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 behavior 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 handling the massive volumes of data generated by these complex systems. Monitoring and analyzing signals from mechanical equipment go beyond mere data collection; they provide a foundation for proactive maintenance strategies. By closely observing signal patterns, experts can detect subtle fault characteristics early on, 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.
The signal processing methods employed in mechanical systems are designed to extract and diagnose faults within the system. These methods aim to assess the operational state of the machinery, mitigating the risk of safety incidents resulting from mechanical failures. Previous research has utilized 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, and random resonance (among others). The application of these sophisticated signal processing techniques enables the extraction and denoising of subtle fault signals, allowing for precise identification of faults that might not be readily apparent. This approach serves to enhance the accuracy of fault recognition and contributes to the overall safety and reliability of mechanical systems.
The scope of Advances in Signal Processing for Mechanical Systems encompasses the following themes:
● 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.
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 behavior 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 handling the massive volumes of data generated by these complex systems. Monitoring and analyzing signals from mechanical equipment go beyond mere data collection; they provide a foundation for proactive maintenance strategies. By closely observing signal patterns, experts can detect subtle fault characteristics early on, 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.
The signal processing methods employed in mechanical systems are designed to extract and diagnose faults within the system. These methods aim to assess the operational state of the machinery, mitigating the risk of safety incidents resulting from mechanical failures. Previous research has utilized 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, and random resonance (among others). The application of these sophisticated signal processing techniques enables the extraction and denoising of subtle fault signals, allowing for precise identification of faults that might not be readily apparent. This approach serves to enhance the accuracy of fault recognition and contributes to the overall safety and reliability of mechanical systems.
The scope of Advances in Signal Processing for Mechanical Systems encompasses the following themes:
● 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.