Signal analysis methods for mechanical fault diagnosis mainly include trend analysis, time domain analysis, frequency domain analysis, envelope analysis method, waterfall diagram and so on.
1, trend analysis
Mainly used to analyze the long-term operation trend of the equipment, in order to predict the health status of the equipment and possible failures.
2, time domain analysis
Mainly used to analyze the signal changes in time, including the signal peak, mean, variance and other parameters.
3, frequency domain analysis
Mainly used to analyze the frequency components of the signal, can be converted to frequency domain signals through the Fourier transform and other methods, in order to analyze the frequency characteristics of the signal.
4, envelope analysis method
Mainly used to analyze the envelope of the signal, in order to extract the characteristic frequency and amplitude of the signal, commonly used in bearing and other rotating equipment fault diagnosis.
5, waterfall diagram
Mainly used to analyze the signal spectrum over time, in order to observe the dynamic changes in equipment.
Two, mechanical fault diagnosis of the signal
Mechanical fault diagnosis of the signal is usually collected from the mechanical equipment, vibration, sound, current, temperature and other physical signals. These signals can reflect the operating status and health of the equipment, through the analysis and processing of these signals, you can identify the type of equipment failure and fault location, so as to realize the fault prediction and diagnosis.
Factors affecting the accuracy of signal analysis methods.
1, noise interference
Noise interference in the signal may affect the clarity and accuracy of the signal. Noise can come from the environment, the equipment itself or interference in the signal acquisition process.
2. Sampling Frequency
The sampling frequency of a signal determines the frequency range that can be captured when the signal is analyzed. If the sampling frequency is too low, it may lead to the loss of signal frequency components or aliasing, thus affecting the accuracy of the analysis results.
3. Signal Distortion
Signals may be distorted during transmission or acquisition, for example, due to faulty sensors, damaged cables, or signal amplifier problems. These distortions may cause changes in the shape, amplitude, or frequency of the signal, thus affecting the accuracy of the signal analysis.
4. Signal Processing Algorithms
Selecting the right signal processing algorithm is very important for accurately analyzing signals. Different algorithms are suitable for different types of signals and faults, and improperly selected algorithms may lead to deviations in the analysis results.