Statistical Process Monitoring with Biogeography-Based Optimization Independent Component Analysis

Xiangshun Li,Cheng Lei,Zhiang Li,Di Wei,Wenlin Wang

doi:10.1155/2018/1729612

Xiangshun Li, Cheng Lei + Show 3 more

Open Access

https://doi.org/10.1155/2018/1729612

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Abstract

Independent Component Analysis (ICA), a type of typical data-driven fault detection techniques, has been widely applied for monitoring industrial processes. FastICA is a classical algorithm of ICA, which extracts independent components by using the Newton iteration method. However, the choice of the initial iterative point of Newton iteration method is difficult; sometimes, selection of different initial iterative points tends to show completely different effects for fault detection. So far, there is still no good strategy to get an ideal initial iterative point for ICA. To solve this problem, a modified ICA algorithm based on biogeography-based optimization (BBO) called BBO-ICA is proposed for the purpose of multivariate statistical process monitoring. The Newton iteration method is replaced with BBO here for extracting independent components. BBO is a novel and effective optimization method to search extremes or maximums. Comparing with the traditional intelligent optimization algorithm of particle swarm optimization (PSO) and so on, BBO behaves with stronger capability and accuracy of searching for solution space. Moreover, numerical simulations are finished with the platform of DAMADICS. Results demonstrate the practicability and effectiveness of BBO-ICA. The proposed BBO-ICA shows better performance of process monitoring than FastICA and PSO-ICA for DAMADICS.

Highlights

Industrial processes play an extremely significant role in the development of economy
When the FastICA algorithm is applied to process monitoring, different initial iterative points would lead to opposite result of diagnosis
Compared with the performance of process monitoring of FastICA and biogeography-based optimization (BBO)-Independent Component Analysis (ICA), it can be concluded that BBO-ICA shows higher fault detection rate (FDR) and lower false alarm rate (FAR) for most of faults

Summary

Introduction

Industrial processes play an extremely significant role in the development of economy. Process monitoring technologies can be divided into three types: model-based, knowledge-based, and data-driven method [1]. The classical FastICA algorithm based on Newton iteration method is employed to extract independent components [13]. When the FastICA algorithm is applied to process monitoring, different initial iterative points would lead to opposite result of diagnosis. To some extent, the result of diagnosis of FastICA algorithm is unreliable because of sensitivity of initial iterative point. A method named BBO-ICA is proposed in this paper to monitor processes by replacing the Newton iteration method for independent components extraction by BBO and achieve more accurate results.

Process Monitoring Based on ICA

BBO-ICA

Simulations

Conclusion