Identification of Critical Components of Complex Product Based on Hybrid Intuitionistic Fuzzy Set and Improved Mahalanobis-Taguchi System

To avoid the decrease of system reliability due to insufficient component maintenance and the resource waste caused by excessive component maintenance,identifying the critical components of complex products is an effective way to improve the efficiency of maintenance activities.Existing studies on identifying critical components of complex products are mainly from two aspects i.e.,topological properties and functional properties,respectively.In this paper,we combine these two aspects to establish a hybrid intuitionistic fuzzy set to incorporate the different types of attribute values.Considering the mutual correlation between attributes,a combination of AHP(Analytic Hierarchy Process)and Improved Mahalanobis-Taguchi System(MTS)is used to determine the λ-Shapley fuzzy measures for attributes.Then,the λ-Shapley Choquet integral intuitionistic fuzzy TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution)method is proposed for calculating the closeness degrees of components to generate their ranking order.Finally,a case study which is about the right gear of airbus 320 is taken as an example to verify the feasibility and effectiveness of this method.This novel methodology can effectively solve the critical components identification problem with different types of evaluation information and completely unknown weight information of attributes,which provides the implementation of protection measures for the system reliability of complex products.

Identifying Critical Components of Combined Cycle Power Plants for Implementation of Reliability-centered Maintenance

Maintenance scheduling and asset management practices play an important role in power systems,specifically in power generating plants.This paper presents a novel riskbased framework for a criticality assessment of plant components as a means to conduct more focused maintenance activities.Critical components in power plants that influence overall system performance are identified by quantifying their failure impact on system reliability,electric safety,cost,and the environment.Prioritization of plant components according to the proposed risk-based method ensures that the most effective and techno-economic investment decisions are implemented.This,in turn,helps to initiate modern maintenance approaches,such as reliability-centered maintenance(RCM).The proposed method is applied to a real combined cycle power plant(CCPP)in Iran,composed of two gas turbine power plants(GTPP)and one steam turbine power plant(STPP).The results demonstrate the practicality and applicability of the presented approach in real world practices.

Dynamic fan-in and fan-out metrics for program comprehension

In this paper, an approach to use run-time information to discover knowledge about software systems thus facilitating program comprehension is presented. Some dynamic metrics based on traces of the subject system execution are proposed. A method of getting these dynamic metrics is introduced, in which instrumentation is implemented by using reflective mechanism based on an open compiler. The system run-time information is captured during the instrumented system running and is then analyzed to get these dynamic metrics. A case study is given to illustrate the use of these dynamic metrics, i.e., identifying critical components of the subject system. These critical components should be focus on user attentions in order to understand the subject system well.