Inteligent IoTSP  Implementation of Embedded ML AI Tensorflow Algorithms on the NVIDIA Jetson Tx Chip This article presents a description of the project and implementation of the system for the execution of online diagnostics of compressors using the methods of artificial intelligence and the Tensorflow library. The main tasks of the system are: online acquisition of process data from the compressor set, online state monitoring (fault detection) of the compressor set based on the analysis of process data and using the classifiers modelled using the Tensorflow library. The system is intended to be a proof of concept, it should show the possibility of using Tensorflow library models running on the Jetson platform for online monitoring of compressor faults. The sample models proposed and prepared during previous research and development projects were used for testing. The algorithms used to identify and detect failures are based on MLP, CNN, SVM and LSTM  keras.

Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised realtime anomaly detection algorithm based on LSTMbased AutoEncoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%85%.