Structural Health Monitoring by Acoustic Emission Technique

Elastic wave, which is formed due to sudden rearrangement of stresses in a material, is called acoustic emission (AE). It is widely used in nondestructive testing (NDT) of materials and structures especially in health monitoring of structures for damage detection. When a body is subjected to an external force (in the form of changing pressure, load, or temperature), any micro fracture inside the body releases energy in the form of AE wave, which is received by sensor and later on is converted to electrical signal for inspection. In early stage, major importance was given on studying the AE characteristics during the deformation and fracture on various materials (by J. Kaiser in Germany in 1950 and B. H. Schofield in the USA in 1954). Nowadays, lots of research are conducting on formulating the theories behind AE formation, propagation, and inspection in various fields as an important health monitoring tool for NDT. In this chapter, I would like to elaborate a “feature outlook of AE” based on past, present, and future perspectives; “AE monitoring” procedure based on theoretical and experimental perspectives; and smart applications in structural health monitoring based on industrial and biostructural perspectives with related figures and tables

Motion Artifact Resistant Mounting of Acoustic Emission Sensors for Knee Joint Monitoring

Among the many diverse methods of recording biological signals, sound and acoustic emission monitoring are becoming popular for data acquisition; however, these sensors tend to be very susceptible to motion artefacts and noise. In the case of joint monitoring, this issue is even more significant, considering that joint sounds are recorded during limb movements to establish joint health and performance. This paper investigates different sensor attachment methods for acoustic emission monitoring of the knee, which could lead to reduced motion and skin movement artefacts and improve the quality of sensory data sets. As a proof-of-concept study, several methods were tested over a range of exercises to evaluate noise resistance and signal quality. The signals least affected by motion artefacts were recorded when using high-density ethylene-vinyl acetate (EVA) foam holders, attached to the skin with double-sided biocompatible adhesive tape. Securing and isolating the connecting cable with foam is also recommended to avoid noise due to the cable movement.Clinical Relevance - The results of this study will be useful in joint AE monitoring, as well as in other methods of body sound recording that involve the mounting of relatively heavy sensors, such as phonocardiography and respiratory monitoring

Motion artifact resistant mounting of acoustic emission sensors for knee joint monitoring

Among the many diverse methods of recording biological signals, sound and acoustic emission monitoring are becoming popular for data acquisition; however, these sensors tend to be very susceptible to motion artefacts and noise. In the case of joint monitoring, this issue is even more significant, considering that joint sounds are recorded during limb movements to establish joint health and performance. This paper investigates different sensor attachment methods for acoustic emission monitoring of the knee, which could lead to reduced motion and skin movement artefacts and improve the quality of sensory data sets. As a proof-of-concept study, several methods were tested over a range of exercises to evaluate noise resistance and signal quality. The signals least affected by motion artefacts were recorded when using high-density ethylene-vinyl acetate (EVA) foam holders, attached to the skin with double-sided biocompatible adhesive tape. Securing and isolating the connecting cable with foam is also recommended to avoid noise due to the cable movement. Clinical Relevance— The results of this study will be useful in joint AE monitoring, as well as in other methods of body sound recording that involve the mounting of relatively heavy sensors, such as phonocardiography and respiratory monitoring

Test-Retest Reliability of Acoustic Emission Sensing of the Knee during Physical Tasks

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises

Test-retest reliability of acoustic emission sensing of the knee during physical tasks

Acoustic emission (AE) sensing is an increasingly researched topic in the context of orthopedics and has a potentially high diagnostic value in the non-invasive assessment of joint disorders, such as osteoarthritis and implant loosening. However, a high level of reliability associated with the technology is necessary to make it appropriate for use as a clinical tool. This paper presents a test-retest and intrasession reliability evaluation of AE measurements of the knee during physical tasks: cycling, knee lifts and single-leg squats. Three sessions, each involving eight healthy volunteers were conducted. For the cycling activity, ICCs ranged from 0.538 to 0.901, while the knee lifts and single-leg squats showed poor reliability (ICC < 0.5). Intrasession ICCs ranged from 0.903 to 0.984 for cycling and from 0.600 to 0.901 for the other tasks. The results of this study show that movement consistency across multiple recordings and minimizing the influence of motion artifacts are essential for higher test reliability. It was shown that motion artifact resistant sensor mounting and the use of baseline movements to assess sensor attachment can improve the sensing reliability of AE techniques. Moreover, constrained movements, specifically cycling, show better inter- and intrasession reliability than unconstrained exercises

Assessment of hip and knee joints and implants using acoustic emission monitoring: A scoping review

