基于改进移频变尺度随机共振的齿轮故障诊断

Improved frequency-shifted and re-scaling stochastic resonance for gear fault diagnosis

为了解决传统的移频变尺度随机共振只对单一参数进行优化,忽略各个参数之间交互作用的不足,提出了一种改进移频变尺度随机共振的算法。首先利用移频变尺度对大参数信号进行预处理;其次以最大信噪比为优化目标,采用改进鱼群算法对系统参数进行同步优化;最终实现齿轮故障微弱特征信号的最优提取。研究结果表明,该算法可以将噪声能量转移到微弱特征信号上,提高信噪比,并对齿轮进行故障诊断,且相对于传统的移频变尺度随机共振以及小波阈值降噪而言更加优越。该文提出的算法可用于强噪声环境下的齿轮故障诊断,为农业机械中齿轮故障诊断研究提供了参考。

With the progress of agricultural technology, agricultural machinery is developing towards the direction of large power, high speed and high efficiency. Due to structural complexity and harsh working conditions, the gear in the agricultural machinery is prone to failure. If the fault cannot be detected accurately, it will result in great economic losses and even much more serious casualties. Yet, the fault feature information is not only weak, but also usually drowned in strong background noise. As a result, it is a challenging project to extract the fault feature information precisely. Different from the traditional de-noising method, the traditional frequency-shifted and re-scaling stochastic resonance method can divert the noise energy to the weak characteristic signal. So it can make the weak characteristic signal increased when the noise was reduced. Then it can precisely realize the aim of detecting the weak characteristic signal which is drowned in strong background noise. Meanwhile, it breaks the limitation of the traditional stochastic resonance due to the frequency-shifted and re-scaling method involved. So, it can be applied under the conditions of large parameters. However, the traditional frequency-shifted and re-scaling stochastic resonance method just does the optimization on the single parameter and neglects the interaction between individual parameter. It cannot sufficiently make full use of the advantages of stochastic resonance in the weak feature signal extraction. So an improved frequency-shifted and re-scaling stochastic resonance method is proposed in this paper. In the proposed method, the signal-noise ratio (SNR) is chosen as the optimization objective, and the improved fish swarm algorithm is used for synchronous optimizationof the frequency-shifted and re-scaling stochastic resonance system parameters. With the progress of the optimization, the precision and speed of the traditional fish swarm algorithm will become lower and lower due to the fixed view and step. So an improved method is taken in this paper and the details are as follows. In the improved fish swarm algorithm, each fish calculates the average of the distances between all other fishes and itself. Then the average value is taken as the visual field of itself, and meanwhile the 0.1 times visual field is taken as the step of itself. In this paper, in order to verify the validity of the method proposed, a numerical simulation is performed firstly. The numerical simulation results show that the method can divert the noise energy to the weak characteristic signal and effectively detect the weak feature information under strong background noise. Secondly, the experimental investigation of a gear with normal teeth and broken teeth is made. We find that compared with the processed vibration signals of the normal teeth and broken teeth, the fault is prone to distinguish. And we can judge that the fault occurs on little gear, which is in agreement with the experimental condition. The experimental results show that the method proposed can not only validly detect the weak information under strong background noise, but also make a diagnosis for the gear fault. Finally, by comparing this proposed method with the traditional frequency-shifted and re-scaling stochastic resonance method and the wavelet threshold de-noising method, we find that this method is much more valid in weak feature detection under strong background noise.