Neural networks in process fault diagnosis

Sorsa, T.; Koivo, H.N.; Koivisto, Hannu

doi:10.1109/21.108299

Cited by 255 publications

(78 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Specialized software is used to develop all the neural networks, in this case, we have used the commercial packages provided by Neural Planner Software and Alyuda Research, LLC that allow a variety of activation functions very used in literature such as; sigmoidal function 52 , linear function 53 or hyperbolic tangent 54 . Likewise, it is also possible to use different of training algorithms, also used extensively in literature, such as Back Propagation 55 , Levenberg-Marquadt 56 , quasi-Newton algorithm 57 , etc.…”

Section: Software Packagementioning

confidence: 99%

Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

et al. 2014

View full text Add to dashboard Cite

show abstract

Section: Software Packagementioning

confidence: 99%

Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Moreover, they can be used as classifiers to perform fault detection and analysis [1]. Neural networks are known to approximate any nonlinear function and they are widely used in nonlinear (and robust) fault diagnosis problems [2][3][4][5][6][7]. They are also able to find the patterns that rule the relationships between the inputs and outputs.…”

Section: Introductionmentioning

confidence: 99%

Feature-based fault detection of industrial gas turbines using neural networks

Rasaienia¹,

Moshiri²,

Moezzi³

2013

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Gas turbine (GT) fault detection plays a vital role in the minimization of power plant operation costs associated with power plant overhaul time intervals. In other words, it is helpful in generating pre-alarms and paves the way for corrective actions in due time before incurring major equipment failures. Hence, finding an efficient fault detection technique that is applicable in the online operation of power plants involved with minor computations is an urgent need in the power generation industry. Such a method is studied in this paper for the V94.2 class of GTs. As the most leading stage for developing a feature-based fault detection system and moving from a fixed time-scheduled maintenance to a condition-based one, principal component analysis is used for dimension reduction in the sensor data space and dimensionless key features are employed instead. One healthy condition and 6 faulty conditions are used to provide a realistic data set that is used for feature extraction, training, and testing artificial neural networks. In the proposed method, multilayer perceptron (MLP) and learning vector quantization (LVQ) networks are used for the fault classification. The good performance of the LVQ networks is presented by properly selecting the network architecture and respective initial weight vectors. When comparing the results of the MLP and LVQ networks for the fault classification, the LVQ network shows better classification results.

show abstract

“…In the food area, they have been used in chemical and sensory analyses (Sorsa et al, 1991, Arteaga and NakaiI, 1993, Liu and Tan, 1999Haugen, 2001), rheology (Goshawk et al, 1998), microbiology (Lou and Nakai, 2001;Cheroutre-Vialette and Lebert, 2002), to predict the quality of food (Lange and Wittemann, 2002;Ni and Gunasekaran, 1998;Mutlu et al, 2011), in biotechnology (Glassey et al, 1994;Zhang et al, 1994;Yuan and Vanrolleghem, 1999), to monitor the authenticity of food (Da Cruz et al, 2009) and to predict the antioxidant activity and classification of tea (Cimpoiu et al, 2011), among others.…”

Section: Introductionmentioning

confidence: 99%

Neural networks to formulate special fats

et al. 2012

View full text Add to dashboard Cite

RESUMEN Utilización de redes neuronales para formular grasas técnicas.Las redes neuronales son una rama de la inteligencia artificial basadas en la estructura y funcionamiento de sistemas biológicos, teniendo como principal característica la capacidad de aprender y generalizar conocimiento. Estas son utilizadas en la resolución de problemas complejos, en los cuales los sistemas computacionales tradicionales presentan una eficiencia baja. Hasta la fecha, han sido propuestas aplicaciones para los más diversos sectores y actividades. En el área de grasas y aceites, la utilización de redes neuronales se ha concentrado principalmente en dos asuntos: la detección de adulteraciones y la formulación de productos grasos. La formulación de grasas para uso específico es el caso clásico de problema complejo donde un experto o grupo de expertos definen las proporciones de cada base, que al ser mezcladas proporcionaran características especificadas para el producto deseado. Algunos sistemas computacionales convencionales están disponibles actualmente para auxiliar a los expertos, sin embargo, estos sistemas presentan algunas deficiencias. En este artículo será descrito con detalles, un sistema para la formulación de productos grasos por redes neuronales (MIX) a partir de 3 o más componentes. Todas las etapas del desarrollo, incluyendo el diseño, construcción, entrenamiento, evaluación y operación de la red serán mostradas. PALABRAS CLAVE: Blending -Formulación -Grasas hidrogenadas -Grasas interesterificadas -Grasas técnicas -Redes neuronales. SUMMARY Neural networks to formulate special fats.Neural networks are a branch of artificial intelligence based on the structure and development of biological systems, having as its main characteristic the ability to learn and generalize knowledge. They are used for solving complex problems for which traditional computing systems have a low efficiency. To date, applications have been proposed for different sectors and activities. In the area of fats and oils, the use of neural networks has focused mainly on two issues: the detection of adulteration and the development of fatty products. The formulation of fats for specific uses is the classic case of a complex problem where an expert or group of experts defines the proportions of each base, which, when mixed, provide the specifications for the desired product. Some conventional computer systems are currently available to assist the experts; however, these systems have some shortcomings. This article describes in detail a system for formulating fatty products, shortenings or special fats, from three or more components by using neural networks (MIX). All stages of development, including design, construction, training, evaluation, and operation of the network will be outlined.

show abstract

Neural networks in process fault diagnosis

Cited by 255 publications

References 22 publications

Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

Density, viscosity and refractive index prediction of binary and ternary mixtures systems of ionic liquid

Feature-based fault detection of industrial gas turbines using neural networks

Neural networks to formulate special fats

Contact Info

Product

Resources

About