This paper presents the design of a fault detection and diagnosis system for a quadrotor unmanned aerial vehicle under partial or total actuator fault. In order to control the quadrotor, the dynamic system is divided in two subsystems driven by the translational and the rotational dynamics, where the rotational subsystem is based on a linear parameter-varying model. A robust linear parameter-varying observer applied to the rotational subsystem is considered to detect actuator faults, which can occur as total failures (loss of a propeller or a motor) or partial faults (degradation). Furthermore, fault diagnosis is done by analyzing the displacements of the roll and pitch angles. Numerical experiments are carried out in order to illustrate the effectiveness of the proposed methodology.

This work proposes a proportional-integral fault estimation observer for nonlinear systems by means of convex structures. Unlike the traditional approaches that consider quadratic Lyapunov functions, this work employs nonquadratic ones. As a result, less conservative conditions in the form of linear matrix inequalities are obtained. Furthermore, an $H_{\mathrm{\infty }}$ performance is employed in order to ensure robustness against sensor noise and disturbances; thus, the designed observer estimates both the state and the actuator faults even in the presence of disturbances. The performance of the proposed methodology is illustrated through a numerical example and a three-tank hydraulic system.

Object tracking is the process of estimating in time N the location of one or more moving element through an agent (camera, sensor, or other perceptive device). An important application in object tracking is the analysis of animal behavior to estimate their health. Traditionally, experts in the field have performed this task. However, this approach requires a high level of knowledge in the area and sufficient employees to ensure monitoring quality. Another alternative is the application of sensors (inertial and thermal), which provides precise information to the user, such as location and temperature, among other data. Nevertheless, this type of analysis results in high infrastructure costs and constant maintenance. Another option to overcome these problems is to analyze an RGB image to obtain information from animal tracking. This alternative eliminates the reliance on experts and different sensors, yet it adds the challenge of interpreting image ambiguity correctly. Taking into consideration the aforementioned, this article proposes a methodology to analyze lamb behavior from an approach based on a predictive model and deep learning, using a single RGB camera. This method consists of two stages. First, an architecture for lamb tracking was designed and implemented using CNN. Second, a predictive model was designed for the recognition of animal behavior. The results obtained in this research indicate that the proposed methodology is feasible and promising. In this sense, according to the experimental results on the used dataset, the accuracy was 99.85% for detecting lamb activities with YOLO${}_{V4}$, and for the proposed predictive model, a mean accuracy was 83.52% for detecting abnormal states. These results suggest that the proposed methodology can be useful in precision agriculture in order to take preventive actions and to diagnose possible diseases or health problems.

This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is achieved by comparing zonotopic sets and evaluating a fault signature matrix. The method accounts for parametric uncertainty and measurement noise, ensuring robustness. Experimental validation on a hydraulic pipeline demonstrated high precision (up to 99.24%), recall (up to 99.20%), and low false positive rates (as low as 0.76%) across various fault scenarios and operational points.

This paper presents a fault diagnosis (FDD) approach based on a Takagi-Sugeno Unknown Input Observer (TS-UIO) that allows for estimation of the states of an active pitch system for a studied wind turbine even in the presence of unknown interference factors. A scheme for FDD is proposed based on the residuals evaluation between the non-linear model of the active pitch system and the Takagi-Sugeno unknown input observer proposed for the detection and isolation of faults in sensors with measurable premise variables. The proposed TS-UIO State Observer is resilient to disturbances and measurement noise due to its unique feature of decoupling unknown inputs, interruptions or undefined factors that affect the behavior of the system under study. This study investigates the effect of load-induced stress on the mechanical blades of a wind turbine, caused by the wind force considered as an unknown disturbance or input to the system given its dependence on weather conditions. The proposed FDD algorithm includes Linear Matrix Inequalities (LMI) ensuring the estimation error dynamics approximates to zero. Successful implementation tests are demonstrated in an active pitch system with reference parameters based on a wind turbine model. The review outlines traditional FDD approaches, including those based on nonlinear models, as well as relatively new methods based on linear sector conditions. Special attention is given to Takagi-Sugeno (TS) methods.