Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems

A novel zonotopic Kalman filter-based actuator fault detection for time delay systems

This paper addresses the problems of state estimation and actuator fault diagnosis in linear discrete time delay systems with actuator faults, and a zonotopic Kalman filter-based actuator fault detection (Z-KF-AFD) algorithm is proposed. First, the relationship between faults and states is calculated by approximating small-range noise. Next, by wrapping non-zero initial states with delays and zero initial faults within a zonotopic set, the zonotopic Kalman filter (ZKF) is iteratively derived to establish the association between current and delayed data. The optimal observer estimator gain for the optimal ZKF is then designed by minimizing the size of the zonotopic set. Subsequently, the fault zonotopic set, which links the state at the current time to the delayed state, is separated. Fault detection is achieved by determining whether zero lies within the bounds of the estimated fault zonotope. Finally, the feasibility of the proposed algorithm is validated through fault diagnosis examples involving a numerical system and a bidirectional DC-DC converter system.

Real-time isolation of physiological tremor using recursive singular spectrum analysis and random vector functional link for surgical robotics

Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms in these instruments often employ nonlinear phase prefilters to isolate the tremor signal. However, these filters introduce phase distortion in the filtered tremor, compromising accuracy. Although improved variants of recursive singular spectrum analysis (RSSA) have addressed the issue of phase distortion, they still face challenges such as reduced generalization performance, large sample delays, and longer computational times. To address these issues, we integrate an accurate and fast random vector functional link (RVFL) with RSSA, referred to as RSSA-RVFL. The proposed approach consists of two main steps: estimation using RSSA and prediction with RVFL. Additionally, we introduce two moving window variants of RSSA-RVFL for real-time implementation. These variants significantly reduce computational costs while delivering the same performance. Experimental results on real tremor data show that our proposed approach achieves an average accuracy of 79.03%, surpassing the benchmark of 70.40%, with a nine-sample delay.

Research on spatial-temporal synergistic sensor fault diagnosis method for top-blowing furnace

Top-blowing furnace systems, characterized by a large number of sensors and harsh working environments, are prone to sensor failures due to factors like component aging and external interference. These failures can significantly impact the system's safe and reliable operation. However, traditional sensor fault diagnosis methods often neglect the exploration of spatial-temporal characteristics and focus solely on learning temporal relationships between sensors, failing to effectively consider their spatial relationships. In this study, we propose a spatial correlation model based on the maximal information-based graph convolutional network (MI-GCN) by constructing a sensor network knowledge graph using maximal mutual information. The MI-GCN leverages the graph convolution mechanism to extract multi-scale spatial features and capture the spatial relationships between sensors. Additionally, we develop a spatial-temporal graph-level prediction model, known as the spatial-temporal graph transformer (STGT), to extract temporal features. By combining the spatial features extracted by the MI-GCN with the temporal features captured by the STGT, accurate predictions can be achieved. Sensor fault diagnosis is conducted by analysing the normalized residuals between the predicted values and the ground truth. Finally, the feasibility and effectiveness of the proposed method are validated using test data from a top-blowing furnace system in the nickel smelting process.

Fault-tolerant prescribed performance control of nonlinear systems with process faults and actuator failures

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach.