Ensemble clustering for fault diagnosis in industrial plants

International audienceIn this paper, we propose an unsupervised ensemble clustering approach for fault diagnosis in industrial plants. The basic idea is to combine multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final ensemble clustering (P*) is unknown. In practice, a Cluster-based Similarity Partitioning Algorithm (CSPA) is employed to quantify the co-association matrix that describes the similarity among the different base clusterings and, then, a Spectral Clustering technique embedding an unsupervised K-Means algorithm is used to find the optimum number of clusters of P* based on Silhouette validity index calculation. The identified clusters allow distinguishing different operational behaviors of the equipment. The proposed approach is verified with respect to an artificial case study representative of the signal trend behavior of an industrial equipment during shut-down operations. The obtained results have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS): the results show that the novel approach is able to identify the final ensemble clustering with a lower misclassification rate than the CSPA-METIS approach

INTEGRATED DETERMINISTIC AND PROBABILISTIC SAFETY ANALYSIS: CONCEPTS, CHALLENGES, RESEARCH DIRECTIONS

International audienceIntegrated deterministic and probabilistic safety analysis (IDPSA) is conceived as a way to analyze the evolution of accident scenarios in complex dynamic systems, like nuclear, aerospace and process ones, accounting for the mutual interactions between the failure and recovery of system components, the evolving physical processes, the control and operator actions, the software and firmware. In spite of the potential offered by IDPSA, several challenges need to be effectively addressed for its development and practical deployment. In this paper, we give an overview of these and discuss the related implications in terms of research perspectives

Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components

International audienceThe development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are 'unlabeled', i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology for the identification of transients of similar characteristics, under the conjecture that faults of the same type lead to similar behavior in the measured signals. The proposed methodology is based on the combined use of Haar wavelet transform, fuzzy similarity, spectral clustering and the Fuzzy C-Means algorithm. A procedure for interpreting the fault cause originating the similar transients is proposed, based on the identification of prototypical behaviors. Its performance is tested with respect to an artificial case study and then applied on transients originated by different faults in the pressurizer of a nuclear power reactor

A Benchmark of Dynamic Reliability Methods for Probabilistic Safety Assessment

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Component- and system-level degradation modeling of digital Instrumentation and Control systems based on a Multi-State Physics Modeling Approach

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Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems

open4We present an original computational method for the identification of prime implicants (PIs) in non-coherent structure functions of dynamic systems. This is a relevant problem for dynamic reliability analysis, when dynamic effects render inadequate the traditional methods of minimal cut-set identification. PIs identification is here transformed into an optimization problem, where we look for the minimum combination of implicants that guarantees the best coverage of all the minterms. For testing the method, an artificial case study has been implemented, regarding a system composed by five components that fail at random times with random magnitudes. The system undergoes a failure if during an accidental scenario a safety-relevant monitored signal raises above an upper threshold or decreases below a lower threshold. Truth tables of the two system end-states are used to identify all the minterms. Then, the PIs that best cover all minterms are found by Modified Binary Differential Evolution. Results and performances of the proposed method have been compared with those of a traditional analytical approach known as Quine-McCluskey algorithm and other evolutionary algorithms, such as Genetic Algorithm and Binary Differential Evolution. The capability of the method is confirmed with respect to a dynamic Steam Generator of a Nuclear Power Plant.Di Maio, Francesco; Baronchelli, Samuele; Vagnoli, Matteo; Zio, EnricoDI MAIO, Francesco; Baronchelli, Samuele; Vagnoli, Matteo; Zio, Enric

Fuzzy C-Means Clustering of Signal Functional Principal Components for Post-Processing Dynamic Scenarios of a Nuclear Power Plant Digital Instrumentation and Control System

International audienceThis paper addresses the issue of the classification of accident scenarios generated in a dynamic safety and reliability analyses of a Nuclear Power Plant (NPP) equipped with a Digital Instrumentation and Control system (I&C). More specifically, the classification of the final state reached by the system at the end of an accident scenario is performed by Fuzzy C-Means clustering the Functional Principal Components (FPCs) of selected relevant process variables. The approach allows capturing the characteristics of the process evolution determined by the occurrence, timing, and magnitudes of the fault events. An illustrative case study is considered, regarding the fault scenarios of the digital I&C system of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The results obtained are compared with those of the Kth Nearest Neighbor (KNN), and Classification and Regression Tree (CART) classifiers