Monitoring the status of the facilities and detecting any faults are considered an important technology in a smart factory. Although the faults of machine can be analyzed in real time using collected data, it requires a large amount of computing resources to handle the massive data. A cloud server can be used to analyze the collected data, but it is more efficient to adopt the edge computing concept that employs edge devices located close to the facilities. Edge devices can improve data processing and analysis speed and reduce network costs. In this paper, an edge device capable of collecting, processing, storing and analyzing data is constructed by using a single-board computer and a sensor. And, a fault detection model for machine is developed based on the long short-term memory (LSTM) recurrent neural networks. The proposed system called LiReD was implemented for an industrial robot manipulator and the LSTM-based fault detection model showed the best performance among six fault detection models.

Photovoltaic systems have become an important source of renewable energy generation. Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable operation planning and proactive power trading. This study builds a model that predicts the amounts of solar power generation using weather information provided by weather agencies. This study proposes a two-step modeling process that connects unannounced weather variables with announced weather forecasts. The empirical results show that this approach improves a base approach by wide margins, regardless of types of applied machine learning algorithms. The results also show that the random forest regression algorithm performs the best for this problem, achieving an R-squared value of 70.5% in the test data. The intermediate modeling process creates four variables, which are ranked with high importance in the post-analysis. The constructed model performs realistic one-day ahead predictions.

Data-driven quality control techniques are being actively developed for implementation in smart factories. Quality prediction during manufacturing processes is a good example of how big data analytics can influence advanced manufacturing environments. In this paper, the problem of classifying manufacturing process conditions into normal and defective products according to defect types is dealt with. Such a quality analysis data set is generally unbalanced because the defective rate is quite low in practice. To solve this imbalanced classification problem, a cost-sensitive decision tree ensemble algorithm is adopted to boost the small number of defective cases and assign a higher cost to the misclassification of defective products than that of normal products. C4.5 decision trees are used as base classifiers, and three cost-sensitive ensembles, AdaC1, AdaC2 and AdaC3, are tried to address the imbalanced classification. A few types of defect conditions in a real-world die-casting data set were predicted through the proposed methods. In these experiments, the cost-sensitive ensembles were able to classify the imbalanced data and detect the defect conditions more precisely and more exactly than 19 algorithms in other classification categories such as classic classifiers and ensembles, cost-sensitive single classifiers and sampling-based ensembles. Especially, the AdaC2-based method mainly outperformed all other classification algorithms in terms of performance measures such as F-measure, G-means and AUC for the die-casting quality condition classification problem.