Integrity Assessment and Control of Offshore Topside Piping: An Expert System Based Approach

2018 ◽  
Author(s):  
R. M. Chandima Ratnayake
Keyword(s):  
Expert System ◽  
Fuzzy Inference ◽  
Control Program ◽  
Technical Condition ◽  
Practical Significance ◽  
Illustrative Case ◽  
Inference System ◽  
Suggested Approach ◽  
Integrity Assessment ◽  
And Control

Downtime has a significant influence on the productive capability of offshore topside operating systems. Integrity assessment and control (IA&C) disciplines face major challenges in implementing a plant integrity control strategy, due to the lack of a methodology for incorporating fuzziness present in the data. To date, the employed IA&C practices face challenges in maintaining uniform quality from one integrity control program to another, due to the variability present in the technical IA&C process, especially among the different integrity assessment experts. Hence, it is vital to use expert systems-based approaches to sustain IA&C activities at an anticipated level and maintain the performance of operating assets at a target level. This manuscript provides a methodology and an illustrative case for how to perform IA&C activities for offshore topside piping. The illustrative case is demonstrated using a fuzzy inference system (FIS). Technical condition (TC) and relative degradation (RD) are selected as the inputs to the FIS for assessing the likelihood of failure (LoF). Expert system-based calculations, and how to use such results for IA&C, are demonstrated. The practical significance of the suggested approach is also discussed.

Adaptable Fuzzy Expert System for Ship Lock Control Support

2016 ◽  
Vol 69 (6) ◽  
pp. 1341-1356 ◽  
Author(s):  
Todor Bačkalić ◽  
Vladimir Bugarski ◽  
Filip Kulić ◽  
Željko Kanović
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Specific Area ◽  
Fuzzy Expert System ◽  
Control Logic ◽  
Extensive Experience ◽  
Inference System ◽  
And Control ◽  
Ship Lock

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

scholarly journals SISTEM PAKAR DIAGNOSA PENYAKIT IKAN GURAME DENGAN MENGGUNAKAN FIS MAMDANI

2021 ◽  
Vol 11 (2) ◽  
pp. 73-80
Author(s):  
Maria Yunita Nesi ◽  
Yampi R Kaesmetan ◽  
Meliana O. Meo
Keyword(s):  
Expert System ◽  
Fuzzy Inference System ◽  
Prevention And Control ◽  
Fuzzy Inference ◽  
The Body ◽  
Parasitic Infections ◽  
Inference System ◽  
The Right ◽  
And Control ◽  
Mamdani Fuzzy Inference System

The carp (Osphronemus Goramy) including fish that was seeded in cultivation. In addition to the price of carp that are relatively more expensive than other fish and it has been easy to carp also has a higher value compared to other freshwater fish. But in the cultivation of carp diseases is one of the serious problems encountered by the fish farmers because it could potentially cause harm. Diseases that attack the carp both are still in the larval or adult forms of which are caused by parasitic infections in the form of fungi, protozoa, worms as well as bacterial infection of Aeromonas hydrophylla, Flexybacter colomnaris, and Mycobacterium sp. The multiplicity of types of disease that can attack the carp and the difficult process of detection because of the similarity of the symptoms caused fish farmers making it difficult to determine the methods of prevention and control of the right to address the disease. Detection of disease of carp is seen on the surface of the body of the fish. Therefore, it takes expert system to detect disease carp by involving technology. One of the methods used in the expert system of fuzzy inference system Mamdani. Fuzzy inference system Mamdani reasoning used in this study because of the handling of the value and accuisition of knowledge representation experts can directly representation in the form of rules, which can be understood when placed on the machine inference. The result of this reasoning is to detect diseases of the carp while delivering the right solution to tackle the disease of carp.

