[5D1] Distinguished overview speaker: Decoding complexity: diagnostic and prognostic approaches in biological and mechanical systems
A Zippo
University of Modena and Reggio Emilia, Italy
Complexity, a defining characteristic of both biological and mechanical systems, arises from the interdependencies and non-linear interactions within these domains. The study of these complexities plays a crucial role in diagnostic and prognostic methodologies, enabling the detection of early-stage anomalies and the prediction of future system behaviours. In mechanical engineering, complexity manifests in powertrain dynamics, vehicle mechanics and structural systems, where the interaction of multiple components often results in non-linear responses such as chaotic vibrations, resonances and bifurcations. These behaviours, if not properly diagnosed, can lead to unexpected wear and failure modes, particularly in gear systems where varying mesh stiffness generates intricate vibration patterns.
Powertrain systems provide an illustrative case of complexity-driven diagnostic and prognostic challenges. Gearboxes, electric motors and other drivetrain components exhibit interrelated interactions that are highly sensitive to minor perturbations, leading to significant effects such as noise, vibration and harshness (NVH) issues. To address these challenges, predictive maintenance strategies leverage advanced signal processing techniques, including vibration analysis and spectral kurtosis, to detect early signs of degradation and predict failure timelines. Moreover, the advent of novel materials, such as metamaterials and high-static-low-dynamic stiffness (HSLDS) isolators, has introduced innovative solutions for mitigating complex vibrations, particularly in aerospace and automotive applications.
Biological systems, especially in biomedical engineering, present even greater diagnostic and prognostic complexities due to the multitude of interacting subsystems maintaining homeostasis, responding to stimuli and adapting over time. A prime example is the study of tremor dynamics in Parkinson’s disease, where the interactions between neural signals, muscle activations and limb mechanics produce highly variable and non-linear motor dysfunctions. Effective diagnostic tools, including multi-body and Simulink co-simulations, experimental electromyography (EMG) data and accelerometric signals, aid in identifying underlying tremor characteristics. Prognostic techniques, such as machine learning algorithms and non-linear analysis methods such as Lyapunov exponents and recurrence quantification analysis, facilitate early detection and disease progression modelling. These insights contribute to the development of control strategies, such as active vibration control and non-invasive stimulation techniques, that aim to mitigate motor symptoms and enhance patient mobility.
The convergence of complexity in biological and mechanical systems is further exemplified in bio-inspired robotics, where principles from biology are applied to the design of adaptive and resilient machines. These hybrid systems leverage sensory feedback and muscle coordination principles to navigate unpredictable environments, emphasising the need for interdisciplinary approaches that combine biology, mechanics and computational modelling.
In conclusion, the integration of diagnostic and prognostic strategies in both biological and mechanical systems is critical for managing complexity and ensuring operational reliability. The ability to model, simulate and predict system behaviours not only advances fundamental understanding but also fosters innovation in healthcare, robotics and engineering applications.
Powertrain systems provide an illustrative case of complexity-driven diagnostic and prognostic challenges. Gearboxes, electric motors and other drivetrain components exhibit interrelated interactions that are highly sensitive to minor perturbations, leading to significant effects such as noise, vibration and harshness (NVH) issues. To address these challenges, predictive maintenance strategies leverage advanced signal processing techniques, including vibration analysis and spectral kurtosis, to detect early signs of degradation and predict failure timelines. Moreover, the advent of novel materials, such as metamaterials and high-static-low-dynamic stiffness (HSLDS) isolators, has introduced innovative solutions for mitigating complex vibrations, particularly in aerospace and automotive applications.
Biological systems, especially in biomedical engineering, present even greater diagnostic and prognostic complexities due to the multitude of interacting subsystems maintaining homeostasis, responding to stimuli and adapting over time. A prime example is the study of tremor dynamics in Parkinson’s disease, where the interactions between neural signals, muscle activations and limb mechanics produce highly variable and non-linear motor dysfunctions. Effective diagnostic tools, including multi-body and Simulink co-simulations, experimental electromyography (EMG) data and accelerometric signals, aid in identifying underlying tremor characteristics. Prognostic techniques, such as machine learning algorithms and non-linear analysis methods such as Lyapunov exponents and recurrence quantification analysis, facilitate early detection and disease progression modelling. These insights contribute to the development of control strategies, such as active vibration control and non-invasive stimulation techniques, that aim to mitigate motor symptoms and enhance patient mobility.
The convergence of complexity in biological and mechanical systems is further exemplified in bio-inspired robotics, where principles from biology are applied to the design of adaptive and resilient machines. These hybrid systems leverage sensory feedback and muscle coordination principles to navigate unpredictable environments, emphasising the need for interdisciplinary approaches that combine biology, mechanics and computational modelling.
In conclusion, the integration of diagnostic and prognostic strategies in both biological and mechanical systems is critical for managing complexity and ensuring operational reliability. The ability to model, simulate and predict system behaviours not only advances fundamental understanding but also fosters innovation in healthcare, robotics and engineering applications.
