scholarly journals Combining chronicle mining and semantics for predictive maintenance in manufacturing processes

Semantic Web ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 927-948 ◽  
Author(s):  
Qiushi Cao ◽  
Ahmed Samet ◽  
Cecilia Zanni-Merk ◽  
François de Bertrand de Beuvron ◽  
Christoph Reich
Keyword(s):  
Data Mining ◽  
Hybrid Approach ◽  
Failure Prediction ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Semantic Gap ◽  
Manufacturing Processes ◽  
Temporal Constraints ◽  
Industrial Data ◽  
The Future

Within manufacturing processes, faults and failures may cause severe economic loss. With the vision of Industry 4.0, artificial intelligence techniques such as data mining play a crucial role in automatic fault and failure prediction. However, due to the heterogeneous nature of industrial data, data mining results normally lack both machine and human-understandable representation and interpretation of knowledge. This may cause the semantic gap issue, which stands for the incoherence between the knowledge extracted from industrial data and the interpretation of the knowledge from a user. To address this issue, ontology-based approaches have been used to bridge the semantic gap between data mining results and users. However, only a few existing ontology-based approaches provide satisfactory knowledge modeling and representation for all the essential concepts in predictive maintenance. Moreover, most of the existing research works merely focus on the classification of operating conditions of machines, while lacking the extraction of specific temporal information of failure occurrence. This brings obstacles for users to perform maintenance actions with the consideration of temporal constraints. To tackle these challenges, in this paper we introduce a novel hybrid approach to facilitate predictive maintenance tasks in manufacturing processes. The proposed approach is a combination of data mining and semantics, within which chronicle mining is used to predict the future failures of the monitored industrial machinery, and a Manufacturing Predictive Maintenance Ontology (MPMO) with its rule-based extension is used to predict temporal constraints of failures and to represent the predictive results formally. As a result, Semantic Web Rule Language (SWRL) rules are constructed for predicting the occurrence time of machinery failures in the future. The proposed rules provide explicit knowledge representation and semantic enrichment of failure prediction results, thus easing the understanding of the inferred knowledge. A case study on a semi-conductor manufacturing process is used to demonstrate our approach in detail. The evaluation of results shows that the MPMO ontology is free of bad practices in the structural, functional, and usability-profiling dimensions. The constructed SWRL rules posses more than 80% of True Positive Rate, Precision, and F-measure, which shows promising performance in failure prediction.

The Generation of the Fuzzy Control Rules Based on Association Rules with Temporal Constraints

2013 ◽  
Vol 385-386 ◽  
pp. 931-934
Author(s):  
Zhi Gang Li ◽  
Feng Li Yang
Keyword(s):  
Data Mining ◽  
Fuzzy Control ◽  
Association Rules ◽  
Association Rule ◽  
Temporal Constraints ◽  
Temporal Constraint ◽  
Industrial Data ◽  
Control Rules ◽  
Mining Methods

In the field of fuzzy control, the generation of fuzzy control rules has always been a problem, because the industrial data is generally expressed in the order of time ,so it strongly depends on the time, it does not take the factors of temporal constraints into account in the previous extracting rule process.This paper uses temporal constraint association rule ,and uses the data mining methods to generate temporal fuzzy control rules. The method is verified by using the MATLAB7.1 ,the simulation shows that the method can achieve good fuzzy control rules.

IMPLEMENTASI ALGORITMA REGRESI LINEAR SEDERHANA DALAM MEMPREDIKSI BESARAN PENDAPATAN DAERAH (STUDI KASUS: DINAS PENDAPATAN KAB. DELI SERDANG)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Fransiskus Ginting ◽  
Efori Buulolo ◽  
Edward Robinson Siagian
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Visual Basic ◽  
Large Contribution ◽  
Simple Linear Regression ◽  
Information Discovery ◽  
Regional Income ◽  
Local Revenue ◽  
Local Taxes ◽  
The Future

Data Mining is an information discovery by extracting information patterns that contain trend searches in a very large amount of data and assist the process of storing data in making a decision in the future. In determining the pattern classification techniques do to collect records (Training set). Regional income is generally derived from local taxes and levies, local taxes are one source of funding for the region on the national average has not been able to make a large contribution to the formation of local revenue. By utilizing Regional Revenue data, it can produce forecasting and predictions of Regional Revenue income in the future to match the reality / reality so that the planned RAPBD can run smoothly. Simple Linear Regression or often abbreviated as SLR (Simple Linear Regression) is one of the statistical methods used in production to make predictions or predictions about the characteristics of quality and quantity to describe the processes associated with data processing for the acquisition of regional income. So that in the testing phase with visual basic net can help in processing valid Regional Revenue Amount data. Keywords: Data Mining, Local Revenue, Simple Linear Regression Algorithm, Visual Basic net 2008

