A Fuzzy Cognitive Maps Tool for Developing a RBI&M Model

Abstract

A proper maintenance plan is directly related to the definition of critical indexes for ensuring a high level of safety and high level in service quality for all equipments in the plants. The traditional approach, according to risk‐based inspection and maintenance (RBI&M), requires that each parameter considered in the definition of critical indexes shall be divided into intervals in order to assign it a score. By the elaboration of these scores, the critical indexes are calculated. However, what are the rules that allow the company the definition of the range and the assignment of the relative score? Are these rules subjective or objectives? Literature in the field highlights that these decisions are often carried out by maintenance managers. In order to overcome this approach, in this paper, a method based on Fuzzy Cognitive Maps (FCMs) is presented. FCMs have been used for structuring and supporting decisional processes. The criticality of equipments is described in terms of concepts affecting its functioning. No ranges or scores are defined, but only structural and functional features are considered in order to define a criticality index. The resulting fuzzy model can be used to analyse, simulate, test the influence of concepts and predict the behaviour of the system. The RBI&M model, proposed in this work, has been analysed through a case study of an Italian refinery Copyright © 2014 John Wiley & Sons, Ltd.

Deciding a Multicriteria Decision-Making (MCDM) Method to Prioritize Maintenance Work Orders of Hydroelectric Power Plants

“…Regarding this uncertainty during decision-making, the fuzzy theory is combined with the main MCDM methods to solve this problem [63]. In other words, fuzzy decision-making is used where vague and incomplete data exist for the solution [64,65].…”

A Plan Recognizing Algorithm based on Fuzzy Cognitive Plan Map

“…It tacks theft for example. It used the average value of running 10000 times (1).for every node in the G, add the concept node to both FCM and SFCM; (2).for every OR-edge in the G, add the edge in SFCM; (3).Set all weight equal 1, then it gets the ESFCM; (4).for every AND-edge in the G, add the edge in FCM; (5).Set edge weight equal thant in G, then it gets the EFCM; (6).…”

“…So plan recognition based on FCPM has favorable expansibility and can be used in more fields. (1).Initialize the instantaneous state vector of concept nodes A equal M; Change=0; (2).If Change ==0,return A; (3).else tempA=A; (4). tempA'=A; (5).…”

“…It is widely used in many fields [1,2,10]. By taking advantage of CM 's reasoning fast, it proposes the concept of Fuzzy Cognitive Plan Map (FCPM) and the recognizing algorithm is based on FCPM.…”

Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants

“…Different enabling technologies of Industry 4.0 such as the Internet of Things (IoT), Cloud Systems and Big Data Analytics contribute to the creation of what is the Digital Twin of a physical process, i.e., a mathematical model able to describe the process, the product or the service in order to carry out analyses and apply company strategies. Digital Twin solutions integrate artificial intelligence, machine learning and software analytics with data collected in production plants to create digital simulation models that are updated when production process parameters or working conditions change [2]. This is a self-learning system, using data collected from various sources: from sensors that transmit operating conditions; from experts, such as engineers with a deep knowledge of the industrial domain; from other fleets of similar machines; as well as integrating historical data related to the past use of plant components [3].…”