Fault diagnosis of machines via parameter estimation and knowledge processing: tutorial paper
Automatica (Journal of IFAC) - Special section on fault detection, supervision and safety for technical processes
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Brief paper: Adaptive divided difference filtering for simultaneous state and parameter estimation
Automatica (Journal of IFAC)
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Sliding mode observers for fault detection and isolation
Automatica (Journal of IFAC)
New developments in state estimation for nonlinear systems
Automatica (Journal of IFAC)
A Recurrent Neural Network for Hierarchical Control of Interconnected Dynamic Systems
IEEE Transactions on Neural Networks
Fault detection and isolation for PEM fuel cell stack with independent RBF model
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
A novel framework based on the use of dynamic neural networks for data-based process monitoring, fault detection and diagnostics of non-linear systems with partial state measurement is presented in this paper. The proposed framework considers the presence of three kinds of states in a generic system model: states that can easily be measured in real time and in-situ, states that are difficult to measure online but can be measured offline to generate training data, and states that cannot be measured at all. The motivation for such a categorization of state variables comes from a wide class of problems in the manufacturing and chemical industries, wherein certain states are not measurable without expensive equipments or offline analysis while some other states may not be accessible at all. The framework makes use of a recurrent neural network for modeling the hidden dynamics of the system from available measurements and uses this model along with a non-linear observer to augment the information provided by the measured variables. The performance of the proposed method is verified on a synthetic problem as well as a benchmark simulation problem.