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Improving risk assessment (RA) for human-robot collaboration (HRC) is crucial, given challenges in existing RA tools. For example, simulation and testing RA tools lack realism due to simplified models, limited dynamic realism, and sensor integration constraints. This study explores how Artificial Intelligence (AI) can enhance risk assessment methods. Using a systematic literature review, the study analyzes three risk assessment methods: PFMEA, HAZOP, and FTA to which AI has been integrated. Results highlight strengths (e.g., systematic process failure identification, thorough hazard identification, complex system modeling) and limitations (e.g., limited coverage of human-robot dynamics, reliance on historical data, adaptability constraints, resource intensity, design dependency, complexity, human factor oversight, data dependency) in addressing HRC risks. Challenges and opportunities of AI in risk assessment are identified, emphasizing reliability, adaptation, safety, and method accuracy. Adhering to standards significantly improves the trustworthiness of AI-driven risk assessments, ensuring consistent, and validated results across diverse HRC scenarios. The study proposes a hybrid approach that combines multiple methods and incorporates image processing as a practical AI feature to enhance risk detection and prevention. The study advances AI-based risk assessment for HRC, offering a comprehensive overview of the current state of the art, highlighting gaps for future research, and suggesting a holistic solution. [ABSTRACT FROM AUTHOR]

Due to their dynamic properties, viscoelastic materials are largely used in different sectors of industry to reduce noise and vibration in mechanical structures. In particular, partial constrained layer damping (PCLD) treatments have been used and studied in order to lessen stationary vibration and ringing noise. In the present work, simulations are performed to predict the effect of PCLD on the initial transient dynamic response of a planar impacted structure. The structure is a simply supported, rectangular aluminum plate impacted at its center with a small steel sphere. A parametric study is carried out based on a finite element model using ABAQUS 6.5 [Dassault Systèmes (ABAQUS Inc., Silicon Valley, CA)], whose results are well correlated with previous experiments. The model reflects the effects of several PCLD parameters on the initial transient response of the padded plate. The results from the parametric study show that the stiffness of the viscoelastic layer is a crucial parameter in the initial transient dynamic response of the impacted plate. It is also shown that the pads locally modify the dynamic properties of the plate thus causing changes in the time evolution of the deformation field. The combined use of PCLD to reduce both the initial transient response of the impacted structure and the ensuing flexural vibration could open the way for a better reduction of impact noise. [ABSTRACT FROM AUTHOR]

Time-frequency fault detection techniques were applied in this study, for monitoring real life industrial bearing. For this aim, an experimental test bench was developed to emulate the bearing rotating motion and to measure the induced vibration signals. Dedicated software was used to analyze the acquired measurements in the time-frequency domain using several distributions with varying resolution. Results showed that each fault type exhibits a specific behavior in the time-frequency domain, which is exploited in the localization of the faulty component. [ABSTRACT FROM AUTHOR]