Unsupervised classification of multichannel profile data using PCA: An application to an emission control system. (August 2018)

• Record Type: Journal Article
• Authors: Pacella, Massimo
• Is Part Of: Computers & industrial engineering. Volume 122(2018)
• Page Start: 161
• Page End: 169
• Publication Date: 2018-08
• Subjects: Unsupervised classification -- Data clustering -- Principal component analysis -- Fault diagnosis; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.05.029
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Data coming from different sensors (multichannel profiles) are available. The high dimensionality of the dataset provides a challenge for clustering. Two multilinear extensions of PCA are considered, to analyze multichannel profiles. The multilinear extensions of PCA may lead to a better classification of data. Abstract: Modern sensing technologies have facilitated real-time data collection for process monitoring and fault diagnosis in several research fields of industrial engineering. The challenges associated with diagnosis of multichannel (multiple) profiles are yet to be addressed in the literature. Motivated by an application of fault diagnosis of an emission control system, this paper proposes an approach for efficient and interpretable modeling of multichannel profile data in high-dimensional spaces. The method is based on unsupervised classification of multichannel profile data provided by several sensors related to a fault event. The final goal is to isolate fault events in a restricted number of clusters (scenarios), each one described by a reference pattern. This can provide practitioners with useful information to support the diagnosis and to find root cause. Two multilinear extensions of principal component analysis (PCA), which can analyze the multichannel profiles without unfolding the original data set, are investigated and compared to regular PCA applied to vectors generated by unfolding the original data set. The effectiveness of multilinear extensions of PCA is demonstrated using an experimental campaign carried out on an emission control system. Results of unsupervised classification show that the multilinear extension of PCA may lead to a classification with better compactness and separation of clusters.