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Bearing fault diagnosis under variable rotational speed via the joint application of windowed fractal dimension transform and generalized demodulation: A method free from prefiltering and resampling

机译:通过窗口分形维变换和广义解调的联合应用,在可变转速下进行轴承故障诊断:一种无需预滤波和重采样的方法

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摘要

The conventional way for bearing fault diagnosis under variable rotational speed generally includes prefiltering, resampling based on shaft rotating frequency and order spectrum analysis. However, its application is confined by three major obstacles: a) knowledge-demanding parameter determination required by prefiltering, b) unavailable shaft rotating frequency for resampling as it is coupled with instantaneous fault characteristic frequency (IFCF) by a fault characteristic coefficient (FCC) which cannot be decided without knowing what fault actually exists, and c) complicated and error-prone resampling process. As such, we propose a new method to address these problems. The proposed method free from prefiltering and resampling mainly contains the following steps: a) extracting envelope by windowed fractal dimension (FD) transform, requiring no prefiltering, b) with the envelope signal, performing short time Fourier transform (STFT) to get a clear time frequency representation (TFR), from which the IFCF and the basic demodulator for generalized demodulation (GD) can be obtained, c) applying the generalized demodulation to the envelope signal with the current demodulator, converting the trajectory of the current time-frequency component into a linear path parallel to the time axis, d) frequency analyzing the demodulated signal, followed by searching the amplitude of the constant frequency where the linear path is situated. Updating demodulator via multiplying the basic demodulator by different real numbers (i.e., coefficient λ) and repeating the steps (c)-(d), the resampling-free order spectrum is then obtained. Based on the resulting spectrum, the final diagnosis decision can be made. The proposed method for its implementation on the example of simulated data is presented. Finally, experimental data are employed to validate the effectiveness of the proposed technique.
机译:在可变转速下进行轴承故障诊断的常规方法通常包括预滤波,基于轴旋转频率的重采样和阶次频谱分析。但是,它的应用受到三个主要障碍的限制:a)预滤波所需的知识要求参数确定,b)由于故障特征系数(FCC)与瞬时故障特征频率(IFCF)耦合而无法重采样的轴旋转频率在不知道实际存在什么故障的情况下就无法确定,并且c)复杂且容易出错的重采样过程。因此,我们提出了一种解决这些问题的新方法。所提出的无预滤波和重采样的方法主要包括以下步骤:a)通过窗口分形维数(FD)变换提取包络,不需要预滤波; b)利用包络信号,执行短时傅立叶变换(STFT)以获得清晰的图像。时频表示(TFR),从中可以获得IFCF和用于通用解调的基本解调器(GD),c)使用当前解调器将通用解调应用于包络信号,转换当前时频分量的轨迹进入平行于时间轴的线性路径,d)对解调信号进行频率分析,然后搜索线性路径所在的恒定频率的幅度。通过将基本解调器乘以不同的实数(即系数λ)并重复步骤(c)-(d),更新解调器,然后获得无重采样的阶谱。基于结果频谱,可以做出最终的诊断决策。提出了以仿真数据为例的实现方法。最后,实验数据被用来验证所提出技术的有效性。

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