Automatic tools for fault detection and diagnostic of HVAC systems

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					Automatic tools for fault detection and diagnostic of HVAC systems for hotel and office
building


Ph.D., H. Vaezi-Nejad, M. Jandon, Ph.D., J.C. Visier,
CSTB (Centre Scientifique et Technique du Bâtiment)
B. Clémençon, F. Diot, J.M. Jicquel
EDF/ARIPA (Electricité De France)

          Summary
          Faults and malfunctioning of HVAC systems in building can lead to important
          waste of energy, decrease of user’s comfort and deterioration of building and
          its facilities. These faults are sometime difficult to detect with present tools and
          can remain in the building during long period of time.
          The aim of our survey is to develop automatic Faults Detection Diagnostic
          (FDD) tools for helping building managers or building engineers to improve
          their task of Buildings facilities supervision.


Introduction
Faults and malfunctioning of HVAC systems in building can lead to important waste of energy,
decrease of user’s comfort and deterioration of building and its facilities. These faults are sometime
difficult to detect with present tools and can remain in the building during long period of time (several
months or years).
The aim of our survey is to develop automatic Faults Detection Diagnostic (FDD) tools for helping
building managers or building engineers to improve their task of buildings’ facilities supervision.
For this survey we have first held an inquiry among buildings managers and maintenance experts to
rank the HVAC systems faults to study on.
Then we have developed FDD tools with the help of Building Management System (BMS) experts
and building managers. We have tested these tools with simulated data and real data collected from
BMS of two test buildings.
Today, we are in the validation phase, testing the tools in-line in the two buildings:
-   In the first building, an hotel building, the manager use the FDD tool,
-   In the second building, a commercial building, an engineer of the maintenance teams uses the
    FDD tool.
The next phase will be to develop more generic and robust tools that can be installed in a set of hotel
and office building.




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This work has been done in the framework of International Energy Agency Annex 34 “Computer-
aided Evaluation of HVAC System Performance: the Practical Application of Fault Detection and
Diagnosis Techniques in Real Buildings” [5].


FDD approach.
In the field of FDD there is two main approaches: top-down or bottom-up approaches [2] and [3].


With the bottom-up approach, the system or equipment to survey is break down into subsystems or
elementary elements in order to identify the causes of malfunctioning. Then, different mathematical
methods (usually based on the comparison of non-faulty model of the element with the real element)
can be used in order to detect the faults of the element [6]. This approach is usually exhaustive,
detailed, make easy the following task of diagnosis (definition of the cause of a fault) and is mainly
maintenance team user oriented. But on the other hand, the detailed work can produce large number of
cases with the difficulty to find a solution or to define the main problem to solve.


The top-down approach is based on the global analyse of the building. The main faults of the building
are defined according to different criteria: waste of energy, discomfort for occupants, deterioration of
building and its facilities, difficulty for the users to find the faults with traditional systems (BMS,
vibration analysis, …), etc. Then, mathematical approach similar to the bottom-up approach can be
used to detect fault [4] and [8]. This approach allows a gradual work (from important to less important
faults) on different faults in the building, an effective definition and orientation of the work on faults
that cause serious consequences in the building and is mainly building manager user oriented. But on
the other hand, the global view makes difficult any detailed diagnosis.


Our FDD method is based on a top-down approach and a development oriented from the beginning to
the end-users needs.
For our development we have followed the seven next phases:
1) Definition of the major faults for managers of hotel and office building equipped of convectors
    and fan coil unit. This list of fault has been established by interviews of a large number of building
    managers and building engineers.
2) Expert analysis of the faults and selection of those that had the most important consequences
    according to fourth criteria:
    -   Comfort,
    -   Energy consumption and its costs,
    -   Deterioration of facilities,
    During this phase, we have selected two buildings, a hotel and an office building also with two
    different BMS' supplier. We have begun the monitoring of these buildings and asked their


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    managers to archive measurements that could serve us in the development of our faults detection
    methods.
3) Development of FDD method based on the expert rules. We have begun to develop for each fault
    a book of specification in order to establish needs and constraints for the fault detection methods.
4) Assessment of FDD method with buildings simulators for hotel and office buildings. We have
    realised for this phase building simulators [7] in order to have databases in normal and faulty
    conditions of the buildings.
    During this phase we have established and structured the databases of hotel and office building.
    The state of our databases is summarised in the following table.

