Fault prognosis is mainly referred to the estimation of the operating time before a failure
occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial …

Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …

Although the partial least squares approach is an effective fault detection method, some
issues of nonlinear process monitoring related to key performance indicators (KPIs) still …

Abstract Advanced Fault Detection (FD) and isolation schemes are necessary to realize the
required levels of reliability and availability and to minimize financial losses against failures …

In this article, a novel fault detection and process monitoring method referred to as artificial
neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are …

Multivariate statistical process monitoring has been widely used in industry. However,
traditional algorithms often ignore the dynamic characteristics of actual industry process …

As a multivariate statistical analysis method, canonical correlation analysis (CCA) performs
well for state monitoring of linear processes, but most industrial processes are nonlinear. To …

Fault detection has long been a hot research issue for industry. Many common algorithms
such as principal component analysis, recursive transformed component statistical analysis …

In order to detect the incipient faults of nonlinear industrial processes effectively, this paper
proposes an enhanced kernel principal component analysis (KPCA) method, called multi …

This paper proposes an air quality prediction system based on the neuro-fuzzy network
approach. Historical time series data are employed to derive a set of fuzzy rules, or …