  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 to empower 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. We further employ a novel
recognition-based loss and relativistic adversarial loss to constraint the
reconstruction of waveforms explicitly. 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.
