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首页> 外文期刊>Sensors Journal, IEEE >Fuzzy Diagnosis Method for Rotating Machinery in Variable Rotating Speed
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Fuzzy Diagnosis Method for Rotating Machinery in Variable Rotating Speed

机译：变速机械旋转机械的模糊诊断方法

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摘要

In order to effectively diagnose faults for rotating machinery in the variable rotating speed, a novel diagnosis method is proposed based on time-frequency analysis techniques, the automatic feature extraction method, and fuzzy inference. The diagnosis sensitivities of three time-frequency analysis methods, namely, the short-time Fourier transform (STFT), wavelet analysis (WA), and the pseudo-Wigner-Ville distribution (PWVD), are investigated for condition diagnosis of rotating machinery. In the case of the bearing diagnosis, the diagnosis sensitivity of the PWVD was found to be highest. An extraction method for instantaneous feature spectrum is proposed using the relative crossing information (RCI), by which the feature spectrum from time-frequency distribution can be automatically extracted by a computer in order to identify among the conditions of a machine. The symptom parameters are also defined in the frequency domain using the feature spectrum extracted by the RCI. The synthetic symptom parameters can be obtained by the least squares mapping (LSM) technique to increase the diagnosis sensitivity of the symptom parameters. Based on the above studies, a fuzzy diagnosis method using sequential inference and possibility theory was also proposed, by which the conditions of machinery can be well identified sequentially. Practical examples of diagnosis for a roller bearing are given in order to verify the effectiveness of the approaches proposed in this paper.
机译：为了有效地诊断变速机中旋转机械的故障，提出了一种基于时频分析技术，特征自动提取和模糊推理的诊断方法。研究了短时傅立叶变换（STFT），小波分析（WA）和拟维格纳-维勒分布（PWVD）这三种时频分析方法对旋转机械状态诊断的敏感性。在轴承诊断的情况下，发现PWVD的诊断灵敏度最高。提出了一种利用相对交叉信息（RCI）的瞬时特征谱提取方法，利用该信息可以通过计算机自动提取时频分布的特征谱，从而在机器的状态之间进行识别。症状参数还使用RCI提取的特征谱在频域中定义。可以通过最小二乘映射（LSM）技术获得综合症状参数，以提高症状参数的诊断敏感性。在上述研究的基础上，提出了一种基于顺序推理和可能性理论的模糊诊断方法，可以很好地顺序识别机械状态。给出了滚动轴承诊断的实际例子，以验证本文提出的方法的有效性。

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