Wyniki wyszukiwania - BazTech

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Availability analysis of vertical milling centre using Markov approach and Monte Carlo simulations

Kumar Girish, Mishra Ravi, Khan Tayyab

Safety and Reliability of Systems and Processes

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2023

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Summer Safety and Reliability Seminar 2023

117--130

EN

The purpose of this article is to investigate the availability of vertical machining centre using a Markovian technique and Monte Carlo simulation (MSC). Availability is a critical performance metric for industrial systems. Conventional methodologies focus for steady-state availability evaluation of mechanical systems. The research analyses transient availability assessment for four different system configurations. Monte Carlo simulation modelling is used to compare the results and future scope is suggested to use the developed MCS based algorithms/codes for non-exponential (time dependant) failure and repair time distributions. The research also investigates the influence of active and passive redundancy on availability, indicating that for the vertical machining centre, parallel architecture with standby redundancy outperforms active load sharing. The chapter includes a sensitivity study that modifies the repair rates of the ball screw and sub-assembly to make the component selection process easier for engineers. The authors believe that this chapter will be useful to maintenance and practising engineers because it will assist them in making informed decisions about system availability, developing maintenance/replacement policies, and determining the repair level required to achieve the desired system availability.

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Degrading systems availability analysis: analytical semi-Markov approach

Kumar Varun, Kumar Girish, Singh Rajesh Kumar, Soni Umang

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 1

195--208

EN

This paper deals with modeling and analysis of complex mechanical systems that deteriorate with age. As systems age, the questions on their availability and reliability start to surface. The system is believed to suffer from internal stochastic degradation mechanism that is described as a gradual and continuous process of performance deterioration. Therefore, it becomes difficult for maintenance engineer to model such system. Semi-Markov approach is proposed to analyze the degradation of complex mechanical systems. It involves constructing states corresponding to the system functionality status and constructing kernel matrix between the states. The construction of the transition matrix takes the failure rate and repair rate into account. Once the steady-state probability of the embedded Markov chain is computed, one can compute the steady-state solution and finally, the system availability. System models based on perfect repair without opportunistic and with opportunistic maintenance have been developed and the benefits of opportunistic maintenance are quantified in terms of increased system availability. The proposed methodology is demonstrated for a two-stage reciprocating air compressor with intercooler in between, system in series configuration.

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Degradation assessment of bearing based on machine learning classification matrix

Kumar Satish, Kumar Paras, Kumar Girish

Eksploatacja i Niezawodność

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2021

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Vol. 23, no. 2

395--404

EN

In the broad framework of degradation assessment of bearing, the final objectives of bearing condition monitoring is to evaluate different degradation states and to estimate the quantitative analysis of degree of performance degradation. Machine learning classification matrices have been used to train models based on health data and real time feedback. Diagnostic and prognostic models based on data driven perspective have been used in the prior research work to improve the bearing degradation assessment. Industry 4.0 has required the research in advanced diagnostic and prognostic algorithm to enhance the accuracy of models. A classification model which is based on machine learning classification matrix to assess the degradation of bearing is proposed to improve the accuracy of classification model. Review work demonstrates the comparisons among the available state-of-the-art methods. In the end, unexplored research technical challenges and niches of opportunity for future researchers are discussed.