• Huijun Zou

      Articles written in Sadhana

    • Research on conceptual design of mechatronic systems

      Yong Xu Huijun Zou Ruiqin Li

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      Based on the fact that function-structure generating and function solving are alternant processes with mutual causality during the conceptual design phase of mechatronic systems, a conceptual design cyclic feedback solving model of a mechatronic system is put forward on the basis of mapping between function layer, effect layer, working principle layer and structure layer. The process of solving single and system functions is analysed. Key technologies of interface matching and function solving are then advanced. Finally, a computer-aided conceptual design automatic software system for mechatronic systems is developed and the conceptual design of a computerised embroidery machine is given as an example.

    • Time-frequency representation based on time-varying autoregressive model with applications to non-stationary rotor vibration analysis

      Long Zhang Guoliang Xiong Hesheng Liu Huijun Zou Weizhong Guo

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      A parametric time-frequency representation is presented based on timevarying autoregressive model (TVAR), followed by applications to non-stationary vibration signal processing. The identification of time-varying model coefficients and the determination of model order, are addressed by means of neural networks and genetic algorithms, respectively. Firstly, a simulated signal which mimic the rotor vibration during run-up stages was processed for a comparative study on TVAR and other non-parametric time-frequency representations such as Short Time Fourier Transform, Continuous Wavelet Transform, Empirical Mode Decomposition, Wigner–Ville Distribution and Choi–Williams Distribution, in terms of their resolutions, accuracy, cross term suppression as well as noise resistance. Secondly, TVAR was applied to analyse non-stationary vibration signals collected from a rotor test rig during run-up stages, with an aim to extract fault symptoms under non-stationary operating conditions. Simulation and experimental results demonstrate that TVAR is an effective solution to non-stationary signal analysis and has strong capability in signal time-frequency feature extraction.

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