AI-enabled acoustic sensing in frictional systems: methods, progress, and perspectives

Acoustic sensing for friction and lubrication monitoring involves the use of acoustic emission, guided waves, and ultrasonic reflection or transmission techniques to probe frictional and lubricated interfaces in a non-intrusive manner. It is particularly important for sealed or in-service mechanical systems, where direct access to the contact zone is limited or impossible. However, traditional acoustic sensing approaches often struggle with the complexity, nonlinearity, and non-stationary nature of friction-induced signals, making reliable interpretation and quantitative analysis challenging. Recent advances in artificial intelligence have significantly enhanced the ability to extract meaningful information from complex acoustic signals generated by friction, lubrication, and wear processes. This review surveys the current state of AI-enabled acoustic sensing for friction and lubrication monitoring in tribological systems. The sensing principles and data acquisition characteristics of acoustic emission, guided waves, and ultrasonic techniques are first summarized. Common signal processing and feature representation strategies are then reviewed, followed by key learning-based methods, including regression and classification frameworks, for quantitative prediction of friction, lubricant film thickness, and wear indicators, as well as identification of lubrication regimes, frictional states, and their transitions. Representative engineering applications in automotive systems, energy and power generation equipment, heavy-duty machinery, and sealed or inaccessible frictional interfaces are also discussed, demonstrating the broad applicability of AI-enabled acoustic sensing under practical operating conditions. Recent findings further indicate a growing shift from purely data-driven models toward physics-guided and mechanism-informed learning, with improved robustness, generalization, and interpretability. The review highlights both the capabilities and limitations of existing approaches and outlines emerging opportunities for intelligent and reliable tribological monitoring.