Developing an Expert System of Failure Analysis

2013 ◽  
Vol 284-287 ◽  
pp. 2375-2379
Author(s):  
Yeong Ho Ho ◽  
Huei Sen Wang ◽  
Hei Chia Wang
Keyword(s):  
Expert System ◽  
Failure Analysis ◽  
Major Elements ◽  
Case Based Reasoning ◽  
Test Results ◽  
Inference Method ◽  
Ranking List ◽  
Document Ranking ◽  
Equipment Failure Analysis ◽  
Inference Methods

An Equipment Failure Analysis Expert System (EFAES) is to be developed to help the engineers diagnose the causes of the failure mechanism and provide a reliable remedy. This system is based on an innovative reasoning approach: integrating the rule-based reasoning (RBR) and the case-based reasoning (CBR) methods The architecture developed in the system consists of six major elements-“Factor and Attribute Editor”, Knowledge Actuation Interface”, “Knowledge Base”, “User Interface”, “Inference Engine” and “Explanation Facility”. Here, the RBR system consists of 46 failure mechanisms and their rules. The CBR system consists of 586 failure cases which are coded and composed from 23 factors and their 265 attributes. Also, this system provides a variety of inference methods which allows retrieving the best answers to users. For the RBR system, performance is directly check the inferred order of the document ranking list. For the CBR system, the effectiveness of each inference method is evaluated by using “Recall”, “Precision”, and “F-Measure” approaches. From the test results, many recommendations are proposed.

Development of an Equipment Failure Identification Expert System with Multiple Reasoning Approaches

2013 ◽  
Vol 479-480 ◽  
pp. 1001-1005
Author(s):  
Yeong Ho Ho ◽  
Huei Sen Wang ◽  
Hei Chia Wang
Keyword(s):  
System Performance ◽  
Equipment Failure ◽  
Test Results ◽  
Inference Method ◽  
Optimal Method ◽  
Ranking List ◽  
Document Ranking ◽  
Reasoning System ◽  
Inference Methods ◽  
F Measure

The goal of the reasoning system in this study is to identify the most similar failure type or failure cases. As a user inputs all possible requirements (attributes), the inference engine of the system carries out its similarity assessment (inference approaches) and ranks rules or cases from the data base. Various inference approaches are chosen to find out the optimal method for the RBR and CBR system. The CBR system offers two types of inference methods which are hierarchical factors, flat factors without weight. For RBR system, there three types of inference methods are chosen, one is complete matched and the others are partial matched approaches which use the inference capability of CBR. The performance of developed system is then evaluated by using the real cases from China Steel Corporation (CSC). For the RBR system, performance is directly check the inferred order of the document ranking list. For the CBR system, the effectiveness of each inference method is evaluated by using Recall, Precision, and F-Measure approaches. From the test results, many recommendations are proposed.

scholarly journals Sistem Pakar Menentukan Maksimal Kalori Harian Berbasis Mobile

2019 ◽  
Vol 1 (2) ◽  
Author(s):  
Teddy Santya ◽  
Cosmas Eko Suharyanto ◽  
Pastima Simanjuntak ◽  
Alex Alfandianto
Keyword(s):  
Expert System ◽  
Everyday Life ◽  
Dietary Habit ◽  
Test Results ◽  
Inference Method ◽  
Web Based ◽  
Forward Chaining ◽  
Server Side ◽  
Registered User ◽  
Flow Of Time

The study was conducted because of many concerning event happened in purpose of losing weight in improper methods. By the flow of time with technologies developed in this century, Smartphone became one of necessity because of the feature by many applications which helps a lot in our everyday life of humanity. The Researcher conducted this research of mobile-based application expert system which helps people properly losing their weight, this expert system is a supporting utility for taking care of dietary habit based on the amount of calorie limit calculated by this system, and recommends suitable and healthy food for users. This system is built with React Native framework. The calculation in this system is based on information of weight, height, and age provided by users, and all prediction will not be done without the provided information and every calculation is handled by web-based server-side processing which is developed using Laravel Framework. Inference method used by the researcher is Forward Chaining. This expert system will validate user's requirement to start working on losing weight by using Forward Chaining Inference method. Every successfully registered user will be able to view their calculated information by this system which is maximum calorie value, and list of healthy and recommended foods information provided by the expert herself. From the test results, the Expert System can solve the problem of giving a way to run a proper diet program by adjusting the diet with the calories needed.

scholarly journals SI-PAKARDI (Sistem Pakar Diagnosa Penyakit Gigi) Menggunakan Metode Forward Chaining

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andrian Eko Widodo ◽  
Suleman Suleman ◽  
Angga Ardiansyah ◽  
Dany Pratmanto ◽  
Sopian Aji ◽  
...  
Keyword(s):  
Expert System ◽  
Expert Knowledge ◽  
Dental Health ◽  
Dental Disease ◽  
Test Results ◽  
Inference Method ◽  
Community Based ◽  
Forward Chaining ◽  
Dental Diseases ◽  
System Knowledge

