Fault Tree is one of the traditional and conventional approaches used in fault diagnosis. By

identifying combinations of faults in a logical framework it’s possible to define the structure

of the fault tree. The same go with Bayesian networks, but the difference of these probabilistic

tools is in their ability to reasoning under uncertainty. Some typical constraints to the

fault diagnosis have been eliminated by the conversion to a Bayesian network. This paper

shows that information processing has become simple and easy through the use of Bayesian

networks. The study presented showed that updating knowledge and exploiting new knowledge

does not complicate calculations. The contribution is the structural approach of faults

diagnosis of turbo compressor qualitatively and quantitatively, the most likely faults are

defined in descending order. The approach presented in this paper has been successfully

applied to turbo compressor, which represent vital equipment in petrochemical plant.

In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

Multi-criteria decision making (MCDM) technique and approach have been a trending topic in decision making and systems engineering to choosing the probable optimal options. The primary purpose of this article is to develop prioritized operators to multi-criteria decision making (MCDM) based on Archimedean t-conorm and t-norms (At-CN&t-Ns) under interval-valued dual hesitant fuzzy (IVDHF) environment. A new score function is defined for finding the rank of alternatives in MCDM problems with IVDHF information based on priority levels of criteria imposed by the decision maker. This paper introduces two aggregation operators: At-CN&t-N-based IVDHF prioritized weighted averaging (AIVDHFPWA), and weighted geometric (AIVDHFPWG) aggregation operators. Some of their desirable properties are also investigated in details. A methodology for prioritization-based MCDM is derived under IVDHF information. An illustrative example concerning MCDM problem about a Chinese university for appointing outstanding oversea teachers to strengthen academic education is considered. The method is also applicable for solving other real-life MCDM problems having IVDHF information.

Lean manufacturing has been the most deliberated concept ever since its introduction. Many organization across the world implemented lean concept and witnessed dramatic improvements in all contemporary performance parameters. Lean manufacturing has been a sort of mirage for the Indian automotive industry. The present research investigated the key lean barriers to lean implementation through literature survey, confirmatory factor analysis, multiple regression, and analytic network process. The general factors to lean implementation were inadequate lean planning, resource constraints, half-hearted commitment from management, and behavioral issues. The most important factor in the context of lean implementation in Indian automotive industry was inadequate lean planning found with the help of confirmatory factor analysis and multiple regression analysis. Further analysis of these extracted factors through analytic network process suggested the key lean barriers in Indian automotive industry, starting from the most important were absence of proper lean implementation methodology, lack of customer focus, absence of proper lean measurement system, inadequate capital, improper selection of lean tools & practices, leadership issues, resistance to change, and poorly defined roles & responsibilities. Though literature identifying various lean barriers are available. The novelty of current research emerges from the identification and subsequent prioritization of key lean barriers within Indian automotive SMEs environment. The research assists in smooth transition from traditional to lean system by identifying key barriers and developing customized framework of lean implementation for Indian automotive SMEs.

The linguistic q-rung orthopair fuzzy (L q-ROF) set is an important implement in the research area in modelling vague decision information by incorporating the advantages of q- rung orthopair fuzzy sets and linguistic variables. This paper aims to investigate the multicriteria decision group decision making (MCGDM) with L q-ROF information. To do this, utilizing Hamacher t-norm and t-conorm, some L q-ROF prioritized aggregation operators viz., L q- ROF Hamacher prioritized weighted averaging, and L q-ROF Hamacher prioritized weighted geometric operators are developed in this paper. The defined operators can effectively deal with different priority levels of attributes involved in the decision making processes. In addition, Hamacher parameters incorporated with the proposed operators make the information fusion process more flexible. Some prominent characteristics of the developed operators are also wellproven. Then based on the proposed aggregation operators, an MCGDM model with L q-ROF context is framed. A numerical example is illustrated in accordance with the developed model to verify its rationality and applicability. The impacts of Hamacher and rung parameters on the achieved decision results are also analyzed in detail. Afterwards, a comparative study with other representative methods is presented in order to reflect the validity and superiority of the proposed approach.

Packet-switched xHaul networks are a scalable solution enabling convergent transport of diverse types of radio data flows, such as fronthaul / midhaul / backhaul (FH / MH / BH) flows, between remote sites and a central site (hub) in 5G radio access networks (RANs). Such networks can be realized using the cost-efficient Ethernet technology, which enhanced with time-sensitive networking (TSN) features allows for prioritized transmission of latency-sensitive fronthaul flows. Provisioning of multiple types of 5G services of different service requirements in a shared network, commonly referred to as network slicing, requires adequate handling of transported data flows in order to satisfy particular service / slice requirements. In this work, we investigate two traffic prioritization policies, namely, flowaware (FA) and latency-aware (LA), in a packet-switched xHaul network supporting slices of different latency requirements. We evaluate the effectiveness of the policies in a networkplanning case study, where virtualized radio processing resources allocated at the processing pool (PP) facilities, for two slices related to enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services, are subject to optimization. Using numerical experiments, we analyze PP cost savings from applying the LA policy (vs. FA) in various network scenarios. The savings in active PPs reach up to 40% − 60% in ring scenarios and 30% in a mesh network, whereas the gains in overall PP cost are up to 20% for the cost values assumed in the analysis.