      Building     Type            Equipment              State                 Exploitation
                 Real Data                         Started               Used to validate
                              Convector
                 Altiport                          since 12/12/98        the FDD tool
                                                   Realized
      Hotel                                                              Used to assess
                 Simulated    Convector            Normal case
                                                                         fault detection method
                 Data                              + 10 Faulty case
                              Fan Coil Unit        Not realized
                 Real Data                         Started               Used to validate
                              Fan Coil Unit
                 EDS                               since 18/02/99        the FDD tool
                                                   Realized
                                                                         Used to assess
                              Convector            Normal case
      Office                                                             fault detection method
                 Simulated                         + 7 Faulty case
                 Data                              Realized
                                                                         Used to assess
                              Fan Coil Unit        Normal case
                                                                         fault detection method
                                                   + 12 Faulty case
                    Table 1. State of the databases used for developing FDD tools .


5) Development of user interfaces for our FDD tools.
6) Evaluation of FDD tools off-line with the real building databases. During this phase we have
    tested and adapted the FDD methods and the FDD users’ interfaces.
7) Evaluation of FDD tools in-line in the building with the help of end-users.


The last phase will be to assess fault detection software on other sites and to develop more generic
FDD tools that can be implemented in a set of hotel and office buildings.


The following graph summarises our development process of FDD tools:




                                                                                                           3
                                                                1

                                                     Inventory &Analyse of
                                                        faults to detect
                                                        with end-users




                        Implement faults                        2                       Fit interface according
                           to detect                                                      to users demands
                                                   Selection of faults to treat
                                                      with BMS suppliers



                                                           Select Fault
                                                             to treat
                                     Correct and
                 4                                              3                   Implement                      5
                                      Adjust the
                                                                                   FDD methods
                                      methods
        Assesment of FDD                              Development of FDD                                   Development of user
      methods with simulation                              methods                                              interface
                                      Test the                                     Adjust FDD
                                      methods                                       methods


                                                                                    Implement
                                                         Adjust methods                          Correct and
                          Correct simulations                                         in site
                                                                                                 adjust FDD
                              or models                                                           methods
                                                                6

                                                   Off-line validation with real
                                                                data



                                                            Implement                           Required link
                                                             in BEMS
                                                                                                Iterative link
                                                                7                               Optional link

                                                      On-Line validation in
                                                           buildings




                                      Figure 1. FDD development process chart.


Buildings and databases description
Buildings description
The main features of our two buildings are the use of electricity as main source of energy and the
individualisation of comfort conditions.


Rooms and offices of these buildings are equipped therefore of individual systems of heating and air-
conditioning: electric convectors in rooms of the hotel and fan coils unit in offices.


These systems are equipped of intelligent room controllers. They allow the transfer toward the BMS
central unit of information useful for the managers to survey its buildings. These information can also
be archived on the central unit in order to permit verifications and the further balances on facilities.
It is therefore possible to have a large number of information on the supervisor: indoor temperatures,
orders of actuators, states, etc. in each room of the building. This important quantity of information is
difficult to analyse by the building manager who has daily just a short time to use the central unit.




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The aim of our survey is to develop automatic FDD tools for helping building managers in order to
facilitate and to improve their usual diagnosis task of the state of their buildings and facilities.


Hotel Databases
For the hotel, we have chosen to monitor 11 rooms. These 11 rooms are distributed on the north front,
the south front and in the different floors. The selected rooms are presented on the next synoptic.




      Figure 2. BMS synoptic of the hotel building representing the rooms and different facilities.


For the hotel building we monitor the following measurements.
Hotel Building
Level                                                            Monitored data
                                     The outside temperature, cyclic ratio of the limiter, the cyclic ratio
Building
                                     of the south and north floor heating system
                                     The indoor temperature, the indoor temperature setpoint, the
Rooms
                                     electricity demand for each convector.
Hot water tanks                      The running permission of heating for the tank, the hot water
(to produce sanitary hot water)      storage temperature, the hot water consumption.
                                     The powers subscribed for each rate, the global consumption of
Electricity energy meters
                                     electric energy during off-peak hours, full hours and peak hours



                                                                                                          5
Hotel Building
Level                                                            Monitored data
                                     and the electric energy consumption for the heating.
                                     The running permission of lighting for 2nd, 3rd, 4th and 5th floor
Other facilities
                                     and the running permission of the Jacuzzi.
                                 Table 2. Database of the hotel building.


Office Building data Base
For the office building, we have chosen to monitor 10 different offices. These 10 offices are
distributed on the Southwest, Northeast front and in the 1st and 2nd floor. Offices selected on the 1st
floor are presented on the next graphic.




            Figure 3. Plan of the second floor of the office building with the selected offices.


