Vibration Data Processing

Vibration Data Processing

Pre-processing of vibration data for classification:

1. Data cleaning: The first step is to remove any missing or inconsistent data. This can be done by imputing missing values, dropping rows with missing values, or using other techniques.
2. Feature extraction: Vibration data often contains a large amount of information that is not useful for classification. Feature extraction is the process of reducing the data to a set of relevant features that can be used for classification.
3. Normalization: It is important to normalize the data so that the features are on a common scale. This can be done using techniques such as min-max scaling or z-score normalization.
4. Resampling: Vibration data is often collected at different sampling rates, which can make it difficult to compare across different signals. Resampling can be used to ensure that all signals have the same sampling rate.
5. Data augmentation: Data augmentation is the process of artificially creating new data from existing data. This can be useful for increasing the size of the data set and making the classifier more robust.
6. Dimensionality reduction: Vibration data often has a high dimensionality, which can make it difficult to classify. Dimensionality reduction techniques such as principal component analysis (PCA) or independent component analysis (ICA) can be used to reduce the dimensionality of the data.

The best way to pre-process vibration data for classification depends on the type of data and the application. Generally, it is important to understand the machine and the kind of data available in order to determine which pre-processing methods should be used 5. Common techniques for analyzing vibration signals include Short-Time Fourier Transform (STFT), Wavelet Transform, and Empirical Mode Decomposition (EMD) 1. Data augmentation can also be used to process raw vibration data 2. Additionally, convolutional neural networks (CNNs) have been used for vibration signals analysis in machining surface applications 3. Finally, feature extraction techniques such as Principal Component Analysis (PCA) can be used to reduce the dimensionality of the data before classification 4. Popular methods to perform time-frequency analysis on vibration signals include the Short-time Fourier Transform (STFT) and Wavelet Transform 3.

The best way to pre process vibration data for classifying them is to use a combination of techniques. One of the most common methods is to use an empirical mode decomposition (EMD) to disintegrate the signal into narrowband IMFs, which can then be applied to a Hilbert transform (HT) to extract the instantaneous frequencies. Another popular method is to use a Fourier transform (FT), which takes a series of FFTs and overlaps them to illustrate how the spectrum (frequency domain) changes with time. Additionally, spectrograms can be used to illustrate how the spectrum of the vibration changes over time. Finally, it is important to consider the resolution in time and frequency as well as the tolerability of artifacts when selecting a technique for vibration signal analysis.

References

• Vibration Data and a Few Techniques to Analyze It | by khushnandan rai | Towards Data Science
• Vibration Analysis Explained | Reliable Plant
• Vibration Measurements: Vibration Analysis Basics
• Vibration Analysis: FFT, PSD, and Spectrogram Basics [Free Download]
• Data Preprocessing for Condition Monitoring and Predictive Maintenance - MATLAB & Simulink