Statistical and Machine Learning Methods for Physical Signal Analysis

Abstract

Physical signal analysis is crucial to scientific work and engineering applications since present-day sensing technologies very often generate huge amounts of measurement data which must be accurately interpreted. The extraction of useful information from complex physical signals has been greatly enhanced in recent decades by advances in statistical signal processing and machine learning. A thorough overview of statistical and machine learning techniques for physical signal analysis is given in this article. It presents basic concepts of physical signals and measurement data, and then explains the traditional statistical signal processing techniques like time-frequency analysis, feature extraction and signal preprocessing. The review then presents an overview of some representative machine learning and deep learning techniques such as Support Vector Machines (SVM), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks and Transformer models and their applications in industrial fault diagnosis, biomedical signal analysis and human activity recognition. With special focus on data quality, model interpretability, computational efficiency, and generalization ability, recent research trends, present constraints, and future research objectives are also covered. In summary, this paper highlights the symbiotic relationship between machine learning and statistical signal processing today in physical signal analysis. It provides readers with a comprehensive understanding of how data-driven learning, signal processing and physical measurement can be integrated to enable intelligent signal analysis and future scientific and engineering applications.