  Sensor signals acquired in the industrial process contain rich information
which can be analyzed to facilitate effective monitoring of the process, early
detection of system anomalies, quick diagnosis of fault root causes, and
intelligent system design and control. In many mechatronic systems, multiple
signals are acquired by different sensor channels (i.e. multi-channel data)
which can be represented by high-order arrays (tensorial data). The
multi-channel data has a high-dimensional and complex cross-correlation
structure. It is crucial to develop a method that considers the
interrelationships between different sensor channels. This paper proposes a new
process monitoring approach based on uncorrelated multilinear discriminant
analysis that can effectively model the multi-channel data to achieve a
superior monitoring and fault diagnosis performance compared to other competing
methods. The proposed method is applied directly to the high-dimensional
tensorial data. Features are extracted and combined with multivariate control
charts to monitor multi-channel data. The effectiveness of the proposed method
in quick detection of process changes is demonstrated with both the simulation
and a real-world case study.
