Abstract—Monitoring is an important aspect of operation
and maintenance in virtually every industrial system. However,
the extent and methods of monitoring vastly vary in different
systems, from fully automated to fully manual. One of the
challenges of automated monitoring is the tediousness of, and
the extent of engineering time and effort required to develop
necessary models or machine learning algorithms for the units to
be monitored. Model-free monitoring, on the other hand, can save
resources and efforts substantially. However, more often than not
they have a very limited scope and application. Such a system
is needed, for example, to monitor entire Heating, Ventilation
and Air Conditioning (HVAC) systems, consisting of different
types of sensors such as temperature, pressure, humidity or
flow sensors. Recently, we proposed the Context-Aware Health
Monitoring (CAH) system for model-free monitoring of any
injective-function black-box, and it was tested successfully on
an AC motor. In this paper, we evaluate the CAH system for
an entirely different industrial use-case, that is, a hydraulic
circuit. The results show the potential for considerable benefits
in monitoring HVAC systems. Moreover, in the light of applying
CAH to different use-cases which may potentially need a different
setup of parameters, we performed a sensitivity analysis on the
values of different parameters in the system. The results show the
robustness of CAH with regard to the values of these parameters