Abnormality localization for industrial processes described by distributed parameter systems
(DPSs) is vital but seldom studied to the best of our knowledge. In this paper, a systematic …

This paper presents a new model-based fault detection and failure prediction framework for
a class of multi-input and multi-output (MIMO) nonlinear distributed parameter systems …

Fault detection for distributed parameter processes (heat processes, fluid processes, etc.) is
vital for safe and efficient operation. On one hand, the existing data-driven methods neglect …

A novel data-driven approach is proposed to localize the abnormality for distributed
parameter systems (DPSs) in this paper. The cross-correlation order of DPSs in the space …

In this paper, a novel method is developed to detect fault and identify its spatial location for a
class of parabolic distributed parameter systems (DPSs) with limited sensors. The normal …

Unknown distributed incipient thermal fault detection and localization are vital to the safe
operation of batteries while they have not been given sufficient attention in existing works …

This paper presents a flatness-based approach to the fault diagnosis for a large class of
parabolic and biharmonic distributed-parameter systems (DPS). The inputs, outputs and …

This paper is concerned with the fault diagnosis problem for general linear heterodirectional
hyperbolic ODE–PDE systems. A systematic solution is presented for additive time-varying …

Identification of abnormal source hidden in distributed parameter systems (DPSs) belongs to
the category of inverse source problems. It is important in industrial applications but seldom …

In this paper, we propose a simultaneous state estimation and fault estimation approach for
a class of first‐order hyperbolic partial integral differential equation systems. Specifically, we …