Unsupervised classification of multichannel profile data using PCA: An application to an emission control system

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.