Monitoring and diagnosis of multi-channel profile data based on uncorrelated multilinear discriminant analysis

Sensors are being widely used in many industrial practices and contain rich information which can be analyzed to detect system anomalies. The outputs of sensors are time-ordered data known as waveform signals, which are also called profiles. Many monitoring methods only focus on a single profile. However, multiple profiles are recorded by different sensor channels in many processes. It is crucial to study methods to analyze the multi-channel profiles. In this paper, uncorrelated multilinear discriminant analysis is suggested for fault detection and diagnosis. Then, the algorithm combined with tensor-to-tensor projection is proposed to make it get better performance in improving the accuracy of detection and reducing the fluctuation of the results in analyzing multi-channel profiles. The proposed method is applied directly to the multi-channel profiles. Discriminative and uncorrelated features are extracted, which are then fed into classifiers to identify different fault types. The effectiveness of the developed method is demonstrated by using both the simulation and a real-world case study. The real profiles in the case study are from a sensor fusion application in multiple forging operation processes, where multi-channel profiles are monitored to detect the faults of missing parts.