A novel machine learning model to design historical-independent health indicators for composite structures

Developing comprehensive health indicators (HIs) for composite structures encompassing various damage types is challenging due to the stochastic nature of damage accumulation and uncertain events (like impact) during operation. This complexity is amplified when striving for HIs independent of historical data. This paper introduces an AI...

Advanced Health Monitoring of Composite Structures Through Deep Learning-Based Analysis of Lamb Wave Data for Developing Health Indicators

A health indicator (HI) serves as an intermediary link between structural health monitoring (SHM) data and prognostic models, and an efficient HI should meet prognostic criteria, i.e., monotonicity, trendability, and prognosability. However, designing a proper HI for composite structures is a challenging task due to the complex damage...

Comparative Study on Semi-Supervised Learning Applied for Anomaly Detection in Hydraulic Condition Monitoring System

Condition-based maintenance is becoming increasingly important in hydraulic systems. However, anomaly detection for these systems remains challenging, especially since that anomalous data is scarce and labeling such data is tedious and even dangerous. Therefore, it is advisable to make use of unsupervised or semi-supervised methods, especially...

A semi-supervised autoencoder framework for joint generation and classification of breathing

Background and objective: One of the main problems with biomedical signals is the limited amount of patient-specific data and the significant amount of time needed to record the sufficient number of samples needed for diagnostic and treatment purposes. In this study, we present a framework to simultaneously generate and classify biomedical...

Partial discharges and noise classification under HVDC using unsupervised and semi-supervised learning

This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted...

A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization

Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in...

Projected estimators for robust semi-supervised classification

For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure proposed in this work does not rely on...