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This paper proposes an adaptive multiclass neurofuzzy classifier (MC-NFC) for fault detection and classification in solar photovoltaic (PV) systems. The designed fuzzy classifier was optimized by seeking for the best numerical values of the parameters that tune its membership functions. The experiments have been conducted on the basis of collected data from a real time PV array emulator (namely array...
This paper presents the design of a super-twisting with a high-gain observer-based state and sensor faults estimations for a continuous stirred tank reactor (CSTR). The proposed schemes transform the original system into two subsystems, the first subsystem includes the nominal plant where a high-gain observer is proposed for state estimation, whereas the second subsystem has only sensor faults where...
This paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm,...
In this paper an approach is developed for the online diagnosis of timed discrete events systems, the formalism used is the labeled timed Petri nets. We have introduced a new concept is: enriched set of possible states. In addition to online diagnosis our approach allows us to avoid the combinatorial explosion of states.
Fault detection needs to be accurate and precise to make right decisions about the systems operation status, unfortunately, monitoring processes via multivariate statistical control (MSPC) such as principal component analysis (PCA) arises the problem of false alarms. One solution to this problem is to increase the confidence intervals of the monitoring indices thresholds; however, doing that will...
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