Low-Power Detection and Classification for In-Sensor Predictive Maintenance Based on Vibration Monitoring

Low-Power Detection and Classification for In-Sensor Predictive Maintenance Based on Vibration Monitoring

In this work, a new custom design of an anomaly detection and classification system is proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform anomaly detection which cooperates with a mixed HW/SW Convolutional Neural Network (CNN) to perform the classification of de...

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Bibliographic Details
Main Authors: Benedetto, L.D (Author), De Vita, A. (Author), Licciardo, G.D (Author), Pau, D. (Author), Vitolo, P. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Anomaly detection
artificial intelligence
Auto encoders
autoencoder
classification
CMOS integrated circuits
Computation theory
Convolution
Convolutional neural network
Feature extraction
Field programmable gate arrays (FPGA)
FPGA
in-sensor computing
In-sensor computing
Integrated circuit design
Low Power
Neural networks
Power detection
Sensor computing
Sensors predictive maintenance
Ultra-low power
ultra-low-power
Vibration monitoring
Online Access:View Fulltext in Publisher

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