Failure Analysis for an Airbag Inflator by Petri Nets

Yang, Shang-Kuo; Liu, T. S.

doi:10.1002/(sici)1099-1638(199705)13:3<139::aid-qre91>3.0.co;2-p

Cited by 27 publications

(7 citation statements)

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“…Performing the procedure on either the multicomponent system as a whole or each of the components individually are both feasible. For a complicated or large system, the proposed method can be performed on those elements in minimum cut sets that are constructed by fault tree analysis or Petri net model for failure [13].…”

Section: Discussionmentioning

confidence: 99%

“…As depicted previously, the system model may be single-component or multicomponent. Whether the failure modules are placed in serial, parallel, or other forms can be determined by system failure analysis [13]. As for a multicomponent system with multiple failure modes, the system can be taken apart to several components and placed the related …”

Section: Discussionmentioning

confidence: 99%

“…] be the state transition matrix of (13), where L À1 denotes the inverse Laplace transform, (13) can be rewritten as…”

Section: Continuous State-space Modelmentioning

confidence: 99%

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Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

Yang

2011

J Fail. Anal. and Preven.

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A 25 kVA generator is one of the most important components in the power system of an indigenous defensive fighter (IDF). However, the generators have failed frequently since being equipped on IDFs. The poor reliability and long repair time of a 25 kVA generator have seriously impacted the availability of IDFs. This study aims to build a failure-processing mechanism for the 25 kVA generators based on the state-estimation function of the Kalman filter. By way of this mechanism, the left-or rightside load of the fighter can be switched from the supplied generator that is going to fail to the other generator before the abnormal generator actually fails. Therefore, this mechanism not only mitigates the impact resulting from the irregular power on systems of the fighter, but also obtains reaction time to shutdown the generator that is going to fail so as to perform a preventive maintenance or other effective actions on the generator such that the further failures can be avoided. In the simulations of this study, the failure times predicted by the Kalman filter are compared with the results obtained by Monte Carlo simulation for judging the prediction accuracy. In order to simulate more accurately, an exponential attenuator is placed at each end of the outputs of both the generator and the Kalman filter so as to conform to the actual aging model. The prediction errors are acceptable. Besides, reliabilities of the generators in four different configurations are compared to know the effect of redundancy design.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

Yang

2011

J Fail. Anal. and Preven.

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“…An event or a fault tree can be very easily transformed into a PN [10][11][12], but it means only to "copy" these classical approaches and add a few new features [13,14]. It does not utilize the full power and all possibilities of the PNs as a modelling language.…”

Section: Introductionmentioning

confidence: 99%

Complex accident scenarios modelled and analysed by Stochastic Petri Nets

Nývlt

Haugen

Ferkl

2015

Reliability Engineering & System Safety

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“…The transition is said to fire, if input places satisfy an enabling condition. Transition firing removes 1 token from all of its input places and puts 1 token into all of its output places [20]. There are 3 types of transitions that are time-based classified [17].…”

Section: A Introductionmentioning

confidence: 99%

A condition-based failure-prediction and processing-scheme for preventive maintenance

Yang

2003

IEEE Trans. Rel.

108

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Abstract-Preventive maintenance (PM) is an effective approach for reliability enhancement. Time-based and condition-based maintenance are two major approaches for PM. In contrast, condition-based maintenance can be a better and more cost-effective type of maintenance than time-based maintenance. However, irrespective of the approach adopted for PM, whether a failure can be detected early or even predicted is the key point. This paper presents a failure prediction method for PM by state estimation using the Kalman filter. To improve preventive maintenance, this study uses a hybrid Petri-net modeling method coupled with fault-tree analysis and Kalman filtering to perform failure prediction and processing. A Petri net arrangement, viz, early failure detection and isolation arrangement (EFDIA), is used; it facilitates alarm, early failure detection, fault isolation, event count, system-state description, and automatic shutdown or regulation. These functions are very useful for health-monitoring and preventive-maintenance of a system. This study implements EFDIA to an application-specific integrated circuit on a Xilinx Demonstration Board. A condition-monitoring system of a thermal power plant is used as an example to demonstrate the proposed scheme. Linking the Kalman filter to the EFDIA Petri net, a condition-based failure prediction and processing scheme has been completed for preventive maintenance. This paper presents a failure prediction and processing scheme for PM via the thermal power-plant example, by using a hybrid Petri net modeling method endowed with fault-tree analysis and Kalman filtering. The FPN (Petri net dealing with system failure) has to be constructed beforehand. The next step is to obtain control charts for all fault places in the FPN in order to prescribe thresholds and increment times for every step in Kalman prediction. Afterwards, the system model of each place in the FPN must be derived to perform Kalman filtering. With these prerequisites, this method can be applied to any system. The proposed Petri net approach not only can achieve early failure detection and isolation for fault diagnosis but also facilitates event count, system state description, and automatic shutdown or regulation. These capabilities are very useful for health monitoring and PM of a system. Since the triggering signal of place of the EFDIA in Section IV ( is a place for the Kalman-predicted indicator value of the sensing signal for the Petri net dealing with system failure) indicates that subsystem # performance is going to reach the prescribed failure threshold, the signal can be provided via the Kalman filtering method in Section III. Linking the Kalman filter to the EFDIA Petri net, a condition-based failure prediction and processing scheme has been completed for preventive maintenance.

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Failure Analysis for an Airbag Inflator by Petri Nets

Cited by 27 publications

References 1 publication

Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

Failure-Processing Scheme Based on Kalman Prediction and Reliability Analysis for IDF-used 25 kVA Generators

Complex accident scenarios modelled and analysed by Stochastic Petri Nets

A condition-based failure-prediction and processing-scheme for preventive maintenance

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