AbstractThe lack of knowledge about dental health and the still limited awareness of the community about dental health, makes some of our community set aside in efforts to prevent or treat dental disease. So we need a system or application that can help people to find out about dental diseases, as well as solutions to overcome these problems. This expert system knowledge base is formed by the if-then rules. The inference method used is forward chaining. This dental disease expert system is based on Android so it can be used anytime and anywhere by the community. Based on the acquisition of expert knowledge obtained 12 rules, 12 diseases, 25 symptoms. Blackbox test results show the application features that are made to run with a fairly good level of success. Unit test results indicate that the application has succeeded in making inferences properly and correctly, in accordance with applicable rules.Keywords: Dental disease, forward chaining, android, expert systemAbstrakMinimnya pengetahuan kesehatan gigi dan masih terbatasnya  kesadaran masyarakat tentang kesehatan gigi, membuat sebagian masyarakat kita mengesampingkan dalam upaya mencegah atau mengobati penyakit gigi. Maka diperlukan suatu sistem atau aplikasi yang dapat membantu masyarakat untuk mengetahui tentang penyakit gigi, serta solusi untuk mengatasi permasalahan tersebut. Basis pengetahuan sistem pakar ini dibentuk dengan aturan if-then. Metode inferensi yang digunakan adalah forward chaining. Sistem pakar penyakit gigi ini dibuat berbasis android agar bisa digunakan kapan saja dan dimana saja oleh masyarakat. Berdasarkan akusisi pengetahuan pakar didapat 12 aturan, 12 penyakit, 25 gejala. Hasil uji blackbox menunjukan fitur-fitur aplikasi yang dibuat berjalan dengan tingkat berhasilan yang cukup baik. Hasil pengujian unit menunjukkan bahwa aplikasi telah berhasil melakukan inferensi dengan baik dan benar, sesuai kaidah yang berlaku. Kata kunci : Penyakit gigi, forward chaining, android, sistem pakar

scholarly journals Sistem Pakar Diagnosis Penyakit Ikan Gurami (Osphronemus Goramy) Menggunakan Case Based Reasoning

2020 ◽  
Vol 7 (4) ◽  
pp. 779
Author(s):  
Adinda Rahmi Saraswati ◽  
Yudha Saintika ◽  
Afandi Nur Aziz Thohari ◽  
Ade Rahmat Iskandar
Keyword(s):  
Expert System ◽  
Nearest Neighbor ◽  
Online News ◽  
Case Based Reasoning ◽  
Fish Disease ◽  
Test Results ◽  
Bacterial Diseases ◽  
Accuracy Test ◽  
Best Value ◽  
Case Based

<p>Ikan Gurami (<em>Osphronemus Goramy)</em> merupakan ikan yang banyak dibudidayakan dan dikomsumsi masyarakat ini menjadi sektor unggulan di beberapa wilayah kabupaten Banyumas. Ikan gurami yang dibudidayakan oleh masyarakat Banyumas, sebenarnya bukan tanpa hambatan. Salah satu hambatan bagi peternak gurami adalah penyakit yang disebabkan oleh bakteri. Pada penelitian ini penulis membuat sistem pakar untuk mendiagnosis penyakit ikan Gurami yang disebabkan bakteri. Penelitian ini menggunakan metode<em> Case Based Reasoning</em> dan <em>Similarity</em> <em>Nearest Neighbor</em> untuk mendapatkan solusi yang terbaik dari kasus yang di identifikasi. Metode tersebut membandingkan antara kasus lama dengan kasus baru dan menghitung suatu nilai <em>similarity </em>kasus. Nilai <em>similarity</em> tertinggi dapat dijadikan kesimpulan untuk kasus yang paling mirip dengan diagnosa pakar. Sehingga dari kedua metode tersebut dapat dihasilkan sistem pakar yang dapat mendiagnosis dan menganalisis sesuai dengan nilai kemiripan gejala terhadap penyakit, serta menampilkan solusi penanganan dari penyakit yang didiagnosis. Hasil pengujian antar kasus dan sistem menggunakan perhitungan <em>similarity</em> mencapai nilai terbaik yaitu 100%. Hasil pengujian akurasi sistem untuk diagnosis yang sesuai dengan pikiran pakar, memperoleh hasil sebesar 93,33% dari 30 kasus yang diuji dengan sistem. Kesimpulan dari hasil tersebut adalah sistem dapat dikatakan layak untuk mendiagnosis penyakit Gurami yang disebabkan bakteri sesuai dengan yang dipikirkan pakar.</p><p> </p><p><em><strong>Abstract</strong></em></p><p><em>Gurami (Osphronemus Goramy) is a fish that is widely cultivated and consumed by the community. This fish is a leading sector in several regions of Banyumas district. Gouramy which is cultivated by the Banyumas people, is actually not without obstacles. One obstacle for gouramy breeders is a disease caused by bacteria. Reporting from the online news portal, circulating in February 2018 circulated that news about Gurami farmers was losing money because thousands of broodstock fish that had been raised to death were attacked by bacterial diseases, namely Aeromoniasis. Experts who handle this are limited, namely only 2 people in the Banyumas Regency.</em><em> </em><em>In this study the authors made an expert system to diagnose Gurami fish disease caused by bacteria. This study uses the Case Based Reasoning (CBR) and Nearest Neighbor methods used to get the best solution from the identified case. The CBR method compares the old case with the new case and calculates a case similarity value. The system was built with 13 symptoms and 3 Gurami diseases caused by bacteria. Each symptom each has a weight of 5, 3, and 1. The highest similarity value can be used as a conclusion for the case most similar to the expert diagnosis. So that from these two methods an expert system can be produced that can diagnose and analyze according to the similarity of symptoms to the disease, as well as display solutions to the treatment of diagnosed diseases. The test results are between cases and the system uses the similarity calculation to achieve the best value of 100%. The results of the system accuracy test for diagnoses that are in accordance with the expert's mind, obtained results of 93.33% from 30 cases tested with the system. The conclusion of these results is that the system can be said to be feasible to diagnose Gurami disease caused by bacteria according to what experts think.</em></p><p><em><strong><br /></strong></em></p>

