Super-Resolution Perception for Industrial Sensor Data

TL;DR

This paper introduces Super-Resolution Perception (SRP), a machine learning approach to reconstruct high-quality industrial sensor data from lower-quality inputs, enhancing system monitoring without additional sensor deployment.

Contribution

It formulates the SRP problem mathematically, proposes a novel neural network (SRPNet) with specialized loss functions, and demonstrates effective high-frequency data reconstruction from low-frequency sensor data.

Findings

SRPNet effectively reconstructs high-frequency data from low-frequency inputs.

Reconstructed data improves appliance monitoring accuracy.

The approach enables upgrading existing industrial systems without new sensors.

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 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.