Text Mining Methodology to Build Dependency Matrix from Unstructured Text to Perform Fault Diagnosis

2016 ◽  
pp. 534-540
Author(s):  
Amruta Kulkarni ◽  
Jyoti Nighot ◽  
Ashish Ramdasi
Keyword(s):  
Fault Diagnosis ◽  
Text Mining ◽  
Dependency Matrix ◽  
Unstructured Text

Design of Automatic Test Platform for the Gyroscope Group Based on Pertinence Matrix

2014 ◽  
Vol 556-562 ◽  
pp. 2567-2570
Author(s):  
He Jia Li ◽  
Xue Wang ◽  
Hai Feng Xu ◽  
Cheng Yao ◽  
Wen Ju Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Performance Test ◽  
Gun Control ◽  
Automatic Test ◽  
Optimal Test ◽  
Fault Dictionary ◽  
Dependency Matrix ◽  
Optimal Criterion ◽  
Test Platform ◽  
Test Points

Aiming the problem of the armored vehicle's gun control system that there are many kinds of internal devices, complex fault reasons ,but no all-around and online fault diagnosis and state inspection mean, The automatic test platform for the gyroscope group with performance test and fault diagnosis for component and circuit is designed .The platform based on dependency matrix and optimal criterion of the maximum failure feature information entropy optimize test points ,choose optimal test points design. Performance test module is created and provides test result information for fault dictionary in fault diagnosis module. Automatic test platform is able to locate the circuit component failure.The platform is tested by actual vehicle experiment, and the results prove the reliability and validity of the platform.

Incorporating Text OLAP in Business Intelligence

2011 ◽  
pp. 77-101 ◽  
Author(s):  
Byung-Kwon Park ◽  
Il-Yeol Song
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Business Intelligence ◽  
Multidimensional Analysis ◽  
Web Pages ◽  
Data Types ◽  
Text Documents ◽  
Text Data ◽  
Platform Architecture ◽  
Unstructured Text

As the amount of data grows very fast inside and outside of an enterprise, it is getting important to seamlessly analyze both data types for total business intelligence. The data can be classified into two categories: structured and unstructured. For getting total business intelligence, it is important to seamlessly analyze both of them. Especially, as most of business data are unstructured text documents, including the Web pages in Internet, we need a Text OLAP solution to perform multidimensional analysis of text documents in the same way as structured relational data. We first survey the representative works selected for demonstrating how the technologies of text mining and information retrieval can be applied for multidimensional analysis of text documents, because they are major technologies handling text data. And then, we survey the representative works selected for demonstrating how we can associate and consolidate both unstructured text documents and structured relation data for obtaining total business intelligence. Finally, we present a future business intelligence platform architecture as well as related research topics. We expect the proposed total heterogeneous business intelligence architecture, which integrates information retrieval, text mining, and information extraction technologies all together, including relational OLAP technologies, would make a better platform toward total business intelligence.

Automatic recommendation of prognosis measures for mechanical components based on massive text mining

2018 ◽  
Vol 14 (4) ◽  
pp. 480-494 ◽  
Author(s):  
Jorge Martinez-Gil ◽  
Bernhard Freudenthaler ◽  
Thomas Natschläger
Keyword(s):  
Fault Diagnosis ◽  
Text Mining ◽  
Design Methodology ◽  
Content Type ◽  
The Past ◽  
Mechanical Component ◽  
The World ◽  
Fault Prognosis ◽  
Mechanical Components ◽  
Unstructured Information

Purpose The purpose of this study is to automatically provide suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Design/methodology/approach Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical components. The major reason is that fault prognosis is an activity that, unlike fault diagnosis, involves a lot of uncertainty and it is not always possible to envision a model for predicting possible faults. Findings This work proposes a solution based on massive text mining for automatically suggesting prognosis activities concerning mechanical components. Originality/value The great advantage of text mining is that makes possible to automatically analyze vast amounts of unstructured information to find corrective strategies that have been successfully exploited, and formally or informally documented, in the past in any part of the world.

scholarly journals Dependency Model-Based Multiple Fault Diagnosis Using Knowledge of Test Result and Fault Prior Probability

2019 ◽  
Vol 9 (2) ◽  
pp. 311 ◽  
Author(s):  
Xiaofeng Lv ◽  
Deyun Zhou ◽  
Ling Ma ◽  
Yongchuan Tang
Keyword(s):  
Fault Diagnosis ◽  
Prior Probability ◽  
Test Results ◽  
Multiple Fault ◽  
Dependency Matrix ◽  
Multiple Faults ◽  
Fault Type ◽  
Multiple Fault Diagnosis ◽  
Fault Dependency ◽  
Dependency Model

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

scholarly journals Editorial for the Special Issue on “Natural Language Processing and Text Mining”

Information ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 279
Author(s):  
Pablo Gamallo ◽  
Marcos Garcia
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Special Issue ◽  
Unstructured Text

Natural language processing (NLP) and Text Mining (TM) are a set of overlapping strategies working on unstructured text [...]

An Evaluation of Unstructured Text Mining Software

2007 ◽  
Author(s):  
Micah J. Crowsey ◽  
Amanda R. Ram ◽  
David H. Gutierrez ◽  
Gregory W. Paladino ◽  
K. P. White
Keyword(s):  
Text Mining ◽  
Unstructured Text

scholarly journals An Efficient Algorithm for Text Mining in Business Intelligence using Machine Learning

2020 ◽  
Vol 9 (6) ◽  
pp. 772-774
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Text Mining ◽  
Business Intelligence ◽  
Efficient Algorithm ◽  
Social Network Sites ◽  
Unstructured Data ◽  
Efficient Approach ◽  
Unstructured Text

Data plays an important role in success of any organization, so organizations required more data to make decision for their planning to improvement. The data that are generating for any organization, in which 80 to 90 percent data belongs to unstructured data type.Text mining is the process that indicate retrieve appealing and unknown information from unstructured text. Social network sites also generate huge amounts of data,with the help of these data people’s behavior and thought easily determine but analysis of these data is a difficult task. This paper proposed an efficient approach for text mining using machine learning.

Research of AMU Unit Fault Testability Based on Multi-Signal Modeling and Fuzzy Decision

2013 ◽  
Vol 760-762 ◽  
pp. 1089-1094 ◽  
Author(s):  
De Xin Zhou ◽  
Ming Yu Song ◽  
Teng Da Ma
Keyword(s):  
Fault Diagnosis ◽  
System Modeling ◽  
Fuzzy Decision ◽  
Dependency Matrix ◽  
Modeling Methodology ◽  
Maintenance Time ◽  
Effective System ◽  
Diagnosis Method ◽  
Fault Dependency ◽  
Set Up

The Multi-signal Flow Graph (MFG) is a simple and effective system modeling methodology, widely used in testability analysis and fault diagnosis field. In order to shorten the maintenance time and reduce the influence of human factors, the MFG method was introduced, and it was used to set up the testability model of aircraft Audio Management Unit (AMU) and found the fault-testability dependency matrix and the fault-fault dependency matrix. Based on dependency matrix and real unit fault message, the trapezoidal fuzzy number algorithm was introduced, thus the new fault diagnosis method was generated. Finally, an example proves that the fault diagnosis algorithm can not only locate the fault more accurately, but also improve the maintenance efficiency.

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