Bearing fault detection and oil debris monitoring by adaptive noise cancellation
Bearing fault detection and oil debris monitoring by adaptive noise cancellation
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DOI：
10.20381/ruor-18797
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发表时间：
2008
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影响因子：
--
通讯作者：
Raymond Wang
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文献类型：
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作者：
Raymond Wang
轴承故障检测对于防止机器故障和确保机器在最佳状态下运行至关重要。然而，被监测的轴承振动信号经常被来自机器中的其它源的干扰信号破坏。自适应噪声抵消器可以用来从干扰信号中提取轴承信号，从而实现轴承故障检测。利用最小均方算法的自适应噪声消除器由于其计算效率也适用于在线监测。自适应噪声抵消器的有效性由其操作参数控制：横向滤波器长度和步长参数。本文建立了确定合适的操作参数的标准。由于自适应噪声抵消器的自回归结构，采用Akaike信息准则来获得横向滤波器长度。自适应滤波器权值的波动会影响自适应噪声抵消器的性能。失调与过滤器重量波动正相关。该方法利用较小的失调值来获得所需的步长参数，以保证自适应噪声抵消器的良好性能。本文还介绍了在稳态环境下实现在线监测中零稳定时间的方法。通过仿真和实验验证了自适应噪声抵消器在方位信号提取中的有效性。电感式油屑监测传感器用于检测润滑油中的金属颗粒。然而，传感器受到干扰振动信号的影响，并且不再能够从所得混合信号中正确地检测金属颗粒特征。为了获得可靠的金属颗粒计数和尺寸，去除干扰振动信号至关重要。油碎片监测信号中的金属颗粒信号是非周期性的，并且可以被认为是宽带信号。因此，可以使用不需要单独的参考输入源的特殊形式的自适应噪声消除器。将延迟并入到所收集的信号以导出参考输入。需要比特征输出信号采样单元的长度更长的延迟值来实现特征输出信号采样单元。
Bearing fault detection is critical in preventing machine failure and ensuring machine is operating in optimal condition. However, the monitored bearing vibration signal is often corrupted by interference signals from other sources in the machine. Adaptive noise canceller can be used to extract the bearing signal from the corrupting interference signals, thus enable bearing fault detection. Adaptive noise canceller utilizing least mean square algorithm is also suitable for on-line monitoring because of its computational efficiency. The effectiveness of the adaptive noise canceller is controlled by its operating parameters: the transversal filter length and the step-size parameter. This thesis establishes the criteria in determining the proper operating parameters. The Akaike information criterion is used to obtain the transversal filter length because of the adaptive noise canceller's autoregressive structure. The adaptive filter weight fluctuations affect the performance of the adaptive noise canceller. The misadjustment positively correlates to the filter weight fluctuations. A small misadjustment value can be used to obtain the required step-size parameter to ensure the satisfactory performance of the adaptive noise canceller. A procedure to achieve zero settling time in on-line monitoring under the stationary environment is also illustrated in this thesis. Simulation and experiments are performed to demonstrate the effectiveness of the adaptive noise canceller in bearing signal extraction. Inductive oil debris monitoring sensor is used to detect metal particles in the lubricating oil. However, the sensor is affected by the interfering vibration signal and the metal particle signatures can no longer be correctly detected from the resulting mixed signal. In order to obtain reliable metal particle counts and sizes, it is critical that the interfering vibration signal is removed. The metal particle signal in the oil debris monitoring signal is non-periodic and can be considered a broadband signal. Thus a special form of the adaptive noise canceller which does not require a separate reference input source can be used. A delay is incorporated to the collected signal to derive the reference input. A delay value longer than the length of the characteristic output signal sampling units is needed to achieve the