Flexibility Discrete Dynamic Bayesian Networks modeling and Inference algorithm

Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

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An anomaly detection technique for business processes based on extended dynamic bayesian networks

Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing - SAC '19 ◽

10.1145/3297280.3297326 ◽

2019 ◽

Cited By ~ 6

Author(s):

Stephen Pauwels ◽

Toon Calders

Keyword(s):

Anomaly Detection ◽

Bayesian Networks ◽

Business Processes ◽

Dynamic Bayesian Networks ◽

Detection Technique ◽

Extended Dynamic

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A dynamic Bayesian network approach for prognosis computations on discrete state systems

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x17712661 ◽

2017 ◽

Vol 231 (5) ◽

pp. 516-533 ◽

Cited By ~ 3

Author(s):

Josquin Foulliaron ◽

Laurent Bouillaut ◽

Patrice Aknin ◽

Anne Barros

Keyword(s):

Complex Systems ◽

Bayesian Networks ◽

Bayesian Network ◽

Multicomponent System ◽

Dynamic Bayesian Network ◽

Dynamic Bayesian Networks ◽

Remaining Useful Life ◽

Predictive Maintenance ◽

Discrete State ◽

Virtual Maintenance

The maintenance optimization of complex systems is a key question. One important objective is to be able to anticipate future maintenance actions required to optimize the logistic and future investments. That is why, over the past few years, the predictive maintenance approaches have been an expanding area of research. They rely on the concept of prognosis. Many papers have shown how dynamic Bayesian networks can be relevant to represent multicomponent complex systems and carry out reliability studies. The diagnosis and maintenance group from French institute of science and technology for transport, development and networks (IFSTTAR) developed a model (VirMaLab: Virtual Maintenance Laboratory) based on dynamic Bayesian networks in order to model a multicomponent system with its degradation dynamic and its diagnosis and maintenance processes. Its main purpose is to model a maintenance policy to be able to optimize the maintenance parameters due to the use of dynamic Bayesian networks. A discrete state-space system is considered, periodically observable through a diagnosis process. Such systems are common in railway or road infrastructure fields. This article presents a prognosis algorithm whose purpose is to compute the remaining useful life of the system and update this estimation each time a new diagnosis is available. Then, a representation of this algorithm is given as a dynamic Bayesian network in order to be next integrated into the Virtual Maintenance Laboratory model to include the set of predictive maintenance policies. Inference computation questions on the considered dynamic Bayesian networks will be discussed. Finally, an application on simulated data will be presented.

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