A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Wu, Dazhong; Jennings, Connor; Terpenny, Janis P.; Gao, Robert X.; Kumara, Soundar

doi:10.1115/1.4036350

Cited by 471 publications

(167 citation statements)

References 44 publications

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“…The insights in the case of support vector regression (SVR) are a bit more complex and too long to be commented here [107]. Some applications to RUL prediction can be seen in [125,90,4,73].…”

Section: Regressionmentioning

confidence: 99%

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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The complexity involved in the process of real-time data-driven monitoring dynamic systems for predicted maintenance is usually huge. With more or less in-depth any data-driven approach is sensitive to data preprocessing, understood as any data treatment prior to the application of the monitoring model, being sometimes crucial for the final development of the employed monitoring technique. The aim of this work is to quantify the sensitiveness of data-driven predictive maintenance models in dynamic systems in an exhaustive way.We consider a couple of predictive maintenance scenarios, each of them defined by some public available data. For each scenario, we consider its properties and apply several techniques for each of the successive preprocessing steps, e.g. data cleaning, missing values treatment, outlier detection, feature selection, or imbalance compensation. The pretreatment configurations, i.e. sequential combinations of techniques from different preprocessing steps, are considered together with different monitoring approaches, in order to determine the relevance of data preprocessing for predictive maintenance in dynamical systems.

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Section: Regressionmentioning

confidence: 99%

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Cernuda

2019

Predictive Maintenance in Dynamic Systems

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“…The preprocessing step includes feature elimination, missing data imputation, normalization, and data division. In the importance measurement step, to measure the importance of each feature, the relevancy between each feature and the failure is analyzed using the random forest algorithm [23][24][25][26]. Then, the feature selection and model building steps are conducted iteratively.…”

Section: Introductionmentioning

confidence: 99%

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

Kwon

Kim

2020

Symmetry

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This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

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“…Traditional manufacturing is gradually developing towards smart manufacturing. Integrating big data, advanced analytical technology, large scale and high-performance computing, and Industrial Internet of Things (IIoT) into traditional manufacturing to produce highly quality customizable products at lower costs is the purpose of smart manufacturing [1]. Accurate prediction of processing product quality is the prerequisite for achieving the smart manufacturing systems or processes.…”

Section: Introductionmentioning

confidence: 99%

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

Lü

Liao

et al. 2019

Complexity

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It is difficult to accurately predict the response of some stochastic and complicated manufacturing processes. Data-driven learning methods which can mine unseen relationship between influence parameters and outputs are regarded as an effective solution. In this study, support vector machine (SVM) is applied to develop prediction models for machining processes. Kernel function and loss function are Gaussian radial basis function and -insensitive loss function, respectively. To improve the prediction accuracy and reduce parameter adjustment time of SVM model, artificial bee colony algorithm (ABC) is employed to optimize internal parameters of SVM model. Further, to evaluate the optimization performance of ABC in parameters determination of SVM, this study compares the prediction performance of SVM models optimized by well-known evolutionary and swarm-based algorithms (differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO), and ABC) and analyzes ability of these optimization algorithms from their optimization mechanism and convergence speed based on experimental datasets of turning and milling. Experimental results indicate that the selected four evaluation indicators values that reflect prediction accuracy and adjustment time for ABC-SVM are better than DE-SVM, GA-SVM, and PSO-SVM except three indicator values of DE-SVM for AISI 1045 steel under the case that training set is enough to develop the prediction model. ABC algorithm has less control parameters, faster convergence speed, and stronger searching ability than DE, GA, and PSO algorithms for optimizing the internal parameters of SVM model. These results shed light on choosing a satisfactory optimization algorithm of SVM for manufacturing processes.

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A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests

Cited by 471 publications

References 44 publications

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

On the Relevance of Preprocessing in Predictive Maintenance for Dynamic Systems

Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things

An Effective ABC‐SVM Approach for Surface Roughness Prediction in Manufacturing Processes

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