Linguistic OWA and two time-windows based fault identification in wide plants

Abstract

Fault detection and diagnosis in industrial processes are challenging tasks that demand effective and timely decision making procedures. The multivariate statistical approaches for fault detection based on data have been very useful. However, they are known to be less powerful for fault diagnosis because they normally require prior knowledge of the problem involved. In this context, this proposal is based on an on-line, distributed fault isolation approach to provide a scored rank of variables considered as responsible for the faults in a more robust and earlier way than usual approaches. The fault isolation is carried out

Identification of natural rubber samples for high-voltage insulation applications

“…PCA ranks the PCs from highest to lowest variance [49], and only retains the first PCs that explain a suitable amount of the total variance, so the remaining ones are disregarded to avoid overfitting [50]. References [45] and [46,51] detail the mathematical background of CVA and PCA, respectively.…”

A Data Fusion Fault Diagnosis Method Based on LSTM and DWT for Satellite Reaction Flywheel

“…is makes the model include a wide range of particular cases so that it can effectively handle the various uncertainties in knowledge acquisition and representation. Sánchez-Fernández et al [31] achieved fault identification based on a scored ranking at two time points: early fault and steady fault. In each case, the OWA linguistic operator based on the regular increasing monotone (RIM) function can find the variables that are responsible for the fault.…”

Métodos de detección y diagnóstico de fallos mediante aproximaciones distribuidas: modelos, métodos y computación

“…Este método propuesto, denominado ORAFI (en inglés, OWA-RIM aggregation based Fault Diagnosis) [Sánchez-Fernández et al, 2018e] consiste en un método de identificación de fallos que realiza una agregación de las diagnosis de 7 métodos de identificación ampliamente usados en los últimos años, como son: Contribuciones de las variables a T 2 [Kourti and MacGregor, 1996] y ϕ [Alcala and Qin, 2009], Error normalizado de la variables [Kourti and MacGregor, 1996], Índices de reconstrucción (para T 2 , Q y ϕ) [Alcala and Qin, 2009], y Diagramas de contribución modificados , a través, de un operador lingüístico (OWA-RIM) usado como un método de toma de decisión multicritero, que además se puede sintonizar por parte del usuario dependiendo del riesgo que quiera asumir al tomar la decisión.…”