Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

In this paper, an improved fault detection method is proposed based on kernel partial least
squares (KPLS) model and generalized likelihood ratio test (GLRT) detection chart in order …

The random forest (RF) classifier, which is a combination of tree predictors, is one of the
most powerful classification algorithms that has been recently applied for fault detection and …

Incipient fault monitoring is becoming very important in large industrial plants, as the early
detection of incipient faults can help avoid major plant failures. Recently, Canonical Variate …

This article proposes a robust end-to-end deep learning-induced fault recognition scheme
by stacking multiple sparse-denoising autoencoders with a Softmax classifier, called stacked …

Determining whether a fault occurs locally or globally is highly important for large-scale
industrial processes involving multiple operating units. Moreover, the complex non-linearity …

In this work, a novel data-driven residual generation based process monitoring method is
proposed for plant-wide process systems which can be partitioned into several sub …

The dynamic kernel principal component analysis (DKPCA) has attracted significant
attention with regards to the monitoring of nonlinear and dynamic industrial processes …

Due to the high available and reliable requirements of petrochemical processes, it is critical
to develop real-time fault detection approaches with high performance. Some machine …

This study investigates the sample value imbalance problem of process monitoring. A fault
detection approach based on variable selection and support vector data description (SVDD) …