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

Transcription

1 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. 1

2 This work has been done in the framework of International Energy Agency Annex 34 Computeraided 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 2

3 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 Hotel Office Real Data Altiport Simulated Data Real Data EDS Simulated Data Convector Convector Fan Coil Unit Fan Coil Unit Convector Fan Coil Unit Started since 12/12/98 Realized Normal case + 10 Faulty case Not realized Started since 18/02/99 Realized Normal case + 7 Faulty case Realized Normal case + 12 Faulty case Used to validate the FDD tool Used to assess fault detection method Used to validate the FDD tool Used to assess fault detection method Used to assess fault detection method 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

4 1 Inventory &Analyse of faults to detect with end-users Implement faults to detect 2 Selection of faults to treat with BMS suppliers Fit interface according to users demands Correct and 4 Implement Adjust the 3 5 FDD methods methods Assesment of FDD methods with simulation Test the methods Select Fault to treat Development of FDD methods Adjust FDD methods Development of user interface Correct simulations or models Adjust methods Implement in site Correct and adjust FDD methods 6 Off-line validation with real data Implement in BEMS Required link 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 airconditioning: 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. 4

5 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 Building The outside temperature, cyclic ratio of the limiter, the cyclic ratio of the south and north floor heating system Rooms The indoor temperature, the indoor temperature setpoint, the electricity demand for each convector. Hot water tanks (to produce sanitary hot water) The running permission of heating for the tank, the hot water storage temperature, the hot water consumption. Electricity energy meters The powers subscribed for each rate, the global consumption of electric energy during off-peak hours, full hours and peak hours 5

6 Hotel Building Level Other facilities Monitored data and the electric energy consumption for the heating. The running permission of lighting for 2nd, 3rd, 4th and 5th floor 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 6

7 Office Building Level Heat Pump Unit Hot water storage tank Monitored data electric energy for the heating and the air-conditioning (during offpeak hours, full hours and peak hours). The departure temperature, the departure setpoint temperature, the return temperature, the change/over state, the water flow rate. The running permission of heating for the tank, the storage water 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... 7

8 We have applied this procedure to the set of faults defined in the following list. Equipment Electric heaters Faults or symptoms Type of Building Comfort Impact Cost Damaging equipment Time Detection Space Complexity boost during the high tariff hours O.B. 0 + to delayed global low electric heater too frequently used O.B./H delayed local/global low Fan coils Electric heaters and fan coils filter's fouling O.B./H delayed local/global low/high simultaneous funtionning of heating and cooling O.B./H delayed local low late boost (during eating or cooling period) O.B delayed local/global low/high overheating during occupancy period O.B./H delayed local/global low underheating during inoccupancy period O.B./H delayed local/global low Mechanical ventilation/ AHU abnormal functioning during inoccupancy O.B./H i delayed global low Hot Water storage heating during the high tariff hours H delayed global low/high derogation has no effect H +++ i 0 on line global high lack of hot water H +++ i 0 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. on line / delayed global high 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 Module 3 Mode estimation 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

9 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. View of the facilities Selection of the time period to analyse View of the hotel rooms A click on this box give access to details about the detected faults An orange signalet Hot Water Tank indicate a low important fault A click on a box give access to specific graphs that help the user to understand the detected fault Figure 5. First window of the FDD tool for hotel building. A red signaled room indicate a serious fault 9

10 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 week Explanation about the detected fault Access to specific graphs that help the user to understand the detected fault Number of fault detected for a room or a facility Type of 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. 10

11 - 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 A generic approach to identifying faults in HVAC plants. ASHRAE Transactions, 102(1), (2) Hyvärinen, J. and al Building Optimisation and Fault Diagnosis (BOFD) source book document - IEA Annex 25). VTT, Finland, ISBN (3) Isermann R., 1984, Process fault detection based on modelling and estimation methods - A survey, Automatica, Vol. 20, n 4, (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 A statistical, rule-based fault detection and diagnostic method for vapor compression air conditioners. International Journal of HVAC&R Research, 3(1), (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 A fault detection tool for school buildings. ASHRAE Transactions. 105(1): pp