Analysis Approach on DMOS Transistor Vth Mismatch Due to Trapped Charges

2015 ◽  
Author(s):  
Hashim Ismail ◽  
Ang Chung Keow ◽  
Kenny Gan Chye Siong
Keyword(s):  
Failure Analysis ◽  
Threshold Voltage ◽  
Gate Oxide ◽  
Analysis Approach ◽  
Test Results ◽  
Root Cause ◽  
Switching Event ◽  
Output Transistor ◽  
Event Based ◽  
Trapped Charge

Abstract An output switching malfunction was reported on a bridge driver IC. The electrical verification testing revealed evidence of an earlier over current condition resulting from an abnormal voltage sense during a switching event. Based on these test results, we developed the hypothesis that a threshold voltage mismatch existed between the sense transistor and the output transistor. This paper describes the failure analysis approach we used to characterize the threshold voltage mismatch as well as our approach to determine the root cause, which was trapped charge on the gate oxide of the sense transistor.

SISTEM PAKAR UNTUK DIAGNOSA PENYAKIT ANAK YANG DIDUKUNG OLEH BAHASA PEMOGRAMAN VISUAL

2017 ◽  
Vol 3 (2) ◽  
pp. 108
Author(s):  
Dian Permata Sari
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Human Knowledge ◽  
Inference Method ◽  
Medical Field ◽  
Forward Chaining ◽  
At Home

<p>Sistem pakar merupakan sistem yang mengadopsi pengetahuan manusia ke komputer yang dirancang untuk memodelkan kemampuan menyelesaikan masalah seperti layaknya seorang pakar. Dengan sistem pakar ini, orang awam pun dapat menyelesaikan masalahnya atau hanya sekedar mencari suatu informasi berkualitas yang sebenarnya hanya dapat diperoleh dengan bantuan para ahli di bidangnya. Salah satunya yaitu dibidang medis untuk mendiagnosapenyakit anak. Mengetahui gejala dari suatu penyakit secara dini dapat menjadi bantuan pertama yang dapat dilakukan para orang tua di rumah jika anak mereka terserang penyakit.Basis pengetahuan disusun sedemikian rupa kedalam database dengan beberapa tabel. Penarikan kesimpulan dalam sistem pakar ini menggunakan metode inferensi <em>forward chaining</em>. Sistem pakar akan memberikan pertanyaan-pertanyaan kepada user berupa gejala dari beberapa penyakit dan user akan menjawab pertanyaan tersebut. Hingga <em>user</em> akan mendapatkan solusi dari hasil pertanyaan tadi. </p><p><em><br /></em></p><p><em>Expert systems are systems that adopt human knowledge into computers designed to model the ability to resolve problems like an expert. Through thisexpert systems,commoner cansolvetheproblem orjustlookingfor a qualityinformationthat can onlybeobtainedwiththehelpofexperts in thefield. One ofthemis in the medical field to diagnosethe children's illness.Knowingthesymptomsofanillnessearly can bethefirstaidto parents if their children stricken withthedisease at home.</em><em>Knowledgebase is arranged into a highlystructureddatabasewithmultipletables. Inferences in this expert system uses forward chaining inference method. Expert systems will provide questions to the user in the form of the symptoms of some diseases and the user will answer that question. Until the user will get the solution of the question.</em></p>

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