scholarly journals Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4429
Author(s):  
Yury Nikitin ◽  
Pavol Božek ◽  
Jozef Peterka
Keyword(s):  
Fuzzy Inference ◽  
Technical Condition ◽  
Numerical Control ◽  
Cnc Machine ◽  
Diagnostic Systems ◽  
Mechatronic Systems ◽  
Inference System ◽  
Electric Drives ◽  
Electrical Drives ◽  
Linguistic Model

The presented paper scientifically discusses the progressive diagnostics of electrical drives in robots with sensor support. The AI (artificial intelligence) model proposed by the authors contains the technical conditions of fuzzy inference rule descriptions for the identification of a robot drive’s technical condition and a source for the description of linguistic variables. The parameter of drive diagnostics for a robotized workplace that is proposed here is original and composed of the sum of vibration acceleration amplitudes ranging from a frequency of 6.3 Hz to 1250 Hz of a one-third-octave filter. Models of systems for the diagnostics of mechatronic objects in the robotized workplace are developed based on examples of CNC (Computer Numerical Control) machine diagnostics and mechatronic modules based on the fuzzy inference system, concluding with a solved example of the multi-criteria optimization of diagnostic systems. Algorithms for CNC machine diagnostics are implemented and intended only for research into precisely determined procedures for monitoring the lifetime of the mentioned mechatronic systems. Sensors for measuring the diagnostic parameters of CNC machines according to precisely determined measuring chains, together with schemes of hardware diagnostics for mechatronic systems are proposed.

Experimental Results of Vibroacoustic Diagnosis Performed with Vibro-Expert System

2018 ◽  
Vol 16 (14) ◽  
pp. 38-51
Author(s):  
Ionel Rusa ◽  
Cornel Marin ◽  
Marius Baidoc
Keyword(s):  
Expert System ◽  
High Power ◽  
High Speed ◽  
Correct Diagnosis ◽  
Technical Condition ◽  
Vibration Sensors ◽  
Data Acquisition And Processing ◽  
And Control ◽  
Modern Technologies ◽  
Proactive Maintenance

Abstract The implementation of proactive maintenance is a necessity required by the development of modern technologies for the monitoring and exploitation of energy facilities and equipment. An important advantage of proactive maintenance is to permanently monitor the technical condition of the plant and equipment by vibration measurements and in the correct diagnosis in order to reasonably plan the required repairs. Turbo-aggregates are autonomous complex installations for producing electricity in refineries that operate in high power and high-speed modes. To monitor and control turbine vibrations, vibration sensors (uniaxial, biaxial and triaxial accelerometers) and proximity sensors (for relative displacements and lasers) are columned on bearing housings that transmit signals to data acquisition and processing systems as well is the Vibro-Expret diagnosis system presented in this paper.

Equipment State Assessment System Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

2015 ◽  
Vol 792 ◽  
pp. 243-247 ◽  
Author(s):  
Alexandra Khalyasmaa ◽  
Artem Aminev ◽  
Dmitry Bliznyuk
Keyword(s):  
Expert Systems ◽  
Fuzzy Inference ◽  
Technical Condition ◽  
Gas Analysis ◽  
Assessment System ◽  
Technical Diagnostics ◽  
Inference System ◽  
Power Stations ◽  
Neuro Fuzzy ◽  
Equipment State

The paper is dedicated to analyze the modern expert systems to assess the technical condition of power stations and substations high-voltage equipment. The main problems of modern expert systems and their possible solutions are determined. As the structure and their basic construction principles are considered. Also this paper proposes an algorithm for the expert system model using fuzzy inference on the basis of technical diagnostics and tests. As a case study of assessment of power transformers state based on dissolved gas analysis in the oil is presented.

