Bio-Tribo-Acoustic Emissions: Condition Monitoring of a Simulated Joint Articulation

The feasibility study of the real-time evaluating the total hip replacement (THR) by using acoustic emission

“…To enhance the realism of simulating tribocorrosion around the hip utilizing AE signals [12], a model was developed with COMSOL modeling software, illustrated in Figure 7. This model features three linear elastic layers that mimic the properties of the skin, muscle, fat, and the implanted prosthesis layers.…”

On the Early and Affordable Diagnosis of Joint Pathologies Using Acoustic Emissions, Deep Learning Decompositions and Prediction Machines

“…Seminal work has been done by researchers who have attempted to track the sound waves-known as biomechanical acoustic emission (AE) waves-that are produced when various joints are flexed, resulting from tissue changes during deformations and movements, and provided signs to suggest that AE can be applied as a means towards assessing the level of degeneration in a joint [10][11][12][13]. The conceptual physics is based on the expectation that the AE events generated by a healthy joint would be less than that of one with a considerable degree of wear within it and can serve as a non-invasive means towards condition monitoring of joint wear [10][11][12][13].…”

“…Contrasting the AE sensing approach to clinical joint diagnosis techniques, it is a passive technique that poses minimal risk of infection and radiation and is affordable, as well as being a non-invasive means towards the acquisition of rich signal information that can be utilised towards the diagnosis and assessment of joint health [14]. AE has predominantly seen traditional use in condition monitoring and maintenance of machinery components, in addition to process monitoring, but recent scientific advancements have seen applications of the technique in areas such as biomechanics and orthopaedics, as mentioned [10][11][12][13][14].…”

“…Tribological research work by Olorunlambe et al [10,11,15] identified the notion that there mainly exists a constrained array of signal processing techniques applied towards ortho-tribological case studies, which they built upon by developing machine learning models that could differentiate between adhesive and abrasive wear conditions. The work by Olorunlambe et al [10,11,15] showcased how machine learning-based methods could distinguish between these two wear conditions from benchtop-based wear conditions in their running in early and steady-state (continuous) stages of wear.…”

“…Tribological research work by Olorunlambe et al [10,11,15] identified the notion that there mainly exists a constrained array of signal processing techniques applied towards ortho-tribological case studies, which they built upon by developing machine learning models that could differentiate between adhesive and abrasive wear conditions. The work by Olorunlambe et al [10,11,15] showcased how machine learning-based methods could distinguish between these two wear conditions from benchtop-based wear conditions in their running in early and steady-state (continuous) stages of wear. The research presented in this paper aims to build on this by designing pattern recognition-based predictive models that can identify and differentiate between the latent and early stages of joint-wear pathologies, a broad class of common joint-wear conditions that includes abrasive, adhesive, burnishing, burnishing-to-scoring transition, and scoring.…”