A neural network based fault detection and identification scheme for pneumatic process control valves

Karpenko, Mark; Sepehri, Nariman

doi:10.1109/icsmc.2001.969794

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Existen algunos trabajos enfocados a buscar la mejor configuración de la ANN. Por ejemplo, Karpenko and Sepehri (2001) realizaron el diagnóstico fallas en una válvula neumática. Ellos propusieron emplear dos capas ocultas con ocho y cuatro neuronas en cada una y usaron la tangente hiperbólica como función de activación.…”

Section: Red Neuronal Artificial Para Fdiunclassified

Diagnóstico de fallas mediante una LSTM y una red elástica

Márquez-Vera

López-Ortega

Ramos-Velasco

et al. 2021

Rev. iberoam. autom. inform. ind.

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El diagnóstico de fallas es importante en los procesos industriales, ya que permite determinar si es necesario detener el proceso en operación y/o proponer un plan de mantenimiento. En el presente trabajo se comparan dos estrategias para diagnosticar fallas. La primera realiza un preprocesamiento de datos usando el análisis de componentes independientes para reducir la dimensión de los datos, posteriormente, se emplea la transformada wavelet para resaltar las señales de falla, con esta información se alimenta una red neuronal artificial. Por su parte, la segunda estrategia, principal contribución de este trabajo, usa una memoria de corto y largo plazo. Esta memoria es alimentada por las variables más significativas seleccionadas mediante una red elástica para usar tanto la norma $L_1$ como la $L_2$. Como ejemplo de aplicación se utilizó el proceso químico Tennessee Eastman, un proceso ampliamente usado en el diagnóstico de fallas. El aislamiento de fallas mostró mejores resultados con respecto a los reportados en la literatura.

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Section: Red Neuronal Artificial Para Fdiunclassified

Diagnóstico de fallas mediante una LSTM y una red elástica

Márquez-Vera

López-Ortega

Ramos-Velasco

et al. 2021

Rev. iberoam. autom. inform. ind.

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“…It means that the model does not provide forecasting values between and . To solve this drawback, several models have to be built at the same time, as shown in figure 3a and in equation (5). It results in a longer algorithm duration.…”

Section: B Artificial Intelligence Tools For Prognosticsmentioning

confidence: 99%

“…The applicability of this kind of approach is however dependent on the representativeness of past experiences that do not always reflect current conditions. 3) Data-driven prognostics aim at transforming real monitoring data into relevant indicators and trends that depict the health of a system [5], [6]. These approaches are a tradeoff between model-based and experience-based approaches.…”

Section: Introductionmentioning

confidence: 99%

Fuel Cells prognostics using echo state network

Morando

Jemeï

Gouriveau

et al. 2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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One remaining technological bottleneck to develop industrial Fuel Cell (FC) applications resides in the system limited useful lifetime. Consequently, it is important to develop failure diagnostic and prognostic tools enabling the optimization of the FC. Among all the existing prognostics approaches, datamining methods such as artificial neural networks aim at estimating the process' behavior without huge knowledge about the underlying physical phenomena. Nevertheless, this kind of approach needs huge learning dataset. Also, the deployment of such an approach can be long (trial and error method), which represents a real problem for industrial applications where realtime complying algorithms must be developed. According to this, the aim of this paper is to study the application of a reservoir computing tool (the Echo State Network) as a prognostics system enabling the estimation of the Remaining Useful Life of a Proton Exchange Membrane Fuel Cell. Developments emphasize on the prediction of the mean voltage cells of a degrading FC. Accuracy and time consumption of the approach are studied, as well as sensitivity of several parameters of the ESN. Results appear to be very promising.

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“…Different types of techniques for Fault Detection and Isolation (FDI) of nonlinear systems were formed and could be applied to pneumatic actuator. In general, the FDI technique monitors some critical, measurable characteristics or parameters are related to the operation of the plant system [1]. When the measurable parameters deviate from their normal values, it is affirmed that a fault has occurred.…”

Section: Introductionmentioning

confidence: 99%

“…If the critical performance parameters are properly selected, there is possibility for identifying each fault. The design technique of an effective FDI system requires that: (i) a method for obtaining performance parameters correlated to the system performances, which have high information about the faults, and (ii) a decision making technique that identifies the specific fault condition pertaining to a particular set of measurable parameters [1].…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Isolation Scheme for Pneumatic Actuator Using Sugeno-Type Fuzzy Inference System

Prabakaran¹,

Kaushik²,

Mouleeshuwarapprabu³

et al. 2014

IJAREEIE

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Fault diagnosis is an ongoing significant research field due to the constantly increasing need for maintain ability, reliability and safety of industrial plants. The pneumatic actuators are installed in harsh environment: high temperature, pressure, aggressive media and vibration, etc. This influenced the pneumatic actuator predicted life time. The failures in pneumatic actuator cause forces the installation shut down and may also determine the final quality of the product. A fuzzy logic based approach is implemented to detect the external faults such as Actuator vent blockage, Diaphragm leakage and in correct supply pressure. The fuzzy system is able to identify the actuator condition with high accuracy by monitoring five parameters. The parameter selection is based on the committee of DAMADICS (Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems). The Fuzzy Inference Systems were implemented in real time using MATLAB and the results prove that the system can effectively classify all the types of external faults.

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A neural network based fault detection and identification scheme for pneumatic process control valves

Cited by 7 publications

References 8 publications

Diagnóstico de fallas mediante una LSTM y una red elástica

Diagnóstico de fallas mediante una LSTM y una red elástica

Fuel Cells prognostics using echo state network

Fault Detection and Isolation Scheme for Pneumatic Actuator Using Sugeno-Type Fuzzy Inference System

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