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

Baraldi, Piero; Zio, Enrico; Gola, Giulio; Roverso, Davide; Hoffmann, Mario

doi:10.1243/1748006xjrr137

Cited by 10 publications

(17 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Properties individually related to a group are, for example, the mutual information content of the signals in the group and the reconstruction performances of the associated model [11,12,16]; global properties of the groups ensemble are, for example, the diversity among the groups and a good redundancy of the signals in the ensemble, i.e., an adequate number of diverse groups containing a same signal [2,10,17,18].…”

Section: Grouping Signals For Diversity and Optimal Ensemble Performancementioning

confidence: 99%

“…by maximizing the correlation of the signals in the groups [11,[16][17][18] and minimizing their reconstruction errors [12]. MOGA approaches have however shown some limitations in guaranteeing the mentioned global ensemble properties, e.g.…”

Section: Grouping Signals For Diversity and Optimal Ensemble Performancementioning

confidence: 99%

“…Indeed, RFSE is a completely random technique in which no optimization of the composition of the individual groups is sought, i.e. no relevance is given, for example, to the correlation between the signals in the groups (as in the filter MOGA search presented in [11,[16][17][18]) or to their capability of efficiently reconstructing one another (as in the wrapper MOGA search presented in [12]). The coverage of all the signals is not a priori guaranteed by the random nature of the RFSE approach itself; nevertheless, since the probability that a signal does not appear in any group is As previously mentioned, an optimal signal grouping structure should ensure on one side a good signal redundancy in the groups (i.e., for any signal there is a suitable number of groups containing it) but, on the other side, a diverse composition of the groups in terms of the signals contained.…”

Section: Injecting and Verifying (Input) Diversity In The Signal Groumentioning

confidence: 99%

“…The procedure is tailored for realistic applications where the number of measured signals is too large to be handled effectively by a single reconstruction model [2,[10][11][12]. The approach is based on the subdivision of the set of signals into overlapping groups, the development of a reconstruction model for each group of signals and the combination of the outcomes of the models within an ensemble approach [13][14][15][16][17][18][19][20][21][22] (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…(i) the groups size: groups made of a reasonably small number of signals are both more accurate and easier to handle [10][11][12][16][17][18];…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A randomized model ensemble approach for reconstructing signals from faulty sensors

Baraldi¹,

Gola²,

Zio³

et al. 2011

Expert Systems with Applications

View full text Add to dashboard Cite

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).

show abstract

Section: Grouping Signals For Diversity and Optimal Ensemble Performancementioning

confidence: 99%

Section: Grouping Signals For Diversity and Optimal Ensemble Performancementioning

confidence: 99%

Section: Injecting and Verifying (Input) Diversity In The Signal Groumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(i) the groups size: groups made of a reasonably small number of signals are both more accurate and easier to handle [10][11][12][16][17][18];…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A randomized model ensemble approach for reconstructing signals from faulty sensors

Baraldi¹,

Gola²,

Zio³

et al. 2011

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control

Baraldi¹,

Cammi²,

Mangili³

et al. 2010

Annals of Nuclear Energy

View full text Add to dashboard Cite

To efficiently control a process, accurate sensor measurements must be provided of the signals used by the controller to decide which actions to actuate in order to maintain the system in the desired conditions. Noisy or faulty sensors must, then, be promptly detected and their signals corrected in order to avoid wrong control decisions. In this work, sensor diagnostics is tackled within an ensemble of Principal Component Analysis (PCA) models whose outcomes are aggregated by means of a local fusion (LF) strategy. The aggregated model thereby obtained is used for both the early detection and identification of faulty sensors, and for correcting their measured values. The fault detection decision logic is based on the Sequential Probability Ratio Test (SPRT). The proposed approach is demonstrated on a simulated case study concerning the pressure and level control in the pressurizer of a Pressurized Water Reactor (PWR). The obtained results show the possibility to achieve an adequate control of the process even when a sensor failure occurs

show abstract

Signal reconstruction by a GA-optimized ensemble of PCA models

Baraldi

Zio

Gola

et al. 2011

Nuclear Engineering and Design

View full text Add to dashboard Cite

On-line sensor monitoring allows detecting anomalies in sensor operation and reconstructing the correct signals of failed sensors by exploiting the information coming from other measured signals. In field applications, the number of signals to be monitored is often too large to be handled effectively by a single reconstruction model. A more viable approach is that of decomposing the problem by constructing a number of reconstruction models, each one handling an individual group of signals. To apply this approach, two problems must be solved: 1) the optimal definition of the groups of signals and 2) the appropriate combination of the outcomes of the individual models. With respect to the first problem, in this work, Multi-Objective Genetic Algorithms (MOGAs) are devised for finding the optimal groups of signals used for building reconstruction models based on Principal Component Analysis (PCA). With respect to the second problem, a weighted scheme is adopted to combine appropriately the signal predictions of the individual models. The proposed approach is applied to a real case study concerning the reconstruction of 84 signals collected from a Swedish nuclear boiling water reactor.

show abstract

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

Cited by 10 publications

References 26 publications

A randomized model ensemble approach for reconstructing signals from faulty sensors

A randomized model ensemble approach for reconstructing signals from faulty sensors

An ensemble approach to sensor fault detection and signal reconstruction for nuclear system control

Signal reconstruction by a GA-optimized ensemble of PCA models

Contact Info

Product

Resources

About