Mario Hoffmann

Genetic algorithms for signal grouping in sensor validation: A comparison of the filter and wrapper approaches

Sensor validation is aimed at detecting anomalies in sensor operation and reconstructing the correct signals of failed sensors, e.g. by exploiting the information coming from other measured signals. In field applications, the number of signals to be monitored can often become too large to be handled by a single validation and reconstruction model. To overcome this problem, the signals can be subdivided into groups according to specific requirements and a number of validation and reconstruction models can be developed to handle the individual groups. In this paper, multi-objective genetic algorithms (MOGAs) are devised for finding groups of signals bearing the required characteristics for constructing signal validation and reconstruction models based on principal component analysis (PCA). Two approaches are considered for the MOGA search of the signal groups: the filter and wrapper approaches. The former assesses the merits of the groups only from the characteristics of their signals, whereas the latter looks for those groups optimal for building the models actually used to validate and reconstruct the signals. The two approaches are compared with respect to a real case study concerning the validation of 84 signals collected from a Swedish boiling water nuclear power plant.

A randomized model ensemble approach for reconstructing signals from faulty sensors

On-line sensor monitoring aims at detecting anomalies in sensors and reconstructing their correct signals during operation. The techniques used for signal reconstruction are commonly based on auto-associative regression models. In full scale implementations however, the number of sensors to be monitored is often too large to be handled effectively by a single reconstruction model. In this paper we propose to tackle the problem by resorting to a pool (ensemble) of reconstruction models, each one handling an individual group of signals. This approach involves two main technical steps: firstly, a procedure for constructing signal groups, and secondly a procedure for combining the outputs of the reconstruction models associated to the groups. For the signal grouping step, a wrapper optimization search is proposed to identify the optimal number of groups in the ensemble and the size of the groups. For the model output aggregation step, a simple arithmetic average is adopted. Ensemble accuracy and robustness is achieved by promoting diversity between the signal groups through the use of the Random Feature Selection Ensemble (RFSE) technique in combination with the Bootstrapping AGGregatING (BAGGING) technique for training data selection. The individual reconstruction models are based on Principal Components Analysis (PCA). The proposed approach has been applied to a real case study concerning 215 signals monitored at a Finnish nuclear pressurized water reactor. The results obtained have been compared with those achieved by an equivalent ensemble of models based on a grouping directly optimized by a Multi-Objective Genetic Algorithm (MOGA).

Robust nuclear signal reconstruction by a novel ensemble model aggregation procedure

Monitoring of sensor operation is important for detecting anomalies and reconstructing the correct values of the signals measured. This can be done, for example, with the aid of auto-associative regression models. However, in practical applications, difficulties arise because of the need for handling large numbers of signals. To overcome these difficulties, ensembles of reconstruction models can be used. Each model in the ensemble handles a small group of signals and the outcomes of all models are eventually combined to provide the final outcome. In this work, three different methods for aggregating the model outcomes are investigated and a novel procedure is proposed for obtaining robust ensemble-aggregated outputs. Two applications are considered concerning the reconstruction of 920 simulated signals of the Swedish Forsmark-3 Boiling Water Reactor (BWR) and 215 signals measured at the Finnish Pressurised Water Reactor (PWR) situated in Loviisa.

Diagnosing faults in nuclear components by an ensemble of feature-diverse fuzzy classifiers

Ensembles of classifiers offer higher classification accuracy than single classifiers. One method for constructing an ensemble is to have the base classifiers work on different feature sets. In this paper, we present a method for selecting the feature sets of the base classifiers by means of a multi-objective genetic algorithm, aimed at maximising the classification performance and the diversity among the classifiers and at minimising the number of features in the subsets. A static voting technique is used to effectively combine the outputs of the base classifiers to construct the ensemble output. The proposed approach is applied to the classification of (simulated) transients in the feedwater system of a boiling water reactor, and the results are compared with those obtained using an optimal single classifier.

On-Line Fault Recognition System for the Analogic Channels of VVER 1000/400 Nuclear Reactors

In this paper, a method for on-line fault diagnosis of analogic channels of the VVER1000/440 monitoring systems is proposed. The method is based on the analysis of the amplitude fluctuations of electrical signals at the output of analogic channels. The advantage of this method is the possibility to perform on-line fault diagnosis of the monitoring system during the normal operation of the nuclear power plant. The method is also considered to be simple enough for practical implementation and use. The paper presents the practical results of the tests carried out to verify the method with the aim of demonstrating that the shape of the histograms of the amplitude fluctuations characterizes different types of sensor faults and component faults in the analogic channels, such as drifts, frozen or noisy signals. The parameters of the histograms of the amplitude fluctuations are used to construct a fault recognition system which is based on the Pearsons chi-square criterion for verifying the probability hypothesis of the discrete random variable.

A model-based ensemble approach to plant-wide online sensor monitoring

Online sensor monitoring aims at detecting anomalies in sensors and reconstructing their correct signals during operation. Since 1994, research at the OECD Halden Reactor Project has focused on the problem of sensor monitoring, eventually developing the PEANO system for signal validation. PEANO combines fuzzy clustering and auto-associative neural networks and has proved successful in a variety of practical applications. Nevertheless, using one single empirical model sets a limit to the number of signals that can be handled at a time. Recently, PEANO has been extended to cover the validation of all the plant signals. This has entailed shifting from a single-model to a model-ensemble approach. This paper illustrates the plant-wide extension of the PEANO system and its practical application to a real case study.