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With monitoring we observe the actual behaviour of the system.

Management Information Systems Part 5: Monitoring and System Identification Prof. Dr.-Ing. Raimar J. Scherer Institute of Construction Informatics. Goal. With monitoring we observe the actual behaviour of the system. The goal is to find out if the function of the system is. as planned

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With monitoring we observe the actual behaviour of the system.

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  1. Management Information SystemsPart 5: Monitoring and System IdentificationProf. Dr.-Ing. Raimar J. SchererInstitute of Construction Informatics

  2. Goal With monitoring we observe the actual behaviour of the system. The goal is to find out if the function of the system is • as planned • sufficient • optimal We compare the actual behaviour with planned behaviour, so we receive the deviations. What we want to know is why the system behaves in another way as planned, calculated and finally designed.

  3. Monitorable Quantities • In a pipe system, we can monitor • Water input at input node • Water consumption at output node • Water throughput at pipe start/end • Pressure at each node • All these quantities are time-dependent Example of water flow record (it is a simulated one, not a realrecord).

  4. Monitoring requirements and methods Need: stationary system for a certain time window Goal: approximate time-dependent, recorded quantities by step-wise constant functions Methods: - Signal Analysis - Statistics, stochastic Methods - Data Mining: classification, subsumption, separation Technology (e.g. RFID system mounted with needed sensors)

  5. Data capturing technology • Continuous capturing of information Construction parts Sensors reading location identification ⇨ Identification & localization using RFID techniques • Collecting and analyzing of the captured informationConsolidation of information Derivation of the actual states Data handling and application optimizing Description of RFID techniques using a suitable tools (e.g. ontology based knowledge base) ⇨

  6. RFID system • RFID system mainly encompasses: • RFID tag (transponder): • Passive transponders, Active and A semi-active tag • Reader: • Handhelds Reader, Stationary Reader • Data processing subsystem (Middleware): • It denotes software that acts as intermediary between the reader and the various application software like Enterprise Resource Planning (ERP) software. Tag Data Chip Antenna Reader Application server Middleware RFID System

  7. Example for acquisition data technology Virtualworld Real world Sector 1 Sector 2 Sector i Processended Strating process Sector n Gate Real-time Data Product ID, Status, timestamp, location… RFID Reader Event layer MobileReader RFID Reader Internet Network Refrence location, work zone… RFIDReader Network Network layer A concept system for embedded RFID technology based on RFID reader Work-zone Site Lay-outlayer

  8. Monitoring data processing Solution, pragmatic and straightforward: • Zero line correction • Trend estimate (by moving average or other low pass filtering) • Moving averageStandard Deviation (STD) • Selection of time windows with nearly constant trend • AND • small STD ! Recommended window length of moving average = 3-5 cycles of the fluctuations

  9. Monitoring data processing Select time windows with nearly constant trend AND small STD! 4 selected windows, however, only window 2 is recommended to be used, due to the strong STD in the other windows. Problem: The selected time windows of all measurement points throuout the system have to coincide 4 1 2 3

  10. Consequences due to Approximations (1) Values correct Measuring Point A B C D Values correct new Approximation D=0 Measuring Point A B C D 10

  11. Consequences due to Approximations (2) Values Measuring Point A B C D Values Measuring Point A B C D 11

  12. Consequences due to Approximations (3) St’Venant principle: In elastic systems approximations are following St’Venant principle: Changing in 1 point, propagates in the neighbourhood points in a decreasing exponential function in the mean. Data Mining: The mean values of different sets of measurements leading to the best trained neural network 12

  13. System Deviations We can compare recorded quantities (actual) with planned quantities (to be) and calculate their deviation DQ = Qto be – Qactual Dp = pto be – pactual With this deviation quantities we can construct an descriptor and classify the system in classes, using data mining methods, i.e. to find out classes like • good performance • sufficient performance • under performance • bad performance • or classes like • no improvement necessary • improvement recommended • improvement urgent • The same can be done considering only subsystems. This is a first step. • As the second step, we want to know what has to be improved. This needs methods called system identification.

