[Data] Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic Emission and Machine Learning

Human joint prostheses face wear and related failure mechanisms due to the complex tribological contact between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo). This study investigates wear mechanisms with the long term goal to predict failure rates in human joint prostheses. Multi-directional pin-on-disc tests were conducted in a medium simulating real in-vivo conditions to analyze the wear behavior of UHMWPE pins sliding against CoCrMo discs. The objectives were to gain insights into wear mechanisms and to classify wear rates. Real-time wear monitoring was enabled using Acoustic Emission (AE) sensors, capturing signals before and after weekly visual inspections over 2.3 million cycles. This approach facilitated continuous wear data collection and wear progression assessment. Integrating AE sensors proved valuable in detecting wear-related signals, enhancing wear progression detection, and aiding in anticipation of failure. This study presents a novel approach for monitoring wear progression in human joint prostheses using two Machine Learning (ML) frameworks. The first framework involved manually extracting time, frequency, and time-frequency domain features from acoustic signatures based on human knowledge. ML classifiers, including Logistic Regression, Support Vector Machine, k-Nearest Neighbor, Random Forest, Neural Networks, and Extreme Gradient Boosting, were applied for wear classification, achieving an average accuracy between 81% to 89%. The second framework introduced a contrastive learning-based Convolutional Neural Network (CNN) with circle loss to enhance wear classification performance. CNN extracted feature maps from the acoustic signatures, which were then used to retrain the ML classifiers. This approach demonstrated superior classification performance of 94% to 96% compared to manual feature extraction. Machine learning techniques enabled accurate wear classification and improved progressive assessment of human joint prostheses. Automated feature extraction using the contrastive learning-based CNN provided better insights into wear patterns and enhanced the predictive capabilities of ML classifiers. This approach can improve the early detection of wear-related failures and enable timely interventions. The successful implementation of AE sensors for real-time monitoring in lab simulated conditions demonstrates their effectiveness in detecting wear-related signals and supporting proactive measures to prevent wear failures. This unique method of monitoring and predicting wear progression using AE and ML in the UHMWPE-CoCrMo pairing enhances understanding of wear mechanisms in UHMWPE. This knowledge can guide the development of more reliable and durable prosthetic joint designs人体关节假体因超高分子量聚乙烯（Ultra-High-Molecular-Weight Polyethylene, UHMWPE）与钴铬钼（Cobalt-Chromium-Molybdenum, CoCrMo）之间复杂的摩擦学接触，面临磨损及相关失效机制问题。本研究围绕磨损机制展开探究，长期目标为预测人体关节假体的失效率。研究团队在模拟体内真实环境的介质中开展多方向销盘试验，以分析UHMWPE销对磨CoCrMo盘的磨损行为。本研究的目标在于深入解析磨损机制并对磨损率进行分类。实验采用声发射（Acoustic Emission, AE）传感器实现实时磨损监测，在230余万次循环周期内，于每周目视检查前后采集声学信号，该方案可实现连续的磨损数据采集与磨损进展评估。研究证实，集成AE传感器可有效检测磨损相关信号，提升磨损进展检测能力，辅助失效预判。本研究提出了一种基于两种机器学习（Machine Learning, ML）框架的人体关节假体磨损进展监测新方法。第一种框架为基于人工先验知识，从声学特征中手动提取时域、频域及时频域特征，随后采用逻辑回归、支持向量机、k近邻、随机森林、神经网络以及极端梯度提升等ML分类器开展磨损分类任务，平均准确率可达81%~89%。第二种框架则引入了结合圆损失函数的基于对比学习的卷积神经网络（Convolutional Neural Network, CNN），以提升磨损分类性能。该框架先由CNN从声学特征中提取特征图，再利用特征图重训练ML分类器。相较于手动特征提取方法，该方案的分类性能更优，准确率可达94%~96%。机器学习技术可实现精准的磨损分类，优化人体关节假体的磨损进展评估。基于对比学习CNN的自动特征提取可更好地解析磨损模式，增强ML分类器的预测能力。该方法可提升磨损相关失效的早期检测效率，实现及时的临床干预。在实验室模拟环境中，AE传感器实时监测方案的成功应用，证实了其在检测磨损相关信号、支撑主动预防磨损失效措施中的有效性。这种结合AE与ML的UHMWPE-CoCrMo配对副磨损进展监测与预测的独特方法，可加深对UHMWPE磨损机制的理解，为研发更可靠、更耐用的人工关节假体设计提供理论指导。