About this Research Topic
The operation of mechanical systems generates various signals, which can manifest in forms such as vibrations, acoustic emissions, pressure fluctuations, temperature changes, and more. Signal processing constitutes a critical aspect in the analysis and comprehension of the behaviour of mechanical systems, particularly in large-scale machinery such as aviation engines, wind turbines, petrochemical equipment, and electric motors. Advanced signal processing techniques are essential for extracting high value information from the massive volumes of data generated by these complex systems. Monitoring and analysing signals from mechanical equipment go beyond mere data collection; they provide a foundation for signal reconstruction, state estimation and system modelling with applications including human decision support, automatic control, and proactive maintenance optimization strategies. For example, by closely observing signal patterns, experts can distinguish subtle fault characteristics from noise, allowing for timely intervention and preventing potential system failures. This approach not only enhances the reliability of mechanical systems but also contributes to the optimization of their operational efficiency and lifespan.
Previous research has successfully utilised conventional signal processing techniques, including time-domain, frequency-domain, and time-frequency analysis methods. Recent studies have focused on advanced signal processing methods for handling mechanical signals. These methods encompass spectral kurtosis, higher-order statistical measures, sparse representation, morphological component analysis, random resonance, among others. This collection aims to collate recent enhancements to these techniques, exploring in particular the use of large datasets to systematically specialise signal processing methods beyond what is achievable with heuristic tuning. The call also provides opportunity for the research community to highlight new applications and the benefit of new techniques in enhancing the performance, safety, and reliability of mechanical systems.
The scope of Advanced Signal Processing Methods in Mechanical Systems encompasses the following themes, particularly where learning from data is exploited:
● Signal processing in manufacturing/machining,
● Machine and structural health monitoring,
● Performance evaluation of mechanical systems,
● Control of vibrations and noise,
● Acoustic emission signal processing,
● Data-driven and model-based prognostics,
● Uncertainty quantification for prognostics,
● Data-driven and Bayesian approaches for signal estimation and prediction,
● Signal processing enabled machine learning methods,
● Weak impulse signal extraction method.
Previous research has successfully utilised conventional signal processing techniques, including time-domain, frequency-domain, and time-frequency analysis methods. Recent studies have focused on advanced signal processing methods for handling mechanical signals. These methods encompass spectral kurtosis, higher-order statistical measures, sparse representation, morphological component analysis, random resonance, among others. This collection aims to collate recent enhancements to these techniques, exploring in particular the use of large datasets to systematically specialise signal processing methods beyond what is achievable with heuristic tuning. The call also provides opportunity for the research community to highlight new applications and the benefit of new techniques in enhancing the performance, safety, and reliability of mechanical systems.
The scope of Advanced Signal Processing Methods in Mechanical Systems encompasses the following themes, particularly where learning from data is exploited:
● Signal processing in manufacturing/machining,
● Machine and structural health monitoring,
● Performance evaluation of mechanical systems,
● Control of vibrations and noise,
● Acoustic emission signal processing,
● Data-driven and model-based prognostics,
● Uncertainty quantification for prognostics,
● Data-driven and Bayesian approaches for signal estimation and prediction,
● Signal processing enabled machine learning methods,
● Weak impulse signal extraction method.
Keywords: Mechanical Systems, Structural health monitoring, Signal processing advances, Signal estimation, Prediction
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
