Vibration test is one of the important means to evaluate the dynamic performance of equipment or structure in engineering field. By collecting and analyzing the vibration signal of the system, we can judge the running state of the equipment, identify potential faults, optimize the design and improve the running safety. Therefore, vibration testing and analysis are widely used in many industries, such as machinery, aviation, construction, electric power and so on.
First, the basic process of vibration testing
Vibration test usually includes the following steps: setting test target, arranging measuring points and installing sensors, collecting data, processing data and analyzing results. Among them, data processing and analysis is the most critical link in the whole process, which directly determines the effectiveness and practicability of the test.
Second, the time domain analysis of vibration signals
In vibration analysis, time domain analysis is the first step. By observing the time history of vibration signals (such as displacement, velocity and acceleration), it can be preliminarily judged whether there is abnormal vibration in the equipment. Commonly used time domain parameters include:
-Peak: reflecting the instantaneous maximum vibration amplitude;
-Effective value (RMS): indicates the vibration energy, which is often used to evaluate the overall vibration level;
-Peak-to-Peak: used to evaluate the dynamic range of vibration;
-Waveform factor and peak factor: used to detect whether there is impact component in the signal.
These indicators help to identify common faults such as imbalance, misalignment and looseness.
Third, frequency domain analysis of vibration signals
In order to understand the vibration characteristics more deeply, time domain signals are usually converted into frequency domain signals for analysis. The commonly used method is to decompose the signal into different frequency components by using the fast Fourier transform (FFT). Frequency domain analysis can identify the vibration energy distribution at different frequencies, thus helping to judge the fault type.
For example:
-Imbalance is usually manifested as significant vibration at the frequency conversion (1×);
-Misalignment may have obvious peak at 2× frequency;
-Gear failure will cause abnormality in gear meshing frequency and its sideband;
-Bearing failure may result in energy concentration at the bearing characteristic frequency (inner ring, outer ring, rolling body frequency).
In addition, the harmonic and sideband information in the frequency spectrum is also of great significance to fault diagnosis.
Fourth, time-frequency analysis and advanced methods
For non-stationary signals, such as start/stop process or off-design operation, the traditional FFT analysis may not be accurate enough. At this time, STFT or Wavelet Transform can be used for time-frequency joint analysis, so as to capture the time-varying characteristics of the signal more accurately.
In recent years, with the development of artificial intelligence and big data technology, vibration data analysis methods based on machine learning have gradually emerged. A large number of vibration data can be classified, clustered and predicted by training model, which can realize automatic identification of equipment state and fault early warning.
V. Conclusion
Vibration testing is not only an important means to evaluate the health status of equipment, but also one of the key technologies to realize predictive maintenance. Through the comprehensive analysis of vibration signals, potential problems can be found in time, the downtime of equipment failure can be reduced, and the production efficiency and system reliability can be improved. With the continuous development of analytical technology, vibration testing will play an increasingly important role in industrial intelligence and digital transformation.