基于MEMS加速度传感器的轴承故障检测

提出了一种通过利用低成本的MEMS加速度传感器进行振动分析，实现检测电动机深沟球轴承多重故障的简易方法。首先分析了轴承多故障特征频率，然后通过快速傅里叶变换算法对轴承出现故障的电动机振动频率进行了分析，从振动频谱中提取故障频率来诊断轴承多重故障的存在。同时，基频分量周围的边带频率分量表明由于故障轴承存在空气间隙。在空载、单相以及失衡电压条件下通过实验对提出的方法进行了研究，结果显示提取出的故障频率与理论值两者十分接近，表明提出的方法能够有效检测并识别出感应电动机的多故障特征。

Bearing fault detection based on MEMS acceleration sensor

A vibration analysis by using MEMS acceleration sensor with low cost, simple method for the detection of multiple faults of motor of deep groove ball bearing. First analyzes the bearing fault characteristic frequency. Then by means of the fast Fourier transform algorithm on bearing appears fault of motor vibration frequency were analyzed. From the vibration spectrum extraction of fault frequency to diagnose multiple faults in bearings. At the same time, the fundamental frequency component around the sideband frequency components show that the fault bearing air gaps. Under no load, and the single-phase voltage unbalance conditions through experiments on the proposed method were studied. The results showed that extract the fault frequency and theoretical value both is very close to, show that the proposed method can effectively detect and identify the multi fault characteristic of the induction motor.