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This paper presents a fault detection and estimation method based on a proportional-integral observer applied to a three-degree-of-freedom mechanical crane. Faults are common in this system and can provoke oscillations that generate a loss of performance and stability. A convex linear parameter varying approach is proposed to stabilize the crane and detect and isolate actuator faults to guarantee the crane's performance. The linear matrix inequalities obtained from candidate Lyapunov functions give sufficient conditions to guarantee the fault estimation method. Finally, numerical simulations are proposed to illustrate the method's performance and applicability. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper develops a data-driven approach for incipient fault diagnosis based on ANFIS and Takagi–Sugeno (TS) interval observers. First, the nonlinear bioreactor system is identified using an adaptive neuro-fuzzy inference system (ANFIS), which results in a set of polytopic TS models derived from measurement data. Second, a bank of TS interval observers is deployed to detect sensor and process faults using adaptive thresholds. Unlike other works that require training fault data, only fault-free data is considered for ANFIS learning in this work. Fault insolation is based on residual generation and evaluated on a fault signal matrix (FSM). Parametric uncertainty and measurement noise are considered to guarantee the method's robustness. The effectiveness of the proposed method is tested on a well-known bioreactor Continuous stirred tank reactor system (CSTR) reference simulator. • The hybrid ANFIS/interval observer method diagnoses incipient faults in bioreactors. • ANFIS identifies bioreactor dynamics and makes convex Takagi–Sugeno models. • ANFIS learning is simplified using only free-fault data, enhancing model precision. • Interval observers with residual analysis achieve fault detection and isolation. [ABSTRACT FROM AUTHOR]