Linear dependence of variables is a commonly used assumption in most diagnostic systems for which many robust methodologies have been developed over the years. In case the system nonlinearities are relevant, fault diagnosis methods, relying on the assumption of linearity, might potentially provide unsatisfactory results in terms of false alarms and missed detections. In recent years, many authors have proposed machine learning (ML) techniques to improve fault diagnosis performance to mitigate this problem. Although very powerful, these techniques require faulty data samples that are representative of any fault scenario. Additionally, ML techniques suffer from issues related to overfitting and unpredictable performance in regions which are not fully explored in the training phase. This paper proposes a non-linear additive model to characterize the non-linear redundancy relationships among the system signals. Using the multivariate adaptive regression splines (MARS) algorithm, these relationships are identified directly from the data. Next, the non-linear redundancy relationships are linearized to derive a local time-dependent fault signature matrix. The faulty sensor can then be isolated by measuring the angular distance between the column vectors of the fault signature matrix and the primary residual vector. A quantitative analysis of fault isolation and fault estimation performance is performed by exploiting real data from multiple flights of a semi-autonomous aircraft, thus allowing a detailed quantitative comparison with state-of-the-art machine-learning-based fault diagnosis algorithms.

An accurate and reliable assessment of muscle fatigue is crucial for understanding its underlying mechanisms, monitoring training adaptations, evaluating rehabilitation progress, and optimizing performance in sports and occupational settings. Over the years, numerous methods and metrics have been developed to quantify and characterize muscle fatigue. This paper comprehensively reviews the various assessment techniques used to measure muscle fatigue, encompassing physiological and functional perspectives based on questionnaires, biosignals, and robotics interfaces. By critically evaluating the strengths, limitations, and applications of these assessment methods, this article aims to provide researchers, clinicians, and practitioners with valuable information for selecting appropriate tools and interpreting the results in the context of research on muscle fatigue. In addition, it highlights emerging technologies and innovative approaches that hold promise in advancing our understanding of muscle fatigue and its implications for human performance.

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.

In this paper PCA and D-PCA techniques are applied for the design of a Data Driven diagnostic Fault Isolation (FI) and Fault Estimation (FE) scheme for 18 primary sensors of a semi-autonomous aircraft. Specifically, Contributions-based, and Reconstruction-based Contributions approaches have been considered. To improve FI performance an inference mechanism derived from evidence-based decision making theory has been proposed. A detailed FI and FE study is presented for the True Airspeed sensor based on experimental data. Evidence Based Filtering (EBF) showed to be very effective particularly in reducing false alarms.

View Video Presentation: https://doi.org/10.2514/6.2022-2046.vid The linear representation of nonlinear systems dynamics has a tremendous potential to enable the estimation, prediction, and control of nonlinear systems using standard and well-known methodologies available for linear systems. Numerical algorithms such as the Extended Dynamic Mode Decomposition (EDMD) has been recently proposed to identify, from experimental data, such nonlinear transformations. The key point of these methods is that the original state space variables are lifted, via a nonlinear transformation (Koopman transformation), in an augmented space where the evolution of the lifted variables is linear. This up-and-coming framework has been applied to the identification of linear prediction models that approximate the nonlinear dynamics of an aerospace system, specifically a CubeSat. Based on this linear representation, simple LQR controllers have been designed to control the dynamics of the nonlinear CubeSat.