Online detection and prognosis are very important for the safe operation of flue gas turbines. Compared with univariate monitoring of the process, multivariate process monitoring is more effective and can capture abnormal situation in the early stage. This paper proposes a new multivariate fault prognosis framework for the flue gas turbine with a hidden fault process based on independent compon...

Historically, actuators’ redundancy was used to deal with faults occurring suddenly in flight systems. This technique was generally expensive, time consuming and involves increased weight and space in the system. Therefore, nowadays, the on-line fault diagnosis of actuators and accommodation plays a major role in the design of avionic systems. These approaches, known as Fault Tolerant Flight Co...

We extend existing theory on robust nonlinear observer design to the class of nonlinear Lipschitz systems where the systems are subject to sensor faults and disturbances. The designed observer is used for robust reconstruction of fault signals. Allowing bounded unknown disturbances to model system uncertainties, it is shown that by adjusting a design parameter we can trade off between fault rec...

This paper focuses on the principle for designing reduced-order fuzzy-observer-based actuator fault reconstruction for a class of nonlinear systems. The problem addressed can be indicated as an approach for a kind of reduced-order fuzzy observer design with special gain matrix structure that depends on a given matching condition specification. Using the Lyapunov theory, the stability conditions...

Contribution analysis in multivariate statistical process monitoring (MSPM) identifies the most responsible variables to the detected process fault. In multivariate contribution analysis, the main challenge of fault isolation is to determine the appropriate variables to be analysed and this usually results in a combinatorial optimisation problem. Reconstruction-based multivariate contribution a...

The traditional approaches for condition monitoring of roller bearings are almost always achieved under Shannon sampling theorem conditions, leading to a big-data problem. The compressed sensing (CS) theory provides a new solution to the big-data problem. However, the vibration signals are insufficiently sparse and it is difficult to achieve sparsity using the conventional techniques, which imp...

The work presented in this paper has been performed under a Spanish research project. The main aim of the tasks, we were responsible of, was the development of a vision subsystem for 2D image preprocessing. These algorithms are the first step of a 3D reconstruction algorithm. These algorithms have been improved by the addition of fault tolerance capabilities. To achieve this goal, the classical...