(325g) Evaluation of Dynamic Ica-Based Sensor Fault Validation Method of Indoor Air Pollutants Data On Energy Consumption of Ventilation System

Millions
of people spend a considerable amount of time in indoor building spaces such as
office, classroom, and subway station. However, due to overcrowding and
inadequate ventilation system, various types of indoor air pollutants
accumulate in the indoor building, specially underground spaces. Therefore, to
ensure people's good health, an accurate monitoring is necessary for regulating
indoor air quality (IAQ) in the underground spaces.

Sensors
are important components of the IAQ monitoring, since they provide data that
are required for continuous monitoring of the IAQ. However, these sensors
suffer reliability problems (namely, sensor
fault) due to extended usage or hostile environment where the sensors are
installed. The sensor fault provides incorrect information while the IAQ
monitoring in the underground spaces. Therefore, sensor fault identification
and reconstruction (namely, sensor fault
validation) are necessary for accurate IAQ monitoring.

Some
studies on IAQ ventilation systems have reported that an amount of energy
consumption of the ventilation system is influenced by the sensor reliability. Once
the IAQ sensor is deteriorated due to the precision degradation, the IAQ level
measured from the faulty sensor is lower than the actual indoor air pollutants
in the building space. Then, the ventilation system misjudges that higher power
is needed to supply more amount of fresh air than the required, and the energy
consumption of ventilation system is increased. It indicates that a consequence
of sensor fault on the energy consumption of ventilation system is critical. Therefore,
a systematic sensor fault validation method is required to satisfy the energy
efficiency as well as people's comfort in the indoor building spaces.

(a)

(b)

Fig. 1.
(a) Hostile environment of the underground space and deteriorated sensor, and
(b) influence of sensor fault on the energy consumption of IAQ ventilation
system

This
study is carried out 1) to identify and reconstruct the faulty sensor of IAQ
measurement and 2) to evaluate the effect of sensor fault validation on the energy
consumption of ventilation system at the underground subway station in Seoul
Metro, South Korea. The IAQ variables measured from the subway station have two
characteristics of non-Gaussian distribution and auto-correlation. In this
study, a sensor fault validation method based on dynamic independent component
analysis (DICA) is proposed to take the dynamic non-Gaussian behavior of the
IAQ variables into account. DICA is an optimal technique which can extract
essential independent factors from the dynamic non-Gaussian distributed system.

Fig.
2 shows a conceptual framework of the proposed method to validate IAQ sensor's
reliability and its reconstruction as well as to predict the energy consumption
of the ventilation system. It consists of three parts: 1) to develop the
DICA-based sensor fault validation model using IAQ data under normal sensing
condition, 2) to apply the developed model to new IAQ data under faulty sensing
condition, and 3) to estimate the energy consumption of ventilation system
using the faulty and validated IAQ data.

Fig. 2. Proposed
framework of validation of IAQ sensor fault and estimation of energy
consumption of ventilation system depending on the sensor reliability

To
detect whether sensor fault occurs or not, squared prediction error (SPE) which
is typical statistics in DICA for detecting abnormal condition is used. If the
sensor fails, the normal correlation inside the DICA model is broken, and then
the SPE increases significantly. Thus, the occurrence of sensor faults is
detected by comparing the SPE values with its threshold. Then, the fault
identification is carried out using DICA-based sensor validity index (namely, DI-SVI). The fault identification finds
out the source of sensor faults from the large number of sensors and the
time-variant characteristics of fault. Finally, DICA-based reconstruction
algorithm is used to reconstruct the faulty sensor to normal.

To
evaluate the influence of sensor fault validation on the energy consumption of
ventilation system, an IAQ ventilation system model developed by Liu et al. [Energy and Buildings, 2013
(accepted)] is used. Fan speed (revolutions per minute, RPM) of the ventilation
system is estimated using the faulty and reconstructed IAQ data, respectively.
Then, the energy consumption of ventilation system is calculated based on the
relation between the fan speed and its energy consumption:

The
first plots in Fig. 3(a) and (b) show the SPE plots of DPCA and DICA models to
detect the sensor fault. Note that the faulty sensor signal was introduced to samples
60 to 110 of particulate matter less than 10mm
(PM₁₀) sensor. To demonstrate the superiority of the DICA-based
method over conventional method, the results obtained using DICA and dynamic
principal component analysis (DPCA) based methods were compared. After the
faulty signal was introduced, both SPE values increase significantly. Note that
the DICA-based SPE detects exactly the occurrence of sensor fault compared to
the DPCA. It highlights that the DICA-based method has better detection accuracy
when detecting the dynamic non-Gaussian distributed fault.

(a)

(b)

Fig. 3.
Detection of sensor fault and reconstruction using (a) DPCA-based SPE and
reconstruction algorithm, and (b) DICA-based SPE and reconstruction algorithm

The
reconstruction of faulty sensor using the reconstruction algorithm based on
DPCA and DICA methods are shown in the second plots of Fig. 3(a) and (b). Table
1 lists errors of the reconstructed PM₁₀ data using both methods.
The reconstructed values in both figures are consistent with the normal
measurements, where the reconstruction error of the DICA-based method is lower
than that of DPCA-based method. This result illustrates that the proposed
method can capture the system dynamics of non-Gaussian IAQ data. In addition,
it provides a way to effectively reconstruct PM₁₀ faulty value where
the reconstructed IAQ can be used in the IAQ ventilation system instead of the
faulty data.

Table 1.
Errors of the reconstructed PM₁₀ data using DICA- and DPCA-based
methods

Table
2 lists the amount of IAQ ventilation system energy consumption with the faulty
and reconstructed IAQ data. Note that the reconstructed PM₁₀ data
using DICA-based method show a better reconstruction performance and lower
ventilation energy consumption compared to the DPCA-based reconstruction as
well as faulty data. Once the faulty sensor is reconstructed, 307 kWh of the
ventilation energy is reduced. It highlights that the accurate reconstruction
of faulty sensor influences on the energy consumption of IAQ ventilation system
directly.

The
result of this study showed that the proposed method could improve the IAQ
monitoring performance as well as reduce the energy consumption of the IAQ
ventilation system in the underground subway station or buildings.

Table 2. Comparison
of ventilation system energy consumption with the faulty and reconstructed IAQ data

ACKNOWLEDGEMENTS:
This work was supported by the Korea Science and Engineering Foundation (KOSEF)
grant funded by the Korean government (MEST) (KRF-2012-001400) and also by the
National Research Foundation of Korea (NRF) grant funded by the Korea
government (MSIP) (No. 2008-0061908).