  14. System Identification Problems: • Each system parameter can take values which are different from its assumed value, hence each parameter is an unknown quantity.Usually we are not able to measure so much quantities, hence we are faced with an over determined math. system • Often we can not measure the system parameter directly. We can only measure a deduced quantity.System parameters are: measurableroughness k: nowater input Qin: yeswater consumption: yes (at all points?)water loss: no • the system can be non-linear • the system can be non-stationary • the system can be stochastic

  15. System Quantification One possible load case:

  16. System Identification Solution: (pragmatic) • assume a deterministic system • consider only time windows with approximate stationary system behaviour, i.e. constant trend, small STD • consider only windows with approximately linear system behaviour • Now the system can be mathematically formulated as a linear matrix equation, which can be solved (inverted) if the matrix is regular.But we are still faced with the problem, that the math. system is over-determined, because we have less measured quantities than unknown system parameters. This means, there exist many system states which are explaining (fitting to) the measured quantities (4) We have to find strategies to reduce the number of unknowns, i.e. to find those system parameters - which have most probably not or little changed - which have most probably considerably changed

  17. System Dependencies We can establish a system influence function - for each system parameter - for each measurable quantity A system influence function • is a deviation of a system state according to its planned behaviour, if only one parameter change. • shows dependencies (relationships, "association rules") between one system parameter and all other parameters. One can identify • the strongest relationship(s) in an ordered form. • certain patterns of deviation between the real system influence function and the system influence function of the planned ('to be') system. (Information Mining methods of pattern recognition, pattern matching)

  18. System influence function for roughness Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of Change of roughness for pipe2 Change of roughness of several pipes

  19. System influence function for roughness p as planned (1) (2) (1) ≡ (2) Q Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of Change of roughness for pipe2 Change of roughness of several pipes

  20. System influence function for roughness (2) Dp (1) DQ (1) ≡ (2) ; DQ = 0 p as planned (1) (2) (1) ≡ (2) Q Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of Change of roughness for pipe2 Change of roughness of several pipes

  21. System influence function for water loss water loss Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of water loss in pipe 2 and/or node 3 Remark: A continuous water loss can not be monitored but only the loss accumulated up to the next measurement point

  22. System influence function for water loss p as planned (1) (1) Q planned water loss Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of water loss in pipe 2 and/or node 3 Remark: A continuous water loss can not be monitored but only the loss accumulated up to the next measurement point

  23. System influence function for water loss Dp (1) DQ (1) p as planned (1) (1) Q planned water loss Qin 1 2 3 4 1 2 3 4 5 Qout2 Qout3 Qout4 Qout5 All parameters are unchanged, except of water loss in pipe 2 and/or node 3 Remark: A continuous water loss can not be monitored but only the loss accumulated up to the next measurement point

  24. Identificators We need identificators. Identificators are • recorded values • derivatives, e.g. changes of curves • trend in curves • any combination of (1) – (3) In the curves of the system influence function we have already learned someidentificators, namely: • DQ is total water loss • change of DQ is local water losslocation is anywhere between the two measurement points • is the pressure loss due to increased roughness; location see (2)

  25. System Identification Strategies (1) Identify water loss in DQ function (2) Then identify change of roughness in Dp functionRemark: Because Q and p are interdependent, but not Q and k. Therfore the expected DpQ due to water loss has to be extracted form from Dp to receive Dproughness (3) investigate as much as possible different system states, to receive mean values and to reduce mal identifications (4a) either an optimization problem is mathematically formulated.- A practical overkill - OR (4b) data mining methods are applied in order to find the most probable changed system quantities

  26. System Identification Strategies (5) Reduce the number of system parameters as much as possible by neglecting all those, which change not very much, i.e. assume for them D=0 (6) repeat this for different system states for identifying the best fitting (right) set of D=0. Different system states have show the same (7) improve this procedure by using the subsystem method, i.e.:dividethe system in subsystems by cutting the branches from the main system. Repeat the above identification procedure for each subsystem as well as the remaining main system

  27. System Identification Strategies (8) repeat the sub system method several times by using different partitions (see methods in data mining for choosing training and test sets) (9) Densify the sensor system by adding new sensors and repeat the recording (10) Move sensors from less important parts of system, where no or little changes are identified to parts where changes are stronger in order to densify the sensor system there. Use the preliminary identified parameters for the less important part of the system or cut those parts if they can be approximated as subsystems

  28. Monitored System We can not (up-to-day) monitor all water output nodes. Therefore we reduce by cutting the total system to the monitored system. At each cutting point we need Q and P - recording or an assumption of Q and P consumption. Thismonitoredsystem for the as planned behaviour is named basic sensor system. For each basic sensor system several measurements could be carried out (not modelled here) resulting in several system states.

