Application of equipment integrity management expert system based on predictive maintenance

2015 ◽  
pp. 369-372
Keyword(s):  
Expert System ◽  
Predictive Maintenance ◽  
Integrity Management ◽  
Management Expert

Computer Based Expert System for Rotating Machinery: Preventive and Predictive Maintenance

1992 ◽  
Author(s):  
El-Adawi S. El-Mitwally ◽  
M. A. Rayan ◽  
N. H. Mostafa ◽  
Yehia M. Enab
Keyword(s):  
Expert System ◽  
Preventive Maintenance ◽  
Rotating Machinery ◽  
Predictive Maintenance ◽  
Production Costs ◽  
Operating Cycle ◽  
Maintenance System ◽  
Computer Based ◽  
Points Of View ◽  
Parameters Of Interest

Abstract At the present time, the maintenance of the equipment becomes an essential task for any production system. This task is becoming more important from both the quantity and the quality points of view, particularly in developing countries. Initiating a maintenance system controlled by the computer will be valuable and effective. The developed expert system is a combination of an intelligent inference engine matched with a database of information. This system will enable the operator to spot instantaneously the parameters of interest. The expert maintenance system will be designed to perform preventive maintenance tasks and detects faults/failure during the operating cycle. Predictive maintenance enables the operator to minimize the shut down time of faulty equipment and hence increases the productivity. Furthermore, the system will minimize the probable human faults and reduce production costs.

Asset Integrity Management - Implementation Plan During Front End Loading Phases

2021 ◽  
Author(s):  
Giorgio Ferrario ◽  
Salvatore Grimaldi
Keyword(s):  
Asset Management ◽  
Operations Management ◽  
Joint Venture ◽  
Lessons Learned ◽  
Predictive Maintenance ◽  
Digital Implementation ◽  
Front End ◽  
Integrity Management ◽  
Advanced Analytics ◽  
Set Up

Abstract Capitalization of lessons learned on Asset Integrity Management during Front End Loading phases of a green field Project Development, by defining plan for implementation of a diagnostic digital tool for reducing downtime and introduce predictive maintenance during Operation. Eni developed a platform of Digital applications for enhanced Operations management by implementing an Integrated Asset Management (IAM) system. Advanced Analytics tool is part of it and is designed for monitoring, foreseeing and preventing production upsets and anomalies; the tool is set up by verification of areas of interest and criticalities, with identification of main equipment data sets and by the implementation and validation of predictive models. Starting from historical data, data scientists supported by experts develop algorithms capable of finding interdependencies between a set of input variables and an output variable (phenomenon to be predicted/monitored), thus detecting anomalies and criticalities. Main areas of benefit are envisaged on Production continuity, capable of predicting problems on static and rotating equipment and giving information on the most impacting variables on the incipient problems. The tool will support technicians to help them preventing failures and out-of-specs events which may cause loss of production or asset integrity issues, with the activation of predictive maintenance and the aim to strive a continuous monitoring and improvement of plant operational performances. An Energy Efficiency predictive model will also be set up, capable of forecasting the future energy performances of the asset through the prediction of the Stationary Combustion of Carbon Dioxide (CO2) emission index (t CO2/kbbl) and providing the list of the main influencing equipment and variables. The plan for implementation of the tool from the Early phases of development help the organization on prioritizing the implementation of Digital tools as part of the execution and realization of the Asset to be delivered to the Operational personnel, by easing the transition and avoiding subsequent retrofitting carrying brownfield works and additional costs. The implementation of Advanced Analytics tool has been embedded in a new green field initiative of a Development Project since Front End Loading phases, thus fostering digital implementation and minimizing deployment costs by including those as part of the Investment Proposal presented to Joint Venture Partners and Authorities.

scholarly journals Android Based Self-Control Management System for Diabetes Mellitus

2018 ◽  
Vol 73 ◽  
pp. 13006
Author(s):  
Devi Astri Nawangnugraeni ◽  
R. Rizal Isnanto ◽  
Oky Dwi Nurhayati
Keyword(s):  
Diabetes Mellitus ◽  
Expert System ◽  
Dietary Pattern ◽  
Self Control ◽  
Public Understanding ◽  
Forward Chaining ◽  
Development Result ◽  
Control Management System ◽  
Management Expert ◽  
Control Management

