An intelligent online fault diagnostic scheme for nonlinear systems

Mok, H.T.; Chan, C.W.; Yang, Zhengyi

doi:10.1007/s11768-008-6108-0

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The online implementation of these diagnosis techniques is becoming an important research topic due to the increasing demand for higher performance, efficiency, reliability and safety of system equipments. An online fault diagnostic scheme for nonlinear systems based on neurofuzzy networks was proposed (Mok et al, 2008). This scheme needed intact historical data about the process operation under various normal and faulty conditions, which are very difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

Duan¹,

Ou²

2013

RJASET

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To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. It pays special attention to meeting two challenges: model development and real-time reasoning. In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic fault tree into an equivalent Bayesian Network (BN) in order to avoid the infamous state space explosion problem. In terms of the real-time reasoning challenge, we adopt a logic compilation based inference algorithm, which compiles the BN into an arithmetic circuit and retrieves answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. Furthermore, we incorporate sensors data into fault diagnosis, cope with the sensors reliability and propose the schemes on how to update the Diagnostic Importance Factor (DIF) and the minimal cut sets. Finally, a case study is given to validate the efficiency of this method.

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Section: Introductionmentioning

confidence: 99%

A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis

Duan¹,

Ou²

2013

RJASET

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“…Li Wenzhu proposed an architecture design method of fault diagnosis system [3]. Nancy University's B. Iung proposed a method to solve the problem of remote maintenance based on Multi-agent [4], doctor KHOO L. P designed fault tree analysis method Based on rough set theory [5][6], Hing Tung Mok analyzed the fuzzy neural network nonlinear systems training strategy of online fault diagnosis, and divided into two phases, the first phase focus on the data acquisition, and the second phase train the fault knowledge [7]. Hanan L. Lutfiyya researched on the commercial distributed fault diagnosis computing environment based on rule [8].…”

Section: Introductionmentioning

confidence: 99%

Research on Online Fault Diagnosis Model Based on IoT in Process Industry

Deng

Chen

2013

AMR

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Obtaining the fault information online may eliminate many potential fatal accidents, in order to inspect and resolve the online fault diagnosis problem in process industry, an online fault diagnosis model based on IoT(Internet of Things) is proposed in the paper. the model is composed as four layer of user layer, application layer, basic technology layer and resources layer, and it can use the resources effectively such as virtual organizations, entity organization, equipment, network resources and so on, the basic technologies such as Internet of things, multiple information collection, knowledge engineering and application technologies were analyzed in the paper too. Finally, an application system is developed, and it proved the model is validity and correctness.

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