Among the greatest challenges in soft sensor development for bioprocesses are variable
process lengths, multiple process phases, and erroneous model inputs due to sensor faults …

This paper presents a data-driven methodology for fault detection and diagnosis (FDD) by
integrating the principal component analysis (PCA) with the Bayesian network (BN). Though …

Noise and high-dimension of process signals decrease effectiveness of those regular fault
detection and diagnosis models in multivariate processes. Deep learning technique shows …

With the increasing demand for electric vehicles, the interest in battery systems is growing. In
order to enable safe operation of these complex energy storage systems, methods of fault …

For complex industrial processes with multiple operating conditions, the traditional
multivariate process monitoring techniques such as principal component analysis (PCA) and …

A dynamic Bayesian network (DBN) based fault detection, root cause diagnosis, and fault
propagation pathway identification scheme is proposed. The proposed methodology …

The power sector in India is the most significant component of the social overhead capital
that effects directly Indian economic through growth of GDP. Since last four decades …

In this paper, a novel data projection method, local and global principal component analysis
(LGPCA) is proposed for process monitoring. LGPCA is a linear dimensionality reduction …

A method of Bayesian belief network (BBN)-based sensor fault detection and identification is
presented. It is applicable to processes operating in transient or at steady-state. A single …

A novel networked process monitoring, fault propagation identification, and root cause
diagnosis approach is developed in this study. First, process network structure is determined …