Low back pain assessment application for construction workers

2015 ◽  
Vol 13 (3) ◽  
pp. 419-434
Author(s):  
H.O. Adeyemi ◽  
S.B. Adejuyigbe ◽  
S.O. Ismaila ◽  
A.F. Adekoya
Keyword(s):  
Low Back Pain ◽  
Back Pain ◽  
Expert System ◽  
Correlation Coefficient ◽  
Fuzzy Inference ◽  
First Aid ◽  
Low Back ◽  
Inference System ◽  
Content Type ◽  
Predicted Risk

Purpose – The purpose of this paper is to develop an expert system capable of assessing risk associated with manual lifting in construction tasks and proffer some first aid advices which are comparable with those obtainable from human experts. Design/methodology/approach – The expert system, musculoskeletal disorders – risk evaluation expert system (MSDs-REES), used Microsoft.Net C# programming language to write the algorithm of the fuzzy inference system with variables load, posture and frequency of lift as inputs and risk of low back pain as the output. The algorithm of the inference engine applied sets of rules to generate the output variable in crisp value. Findings – The result of validation, between the human experts’ calculated risk values and MSDs-REES-predicted risk values, indicated a correlation coefficient of 0.87. Between the predicted risk values generated using MSDs-REES and the existing package (MATLAB version 7.8), there was a strong positive relationship statistically with correlation coefficient of 0.97. Originality/value – The study provided a very simple expert system which has the ability to provide some medical-related injury prevention advice and first aid information for injury management, giving it a unique attribute over the existing applications.

scholarly journals Design and Implementation of a Fuzzy Expert System for Diagnosing Breast Cancer

2019 ◽  
pp. 1-14 ◽  
Author(s):  
F. M. Okikiola ◽  
E. E. Aigbokhan ◽  
A. M. Mustapha ◽  
I. O. Onadokun ◽  
O. A. Akinade
Keyword(s):  
Breast Cancer ◽  
Expert System ◽  
Fuzzy Inference ◽  
Treatment Recommendation ◽  
Fuzzy Expert System ◽  
Cancer Type ◽  
Inference System ◽  
Java Programming ◽  
New Information ◽  
High Level

The death rate is caused by breast cancer in women is increasingly high and growing. A number of people are getting to lose this part of their body due to late diagnosis of this disease. This therefore requires the development of an efficient and accurate diagnosis approach that will aid providing the knowledge of the type of breast cancer type and severity in order to reduce the mortality rate through the disease. This need serves as the major motivation for this work. In this paper, we proposed a fuzzy expert system for diagnosis of and treatment recommendation of breast cancer problems which provide physicians and patients with information of the cancer type and treatment recommendation. The application was designed using JAVA programming language, MATLAB and SQLite database engine. This application permits update of new information as a means of knowledge. The evaluation showed that the inclusion of the fuzzy inference system improved the accuracy and precision of the system from 0.8 to 0.9. The system is user-friendly and has high level of acceptability from the validation conducted at the end of the research.

scholarly journals Expert System Models for Forecasting Forklifts Engagement in a Warehouse Loading Operation: A Case Study

2016 ◽  
Vol 28 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Dejan Mirčetić ◽  
Nebojša Ralević ◽  
Svetlana Nikoličić ◽  
Marinko Maslarić ◽  
Đurđica Stojanović
Keyword(s):  
Expert System ◽  
Customer Service ◽  
Fuzzy Inference ◽  
Service Level ◽  
Inference System ◽  
New Approach ◽  
Neuro Fuzzy ◽  
Customer Service Level ◽  
Distribution Chain

The paper focuses on the problem of forklifts engagement in warehouse loading operations. Two expert system (ES) models are created using several machine learning (ML) models. Models try to mimic expert decisions while determining the forklifts engagement in the loading operation. Different ML models are evaluated and adaptive neuro fuzzy inference system (ANFIS) and classification and regression trees (CART) are chosen as the ones which have shown best results for the research purpose. As a case study, a central warehouse of a beverage company was used. In a beverage distribution chain, the proper engagement of forklifts in a loading operation is crucial for maintaining the defined customer service level. The created ES models represent a new approach for the rationalization of the forklifts usage, particularly for solving the problem of the forklifts engagement incargo loading. They are simple, easy to understand, reliable, and practically applicable tool for deciding on the engagement of the forklifts in a loading operation.

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