Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES)...Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.展开更多
This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a var...This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.展开更多
基金supported by TATA Consultancy Services(TCS),India through TCS Research Fellowship Program
文摘Fault detection and diagnosis(FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy(AR), Principal component analysis(PCA), Discrete event system(DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters.Due to various reasons like faults, communication errors etc.,sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc.,have mechanisms to handle measurement inconsistency, however,they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent(MI)parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system(MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol(DHCP) server attack by mapping the attack to a fault in the DES framework.
文摘This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.
