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Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

机译：具有振动和声发射信号的多特征熵距离方法，用于滚动轴承故障的过程特征识别

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摘要

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time-frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner-outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.
机译：为了准确揭示滚动轴承的运行状态，提出了一种时域，频域和时频域的四个信息熵和两种信号的融合的多特征熵距离法，用于滚动轴承故障的过程特征分析和诊断。包括振动信号和声发射信号。研究了多特征熵距离法，给出了用多特征熵距离法诊断滚动轴承故障的基本思路。通过转子仿真试验台，获得了在不同转速下六个滚动轴承故障（球故障，内圈故障，外圈故障，内球故障，内外故障和正常）的振动和声发射信号。针对多特征熵距离法，对滚动轴承故障进行了过程诊断。分析结果表明，特征熵距离充分反映了滚动轴承故障随转速变化的过程特征。在滚动轴承故障诊断中，具有振动和声发射信号的多特征熵距离比单一信号（振动或声发射信号）更好地报告了信号特征；提出的多特征熵距离方法具有较高的诊断精度和较强的鲁棒性（抗噪能力）。该研究为滚动轴承和其他旋转机械的过程特征提取和故障诊断提供了一种新颖而有用的方法。

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