Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory

Basir, Otman; Yuan, Xiaohong

doi:10.1016/j.inffus.2005.07.003

Cited by 403 publications

(187 citation statements)

References 13 publications

Supporting

Mentioning

186

Contrasting

Unclassified

Order By: Relevance

“…Multi-sensor data fusion covers the problem of combining sensor data from different sources into one consistent model. The main questions of sensor fusion are [2]:…”

Section: Multi-sensor Data Fusionmentioning

confidence: 99%

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Gerdes

Galar

Scholz

2016

EiN

View full text Add to dashboard Cite

IntroductionThis paper presents a condition monitoring system with sensor optimization capabilities to prevent unscheduled delays in the aircraft industry. Unscheduled delays cost airlines a great deal of money but can be prevented by condition monitoring [11]. The aim was to develop a simple condition monitoring system that can be understood by humans and modified by experts to incorporate knowledge that is not in the learning data set, using decision trees as the main tool. Decision Gerdes M, GAlAr d, scholz d. decision trees and the effects of feature extraction parameters for robust sensor network design. eksploatacja i Niezawodnosc - Maintenance and reliability 2017; 19 (1): 31-42, http://dx.doi.org/10.17531/ein.2017.1.5. trees satisfy the requirements and provide a ranking of data sources for condition monitoring.The first section of the paper gives the motivation for developing a condition monitoring system with sensor optimization capabilities and explains the basic concepts of the proposed method. The second section explains the method in detail. Section three discusses the experiments validating it. The results of the validation experiments are given in section four. The paper concludes with a discussion of the results. New and better monitoring approaches are required for condition monitoring, because systems are becoming more complex and more difficult to monitor [32]. Condition monitoring requires reliable sensors. To obtain enough sensing data, special attention should be given to optimizing sensor allocation to ensure system diagnosability, lower sensing cost and reduce time to diagnosis [37]. Sensors can be used to determine the system health of control systems; a failed sensor can lead to a loss of process control [18]. The information about a system is incomplete, if a sensor fails. Complex systems are often monitored by multiple sensors. An advantage of a multi sensor system is that a single failed sensor shows its effects in multiple sensors [18].This means the system condition is defined by all information from the sensors. However, the system's health status becomes uncertain when a sensor fails or sends wrong data. This could trigger incorrect maintenance, including maintenance on a part with no failure, as well as long maintenance times to find the correct fault or not noticing the fault at all.The Safety Integrity Level (SIL) defines the probability that the system safety function for a Safety Instrumented System (SIS) can be executed. There are four SILs; level four is the level with the highest probability that an SIS can be performed. Sensor failure detection (sensor validation) is a critical part of the safety function of a system. When a failure is detected SIS is put into a safe state to avoid risk and damage to humans and machines [14,13].Redundancy is used to reduce the risk of model uncertainty [5]. One way to create sensor redundancy is hardware redundancy; another is analytical redundancy [5]. Analytical redundancy assumes multiple sensors deliver the same information, and,...

show abstract

“…Multi-sensor data fusion covers the problem of combining sensor data from different sources into one consistent model. The main questions of sensor fusion are [2]:…”

Section: Multi-sensor Data Fusionmentioning

confidence: 99%

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Gerdes

Galar

Scholz

2016

EiN

View full text Add to dashboard Cite

show abstract

“…DEFINITION 3. Extended D-S evidence model with importance factors: 2 are two independent evidences. Then, the combination of E 1 and …”

Section: Extended Dempster-shafer Theory Of Evidencementioning

confidence: 99%

“…In this paper, we seek a way to bridge this gap by using Dempster-Shafer mathematical theory of evidence (D-S theory), which offers an alternative to traditional probability theory for representing uncertainty [18]. D-S theory has been adopted as a valuable tool for evaluating reliability and security in information systems [19], [14] and by other engineering fields [2], where precise measurement is impossible to obtain or expert elicitation is required. D-S theory has several characteristics.…”

Section: Introductionmentioning

confidence: 99%

Risk-Aware Response for Mitigating MANET Routing Attacks

Zhao

Ahn

et al. 2010

2010 IEEE Global Telecommunications Conference GLOBECOM 2010

View full text Add to dashboard Cite

Abstract-Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naïve fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naïve fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a riskaware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factor. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of the packet delivery ratio and routing cost.

show abstract

“…DS theory can operate without priori knowledge and conditional probability, which is considered an inexact derivation of probability theory and Bayesian reasoning [2]. Hence, DS theory is applied to various smart areas, like monitoring systems [3,4], decision making [5,6], fault diagnosis [7,8], target tracking [9,10], and so forth. The wide application of DS theory has attracted a lot of scholars.…”

Section: Introductionmentioning

confidence: 99%

Improvement of DS Evidence Theory for Multi-Sensor Conflicting Information

Chen

2017

Symmetry

View full text Add to dashboard Cite

Abstract:A new DS (Dempster-Shafer) combination method is presented in this paper. As data detected by a single sensor are characterized by not only fuzziness, but also partial reliability, the development of multi-sensor information fusion becomes extremely indispensable. The DS evidence theory is an effective means of information fusion, which can not only deal with the uncertainty and inconsistency of multi-sensor data, but also handle the inevitably ambiguity and instability under noise or possible interference. However, the application of DS evidence theory has some limitations when multi-sensor data are conflicting. To address this issue, the DS evidence theory is modified in this paper. Adopting the idea of cluster analysis, we firstly introduce the Lance distance function and spectral angle cosine function to revise original evidence separately before the combination of evidence. Then, based on the modifications of original evidence, an improved conflict redistribution strategy is ulteriorly raised to fuse multi-sensor information. Finally, the numerical simulation analyses demonstrate that the improvement of the DS evidence theory available in this paper overcomes the limitations of conventional DS evidence theory, and realizes more reliable fusion with multi-sensor conflicting information compared to the existing methods.

show abstract

Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory

Cited by 403 publications

References 13 publications

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Decision trees and the eﬀects of feature extraction parameters for robust sensor network design

Risk-Aware Response for Mitigating MANET Routing Attacks

Improvement of DS Evidence Theory for Multi-Sensor Conflicting Information

Contact Info

Product

Resources

About