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中文摘要：
为了解决机械设备中早期故障和复合故障识别的难题，提高故障诊断的准确率，利用经验模式分解（Empirical mode decomposition，EMD）、改进的距离评估技术、自适应神经模糊推理系统（Adaptive neuro-fuzzy inference system，ANFIS）和遗传算法（Genetic algorithm，GA）等技术，提出一种综合运用多征兆域特征集和多个分类器的混合智能诊断模型。该模型在特征提取之前，利用滤波、EMD、解调等预处理技术挖掘潜藏在动态信号中的故障信息；从原始振动信号和预处理信号中，分别提取从不同侧面表征设备运行状态的时域和频域统计特征，得到6个特征集。采用提出的一种改进的距离评估技术选择特征，从6个原始特征集中相应地筛选出6个敏感特征集。将6个敏感特征集输入到基于GA组合的多个ANFIS分类器以得到最终的诊断结果。该模型在电力机车轮对轴承的故障诊断中实现了轴承不同故障类型、不同故障程度，以及复合故障的可靠识别，获得了满意的诊断结果。应用结果也验证了基于改进的距离评估技术的特征选择方法的有效性。
英文摘要：
In order to solve the difficult problem of identifying incipient fault and compound fault for mechanical equipment, and improve diagnosis accuracy, a novel hybrid intelligent diagnosis model based on multiple feature sets from different symptom domains and multiple classifier combination, is proposed. This model combines empirical mode decomposition （EMD）, the improved distance evaluation technique, adaptive neuro-fuzzy inference system （ANFIS） and genetic algorithm （GA） techniques etc. Prior to feature extraction, several signal preprocessing techniques, i.e. filtration, EMD and demodulation etc., are employed to excavate the underlying fault information from dynamic signals. Time-domain and frequency-domain statistical features that reflect the equipment operation conditions from various aspects are extracted and six feature sets are obtained. In succession, the improved distance evaluation technique is proposed, and with it, six sensitive feature sets are selected from the six original feature sets, respectively. The six sensitive feature sets are input into the multiple ANFISs combined by GA to attain the final diagnosis result. The application to fault diagnosis of locomotive wheel pair bearings shows the model is able to reliably recognize not only different fault categories and severities but also the compound faults. Thus, a desired diagnosis effect is obtained via the hybrid model. Moreover, the application result also validates the power of the proposed feature selection method based on the improved distance evaluation technique.
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