Difficulties in Fault-Tree Synthesis for Process Plant

Andow, P.K.

doi:10.1109/tr.1980.5220684

Cited by 34 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Основные ограничения и затруднения, присущие методу диагностирования с помощью ДО, обсуждаются в работах [62,63]. Учет неопределенности во входных данных и множественность состояний рассматриваются в работах [64][65][66][67][68].…”

Section: рис 2 множество возможных интерпретаций пнг при построенииunclassified

Математические Модели, Используемые В Задачах Диагностики Технологических Систем

Тоичкин¹,

Toichkin²,

Палюх³

et al. 2015

ППС

View full text Add to dashboard Cite

The paper deals with mathematical models used in the diagnostics of the technological system states: structurallogical model; causal model; state space model; interval parametric models. The problems of diagnostics states and control of technological safety of chemical production are discussed. It is shown that the core of modern management system of technological safety of chemical production, is a diagnostic system of chemical-technological process states and are some common features of the establishment and use of diagnostic systems for chemical-technological process. The conclusion about the need to build diagnostic systems based on chemical-technological process class of discrete models, which allow the analysis of the system availability under uncertainty information about diagnosed object.

show abstract

Section: рис 2 множество возможных интерпретаций пнг при построенииunclassified

Математические Модели, Используемые В Задачах Диагностики Технологических Систем

Тоичкин¹,

Toichkin²,

Палюх³

et al. 2015

ППС

View full text Add to dashboard Cite

show abstract

“…From Equation 6 and Table 1 it can be seen that if the gradient residual after the fault report was less than -0.0203cm/sec a leak would be verified. As this condition has been satisfied the presence of a leak in the LH auxiliary tank can be confirmed.…”

Section: Verifying Leak Arisingsmentioning

confidence: 99%

“…The digraph technique, proposed by Lapp and Powers [5], models a system using the process variables present within the system, the relationships between them and the respective influences on the variables from within and outside the system. While this allows detailed system models to be produced, the technique is limited by its lack of flexibility, issues with two-way flow and is not suited for application to phased mission systems [4] [6]. The Petri net (PN) modelling technique was devised in the PhD of Carl Petri [7].…”

Section: Introductionmentioning

confidence: 99%

A Petri Net Approach to Fault Verification in Phased Mission Systems using the Standard Deviation Technique

Lloyd

Andrews

Remenyte‐Prescott

et al. 2012

Quality & Reliability Eng

View full text Add to dashboard Cite

Health management systems are now standard aspects of complex systems. They monitor the behaviour of components and sub-systems and in the event of unexpected system behaviour diagnose faults that have occurred. Although this process should reduce system downtime it is known that health management systems can generate false faults that do not represent the actual state of the system and cause resources to be wasted. The authors propose a process to address this issue in which Petri nets are used to model complex systems. Faults reported on the system are simulated in the Petri net model to predict the resultant system behaviour. This behaviour is then compared to that from the actual system. Using the standard deviation technique the similarity of the system variables is assessed and the validity of the fault determined. The process has been automated and is tested through application to an experimental rig representing an aircraft fuel system. The success of the process to verify genuine faults and identify false faults in a multi-phase mission is demonstrated. A technique is also presented that is specific to tank leaks where depending on the location and size of the leak, the resulting symptoms will vary.

show abstract

“…There are many methods for this, such as neural networks [6], Petri nets [7,15], fault tree [8], decision tree [9], etc., but there are some deficiencies in all respects. In this paper, the classical Bayesian network learning algorithm (K2 algorithm [11]) for fault detection [10] is applied to learn the sensorless drive network structure according to the data after the feature selection and weighting.…”

Section: Introductionmentioning

confidence: 99%

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

Zhou

Ling

2017

MATEC Web Conf.

View full text Add to dashboard Cite

Abstract. Sensorless Drive Diagnosis can be used to assess the process data without the need for additional costintensive sensor technology, and you can understand the synchronous motor and connecting parts of the damaged state. Considering the number of features involved in the process data, it is necessary to perform feature selection and reduce the data dimension in the process of fault detection. In this paper, the MOEA / D algorithm based on multiobjective optimization is used to obtain the weight vector of all the features in the original data set. It is more suitable to classify or make decisions based on these features. In order to ensure the fastness and convenience sensorless drive diagnosis, in this paper, the classic Bayesian network learning algorithm-K2 algorithm is used to study the network structure of each feature in sensorless drive, which makes the fault detection and elimination process more targeted.

show abstract

Difficulties in Fault-Tree Synthesis for Process Plant

Cited by 34 publications

References 9 publications

Математические Модели, Используемые В Задачах Диагностики Технологических Систем

Математические Модели, Используемые В Задачах Диагностики Технологических Систем

A Petri Net Approach to Fault Verification in Phased Mission Systems using the Standard Deviation Technique

Fault detection Based Bayesian network and MOEA/D applied to Sensorless Drive Diagnosis

Contact Info

Product

Resources

About