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Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram

机译：基于遗传算法和快速谱图相结合的滚动轴承故障诊断

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摘要

The rolling element bearing is a key part in many mechanical facilities and the diagnosis of its faults is very important in the field of predictive maintenance. Till date, the resonant demodulation technique (envelope analysis) has been widely exploited in practice. However, much practical diagnostic equipment for carrying out the analysis gives little flexibility to change the analysis parameters for different working conditions, such as variation in rotating speed and different fault types. Because the signals from a flawed bearing have features of non-stationarity, wide frequency range and weak strength, it can be very difficult to choose the best analysis parameters for diagnosis. However, the kurtosis of the vibration signals of a bearing is different from normal to bad condition, and is robust in varying conditions. The fast kurtogram gives rough analysis parameters very efficiently, but filter centre frequency and bandwidth cannot be chosen entirely independently. Genetic algorithms have a strong ability for optimization, but are slow unless initial parameters are close to optimal. Therefore, the authors present a model and algorithm to design the parameters for optimal resonance demodulation using the combination of fast kurtogram for initial estimates, and a genetic algorithm for final optimization. The feasibility and the effectiveness of the proposed method are demonstrated by experiment and give better results than the classical method of arbitrarily choosing a resonance to demodulate. The method gives more flexibility in choosing optimal parameters than the fast kurtogram alone.
机译：滚动轴承是许多机械设备中的关键部件，其故障的诊断在预测性维护领域非常重要。迄今为止，共振解调技术（包络分析）已在实践中得到广泛利用。但是，用于进行分析的许多实用的诊断设备几乎没有灵活性来改变不同工作条件下的分析参数，例如转速变化和不同故障类型。由于来自缺陷轴承的信号具有不稳定，频率范围宽和强度弱的特点，因此很难选择最佳的分析参数进行诊断。但是，轴承的振动信号的峰度不同于正常条件或恶劣条件，并且在变化的条件下具有鲁棒性。快速峰图可以非常有效地提供粗略的分析参数，但不能完全独立选择滤波器的中心频率和带宽。遗传算法具有很强的优化能力，但是除非初始参数接近最佳值，否则遗传算法会很慢。因此，作者提出了一种模型和算法，该模型和算法将快速峰图用于初始估计，并将遗传算法用于最终优化，从而设计出用于最佳共振解调的参数。实验证明了该方法的可行性和有效性，与经典选择谐振进行解调的经典方法相比，具有更好的效果。与仅使用快速峰图相比，该方法在选择最佳参数方面具有更大的灵活性。

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