Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques
One-class svms for document classification
The Journal of Machine Learning Research
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Brief paper: Nonlinear robust fault reconstruction and estimation using a sliding mode observer
Automatica (Journal of IFAC)
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
ACM Computing Surveys (CSUR)
On Preprocessing Multi-channel Sensor Data for Online Process Monitoring
CIMCA '08 Proceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information Sciences: an International Journal
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools
SparseFIS: data-driven learning of fuzzy systems with sparsity constraints
IEEE Transactions on Fuzzy Systems
Fault-Diagnosis Applications: Model-Based Condition Monitoring Actuators, Drives, Machinery, Plants, Sensors, and Fault-tolerant Systems
Combining Uncertainty Sampling methods for supporting the generation of meta-examples
Information Sciences: an International Journal
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We propose a residual-based approach for fault detection at rolling mills based on data-driven soft computing techniques. It transforms the original measurement signals into a model space by identifying the multi-dimensional relationships contained in the system. Residuals, calculated as deviations from the identified relations and normalized with the model uncertainties, are analyzed on-line with incremental/decremental statistical techniques. The identification of the models and the fault detection concept are conducted solely based on the on-line recorded data streams. Thus, neither annotated samples nor fault patterns/models, which are often very time-intensive and costly to obtain, need to be available a priori. As model architectures, we used pure linear models, a new genetic variant of Box-Cox models (termed as Genetic Box-Cox) reflecting weak non-linearities and Takagi-Sugeno fuzzy models being able to express more complex non-linearities, which are trained with sparse learning techniques. This choice gives us a clue about the degree of non-linearity contained in the system. Our approach is compared with several state-of-the-art approaches including a PCA-based approach, a univariate time-series analysis, a one-class SVM (fault-free) pattern recognizer in the signal space and a combined approach based on time-series model parameter changes.