S U M M A R YWhen the signal-to-noise (S/N) ratio is less than −3 dB or even 0 dB, seismic events are generally difficult to identify from a common shot record. To overcome this type of problem we present a method to detect weak seismic signals based on the oscillations described by a chaotic dynamic system in phase space. The basic idea is that a non-linear chaotic oscillator is strongly immune to noise. Such a dynamic system is less influenced by noise, but it is more sensitive to periodic signals, changing from a chaotic state to a large-scale periodic phase state when excited by a weak signal. With the purpose of checking the possible contamination of the signal by noise, we have performed a numerical experiment with an oscillator controlled by the Duffing-Holmes equation, taking a distorted Ricker wavelet sequence as input signal. In doing so, we prove that the oscillator system is able to reach a large-scale periodic phase state in a strong noise environment. In the case of a common shot record with low S/N ratio, the onsets reflected from a same interface are similar to one other and can be put on a single trace with a common reference time and the periodicity of the so-generated signal follows as a consequence of moveout at a particular scanning velocity. This operation, which is called 'horizontal dynamic correction' and leads to a nearly periodic signal, is implemented on synthetic wavelet sequences taking various sampling arrival times and scanning velocities. Thereafter, two tests, both in a noisy ambient of −3.7 dB, are done using a chaotic oscillator: the first demonstrates the capability of the method to really detect a weak seismic signal; the second takes care of the fundamental weakness of the dynamic correction coming from the use of a particular scanning velocity, which is investigated from the effect caused by near-surface lateral velocity variation on the periodicity of the reconstructed seismic signal. Finally, we have developed an application of the method to real data acquired in seismic prospecting and then converted into pseudo-periodic signals, which has allowed us to discriminate fuzzy waveforms as multiples, thus illustrating in practice the performance of our working scheme.

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High voltage circuit breaker is one of the most significant devices and its health status will impact security of the power system. In this paper, the method of high voltage circuit breakers mechanical fault diagnosis is discussed, fault diagnosis method based on vibration signal is proposed. Firstly, the collected acoustic signals are proceed by blind source separation processing through fast independent component analysis. Then, the acoustic signal feature vector is extracted by improved ensemble empirical mode decomposition (EEMD) and the residual signal is filtered by fractional differential. Finally, the feature vectors are input into support vector machine (SVM) for fault diagnosis. Experiment shows that the proposed method can get more precise fault classification to high voltage circuit breakers.

In view of the simulation problems of pressure control logic module in PWR nuclear power plant, a complete simulation model has been designed by using simulation software package Simulink. Based on the simulation test of the typical test signal, the model is proved to be effective and correct.