A Novel Adaptive Sensor Fault Estimation Algorithm in Robust Fault Diagnosis.

The paper deals with a robust sensor fault estimation by proposing a novel algorithm capable of reconstructing faults occurring in the system. The provided approach relies on calculating the fault estimation adaptively in every discrete time instance. The approach is developed for the systems influenced by unknown measurement and process disturbance.

Towards Simultaneous Actuator and Sensor Faults Estimation for a Class of Takagi-Sugeno Fuzzy Systems: A Twin-Rotor System Application.

The paper is devoted to the problem of estimating simultaneously states, as well as actuator and sensor faults for Takagi-Sugeno systems. The proposed scheme is intended to cope with multiple sensor and actuator faults. To achieve such a goal, the original Takagi-Sugeno system is transformed into a descriptor one containing all state and fault variables within an extended state vector.

Fault diagnosis of an automated guided vehicle with torque and motion forces estimation: A case study.

The paper is devoted to developing a new fault detection scheme for an Automated Guided Vehicle (AGV) on the basis of so-called virtual sensors (VSs) which provide the information regarding the current status of a vehicle. This set contains the estimates of lateral and longitudinal forces as well as the torque. The paper proposes a novel robust VSs design scheme which yields such estimates taking into account inevitable disturbances/noise and modelling uncertainty without any knowledge about tire models used in the AGV.

A Combined H 2 / H ∞ Approach for Robust Joint Actuator and Sensor Fault Estimation: Application to a DC Servo-Motor System.

The main objective of this paper is to develop an actuator and sensor fault estimation framework taking into account various uncertainty sources. In particular, these are divided into three groups: sensor measurement noise, process-external exogenous disturbances, as well as unknown fault dynamics. Unlike the approaches presented in the literature, here they are not processed in the same way but treated separately in a suitably tailored fashion.

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system.

The paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems.

Toward the training of feed-forward neural networks with the D-optimum input sequence.

The problem under consideration is to obtain a measurement schedule for training neural networks. This task is perceived as an experimental design in a given design space that is obtained in such a way as to minimize the difference between the neural network and the system being considered. This difference can be expressed in many different ways and one of them, namely, the D-optimality criterion is used in this paper.