Super-Resolution Perception for Industrial Sensor Data

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.