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首页> 外文期刊>Shock and vibration >Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization
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Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second-Order Stochastic Resonance Method with Barrier Height Optimization

机译：通过双阶随机共振法通过具有阻挡高度优化的二阶随机共振方法，滚动轴承的滚动轴承故障检测弱

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摘要

The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.
机译：随机共振（SR）方法广泛应用于旋转机器的故障特征提取，其能够通过潜在的井功能通过能量变换来提高弱故障检测性能。潜在的井函数大多是固定的，以降低计算复杂性，并且具有固定潜在井参数的SR方法在稳定的工作条件下具有更好的性能。当变量工作条件下的故障频率发生变化时，信号处理效果与固定参数不同，导致故障检测中的错误。本文提出了一种具有潜在井参数优化的额外的二阶自适应一般可变尺度随机共振（USAGVSR）方法。对于具有不同故障频率的输入信号，通过蚁群算法计算和优化与屏障高度相关的潜在井参数。在此基础上，对未透明的因子和步长进行了进一步的优化，以获得更好的故障检测性能。研究了具有不同故障类型和不同工作条件的案例，通过实验验证了所提出的方法的性能。结果证明了该方法具有更好的弱故障特征提取性能，可以准确地识别输入信号中的不同故障类型。该方法在弱故障提取和分类中证明是有效的，并且在滚动轴承的故障特征识别方面具有良好的应用前景。

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