For the office building we monitor the following measurements.
Office Building
Level                                                            Monitored data
Building                             The outside temperature
                                     The indoor temperature, the indoor setpoint temperature, the state
Offices                              of the fan coil unit (start/stop), the percentage of hot and of cold
                                     demand and ventilation speeds, the change/over state.
                                     The supply temperature, the supply setpoint temperature, the state
AHU                                  of the supply and return fan, the control signal of the hot and cold
                                     coils valves, the control signal of electrical coils.
Electricity energy meters            The powers subscribed for each rate, the global consumption of




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Office Building
Level                                                           Monitored data
                                    electric energy for the heating and the air-conditioning (during off-
                                    peak hours, full hours and peak hours).
                                    The departure temperature, the departure setpoint temperature, the
Heat Pump Unit
                                    return temperature, the change/over state, the water flow rate.
                                    The running permission of heating for the tank, the storage water
Hot water storage tank
                                    temperature
                                Table 3. Database of the office building.


All those information monitored by the BMS are treated by the FDD tools and the results of the
analysis are presented to the building managers. Thus, the building managers can decide if any action
need to be implemented: maintenance task, using BMS for further investigation and more detailed
diagnostic, waiting for more FDD results before decision, …


Fault detection method
The FDD method we have used is based on the following approach.
1) To define the specific symptom for each fault to detect
    The definition of a specific symptom for each fault implies to determine the variable or the
    parameter of the installation that can highlight the presence of this fault by an abnormal behaviour.
    Example of symptom: temperature too low in occupation.
2) To define conditions of validity for each fault in order to limit the false alarms
    The definition of validity conditions for each fault implies to determine what are modes of the
    building for which the presence of a symptom means the existence of the fault.
    Example of validity condition: to validate the detection of low temperature during occupation we
    need to be in occupation mode, not in boost mode, to be in normal outside temperature mode (not
    too low), etc.
3) To define for each fault the likely causes.
    The definition of likely causes is an "up-to-date" procedure that is gained in the time. It consists to
    the characterisation of reasons that produces a given fault.
    Example of causes: the heating system is manually switched off, open window, control is out of
    order...




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We have applied this procedure to the set of faults defined in the following list.
                                                              Type of                   Impact                     Detection
 Equipment                  Faults or symptoms                                                   Damaging                               Complexity
                                                              Building     Comfort     Cost                   Time          Space
                                                                                                 equipment
   Electric
                boost during the high tariff hours              O.B.           0     + to +++        0       delayed        global          low
   heaters

                electric heater too frequently used           O.B./H           +       +++           0       delayed     local/global       low

                filter's fouling                              O.B./H           +         +          +++      delayed     local/global    low/high
  Fan coils
                simultaneous funtionning of heating
                                                              O.B./H           +       +++           0       delayed         local          low
                and cooling
                late boost (during eating or cooling
                                                                O.B.         +++        ++           0       delayed     local/global    low/high
                period)
    Electric
 heaters and    overheating during occupancy period           O.B./H          ++       +++           0       delayed     local/global       low
   fan coils
                underheating during inoccupancy
                                                              O.B./H           0         0          +++      delayed     local/global       low
                period

 Mechanical
                abnormal functioning during
 ventilation/                                                 O.B./H           i       +++           +       delayed        global          low
                inoccupancy
    AHU

                storage heating during the high tariff
                                                                 H             0       +++           0       delayed        global       low/high
                hours
 Hot Water      derogation has no effect                         H           +++         i           0       on line        global         high
                                                                                                             on line /
                lack of hot water                                H           +++         i           0                      global         high
                                                                                                             delayed
                H    hotel                                                 +++ major impact
                O.B. offices building                                      ++ medium impact
                                                                           + low impact
                                                                           0 no impact
                                                                           i  indirect impact

                                                       Table 4. List of faults to detect.


Presentation of the FDD Tool
The FDD tools are composed of six main modules presented in the following flow chart.
                                                                         Acquisition Interface

                                              Module 1        Transfert Data from BMS Database to
                                                                         FDD database


                                                                             Data Filtring

                                              Module 2       Eliminate inconsistant values and filter
                                                              data with a moving average window


                                                                           Mode estimation
                                              Module 3
                                                                 Estimation of operating modes



                                                                         Threshold estimation

                                              Module 4
                                                                 Estimation of FDD thresholds



                                                                         FDD rules application

                                              Module 5
                                                                           Apply FDD rules



                                                                       Diagnostic application

                                              Module 6
                                                                     Suggest likely fault causes


                                            Figure 4. Main modules of the FDD tools.


