A distributed fault diagnosis method based on digraph models: Steady-state analysis

Mohindra, Sanjeev; Clark, Peter

doi:10.1016/0098-1354(93)80014-e

Cited by 34 publications

(17 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this framework a fault tree or a signed digraph is often used to express the in¯uences between variables (see e.g. Iri et al 1979 or Mohindra andClark 1993). Each node of the digraph represents a variable, and the causal relationships between adjacent nodes are indicated by the sign of the connecting arc.…”

Section: Introductionmentioning

confidence: 99%

Fault identification: An approach based on propagation digraphs

Scattoloni

2000

International Journal of Systems Science

View full text Add to dashboard Cite

his paper presents a method of fault identi® cation in complex industrial processes. T he fault propagation is modelled by means of a digraph, whose nodes can represent devices of the plant or their failure modes, consistency relations or connecting elements. Under the main assumption of single faults and assuming knowledge of the minimum and maximum propagation times between adjacent nodes, the proposed algorithm allows one to reduce the time required for fault isolation and, in some cases, guarantees the identi® cation of the fault origin also in cyclic digraphs.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault identification: An approach based on propagation digraphs

Scattoloni

2000

International Journal of Systems Science

View full text Add to dashboard Cite

show abstract

“…The analytical model-based, 1-5 causal analysis, 6,7 and pattern recognition [8][9][10][11][12][13] are the main groups of fault diagnosis approaches. Chemical processes are often characterized by nonlinear behavior, noisy inputs, and unknown parameters.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Single-Variate Systems Using a Wavelet-Based Pattern Recognition Technique

Akbaryan

Bishnoi

2001

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

A pattern recognition-based methodology is presented for fault diagnosis of a single-variate and dynamic system. A group of wavelet coordinates discriminating the classes of events most efficiently among other wavelet coordinates are determined according to the linear discriminant basis (LDB) method and a principal component analysis (PCA) technique. The proposed feature extractor couples the LDB method with the double wavelet packet tree in order to determine the best configuration of pattern windows causing the most discrimination among classes. The lifting scheme-based wavelet filters are used so that the required computation time is reduced significantly without degrading the robustness of the method. To reduce the size of the feature space, the wavelet coordinates are projected into a new low-dimensional space, by using a PCA technique, where minimum correlation exists among the new space variables. The tuning of some parameters, which affect the performance of the approach, is also discussed. The feature classifier is a binary decision tree that employs a soft-thresholding scheme for recognition of a noisy input pattern. The performance of the proposed technique is examined by a classification benchmark problem, and the faults classification problems for the Tennessee Eastman process. It is observed that the proposed pattern recognition methodology succeeds satisfactorily to classify the noisy input pattern into the known classes of events.

show abstract

“…A method for distributed fault diagnosis has been developed by Mohindra and Clark (1993). The diagnostic search is conducted by a parallel scheme in which every sensor and controller is a smart node participating in the search for possible explanations of observed anomalies.…”

Section: Z3 Z3 Hazard Emulation Applicationsmentioning

confidence: 99%

“…Other approaches consider a variety of facets, e. g. distributed fault diagnosis (Mohindra and Clark 1993), providing explanations for the fault (Tarifa and Scenna 1997) and multiple fault scenarios (Vedam and Venkatasubramaniam 1997).…”

Section: Z3 Z3 Hazard Emulation Applicationsmentioning

confidence: 99%

Creating Signed Directed Graph Models for Process Plants

Palmer

Chung

2000

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

The concept of qualitative modeling is briefly described. This paper focuses on the use of signed directed graphs to model fault propagation in process plants. It is common to use a unit-based approach for modeling. Two kinds of problems, which arise when constructing unit-based qualitative models, are identified: (1) The models are difficult to construct. (2) Ambiguities due to multiple causal paths may occur when the models are combined. A method that allows models to be built simply and correctly is proposed. The method incorporates features from the knowledge acquisition field, and a computer-aided modeling tool, Equipment Model Builder, has been built to support this method. A new modular approach is presented which overcomes the problem of ambiguous inference when qualitative models are combined. Two case studies were used to evaluate the method developed and the support tool.

show abstract

A distributed fault diagnosis method based on digraph models: Steady-state analysis

Cited by 34 publications

References 11 publications

Fault identification: An approach based on propagation digraphs

Fault identification: An approach based on propagation digraphs

Fault Diagnosis of Single-Variate Systems Using a Wavelet-Based Pattern Recognition Technique

Creating Signed Directed Graph Models for Process Plants

Contact Info

Product

Resources

About