Planetary Gearbox Fault Signal Analysis and Fault Diagnosis

1. Composition and working principle of planetary gearbox

The planetary gearbox is composed of planetary gears, sun gears, internal gears, external gears, etc. Its working principle is that the sun gear, planetary gears and internal gears transmit the driving force to the external gears through the cam to achieve the transmission of different speeds and torques.

2. Fault signal characteristics of planetary gearbox

2.1. Significant increase in noise: After a fault occurs in the planetary gearbox, the noise will increase significantly, which is caused by the friction and wear of the faulty gear.

2.2. Temperature increase: After the planetary gearbox fails, the operating efficiency decreases, which will cause excessive heat and cause the temperature of the entire system to rise.

2.3. Abnormal vibration signal: Abnormal vibration signal is one of the most significant fault characteristics. The increase in friction of the faulty gear will cause a larger vibration signal.

3. Experimental method for fault diagnosis of planetary gearbox

3.1. Vibration analysis method: By analyzing the vibration signal, the location and type of the fault can be determined, so as to locate the problem and deal with it.

3.2. Sound analysis method: By analyzing the sound signal, the abnormal sound emitted by the faulty gear can be detected, and the severity of the fault can be judged according to the type and size of the sound.

3.3. Temperature analysis method: Monitor the operating temperature of the planetary gearbox. When it rises abnormally, it means that the fault is serious and needs to be repaired or replaced.

3.4.Edge filtering waveform analysis method: This method can analyze the edge of the faulty gear to determine the type and degree of the gear fault.

4. Experimental technology for fault diagnosis of planetary gearbox

4.1. Feature extraction technology: Determine the location and type of fault by analyzing the characteristics of the signal, such as using wavelet analysis, Fourier analysis and adaptive filtering to extract features.

4.2. Pattern recognition technology: Establish a fault pattern recognition model through training samples to obtain more accurate fault judgment results.

4.3. Artificial intelligence technology: Neural networks, genetic algorithms and other technologies can be used for fault prediction and diagnosis.

In summary, the analysis of planetary gearbox fault signals and the introduction of fault diagnosis experimental methods and technologies can effectively improve the operating efficiency and reliability of planetary gearboxes.

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