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The sustaining evolution of sensing and advancement in communicationstechnologies have revolutionized prognostics and health management for variouselectrical equipment towards data-driven ways. This revolution delivers apromising solution for the health monitoring problem of heat pump (HP) system,a vital device widely deployed in modern buildings for heating use, to timelyevaluate its operation status to avoid unexpected downtime. Many HPs werepractically manufactured and installed many years ago, resulting in fewersensors available due to technology limitations and cost control at that time.It raises a dilemma to safeguard HPs at an affordable cost. We propose a hybridscheme by integrating industrial Internet-of-Things (IIoT) and intelligenthealth monitoring algorithms to handle this challenge. To start with, an IIoTnetwork is constructed to sense and store measurements. Specifically,temperature sensors are properly chosen and deployed at the inlet and outlet ofthe water tank to measure water temperature. Second, with temperatureinformation, we propose an unsupervised learning algorithm named mixture slowfeature analysis (MSFA) to timely evaluate the health status of the integratedHP. Characterized by frequent operation switches of different HPs due to thevariable demand for hot water, various heating patterns with different heatingspeeds are observed. Slowness, a kind of dynamics to measure the varying speedof steady distribution, is properly considered in MSFA for both heating patterndivision and health evaluation. Finally, the efficacy of the proposed method isverified through a real integrated HP with five connected HPs installed tenyears ago. The experimental results show that MSFA is capable of accuratelyidentifying health status of the system, especially failure at a preliminarystage compared to its competing algorithms.

IIoT-Enabled Health Monitoring for Integrated Heat Pump System Using Mixture Slow Feature Analysis