Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

Arif, Fahmi; Suryana, Nanna; Hussin, Burairah

doi:10.1016/j.ieri.2013.11.029

Cited by 23 publications

(6 citation statements)

References 12 publications

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“…The dataset provided by UCI Machine Learning Repository (http://archive.ics.uci.edu/mL) is related to a semiconductor manufacturing process. The dataset consists of 1567 observations, and each observation is made up of 591 features representing manufacturing operations of a single semiconductor [56]. The data were collected from the continuous monitoring process using sensors and metrology equipment along the semiconductor manufacturing line.…”

Section: Steel Plates Faults Datasetmentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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Machinery diagnostics and prognostics usually involve the prediction process of fault-types and remaining useful life (RUL) of a machine, respectively. The process of developing a data-driven diagnostics and prognostics method involves some fundamental subtasks such as data rebalancing, feature extraction, dimension reduction, and machine learning. In general, the best performing algorithm and the optimal hyper-parameters suitable for each subtask are varied across the characteristics of datasets. Therefore, it is challenging to develop a general diagnostic/prognostic framework that can automatically identify the best subtask algorithms and the optimal involved parameters for a given dataset. To resolve this problem, we propose a new framework based on an ensemble of genetic algorithms (GAs) that can be used for both the fault-type classification and RUL prediction. Our GA is combined with a specific machine-learning method and then tries to select the best algorithm and optimize the involved parameter values in each subtask. In addition, our method constructs an ensemble of various prediction models found by the GAs. Our method was compared to a traditional grid-search over three benchmark datasets of the fault-type classification and the RUL prediction problems and showed a significantly better performance than the latter. Taken together, our framework can be an effective approach for the fault-type and RUL prediction of various machinery systems.

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Section: Steel Plates Faults Datasetmentioning

confidence: 99%

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Trinh

Kwon

2020

Applied Sciences

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“…There are many advantages [32] to classify a mass of data by using the ID3 algorithm, such as strong intuitive nature, easy decomposability and so on, but the ID3 algorithm also has some disadvantages, for example: (1) When using the information gain as attribute selection method, ID3 tends to choose the attribute that has more values [33] because the value of the information gain equation of this type of attribution will be larger than that of others. (2) As is known to all, if the logarithmic expression and the four arithmetic operations are the two equivalent expressions of one function, the running time of the four arithmetic operations is faster than that of the logarithmic expression [34].…”

Section: Algorithm 1: Id3 Algorithmmentioning

confidence: 99%

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

Wang

Song

et al. 2017

Algorithms

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Abstract:The decision tree algorithm is a core technology in data classification mining, and ID3 (Iterative Dichotomiser 3) algorithm is a famous one, which has achieved good results in the field of classification mining. Nevertheless, there exist some disadvantages of ID3 such as attributes biasing multi-values, high complexity, large scales, etc. In this paper, an improved ID3 algorithm is proposed that combines the simplified information entropy based on different weights with coordination degree in rough set theory. The traditional ID3 algorithm and the proposed one are fairly compared by using three common data samples as well as the decision tree classifiers. It is shown that the proposed algorithm has a better performance in the running time and tree structure, but not in accuracy than the ID3 algorithm, for the first two sample sets, which are small. For the third sample set that is large, the proposed algorithm improves the ID3 algorithm for all of the running time, tree structure and accuracy. The experimental results show that the proposed algorithm is effective and viable.

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“…Beside single-point and multi-point, different approach is proposed by [24]. They proposed a cascade approach to develop quality prediction model for MMS.…”

Section: Related Workmentioning

confidence: 99%

“…This method is design based on the variable relationships in MMS. Reference [24] explained the condition of variable relationships in MMS as follows:…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the use of SMS approach to measure quality in MMS can be misleading and ineffective due to the cumulative effect in a workstation as the result of the existence of preceding manufacturing operation in previous workstation [23]. Reference [24] proposed a framework of Cascade Quality Prediction Method (CQPM) for developing quality prediction. However, the accuracy of the prediction model that has been developed using CQPM has not been proved and the techniques that can be employed by this model have not been investigated as well.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

Arif¹,

Suryana²,

Hussin³

2013

IJCA

Self Cite

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Quality prediction model has been developed in various industries to realize the faultless manufacturing. However, most of quality prediction model is developed in single-stage manufacturing. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing effectively. This study is intended to propose combination of multiple PCA+ID3 algorithm to develop quality prediction model in MMS. This technique is applied to a semiconductor manufacturing dataset using the cascade prediction approach. The result shows that the combination of multiple PCA+ID3 is manage to produce the more accurate prediction model in term of classifying both positive and negative classes. General TermsData Mining, Prediction Model.

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Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

Cited by 23 publications

References 12 publications

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

A Data-Independent Genetic Algorithm Framework for Fault-Type Classification and Remaining Useful Life Prediction

Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree

A Data Mining Approach for Developing Quality Prediction Model in Multi-Stage Manufacturing

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