibration signals, and to detect localized gear damage. The properties of the amplitude and frequency modulated signals in the cyclic frequency domain are summarized in order to investigate the differences between the modulation features of normal and faulty gearbox vibration signals. Gear tooth spalling is detected by the presence of many sidebands in a zero-lag time-slice of the cyclic autocorrelation function, thereby indicating an increase in the degree of modulation effect. The damage source is located by the spacing of the sidebands.

Abstract: The demodulation analysis has been extensively used for gear diagnosis. However these techniques mainly deal with the amplitude-modulated signal instead of the frequency-modulated signal. Due to the symmetrical phase relationship of the sidebands, the amplitude-demodulated methods are not suitable for the frequency-modulated signal. This paper introduces the theory of cyclostationary processes as a powerful frequency-demodulation tool for the diagnosis of gears. The Cyclic Autocorrelation Function (CAF) is an important second-order cyclic statistics and acts as an efficient parameter to the frequency-demodulated analysis. In this paper, the CAF of frequency-modulated signal is deduced carefully. Through the discussion of frequency feature of the CAF slice at different cyclic frequency, two useful conclusions have been arrived about the frequency-demodulation. Firstly, the CAF slice at even multiples of the modulator-frequency can demodulate the frequency-modulated signal directly. Secondly, the amplitude-demodulated methods are suitable for the CAF slice of frequency-modulated signal at some special cyclic frequencies, which are equal to odd multiples of the modulator-frequency or close to the double carrier-frequency. These features of the CAF slice mentioned above overcome the invalidation of amplitude-demodulated methods for the frequency-modulated signal and increase it’s application range in engineering. Application in simulated and experimental data from a gear rig verifies the effectiveness of the frequency-demodulated method based on cyclostationarity.