Nuclear power industries have increasing interest in using fault detection and diagnosis
(FDD) methods to improve safety, reliability, and availability of nuclear power plants (NPP) …

In this paper, the development of data-driven design of process monitoring and fault
diagnosis (PM-FD) systems is reviewed and some recent results are presented. A major …

In this work, we propose and develop data-driven explicit state-space based fault detection,
isolation and estimation filters that are directly identified and constructed from only the …

Despite the general acknowledgment in the Fault Detection and Isolation (FDI) literature that
FDI are typically accomplished in two steps, namely residual generation and residual …

This paper deals with subspace method aided data-driven design of robust fault detection
and isolation systems. The basic idea is to identify a primary form of residual generators …

Detection of faults that appear as additive unknown input signals to an unknown LTI discrete-
time MIMO system is considered. State of the art methods consist of the following steps. First …

In order to maximize the customer satisfaction and profit as well as to obey government
regulations, the complexity and automation degree of modern industrial processes are …

Classical methods for estimating additive faults are based on state-space models, eg,
moving horizon estimation (MHE) and unknown input observers (UIOs). This paper …

In this paper, a scheme for the design of a fault-tolerant control architecture using process
data is proposed, whose core is an observer-based realization of the Youla …

This paper proposes an alternative fault detection (FD) scheme, in which the so-called
residual signals are generated by means of a projection of process input data. This is the …