Objectives: Population ageing and the subsequent increase of joint disorders prevalence requires the development of non-invasive and early diagnostic methods to enable timely medical assistance and promote healthy aging. Over the last decades, acoustic emission (AE) monitoring, a technique widely used in non-destructive testing, has also been introduced in orthopedics as a diagnostic tool. This review aims to synthesize the literature on the use of AE monitoring for the assessment of hip and knee joints or implants, highlighting the practical aspects and implementation considerations. Methods: this review was conducted as per the PRISMA statement for scoping reviews. All types of studies, with no limits on date of publication, were considered. Articles were assessed and study design parameters and technical characteristics were extracted from relevant studies. Results: conducted search identified 1379 articles and 64 were kept for charting. Seven additional articles were added at a later stage. Reviewed works were grouped into studies on joint condition assessment, implant assessment, and hardware or software development. Native knees and hip implants were most commonly assessed. The most researched conditions were osteoarthritis, implant loosening or squeaking in vivo and structural damage of implants in vitro. Conclusion: in recent years, AE monitoring showed potential of becoming a useful diagnostic tool for lower limb pathologies. However, further research is needed to refine the existing methods and assess their feasibility in early diagnostics. Significance: The current state of research on AE monitoring for hip and knee joint assessment is described and future research directions are identified

Acoustic emissions: diagnosing tribological phenomenon in artificial joint materials

Studies have shown that many reported causes of failure of artificial joints such as hip, knee and spine are wear and friction related. Current modes of diagnosing failed artificial joints involve the use of imaging techniques like X-rays and CT scans, which although effective, are costly, time-consuming and harmful to patient health due to frequent exposure to radiation. There is the added limitation of the delay experienced before signs of failure become visible, causing further discomfort to the patient and, at times, health complications resulting from possible migration of wear debris into blood tissues. These complications have necessitated the need for a simpler and more dynamic system for identifying and diagnosing failed artificial joints, which is where the acoustic emission (AE) testing has shown promise. AE testing is a non-destructive test method used to detect the onset and progression of mechanical flaws that has proven advantageous in the analysis and understanding of tribological interactions in mechanical systems. In recent times, it has been increasingly used in the study of the tribology of artificial and natural human joints thereby showing potential as a tool for the identification and diagnosis of failed artificial joints. Thus, this research aimed to use AE to monitor the tribological characteristics of artificial joint materials as a first step toward using AE to diagnose artificial and natural joint pathologies. To gain an initial understanding of how AE features can be related to tribological mechanisms such as friction, in particular, a bio-tribo-acoustic tests system was developed. This enabled the acquisition of AE signals during biotribological testing of artificial joint materials. This proof-of-concept study showed that time-dependent (TDD) AE features can be used to predict the friction profile of a simulated polymer-metal artificial joint articulation. The prediction was carried out using a Non-linear Auto Regression with Exogeneous inputs (NARX) model. During testing of the trained model, predicted data had R2 values of 94% in tests on PEEK reciprocating at 2 Hz test and 98.6% for UHMWPE at 2 Hz. These regression results support the hypothesis that AE TDD features can be used to predict the friction profile which can then be related to the wear behaviour of the simulated joint articulation. Having proved the potential of AE as a biotribological diagnostic tool, the next step is to be able to use the acquired AE signals to identify the perceived damage mode prompting the need for a method by which AE signals can be differentiated according to different wear mechanisms. To this end, AE signals from adhesive and abrasive wear, simulated under controlled joint conditions, were classified using supervised learning. Principal component analysis was used to derive uncorrelated AE features and then classified using three methods – logistic regression, k-nearest neighbours and back propagation (BP) neural network. The BP network emerged as the best performing network with a classification accuracy of 98%. One of the limitations of traditional artificial neural networks (ANN) such as the BP network is the complex feature engineering required to obtain a model with high accuracy and high sensitivity. To mitigate this, deep transfer learning, with GoogLeNet as the base convolutional neural network (CNN) model, was used to classify AE signals from simulated damage mechanisms observed in retrieved polyethylene inserts of failed knee implants - burnishing and scratching wear. It was found that using CNN to extract features to be trained with an SVM model obtained a higher classification accuracy (99.3%) than just training with CNN model (96.5%). The work presented in this thesis has shown that AE testing can be used to monitor the tribological properties of simulated articulating joint surfaces. With machine learning and deep transfer learning techniques, models with high accuracy and high sensitivity can be built to classify the acquired AE signals based on simulated real-life artificial joint damage modes. This confirms the initial hypothesis that with AE testing, a more dynamic, highly specific and highly sensitive process of identifying and diagnosing artificial joint pathologies can be developed, thereby reducing patient discomfort and NHS expenditure