Reliability analysis of pulping system using Petri nets

Sachdeva, Anish; Kumar, Dinesh; Kumar, Pradeep

doi:10.1108/02656710810898667

Cited by 31 publications

(21 citation statements)

References 34 publications

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“…A model-based approach was proposed by Portugal et al (2005) using Stochastic Petri nets (SPNs) for dependability analysis of controller area network in the presence of transient faults occurred during communication. Sachdeva et al (2008) proposed a method based on PNs for reliability evaluation of the pulping system in the paper industry. Interactions between different active and standby units of the system were modelled using Stochastic Reward Nets.…”

Section: Applications As Standalone Approachesmentioning

confidence: 99%

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

2019

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System safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.

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Section: Applications As Standalone Approachesmentioning

confidence: 99%

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

2019

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“…The modeling power of PNs is versatile. For example, Sachdeva et al [15] and Caterino et al [16] utilized PNs to analyze the reliability of a pulping system and a guillotine machine for retaining safe activities.…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

Chen¹,

Liu

Chen³

2019

Applied Sciences

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This study proposes a cognition-adaptive approach for the administrative control of human-machine safety interaction through Internet of Things (IoT) data. As part of Industry 4.0, a human operator possesses various characteristics, but cannot be consistently understood as well as a machine. Thus, human-machine interaction plays an important role. This study focuses on incumbent challenges on the basis of estimated mental states. Given the operation logs from data recording hardware, a Hidden Markov model on top of a human cognitive model was trained to capture a production line worker’s sequential faults. Our study found that retaining workers’ attention is insufficient and tracking the state of perception is key to accomplishing production tasks. A safe workflow policy requires attention and perception. Accordingly, our proposed Petri Net enhances operation safety and improves production efficiency.

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“…As well as many techniques/ methodology are used to evaluate the reliability, some of these are supplementary variable technique, Petri nets, stochastic reward nets, regenerative point technique etc. [7]- [15]. All these conventional methods evaluate the reliability parameters accurately but today's demand is to update the output and minimize the errors/ failures and optimize the results.…”

Section: Introductionmentioning

confidence: 99%

Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture

Gupta¹,

Ekata²,

Batra³

2019

IJITEE

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The world’s ever increasing demand for energy and abating global warming, suitable renewable sources of energy are highly in demand. The wastes from industries such as plant’s biomass could meet the energy requirements. In this paper authors analyze bio fuel plant system which produces ethanol fuel. This system is divided into various subsystems considering multiple phases in the production of ethanol. The structure of this system consists of interconnected networks of components on very large dimensional scales escalates the complexity of systems that can increase the degradation of system's functioning. In view of this, one of the computational intelligence approach, neural network (NN), is useful in predicting various reliability parameters. To improve the accuracy and consistency of parameters, Feed Forward Back Propagation Neural Network (FFBPNN) is used. All types of failures and repairs follow exponential distributions. System state probabilities and other parameters are developed for the proposed model using neural network approach. Failures and repairs are treated as neural weights. Neural network's learning mechanism can modify the weights due to which these parameters yield optimal values. Numerical examples are included to demonstrate the results. The iterations are repeated till the convergence in the error tends up to 0.0001 precision using MATLAB code. The reliability and cost analysis of the system can help operational managers in taking the decision to implement it in the real time systems

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Reliability analysis of pulping system using Petri nets

Cited by 31 publications

References 34 publications

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

Human-Machine Interaction: Adapted Safety Assistance in Mentality Using Hidden Markov Chain and Petri Net

Estimate Reliability Parameters in Bio_Fuel Plant Using Neural Network Architecture

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