scholarly journals Using Data Mining Methods for Predicting Sequential Maintenance Activities

2018 ◽  
Vol 8 (11) ◽  
pp. 2184 ◽  
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Data Mining ◽  
Spare Parts ◽  
Threshold Values ◽  
Data Mining Approach ◽  
History Of ◽  
Maintenance Activities ◽  
Using Data ◽  
Subsequent Failure ◽  
Sequential Maintenance

A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.

Application of data mining for spare parts information in maintenance schedule: a case study

2019 ◽  
Vol 30 (7) ◽  
pp. 1055-1072
Author(s):  
U.C. Moharana ◽  
S.P. Sarmah ◽  
Pradeep Kumar Rathore
Keyword(s):  
Data Mining ◽  
Industrial Applications ◽  
Spare Parts ◽  
Sequential Patterns ◽  
Historical Records ◽  
Threshold Values ◽  
Content Type ◽  
Maintenance Activity ◽  
Maintenance Activities

Purpose The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values. Design/methodology/approach A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts. Findings This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity. Research limitations/implications Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study. Practical implications The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities. Originality/value This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

scholarly journals Retraction: Rezig, S. et al. (2018). Using Data-Mining Methods for Predicting Sequential Maintenance Activities. Applied Sciences, 8(11), 2184

2019 ◽  
Vol 9 (4) ◽  
pp. 633
Author(s):  
Sadok Rezig ◽  
Zied Achour ◽  
Nidhal Rezg
Keyword(s):  
Applied Sciences ◽  
Data Mining ◽  
Research Project ◽  
Mining Methods ◽  
Maintenance Activities ◽  
Using Data ◽  
The University ◽  
Sequential Maintenance

After the results from a research project with former students of the university were published in Applied Sciences [...]

scholarly journals Accurate Prediction of Protein Functional Class from Sequence in theMycobacterium tuberculosisandEscherichia coliGenomes Using Data Mining

Yeast ◽  
2000 ◽  
Vol 1 (4) ◽  
pp. 283-293 ◽  
Author(s):  
Ross D. King ◽  
Andreas Karwath ◽  
Amanda Clare ◽  
Luc Dehaspe
Keyword(s):  
Data Mining ◽  
Evolutionary History ◽  
Inductive Logic ◽  
Rule Learning ◽  
Functional Class ◽  
E Coli ◽  
Data Mining Approach ◽  
History Of ◽  
Using Data ◽  
Insight Into

The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on theM. tuberculosisandE. coligenomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function inM. tuberculosisand 24% of those inE. coli, with an estimated accuracy of 60–80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history ofM. tuberculosisandE. coli.

scholarly journals Accurate Prediction of Protein Functional Class from Sequence in the Mycobacterium tuberculosis and Escherichia coli Genomes Using Data Mining

Yeast ◽  
2000 ◽  
Vol 1 (4) ◽  
pp. 283-293
Author(s):  
Ross D. King ◽  
Andreas Karwath ◽  
Amanda Clare ◽  
Luc Dehaspe
Keyword(s):  
Data Mining ◽  
Evolutionary History ◽  
Inductive Logic ◽  
Rule Learning ◽  
Functional Class ◽  
E Coli ◽  
Data Mining Approach ◽  
History Of ◽  
Using Data ◽  
Insight Into

The analysis of genomics data needs to become as automated as its generation. Here we present a novel data-mining approach to predicting protein functional class from sequence. This method is based on a combination of inductive logic programming clustering and rule learning. We demonstrate the effectiveness of this approach on the M. tuberculosis and E. coli genomes, and identify biologically interpretable rules which predict protein functional class from information only available from the sequence. These rules predict 65% of the ORFs with no assigned function in M. tuberculosis and 24% of those in E. coli, with an estimated accuracy of 60–80% (depending on the level of functional assignment). The rules are founded on a combination of detection of remote homology, convergent evolution and horizontal gene transfer. We identify rules that predict protein functional class even in the absence of detectable sequence or structural homology. These rules give insight into the evolutionary history of M. tuberculosis and E. coli.

scholarly journals Predicting COVID-19 Incidence Using Data Mining Techniques: A case study of Pakistan

2020 ◽  
Vol 11 (4) ◽  
pp. 168-184
Author(s):  
Saba NOOR ◽  
◽  
Waseem AKRAM ◽  
Touseef AHMED ◽  
Qurat-ul-Ain Qurat-ul-Ain ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Wuhan City ◽  
Data Mining Approach ◽  
Life Threatening ◽  
Precautionary Measures ◽  
The Government ◽  
Predictive Data Mining ◽  
Using Data

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic.

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