Fault diagnosis of multi-source signal systems is critical for the safe and reliable operation of
modern industrial systems, and accurate fault diagnosis of systems based on multi-source …

In this paper, we present a unified framework for sensor validation, which is an extremely
important module in the engine health management system. Our approach consists of …

A fault diagnosis method for multi-source sensors in complex systems was proposed. Based
on the correlation between multi-source sensor data, a convolutional neural network (CNN) …

Recently, the well-known k-nearest neighbor (kNN) rule has been successfully applied to
the fault detection of industrial processes with nonlinear, multimode, and non-Gaussian …

In industrial processes a vast variety of different sensors is increasingly used to measure
and control processes, machines, and logistics. One way to handle the resulting large …

Traditional redundancy detection methods are challenging to achieve fault detection due to
high modeling complexity. Aiming at this problem, a data-driven engine sensor fault …

Recently, advanced sensing techniques ensure a large number of multivariate sensing data
for intelligent fault diagnosis of machines. Given the advantage of obtaining accurate …

Sensor and actuator self-validation is a critical step in system control and fault diagnostics. If
sensors do not work properly, one cannot rely on their outputs to further deduce system …

This paper presents a data-driven method based on non-linear principal component
analysis to detect and isolate multiple sensor faults. The RBF-NLPCA model is obtained by …

This paper presents a systematic deterministic learning-based rapid sensor fault detection,
isolation and accommodation (SFDIA) scheme for a class of nonlinear systems with …