Abstract
Fault diagnosis systems are necessary in industrial plants to reach high economic profits and high levels of industrial safety. For achieving these aims, it is necessary a fast detection and identification of faults that occur in the plants. However, the performance of the fault diagnosis systems, are affected by the presence of noise and missing information on the measured variables from the industrial systems. In this paper, a novel methodology for fault diagnosis in industrial plants is proposed by using computational intelligence tools. The proposal presents a robust behavior in the presence of missing data and noise in the measurements by achieving high levels of performance. The imputation process prior to the diagnosis of failures is carried out online, this being one of the advantages.
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Ortiz Ortiz, F.J., Rodríguez-Ramos, A., Llanes-Santiago, O. (2022). A Robust Fault Diagnosis Method in Presence of Noise and Missing Information for Industrial Plants. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_4
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