Pointwise profile monitoring considering covariates based on Gaussian process

Abstract

In the intelligent manufacturing process, sensors are increasingly becoming an important means of measurement and data acquisition. Profile data from sensors are generally huge, irregular, and correlated. Meanwhile, profiles are usually affected by many external factors, which are referred to as covariates in this paper. Existing statistical process control methods are mainly based on statistical models, which have many limitations in dealing with such complex profile data. Motivated by the real life problem of industrial busbar temperature monitoring, this paper proposes a nonparametric pointwise profile monitoring scheme for unbalanced data considering time varying covariates. The developed Gaussian process-based model provides flexibility for predicting autocorrelated profiles. To incorporate time varying covariates, the Gaussian process model kernel is modified by an automatic relevance determination structure. The control chart is then constructed based on the difference between the observed and predicted profiles. Instantaneous anomalies within a profile can be detected by pointwise monitoring without waiting for the entire profile data collection. The effectiveness and applicability of the proposed monitoring scheme are validated by both simulations and a real case of busbar temperature monitoring in the workshop of an automotive manufacturer.