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.

中文翻译：

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基于二阶随机共振法和障碍高度优化的滚动轴承在不同工况下的弱故障检测

随机共振（SR）方法广泛应用于旋转机械故障特征提取，通过势阱函数进行能量转换，能够提高弱故障检测性能。势阱函数大多设置固定以降低计算复杂度，而势阱参数固定的SR方法在稳定工况下具有更好的性能。当故障频率在可变工况下发生变化时，固定参数下信号处理效果不同，导致故障检测错误。本文提出了一种带势井参数优化的欠阻尼二阶自适应通用变尺度随机共振(USAGVSR)方法。对于不同故障频率的输入信号，通过蚁群算法计算和优化与势垒高度相关的势阱参数。在此基础上，进一步优化无阻尼因子和步长，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，并通过实验验证了所提出方法的性能。结果表明，该方法具有较好的弱故障特征提取性能，能准确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被证明是有效的，在滚动轴承故障特征识别中具有良好的应用前景。对无阻尼因子和步长进行进一步优化，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，并通过实验验证了所提出方法的性能。结果表明，该方法具有较好的弱故障特征提取性能，能准确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被证明是有效的，在滚动轴承故障特征识别中具有良好的应用前景。对无阻尼因子和步长进行进一步优化，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，并通过实验验证了所提出方法的性能。结果表明，该方法具有较好的弱故障特征提取性能，能准确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被证明是有效的，在滚动轴承故障特征识别中具有良好的应用前景。结果表明，该方法具有较好的弱故障特征提取性能，能准确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被证明是有效的，在滚动轴承故障特征识别中具有良好的应用前景。结果表明，该方法具有较好的弱故障特征提取性能，能准确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被证明是有效的，在滚动轴承故障特征识别中具有良好的应用前景。