  The sustaining evolution of sensing and advancement in communications
technologies have revolutionized prognostics and health management for various
electrical equipment towards data-driven ways. This revolution delivers a
promising solution for the health monitoring problem of heat pump (HP) system,
a vital device widely deployed in modern buildings for heating use, to timely
evaluate its operation status to avoid unexpected downtime. Many HPs were
practically manufactured and installed many years ago, resulting in fewer
sensors 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 hybrid
scheme by integrating industrial Internet-of-Things (IIoT) and intelligent
health monitoring algorithms to handle this challenge. To start with, an IIoT
network is constructed to sense and store measurements. Specifically,
temperature sensors are properly chosen and deployed at the inlet and outlet of
the water tank to measure water temperature. Second, with temperature
information, we propose an unsupervised learning algorithm named mixture slow
feature analysis (MSFA) to timely evaluate the health status of the integrated
HP. Characterized by frequent operation switches of different HPs due to the
variable demand for hot water, various heating patterns with different heating
speeds are observed. Slowness, a kind of dynamics to measure the varying speed
of steady distribution, is properly considered in MSFA for both heating pattern
division and health evaluation. Finally, the efficacy of the proposed method is
verified through a real integrated HP with five connected HPs installed ten
years ago. The experimental results show that MSFA is capable of accurately
identifying health status of the system, especially failure at a preliminary
stage compared to its competing algorithms.
