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Design of an auto-associative neural network by using design of experiments approach

  • KES 2008
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Abstract

Advanced monitoring systems enable integration of data-driven algorithms for various tasks, for e.g., control, decision support, fault detection and isolation (FDI), etc. Due to improvement of monitoring systems, statistical or other computational methods can be implemented to real industrial systems. Algorithms which rely on process history data sets are promising for real-time operation especially for online process monitoring tasks, e.g., FDI. However, a reliable FDI system should be robust to uncertainties and small process deviations, thus, false alarms can be avoided. To achieve this, a good model for comparison between process and model is needed and for easier FDI implementation, the model has to be derived directly from process history data. In such cases, model-based FDI approaches are not very practical. In this paper a nonlinear statistical multivariate method (nonlinear principal component analysis) was used for modeling, and realized with auto-associative artificial neural network (AANN). A Taguchi design of experiments (DoE) technique was used and compared with a classic approach, where according to the analysis best AANN model structure was chosen for nonlinear model. Parameters that are important for neural network’s performance have been included into a joint orthogonal array to consider interactions between noise and control process variables. Results are compared to AANN design recommendations by other authors, where obtained nonlinear model was designed for reliable fault detection of very small faults under closed-loop conditions. By using Taguchi DoE robust design on AANN, an improved and reliable FDI scheme was achieved even in case of small faults introduced to the system. The accuracy and performance of AANN and FDI scheme were tested by experiments carried out on a real laboratory hydraulic system, to validate the proposed design for industrial cases.

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Abbreviations

FDI:

Fault detection and isolation

PCA:

Principal component analysis

NLPCA:

Nonlinear PCA

AANN:

Auto-associative artificial neural network

DOE:

Design of experiments

FPE:

Final prediction error

AIC:

Information theoretic criterion

OA:

Orthogonal array

ANOVA:

Analysis of variance

S/N:

Signal to noise ratio

OPC:

OLE for process control

PLC:

Programmable logic controller

LM:

Levenberg–Marquardt backpropagation

GD:

Gradient descent backpropagation

GDX:

Gradient descent backpropagation with adaptation

Tansig:

Hyperbolic tangent sigmoid transfer function

Logsig:

Logarithmic sigmoid transfer function

Purelin:

Linear transfer function

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Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000, Maribor, Slovenia

    Božidar Bratina, Nenad Muškinja & Boris Tovornik

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  1. Božidar Bratina
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  2. Nenad Muškinja
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  3. Boris Tovornik
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Correspondence to Božidar Bratina.

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Bratina, B., Muškinja, N. & Tovornik, B. Design of an auto-associative neural network by using design of experiments approach. Neural Comput & Applic 19, 207–218 (2010). https://doi.org/10.1007/s00521-009-0287-6

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  • DOI: https://doi.org/10.1007/s00521-009-0287-6

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