Bearing vibration is very sensitive to bearing damage, such as drops, indentations, rust, cracks, fatigue, etc., which will all play an important role in bearing and vibration measurements. Therefore, the size of the vibration can be measured by using a special bearing vibration measuring instrument (frequency analyzer, etc.), and the specific circumstances of the abnormality can be inferred from the frequency distribution. The measured values vary depending on the usage conditions of the bearings or the location of the sensor installation, etc. Therefore, it is necessary to analyze and compare the measured values of each machine in advance to determine the judgment criteria.
There are many detection and diagnosis technologies for rolling bearing faults, such as vibration signal detection, lubricating oil analysis and detection, temperature detection, acoustic emission detection, etc. Among equipment diagnosis methods, diagnosis technology based on vibration signals is widely used. This technology is divided into two types: simple diagnosis method and precision diagnosis method.
Simple diagnosis uses various parameters of the vibration signal waveform, such as amplitude, crest factor, crest factor, probability density, kurtosis coefficient, etc., as well as various demodulation techniques to make a preliminary judgment on the bearing to confirm whether there is a fault;
Precision diagnosis uses various modern signal processing methods to determine the fault type and cause of the bearing that is considered to be faulty in simple diagnosis.
The following is a detailed introduction to the two methods:
1. Simple diagnosis method
(1) Amplitude value diagnosis method
The amplitude value mentioned here refers to the peak value XP, the mean value This is the simplest and most commonly used diagnostic method, which is diagnosed by comparing the measured amplitude value with the value given in the judgment standard.
The peak value reflects the maximum amplitude at a certain moment, so it is suitable for fault diagnosis with instantaneous impact such as surface pitting damage.
The diagnostic effect of the average value is basically the same as that of the peak value. Its advantage is that the detection value is more stable than the peak value, but it is generally used when the rotation speed is higher (such as above 300r/min).
The root mean square value is averaged over time, so it is suitable for fault diagnosis where the amplitude value changes slowly with time, such as wear.
(2) Probability density diagnostic method
The probability density curve of the amplitude of a fault-free rolling bearing is a typical normal distribution curve; but once a fault occurs, the probability density curve may be skewed or dispersed.
(3) Diagnostic method of kurtosis coefficient
A fault-free bearing whose amplitude satisfies the normal distribution law has a kurtosis value of approximately 3. With the occurrence and development of faults, the kurtosis value has a similar changing trend to the crest factor. The advantage of this method is that it has nothing to do with the rotation speed, size and load of the bearing, and is mainly suitable for the diagnosis of pitting corrosion faults.
(4) Form factor diagnostic method
Crest factor is defined as the ratio of peak to average (XP/X). This value is also one of the effective indicators for simple diagnosis of rolling bearings.
(5) Crest factor diagnostic method
Crest factor is defined as the ratio of peak value to root mean square value (XP/Xrms). The advantage of this value for simple diagnosis of rolling bearings is that it is not affected by bearing size, speed and load, nor is it affected by changes in sensitivity of primary and secondary instruments such as sensors and amplifiers. This value is suitable for diagnosing pitting corrosion faults. By monitoring the changing trend of XP/Xrms values over time, rolling bearing faults can be effectively predicted early and the development and changing trends of faults can be reflected. When the rolling bearing has no fault, XP/Xrms is a small stable value; when the bearing is damaged, an impact signal will be generated and the vibration peak value will increase significantly, but the root mean square value will not increase significantly at this time. , so XP/Xrms increases; when the fault continues to expand and the peak value gradually reaches the limit value, the root mean square value begins to increase, and XP/Xrms gradually decreases until it returns to the size without faults.
2. Precision diagnostic method
(1) Low-frequency signal analysis method
Low-frequency signals refer to vibrations with frequencies below 8kHz. Generally, acceleration sensors are used to measure the vibration of rolling bearings, but the vibration speed is analyzed for low-frequency signals. Therefore, the acceleration signal must be converted into a speed signal by an integrator after passing through a charge amplifier, and then pass through a low-pass filter with an upper cutoff frequency of 8 kHz to remove the high-frequency signal. Finally, the frequency component is analyzed to find the characteristic frequency of the signal. diagnosis.
(2) Medium and high frequency signal demodulation analysis method
The frequency range of the intermediate frequency signal is 8~20kHz, and the frequency range of the high frequency signal is 20~80kHz. Since acceleration can be directly analyzed for mid- and high-frequency signals, after the sensor signal passes through the charge amplifier, the low-frequency signal is directly removed by a high-pass filter, then demodulated, and finally frequency analysis is performed to find the characteristic frequency of the signal.
