Deepnautilus: A Deep Learning Based System For Nautical Engines' Live Vibration Processing

Recent advances in sensor technologies and data analysis techniques allow reliable and efficient systems for the early diagnosis of breakdowns in the production chain of the car industry and, more generally, of engines. The performance of these systems is based fundamentally on the quality of the features extracted and on the learning technique. In this paper, we show our preliminary, but encouraging, results carried out through our research effort in the field of using deep neural network to recognize and eventually predict engine failures. We present the prototypal blueprint of the system DeepNautilius devoted to detect failures in marine engines using deep learning with the ambitious goal of reducing marine pollution. Our envision comprises a distributed sensor data acquisition system based on the fog/edge/cloud computing paradigm, with a consistent part of the computation located on the edge side. While our architectural approach is described as a design oriented issue, in this work we present our experience with the deep neural network (DNN) computational core, using a literature dataset from an air compression engine. We demonstrate that our approach is not only comparable with the one in literature but is even better performing.