(552c) Fault Detection and Isolation and Optimal Parking for HVAC Systems

Fault detection
and isolation and optimal parking for HVAC systems

Hadi
Shahnazari, Craig McDonald, Prashant Mhaskar^*, John House and Tim
Salsbury

Government
regulations and initiatives have placed a large emphasis on the reduction of
energy consumption and increase in energy efficiency. Heating, ventilation, and
air-conditioning (HVAC) systems are responsible for 40-50% of total building
energy consumption. In general, 15 to 20% per annum of energy consumption can
be reduced by efficient and optimal operation of buildings. A major factor
contributing to this inefficiency are undiagnosed faults (such as stuck
dampers) being compensated through wasted, energy intensive effort (higher
static pressures). In US alone, the fault detection and isolation (FDI) and
fault tolerant control methods are estimated to be capable of saving 10-40% of
HVAC energy consumption (see e.g., [1]).

These
realizations have motivated significant research effort on devising FDI
frameworks for HVAC systems (see e.g., [1], [2], [3] and [4]). In [2], a fault
detection tool is proposed that uses a set of expert rules derived from mass
and energy balances to detect faults in air handling units (AHUs). A subset of
the expert rules which correspond to that mode of operation are then evaluated
to determine whether a fault exists. In [3], a principal component analysis
(PCA) based approach is presented to detect single sensor faults in heating,
ventilation and air conditioning (HVAC) systems and faults are isolated using
joint angle plot, which compares the new fault vector with known ones in the
library. In [4], a PCA approach is used to extract the correlation of measured
variables in heating/cooling billing system and reduce the dimension of
measured data. Square prediction error (SPE) statistic is used to detect sensor
faults in the system. Then, sensor validity index (SVI) is employed to identify
faulty sensors and a reconstruction algorithm is presented to recover the
correct data of faulty sensor in accordance with the correlations among system
variables. In [5], a combination of PCA and wavelet transform has been used for
FDI of HVAC systems. To this end, at first wavelet transform has been used to
remove effect of normal weather conditions changes from data. Then PCA has been
used as a data driven methodology for fault detection and isolation. The
existing results, however, consider only isolation of single fault scenarios or
at best multiple sensor faults or multiple actuator faults, and do not consider
simultaneous actuator and sensor faults.

There
also exist results on fault tolerant control (FTC) of HVAC systems. In [6], the
control design compensates for the fault effect as much as possible by
switching between different control modes available in the air handling unit
design. In [7] and [8], single sensor faults are diagnosed via estimation of
healthy value of sensors using PCA method and handled by utilizing the healthy
values of sensors in the closed loop, upon fault isolation. In [9], the fault
tolerant control design is based on real time estimation of the fault
magnitude, and determining MPC constraints (input constraints) based on those
values. In this way, MPC design explicitly accounts for fault effect. These
fault-tolerant control approaches, however, are all predicated on the idea of
maintaining nominal operation, which might simply be impossible, or very
expensive, in case of certain faults. Recently, safe-parking based approaches
for fault-tolerant control have been proposed (see e.g., [10] and [11]) that
park the process at an appropriate operating point, instead of trying to
maintain nominal operation. These ideas, however, have not been applied to HVAC
systems. In summary there is lack of results in simultaneous actuator and
sensor fault detection and isolation and optimal parking of HVAC systems to
minimize energy expenditure.

Motivated
by the above considerations, in this work, we design and implement an
integrated framework for fault diagnosis and fault handling in HVAC system. The
results include simulations as well as application to a room at McMaster
University (room 207 of Hamilton Hall). To this end, first, we identify a
linear model for the room HVAC system using the existing data. Then we design a
model based FDI framework based on the methodology proposed in [12]. The key
idea is to exploit analytical redundancy in the system through state observer
design. We consider subset of actuator and sensor faults and design observers
that only use information of inputs and outputs that are not subject to fault.
Then we generate residuals that are only sensitive to a subset of fault. The
fault is detected if the corresponding residuals breach their thresholds and
isolated using a bank of residuals and a logic rule. We also compare the result
obtained using model based approaches for fault detection and isolation (FDI)
with a principal component analysis (PCA) based approach (see e.g., [13]).
Finally, to minimize energy expenditure, we design and implement an optimal
parking approach that minimizes energy expenditure in the presence of a fault.

References

[1] Jeffrey Schein,
Steven T Bushby, Natascha S Castro, and John M House. A rule-based fault
detection method for air handling units. Energy and Buildings, 38(12):
1485-1492, 2006

[2] John M House,
Hossein Vaezi-Nejad, and J Michael Whitcomb. An expert rule set for fault
detection in air-handling units. Transactions-American Society of Heating
Refrigerating and Air Conditioning Engineers, 107(1):858-874, 2001.

[3] Zhimin Du and
Xinqiao Jin. Detection and diagnosis for sensor fault in HVAC systems. Energy
Conversion and Management, 48(3):693-702, 2007.

[4] Youming Chen and
Lili Lan. Fault detection, diagnosis and data recovery for a real building
heating/cooling billing system. Energy Conversion and Management, 51(5):1015-1024,
2010.

[5] Shun Li and Jin
Wen. A model-based fault detection and diagnostic methodology based on PCA
method and wavelet transform. Energy and Buildings, 68:63-71, 2014.

[6] John E Seem.
Integrated control and fault detection of HVAC equipment, May 1 2001. US Patent
6,223,544.

[7] Xiaoli Hao,
Guoqiang Zhang, and Youming Chen. Fault-tolerant control and data recovery in
HVAC monitoring system. Energy and buildings, 37(2):175-180, 2005.

[8] Xinqiao Jin and
Zhimin Du. Fault tolerant control of outdoor air and AHU supply air temperature
in VAV air conditioning systems using PCA method. Applied Thermal Engineering,
26(11):1226-1237, 2006.

[9] Sorin C Bengea,
Pengfei Li, Soumik Sarkar, Sergey Vichik, Veronica Adetola, Keunmo Kang, Teems
Lovett, Francesco Leonardi, and Anthony D Kelman. Fault-tolerant optimal
control of a building HVAC system. Science and Technology for the Built Environment,
21(6):734-751, 2015.

[10] Rahul Gandhi and
Prashant Mhaskar. Safe-parking of nonlinear process systems. Computers &
Chemical Engineering, 32(9):2113-2122, 2008.

[11] Miao Du and
Prashant Mhaskar. A safe-parking and safe-switching framework for fault
tolerant control of switched nonlinear systems. International Journal of
Control, 84(1):9-23, 2011.

[12] Miao Du, James
Scott, and Prashant Mhaskar. Actuator and sensor fault isolation of nonlinear
process systems. Chemical Engineering Science, 104:294-303, 2013.

[13] Seongkyu Yoon and
John F MacGregor. Statistical and causal model-based approaches to fault
detection and isolation. AIChE Journal, 46(9):1813-1824, 2000.