Reliability in the determination of gas turbine operating state

Spina, Pier Ruggero

doi:10.1109/cdc.2000.914203

Cited by 7 publications

(2 citation statements)

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“…State-of-art mathematical methods from hard and soft computing area are used to detect, separate and identification (Zhou, 2003) the different faults of industrial systems (Azhar, 2010) or dynamic systems (Nicholson, 1992). Several papers can be found which use Kalman filter (Spina, 2000), parity equations (Pranatyasto, 2001), principal component analyses (Zhang, 2003) for fault detection and diagnosis. Other authors use width spectrum of different artificial intelligence methods like: Bayes networks (Mehranbod, 2005), probabilistic neural networks (Jabbari, 2007), multilayer perceptron (MLP) (Zhang, 2006) or fuzzy logic (Amann, 2001).…”

Section: Introductionmentioning

confidence: 99%

Diagnoses of additive faults of serial wounded motor using artificial intelligence methods

Füvesi¹,

Kovács²

2014

RIiM

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In this paper an artificial neural network based technique will be introduce, which is capable to separate the different types of faulty state of the analysed system and generate signs to alarm the user about the failures in the system. The method can detect, separate, and identify the faults in the system. Large datasets were developed to train the separator networks. To speed up the training process of separator network, active learning method was applied. To find the separator mathematical structure weakness, a complex test process was used where the size of the different faults was varied and the performance of the structure was examined. The examination has two parts: first the appearance and termination of the faults were tested; later the estimation of the fault size was checked. The separator technique needs mathematical models of the analysed system. In our case, the models were also based on feed forward neural networks.

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Section: Introductionmentioning

confidence: 99%

Diagnoses of additive faults of serial wounded motor using artificial intelligence methods

Füvesi¹,

Kovács²

2014

RIiM

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“…For this reason, two techniques for measurement validation are presented in the paper: the first one is based on the use of acceptability bands [43,53], while the other uses statistical-based methods for outlier identification [44]. Analytical redundancy techniques for sensor fault detection and isolation can also be used [54]. In any case, if there is insufficient data or measurement accuracy is low, a "reality check" should be always made to verify plausibility of the information obtained through the GPAbased tool.…”

Section: Introductionmentioning

confidence: 99%

Gas Turbine Health State Determination: Methodology Approach and Field Application

Pinelli

Spina

Venturini

2012

International Journal of Rotating Machinery

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A reduction of gas turbine maintenance costs, together with the increase in machine availability and the reduction of management costs, is usually expected when gas turbine preventive maintenance is performed in parallel to on-condition maintenance. However, on-condition maintenance requires up-to-date knowledge of the machine health state. The gas turbine health state can be determined by means of Gas Path Analysis (GPA) techniques, which allow the calculation of machine health state indices, starting from measurements taken on the machine. Since the GPA technique makes use of field measurements, the reliability of the diagnostic process also depends on measurement reliability. In this paper, a comprehensive approach for both the measurement validation and health state determination of gas turbines is discussed, and its application to a 5 MW gas turbine working in a natural gas compression plant is presented.

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Literature Review

Lemma¹

2017

A Hybrid Approach for Power Plant Fault Diagnostics

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Reliability in the determination of gas turbine operating state

Cited by 7 publications

References 20 publications

Diagnoses of additive faults of serial wounded motor using artificial intelligence methods

Diagnoses of additive faults of serial wounded motor using artificial intelligence methods

Gas Turbine Health State Determination: Methodology Approach and Field Application

Literature Review

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