In order to diagnose the fault of roller group, we have designed an array pressure sensor and a scan circuit with weak crosstalk of the column signal for collecting pressure distribution data. Furthermore, these data are processed via both the probabilistic neural network (PNN) fault diagnosis model and the back propagation (BP) neural network fault diagnosis model. Experimental results reveal that the PNN fault diagnosis model has more accuracy and shorter period of constructing and training model than BP fault diagnosis model. Our research opens up a new way for roller fault detection from via indirect information (such as sound, vibration and other signals) to via direct digitization pressure image. This method reduces the interference of environmental noise on fault detection. It is more accurate, intuitive, and potential for the applications in all kinds of mechanical fault detection.

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.

In the wireless sensor network, every node is energy and resource limited and the received signal is weak, which requires less complexity and low computation signal combination algorithm to improve the signal-to-noise ratio (SNR) of the combined output signal. Considering the computation and time-varying delay, this paper proposes an adaptive feedback time-delay alignment method based on equalizer taps to decrease the SNR loss caused by time-delay difference. The relationship between the equalizer taps and time-delay difference is derived according to the Nyquist first criterion and the gradient descent algorithm is adopted to iteratively adjust the time delay of one antenna signal. The numerical results show that the algorithm can almost eliminate the time difference and the SNR loss decreases. The bit error rate (BER) performance is obviously improved.

In this paper, aiming at the poor performance of the existing symbol rate estimation methods when SNR is low, we proposed a method based on the timing square which can be simply calculated and applied to MASK/MPSK/MQAM modulated signal, improved the performance of signal symbol rate estimation compared with the existing method. Based on the principle of the timing square, the signal modules were squared to calculate the fourier coefficient modulus corresponding to different sampling ratios. Then the characteristic line was searched in the transform spectrum which contains symbol rate information and obtained an estimation of the symbol rate. The impact of the roll-off factor and the carrier wave was analyzed and solution was proposed in this paper. Simulation results show that the performance of symbol rate estimation of the improved method is better than the original method, the wavelet transform method and the cyclostationary method in low SNR and low roll-off factor environment.

This paper deals with maximum-likelihood (ML) detection of symbol sequence in the absence of synchronization information. A novel iterative scheme is proposed to obtain the ML estimates of the symbols without an estimation of synchronization parameters. Instead of the optimal sampling point recovery and explicit carrier phase compensation, the detection of symbols employs the direct calculation of the matched filter output, eliminating the need for a separate synchronizer. The detection problem is treated as ML estimation from incomplete data, which is solved by means of an iterative scheme based on the expectation-maximization algorithm. The proposed scheme is compared with conventional non-data-aided and iterative ML synchronizers. Accordingly, the simulation results indicate that the proposed detector enables improvements on both the bit error rate and convergence property.