scholarly journals Fault Detection in Manufacturing Companies with Ensemble Machine Learning Method

2021 ◽  
Vol 4 (13) ◽  
pp. 3728-3741
Author(s):  
Deniz Demircioğlu Diren ◽  
Semra Boran
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Machine Learning Method ◽  
Learning Method ◽  
Manufacturing Companies ◽  
Ensemble Machine Learning

Estimations of Long-Term nss-SO42– and NO3– Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method

2020 ◽  
Vol 54 (18) ◽  
pp. 11118-11126
Author(s):  
Xingcheng Lu ◽  
Dehao Yuan ◽  
Yiang Chen ◽  
Jimmy C.H. Fung ◽  
Wenkai Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
East Asia ◽  
Machine Learning Method ◽  
Learning Method ◽  
Ensemble Machine Learning

scholarly journals Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China

2020 ◽  
Vol 10 (11) ◽  
pp. 4016 ◽  
Author(s):  
Xudong Hu ◽  
Han Zhang ◽  
Hongbo Mei ◽  
Dunhui Xiao ◽  
Yuanyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Landslide Susceptibility ◽  
Southwest China ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Statistical Measures ◽  
Ensemble Machine Learning

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson’s correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models’ performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

Forecast of Temperature using Ensemble Machine Learning Method

2019 ◽  
Vol 14 (2) ◽  
pp. 129-143
Author(s):  
Yuseon Hwang ◽  
◽  
Chansoo Kim
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Ensemble Machine Learning
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