                                                                                                                                                     8
The module “Acquisition Interface” ensures the transfer of data between the BMS databases and FDD
database. This module needs to be adapted different type of BMS (different types of databases).
The module “Data Filtering” eliminates inconsistent data and filtered data with a moving average
filter.
The module “Mode estimation” helps to predict the different running modes of the building and its
facilities (occupation/non-occupation modes, boost mode, heating/cooling modes, etc.).
The module “Threshold estimation” calculates the thresholds for FDD rules according to the user
sensibility choices, the different setpoints or estimated modes.
The module “FDD rules application” detect the different faults according to expert rules, the
thresholds and the modes estimated by the previous modules.
Finally, the module “Diagnostic application” suggest likely diagnostic for the detected faults.


The FDD tools are developed with C language for the calculation part. The user interface is at the
present builds with MS Excel.
In the first view (first window) of the FDD tools, the building manager can know quickly if there is
any fault detected, where are the faults (location in the building) and the seriousness of the detected
fault.
                                                      Selection of the
                                                                                            View of the hotel
               View of the                            time period to
                                                                                            rooms
               facilities                             analyse




                                  An orange signalet Hot           A click on a box give
                                                                      access to specific                        A red signaled room
                                   Water Tank indicate a
                                                                   graphs that help the                         indicate a serious fault
  A click on this box give            low important fault
                                                                 user to understand the
 access to details about
                                                                           detected fault
       the detected faults

                             Figure 5. First window of the FDD tool for hotel building.



                                                                                                                                9
If the building manager needs more details, he can get information about the time of the detection of
the faults and the type of detected faults (explanation about the faults).
                          Fault detection           Explanation about
                          week                      the detected fault




                                                     Number of fault
                                                                            Type of detected
            Access to specific                       detected for a
                                                                            fault
          graphs that help the                       room or a facility
        user to understand the
                 detected fault


                              Figure 6. Second window of the FDD tool for hotel building.


Conclusions
We have developed two FDD tools, FDD_Hotel and FDD_Office, for our two different buildings
(hotel and office buildings) and users. In the hotel building, the user is the manager and in the office
building, an engineer of the maintenance teams.
In the two cases we have tried to adapt the tools to the end-users’ demands:
-   Easy to use.
-   Presentation of the results on a long period of time :
    -    4 weeks for the hotel managers who works on a weekly-based period. The HVAC equipment
         supervision is a secondary task for the hotel manager as her main job is to give to her client a
         comfortable stay.
    -    1 month for the maintenance engineer who generally use the tools more often (several times
         during the month period). But the maintenance engineer has to work on different buildings
         and he needs at the end of each month a global result in other to plan its different tasks.




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-   Possibility to select or unselect the faults to detect and to set the sensibility of fault detection
    method (in order to view only high, medium or low important faults). These procedures help the
    user to organise and prioritise its maintenance tasks and to decrease the rate of false alarms.
-   Possibility to access to details for a better understanding of the fault detection process.


Today, and after a first phase of off-line validation we are in the phase of testing the tools in line in the
buildings.
The next steps will be to test the tools in on a larger sample of buildings in order to have more generic
tools that could be implemented in a set of buildings.


References

(1) Dexter A L, Benouarets M. 1996. A generic approach to identifying faults in HVAC plants.
    ASHRAE Transactions, 102(1), 550-556.
(2) Hyvärinen, J. and al. 1996 Building Optimisation and Fault Diagnosis (BOFD) source book
    document - IEA Annex 25). VTT, Finland, ISBN 952-5004-10-4.
(3) Isermann R., 1984, Process fault detection based on modelling and estimation methods - A survey,
    Automatica, Vol. 20, n°4, 387-404.
(4) Li X., Visier J.C., Vaezi-Nejad H., A Neural Network Prototype for Fault detection and Diagnosis
    of Heating Systems, Ashrae Winter meeting 1997, Philadelphia, in Ashrae Transactions (to be
    published).
(5) Pakanen J., Dexter A. L., and al 2001, Computer-aided Evaluation of HVAC System Performance:
    the Practical Application of Fault Detection and Diagnosis Techniques in Real Buildings, source
    book document, To appear in IEA source book.
(6) Rossi T M and Braun J E. 1997. A statistical, rule-based fault detection and diagnostic method for
    vapor compression air conditioners. International Journal of HVAC&R Research, 3(1), 19-37.
(7) Vaezi-Nejad H., Jandon M., Visier J.C., Clemençon B, and al, Real Time Simulation of a Building
    with Electrical Heating System or Fan Coil Air Conditioning System, C, CLIMA 2000,
    BRUXELLES, 31/08-2/09/97.
(8) Visier J C., Vaezi-Nejad H., Corrales P. 1999. “A fault detection tool for school buildings.”
    ASHRAE Transactions. 105(1): pp. 543-554.




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