One of the biggest cause of death is Diabetes Mellitus caused by a lack of public understanding of the symptoms of the disease, so that the diagnosis of the disease is not done as early as possible. This paper presents the research and the development of an Android based self-control management expert system for Diabetes Mellitus patients. This expert system purposed to diagnose Diabetes Mellitus disease based on symptoms experienced and to manage the dietary pattern in patients. The method used to develop expert system is forward chaining method. Implementation of the forward chaining method begins with gathering information then applying reasoning as a determinant of diagnosis conclusions using rule based If-Then. The development result is an expert system that can be used to diagnose Diabetes Mellitus and can be used to determine the dietary pattern in patients who are implemented on Android based mobile devices. This system shows more specific results in determining the diagnosis of the disease based on 4 types of Diabetes Mellitus. In addition, more specific in determining dietary pattern such as showing the number of calories, food levels and variations of food that can be consumed by patients.

Exercise countermeasure protocol management expert system

1993 ◽  
Vol 39 (3-4) ◽  
pp. 217-223 ◽  
Author(s):  
Laurie Webster ◽  
Jen-Gwo Chen ◽  
Luis Flores ◽  
Simon Tan
Keyword(s):  
Expert System ◽  
Management Expert

scholarly journals Purwarupa Sistem Pakar dengan Mamdani Product untuk Menentukan Menu Harian Penderita DM

2013 ◽  
Vol 7 (1) ◽  
pp. 45
Author(s):  
Nur Hasanah ◽  
Retantyo Wardoyo
Keyword(s):  
Fuzzy Logic ◽  
Expert System ◽  
Inference Engine ◽  
Rule Based ◽  
Knowledge Based ◽  
Menu Planning ◽  
Model Complex ◽  
Uncertainty Method ◽  
Management Expert ◽  
Diabetes Melitus

AbstrakPada 2025 diperkirakan 12,4 juta orang yang mengidap Diabetes Melitus (DM) di Indonesia. Perencanaan makan merupakan salah satu pilar dalam pengelolaan DM. Sistem pakar dapat berfungsi sebagai konsultan yang memberi saran kepada pengguna sekaligus sebagai asisten bagi pakar. Logika fuzzy fleksibel, memiliki kemampuan dalam proses penalaran secara bahasa dan memodelkan fungsi-fungsi matematika yang kompleks. Penelitian ini bertujuan menerapkan metode ketidakpastian logika fuzzy pada purwarupa sistem pakar untuk menentukan menu harian. Manfaat penelitian ini adalah untuk mengetahui keakuratan mesin inferensi Mamdani Product.            Pendekatan basis pengetahuan yang digunakan pada sistem pakar ini adalah dengan Rule-Based Reasoning. Proses inferensi pada sistem pakar menggunakan logika fuzzy dengan mesin inferensi Mamdani Product. Fuzzifier yang digunakan adalah Singleton sedangkan defuzzifier yang digunakan adalah Rata-Rata Terpusat. Penggunaan kombinasi Singleton fuzzifier, mesin inferensi Product dan defuzzifier Rata-Rata Terpusat yang digunakan pada sistem pakar dapat diterapkan untuk domain permasalahan yang dibahas. Meskipun demikian, terdapat kemungkinan Singleton fuzzifier tidak dapat memicu beberapa atau semua aturan. Jika semua aturan tidak dapat dipicu maka tidak dapat disimpulkan kebutuhan kalori hariannya. Kata kunci— sistem pakar, logika fuzzy, mamdani product, diabetes, menu  AbstractIt is predicted that 12.4 million people will suffer from Diabetes Mellitus (DM) in Indonesia in 2025. Menu planning is one of the important aspects in DM management. Expert system can be used as a consultant that gives suggestion to users as well as an assistant for experts. Fuzzy logic is flexible, has the ability in linguistic reasoning and can model complex mathemathical functions. This research aims to implement fuzzy logic uncertainty method into expert sistem prototype to determine diabetic daily menu. The advantage is to find out the accuracy of Mamdani Product inference engine. The knowledge-based approach in this expert system uses Rule-Based Reasoning. The inference process employs fuzzy logic making use of Mamdani Product inference engine. The fuzzifier used is Singleton while defuzzifier is Center Average.            The combination of Singleton fuzzifier, Mamdani Product inference engine and Center Average defuzzifier that is used can be applied in the domain of the problem under discussion. In spite of the case, there is possibility that Singleton fuzzifier can’t trigger some or all of the rules. If all of the rules can’t be triggered then the diabetic daily menu can’t be concluded. Keyword— expert system, fuzzy logic, mamdani product, diabetes, menu

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