Industrial Internet of Things (IIoT) systems connect a plethora of smart devices, such as
sensors, actuators, and controllers, to enable efficient industrial productions in manners …

Abstract Model-based methods are widely used for the fault diagnosis of stochastic dynamic
systems by simply using the input–output relationship of the system. Despite encouraging …

Using multi-source sensing data based on the Internet of Things (IoT) with artificial
intelligence and big data processing technology to achieve predictive maintenance of …

Timely and accurate fault diagnosis plays a critical role in today's smart manufacturing
practices, saving invaluable time and expenditure on maintenance process. To date …

In this paper, three model-based fault diagnosis algorithms for robotic systems are designed,
compared, simulated, and implemented. The first algorithm is based on a nonlinear adaptive …

This paper presents a digital twin-driven fault diagnosis approach based on a graphical
model and an adaptive extended Kalman filter algorithm for autonomous surface vehicles. In …

Diagnostics and prognostics have a significant role in the reliability enhancement of systems
and are focused topics of active research. Engineered systems are becoming more complex …

This paper presents an approach to improve the reliability and efficiency of autonomous
surface vessels (ASV) by leveraging predictive digital twin and state estimation techniques …

This paper introduces a new adaptive Kalman filter for nonlinear systems. The proposed
method is an adaptive version of the square-root unscented Kalman filter (Sr-UKF). The …

This article proposes an integrated approach of model-based and data-driven gap metric
fault detection and isolation in a stochastic framework. For actuator and sensor faults, an …