Research on Persona-Based User Requirement in the Future Exploratory Products Design

2014 ◽  
Vol 912-914 ◽  
pp. 1710-1713
Author(s):  
Qing Zhang ◽  
Sui Huai Yu ◽  
Ming Jiu Yu
Keyword(s):  
Data Mining ◽  
User Modeling ◽  
Modeling Method ◽  
Mobile Internet ◽  
User Requirement ◽  
Operating Rules ◽  
The Future ◽  
Key Factor

During the design processing of the future exploratory products, requirements from users seems to be a key factor for products availability achievement. As a practical user modeling method, Persona may accomplish the potential needs data mining effectively based on the analyzing of users. This review mainly focused on how to apply the persona in the exploratory products investigation to acquire useful information from the products design. The method to establish persona and the operating rules were also discussed in this article. The concept of the mobile internet device in future was used as an case to demonstrate the persona mentioned above.

Proving the Labeled Interval Calculus for Inferences on Catalogs

1990 ◽  
Author(s):  
Walid Habib ◽  
Allen C. Ward
Keyword(s):  
Mechanical Design ◽  
Formal System ◽  
Constraint Propagation ◽  
Operating Conditions ◽  
Manufacturing Processes ◽  
Entire System ◽  
High Level Language ◽  
Design Problems ◽  
High Level ◽  
Interval Constraint

Abstract The “labeled interval calculus” is a formal system that performs quantitative inferences about sets of artifacts under sets of operating conditions. It refines and extends the idea of interval constraint propagation, and has been used as the basis of a program called a “mechanical design compiler,” which provides the user with a “high level language” in which design problems for systems to be built of cataloged components can be quickly and easily formulated. The compiler then selects optimal combinations of catalog numbers. Previous work has tested the calculus empirically, but only parts of the calculus have been proven mathematically. This paper presents a new version of the calculus and shows how to extend the earlier proofs to prove the entire system. It formalizes the effects of toleranced manufacturing processes through the concept of a “selectable subset” of the artifacts under consideration. It demonstrates the utility of distinguishing between statements which are true for all artifacts under consideration, and statements which are merely true for some artifact in each selectable subset.

A Hybrid Algorithm of Mining Closed Itemsets for Large Databases

2011 ◽  
Vol 145 ◽  
pp. 292-296
Author(s):  
Lee Wen Huang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Execution Time ◽  
Hybrid Algorithm ◽  
Hybrid Approach ◽  
Market Basket Analysis ◽  
Market Basket ◽  
Large Databases ◽  
Closed Itemsets ◽  
Simulation Results

Data Mining means a process of nontrivial extraction of implicit, previously and potentially useful information from data in databases. Mining closed large itemsets is a further work of mining association rules, which aims to find the set of necessary subsets of large itemsets that could be representative of all large itemsets. In this paper, we design a hybrid approach, considering the character of data, to mine the closed large itemsets efficiently. Two features of market basket analysis are considered – the number of items is large; the number of associated items for each item is small. Combining the cut-point method and the hash concept, the new algorithm can find the closed large itemsets efficiently. The simulation results show that the new algorithm outperforms the FP-CLOSE algorithm in the execution time and the space of storage.

scholarly journals Corporate governance in Poland

2009 ◽  
Vol 7 (2) ◽  
pp. 387-394 ◽  
Author(s):  
Tom Mortimer
Keyword(s):  
Corporate Governance ◽  
Future Development ◽  
Traditional Approach ◽  
Hybrid Approach ◽  
The State ◽  
The Future ◽  
Stakeholder Model ◽  
State Models

This article considers the traditional approach to the ’state’ Models of corporate governance, namely shareholder Model and stakeholder Model. It then considers the extent to which developments in a recent accession EU country, Poland, reflects either of these Models or adopts a hybrid approach. It then offers proposals for the future development of corporate governance within Poland.

scholarly journals Disease Prediction System using Data Mining Hybrid Approach

2016 ◽  
Vol 4 (9) ◽  
pp. 48-51 ◽  
Author(s):  
Rahul Patil ◽  
Pavan Chopade ◽  
Abhishek Mishra ◽  
Bhushan Sane ◽  
Yuvraj Sargar
Keyword(s):  
Data Mining ◽  
Hybrid Approach ◽  
Disease Prediction ◽  
Prediction System ◽  
Using Data
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