Modelling Issue in Fault Diagnosis

Patan, Krzysztof

doi:10.1007/978-3-540-79872-9_2

Cited by 9 publications

(27 citation statements)

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“…One of the approaches to build robust models is the model identification with limited value of identification error [46,47]. The approach based on determination of statistical error bounds has the greatest practical importance [7,[48][49][50][51][52][53].…”

Section: Fault Detectionmentioning

confidence: 99%

Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

Kościelny¹,

Syfert²

2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. The survey presents a selection of the methods of the fault detection and isolation suitable to be useful for the diagnostics of the complex, large scale industrial processes. The paper focuses on these methods that have appropriately high level of potential applicability in industrial practice. The novelty of the paper relies on the discussion of the dependency of the level of knowledge about diagnosed process and recommended diagnostic approaches. Appropriate recommendations were given in the convenient form of the table.

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Section: Fault Detectionmentioning

confidence: 99%

Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

Kościelny¹,

Syfert²

2014

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…Neural networks can filter noise and disturbance; they can provide a stable diagnostic, failures without traditional types of models, extremely sensitive, and economic efficiency due to insignificant computing and design effort. Another desirable feature of neural networks is that exact patterns are not required to reach the decision stage [1], [2], [4], [5], [7], [8], [11]. In a typical operation, the process model can only be approximate and critical measurements may be capable of internally crunching functional relationships that represent processes, filtering noise, and managing correlations.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, many such input/ target pairs are used to train a network, this type of neural network is called a supervised learning network. The classical mathematical model of such a neural network is described by the following equation, [1]:…”

Section: Introductionmentioning

confidence: 99%

An approach of classification and parameters estimation, using neural network, for lubricant degradation diagnosis

2018

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Abstract. This paper addresses a delicate problem, namely the diagnosis of the state of the oils in the industrial systems, namely the machine tools. Based on measurements (the data set contains over five million records), within a Machine Intelligence for Diagnosis Automation (MIDA) project funded by the National Program PN II, ERA MANUNET: NR 13081221 / 13.08.2013, several applications of MATLAB toolbars are being developed in the field of artificial intelligence, specifically using the Support Vector Machine algorithms and neural networks. The tests were carried out on several distinct situations, followed by validation and verification tests on the devices designed and developed within the project (MIDA, Monitoil).

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“…Furthermore the AR techniques can be classified as quantitative model-based methods and qualitative model-based ones. In recent years, extensive researches have been performed on quantitative model-based methods [ 18 , 19 ] and qualitative model-based methods [ 20 – 22 ]. Generally these methods can be classified as observer-based, signal processing, expert system, and artificial intelligence approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Generally these methods can be classified as observer-based, signal processing, expert system, and artificial intelligence approaches. Over the past two decades, artificial neural networks have been studied extremely by researchers and they have been used successfully for modeling and control of dynamical systems [ 20 , 23 ]. Also they were used to design FDSs [ 20 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

Talebi

Sadrnia

Darabi

2014

Computational Intelligence and Neuroscience

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Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.

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Modelling Issue in Fault Diagnosis

Cited by 9 publications

References 0 publications

Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

Application properties of methods for fault detection and isolation in the diagnosis of complex large-scale processes

An approach of classification and parameters estimation, using neural network, for lubricant degradation diagnosis

Robust Fault Detection of Wind Energy Conversion Systems Based on Dynamic Neural Networks

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