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In this paper a decentralized model-free method for real-time fault diagnosis applicable in robotic systems has proposed. This method can discriminate any predefined fault type as well as novel faults in actuators and robot components; localization and decreasing of computational burden is guaranteed by distribution of diagnostic agents and their interactions with supervisory system. The generalization...
Diagnosing a system fault before it deteriorates the system performance is crucial for the reliability and safety of many industrial systems. This paper develops an online decentralized fault isolation system for a complex chemical process which has several diagnosing agents for different areas of the process. Each diagnostic agent based on multiple adaptive neuro-fuzzy inference system (ANFIS) assigned...
This paper proposes a novel method based on multiple adaptive neuro-fuzzy in combination of statistic method to detect and diagnose the faults occurring in complex dynamical systems. The basic idea is to use PCA to extract the features for reducing the complexity of the data achieved from a process. The most superior features are fed into multiple ANFIS to identify different faulty conditions in order...
In this paper, an online fault diagnosis for a complex dynamical systems integrating adaptive neuro-fuzzy inference system (ANFIS) and using independent component analysis (ICA) for feature extracting is presented. In this approach, using ICA provide salient features selected from raw measured data sets. Subsequently, the most superior extracted features are fed into multiple ANFIS in order to identify...
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