An Improved Accuracy and Efficiency Based Defect Detection Method for Industrial Signal Processing

Defect detection is an important part of industrial signal processing that uses various techniques to identify abnormalities in signals. This is done by using a combination of feature extraction, signal analysis, and machine learning algorithms. It can be used in industrial applications such as quality assurance, predictive maintenance, and safety activities. Feature extraction techniques are used to identify features from signals which then used for further analysis. These features can be base waveforms, frequency components, waveform trends, and amplitudes. Signal analysis techniques such as discrete-time Fourier transform, wavelet transform, and fast Fourier transform are used to extract additional information from signals. Machine learning techniques are used to analyze extracted features to detect anomalies in data. This helps in making decisions and optimizing performance of industrial systems.