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.

The first and highly stereoselective asymmetric total syntheses of eight-membered ring ether marine natural products (+)-3-(E)-pinnatifidenyne and (+)-3-(Z)-pinnatifidenyne have been accomplished. Notable features of our syntheses include a novel and efficient construction of oxocene 5 by a highly stereo- and regioselective internal alkylation and direct ketone synthesis of ketone 16 from the alpha-alkyloxy amide moiety in oxocene 5.

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