  29. Sensor System PQ • Each sensor system have to be partionable into two subsystems: • a basic sensor system and an investigating Sensor system. • Basic sensor system PQ PQ PQ PQ PQ Sensor system The total monitored system / subsystem have to be chosen. The related input and output nodes (incl the cutted nodes) have to be determined, where sensors have to be allocated in order to obtain a fully deteminable (computable) system. This is a system investigating case equivalent to a load case.

  30. Sensor System PQ 2. Investigating sensor system PQ PQ Q Q P PQ PQ PQ Q P In addition to the basic sensor system we can allocate additional sensors, which allow us to investigate the system. For each monitored value, we can evaluate one system parameter. If we would do so, we would assume, that the total actual (delta) behaviour of the system we observe with this one sensor can be explained with one altered system parameter. This would be mathematically correct but in reality wrong, because one model assumption of a 1:1 relationship is wrong ( System-Strategy).

  31. Evaluation process • Store all data from each monitoring campaigns. • Retrieve selected data from monitoring campaigns • and instantiate a basic sensor system and a complementary • investigating sensor system. • Release a set of system parameters to be the unknown • parameters of the system and adjust them to the monitored • system state. • Repeat this for a different set of system parameters. • Repeat this for different parts of basic / investigating • sensor systems. • Repeat this for several monitoring campaigns.

  32. Monitoring

  33. Data Dimension for Evaluation Sensor System System design values constrains Req. values Monitoring n Cases 1 . MC : P1Q 2 . MC 3 . MC 4 . System Investigation 1.1 S I : Selected (sub) system Retrieve design values Retrieve monit. Values 1.2 1.3 Data Dimension for Evaluation Evaluation Keval 1.11 EV :{ΔP,ΔQ} {Qloss, Kact} 1.12 EV 1.13 EV deduced Updated System {Qloss , Kact}

  34. Sensor System Planning Sensors are always limited, because of • procurement • installation • maintenance • signal processing • costs Planning of a Sensor system should include always update campaigns of the sensor system, i.e. densifying of the system at hot areas. This includes movement of sensors

  35. Resume System identification needs information management of • many sensor system states • many influence function studies ( = system simulations) These system states and influence functions have to be classified, clustered and analysed in different combinations (clusters) in order to find out the most liable parameter set and the related values best explaining the monitored data. Therefore system states and influence function data are to be stored and managed by a data base system. Influence functions can be seen as a particular system state and hence have the same data structure

  36. Data Management for Design 1) System design values (= plain system data model) Topology, length, pipe parameters, etc. 2) Required values max pressure in pipe min pressure at output 3) System load parameters Water consumption, pressure at the input 4) System behaviour value ( = reaction values) pressure at output pressure at node, in pipe. water input water put through

  37. Data Management for Monitoring 5) Monitoring of system states • water input • pressure at node • water consumption • water put through • pressure at output

  38. Data Management for System State • 6) Investigated System Case • Retrieve of selected system the design parameters (1) • Retrieve of selected system the monitored values • which are to be used as load parameters (3) • Calculate expected (as planned) system behaviour values (4) • Store system behaviour values (4) • Retrieve of complementary monitored (actual) values • Calculate ΔQ, Δp values. • Report in graphical form Q , p , ΔQ , Δp diagrams.

  39. Data Management for Evaluation • 7) System identification (Evaluation Case) • Release a set of system parameters (K , Qloss)(determined from the strategy). • Calculate the released system parameters. • Store the calculated system parameters. Repeat step 6 Repeat step 5-6 Repeat step 4-6

  40. Data Management for Decision • 8) Decision • Retrieve recalculated system parameters • Use statistics and data mining method + eye-methods • (including graphical representation). • to determine the most probable actual new system • parameters. • Store the new system parameters as the actual version • of the supply system.

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