Super-Resolution Perception for Industrial Sensor Data
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by
Jinjin Gu, Guolong Liu, Gaoqi Liang, Junhua Zhao
2018
Abstract
In this paper, we present the problem formulation and methodology framework
of Super-Resolution Perception (SRP) on industrial sensor data. Industrial
intelligence relies on high-quality industrial sensor data for system control,
diagnosis, fault detection, identification and monitoring. However, the
provision of high-quality data may be expensive in some cases. In this paper,
we propose a novel machine learning problem - the SRP problem as reconstructing
high-quality data from unsatisfactory sensor data in industrial systems.
Advanced generative models are then proposed to solve the SRP problem. This
technology makes it possible for empowering existing industrial facilities
without upgrading existing sensors or deploying additional sensors. We first
mathematically formulate the SRP problem under the Maximum a Posteriori (MAP)
estimation framework. A case study is then presented, which performs SRP on
smart meter data. A network namely SRPNet is proposed to generate
high-frequency load data from low-frequency data. Experiments demonstrate that
our SRP model can reconstruct high-frequency data effectively. Moreover, the
reconstructed high-frequency data can lead to better appliance monitoring
results without changing the monitoring appliances.
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