Sensor Fault Detection, Diagnosis and Validation - A Survey

2012 ◽  
Vol 229-231 ◽  
pp. 1265-1271 ◽  
Author(s):  
Zhi Gang Yao ◽  
Li Cheng ◽  
Qing Lin Wang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Principal Components ◽  
Sufficient Conditions ◽  
Data Driven ◽  
Sensor Fault ◽  
Validity Index ◽  
Sensor Fault Detection ◽  
Multiple Sensor ◽  
Necessary And Sufficient

This paper provides an overview and analysis of data-driven sensor fault detection, diagnosis and validation from the application viewpoint. The typical sensor fault detection indices in the literature and the fundamental issues of necessary and sufficient conditions for detectability, reconstructability, identifiability and isolatability are analyzed. The main objective is to study the essential and important algorithms and techniques for single or multiple sensor fault diagnosis and validation. The issues of optimal principal components, sensor validity index, maximized sensitivity, as well as robust sensor fault diagnosis, etc. are discussed. Additional focuses are summarized at the end of the paper for future investigation.

Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

2020 ◽  
pp. 1-1
Author(s):  
Hossein Darvishi ◽  
Domenico Ciuonzo ◽  
Eivind Roson Eide ◽  
Pierluigi Salvo Rossi
Keyword(s):  
Fault Detection ◽  
Data Driven ◽  
Sensor Fault ◽  
Digital Twins ◽  
Modular Data ◽  
Sensor Fault Detection

Adaptive Observer Design for Sensor Fault Detection and Reconstruction

2019 ◽  
Vol 20 (9) ◽  
pp. 515-523
Author(s):  
N. Bedioui ◽  
R. Houimli ◽  
M. Besbes
Keyword(s):  
Fault Detection ◽  
Sufficient Conditions ◽  
Main Idea ◽  
Observer Design ◽  
Adaptive Observer ◽  
Linear Matrix ◽  
Sensor Fault ◽  
Sensor Fault Detection ◽  
Robust Adaptive ◽  
The Stability

A new approach is presented for sensor fault detection reconstruction and state estimation. The system considered is linear polytopic parameter-vary ing (LPV) system. The main idea is the design of a novel robust adaptive observer based on and polyquadratic formulation with a new set of relaxation. Sufficient conditions are given by a set of Linear Matrix Inequalities (LMI) in order to guarantee the stability of the system and the asymptotic convergence of the fault error. A simulation example has been studied to illustrate the proposed methods by detecting constant and variable sensor fault.

scholarly journals A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

2016 ◽  
Author(s):  
Pyung Soo Kim
Keyword(s):  
Fault Detection ◽  
Motor System ◽  
Hardware Implementation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Filter Structure ◽  
Sensor Fault Detection ◽  
Multiple Sensor ◽  
Finite Memory ◽  
Residual Generation

In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Automotive Wire Control Steering Sensor Fault Detection Algorithm Research

2015 ◽  
Vol 727-728 ◽  
pp. 708-711
Author(s):  
Zhi Ping Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Large Scale ◽  
Structural Changes ◽  
Detection Algorithm ◽  
Steering System ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Sensor Fault ◽  
Sensor Fault Detection

This article to cancel after the mechanical connections between steering wheel and steering, wire control steering system security and reliability problems, put forward on the basis of the analytical redundancy software sensor method of wire control steering system. In order to solve the compared with the traditional steering system in terms of reliability and safety of the problems of structural changes, the wire control steering system of the main sensor fault diagnosis methods are studied. In wire control steering system associated with the vehicle dynamics model is established under the premise of hypothesis testing to double adaptive fading Kalman filtering technology as a platform, combined with according to the working state of each sensors to determine fault feature vector, to build the main sensor wire control steering automobile fault diagnosis method of residual error threshold. For fault diagnosis of automobile EPS sensor, the BP neural network is put forward to EPS sensor for auto are introduced in the fault diagnosis. For large-scale wireless sensor networks (WSN), reduce the fault detection accuracy, and larger load of communication problems, according to the spatial and temporal correlation characteristics of sensor nodes, proposes a distributed sensor fault detection algorithm based on cluster. These algorithms for sensor fault detection is of great significance.

Research on PCA Based Sensor Fault Detection and its Application

2013 ◽  
Vol 404 ◽  
pp. 508-513
Author(s):  
Li Cheng ◽  
Zhi Gang Yao
Keyword(s):  
Fault Detection ◽  
Prediction Error ◽  
Data Driven ◽  
Sensor Fault ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Complex Process ◽  
Sensor Fault Detection ◽  
Multivariate Statistical Process Monitoring ◽  
Statistical Process Monitoring

This paper deals with problem related to fault detection in complex process with various sorts of sensors. Not model based but data-driven multivariate statistical process monitoring approach PCA is proposed, fault detection indices statistic and square prediction error are discussed and their complementary relationship is also presented. Availability and reliability of the method proposed in this paper is verified by experimental example.

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