CN103807980B - A kind of method predicting central air-conditioning exception - Google Patents
A kind of method predicting central air-conditioning exception Download PDFInfo
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- CN103807980B CN103807980B CN201410077544.8A CN201410077544A CN103807980B CN 103807980 B CN103807980 B CN 103807980B CN 201410077544 A CN201410077544 A CN 201410077544A CN 103807980 B CN103807980 B CN 103807980B
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Abstract
The present invention discloses a kind of method predicting central air-conditioning exception, comprise: S1, monitor each room refrigeration or heat whether abnormal, and to mark with room Outlier factor: if exception, then room Outlier factor is labeled as 1, if normal, then room Outlier factor is labeled as 0; The unit exception factor F:F=r/R of each equipment in S2, the cold calculating central air-conditioning or heat transfer piping, wherein r represents that the abnormal room number that this equipment controls, R represent total room number that this equipment controls; S3, be abnormal nodes by the device flag of n≤F≤1, to carry out manual detection investigation, wherein, n is predetermined value, and 0 < n≤1.By this method, attendant only need have and to go during abnormal nodes to detect in discovering device, and amount detection is few, and solves because of can not the abnormal and problem that wastes energy of Timeliness coverage.
Description
Technical field
The present invention relates to the abnormality detection of air-conditioning equipment, particularly relate to a kind of method predicting central air-conditioning exception.
Background technology
Central air-conditioning has been widely used in various heavy construction, but a lot of central air-conditioning in operation after several years because be negligent of safeguarding, can unit exception be produced, cause cooling or heating effect undesirable, affect the comfort level of user, also can waste energy.At present, the preservation & testing of central air-conditioning mainly still relies on traditional mode: made regular check on by air-conditioning attendant and safeguarded, but because central air-conditioning is installed and the complicated complexity of structure, in addition conditioner terminal air blower fan quantity is many, and this mode needs regularly to check all air-conditioning equipments, comprise normal operation and abnormal running, detection efficiency is low and cost is high.Another kind of mode is then that just to attendant's inspection declaration, now abnormal conditions often relatively seriously, cause energy waste when user feels that air-conditioning has a problem (such as cannot freeze).
Summary of the invention
The present invention is directed to low, that cost the is high present situation of existing method for detecting abnormality efficiency and propose a kind of analytical method completed by computer, can than the more effectively unusual circumstance in time of traditional method, thus remind air-conditioning attendant to safeguard, reduce energy waste.
The invention provides following technical scheme and solve above-mentioned technical problem:
Predict a method for central air-conditioning exception, comprise the following steps:
S1, monitor each room refrigeration or heat whether abnormal, and to mark with room Outlier factor: if exception, then room Outlier factor is labeled as 1, if normally, then room Outlier factor is labeled as 0;
The unit exception factor F:F=r/R of each equipment in S2, the cold calculating central air-conditioning or heat transfer piping, wherein r represents that the abnormal room number that this equipment controls, R represent total room number that this equipment controls;
S3, be abnormal nodes by the device flag of n≤F≤1, to carry out manual detection investigation, wherein, n is predetermined value, and 0 < n≤1.
Preferably, described central air-conditioning has the wind blower coil tube temperature controller and temperature sensor that are connected with server end, described temperature sensor is in order to gather indoor temperature, and described wind blower coil tube temperature controller is in order to be sent to described server end by the duty of the described indoor temperature collected and fan coil; Described step S1 comprises:
S11, obtain the operating state data of temperature data in each room and fan coil;
S12, the room temperature delta data be extracted under described fan coil running status, judge whether the variations in temperature in each room meets desired value according to temperature variation data, thus judge room refrigeration or heat whether extremely also mark with room Outlier factor: if abnormal, then room Outlier factor is 1, if normal, then room Outlier factor is 0.
Preferably, described step S12 comprises:
Extract the temperature data set of m the time period that described fan coil runs, wherein comprise several discrete temperature data points (t, T) in each temperature data set, T represents the temperature in t room;
Respectively linear regression is carried out to the temperature data points in each temperature data set, obtain the time dependent linear equation T of m temperature T corresponding to a described m time period
i=a
i* t
i+ b
i, wherein i=1,2 ... m, a
iand b
ibe constant;
When air-conditioning is refrigeration mode, be calculated as a of negative
iaccount for a
ithe percentage of total number, and the negative a that will obtain
ipercentage and the threshold value preset compare: if negative a
ipercentage is more than or equal to described threshold value, then judge that this room refrigeration is normal, described room Outlier factor is 0, otherwise described room Outlier factor is 1;
When air-conditioning is heating mode, be calculated as a of positive number
iaccount for a
ithe percentage of total number, and the positive number a that will obtain
ipercentage and described threshold value compare: if positive number a
ipercentage is more than or equal to described threshold value, then judge that this room heats normally, described room Outlier factor is 0, otherwise described room Outlier factor is 1.
Preferably, described n=1.
Preferably, in described step S12, when linear regression is carried out to the temperature data points in described temperature data set, adopt least square method.
Preferably, described threshold value is 70% ~ 90%.
Preferably, described threshold value is 80%.
Preferably, the operating state data of described fan coil is (U, t), U represents the duty of fan coil described in t, and the value of U be 0,1,2 or 3, U=0 represent stopping, U=1 represents that low wind runs, and U=2 represents that apoplexy is run, and U=3 represents that high wind runs.
The method of prediction central air-conditioning exception provided by the invention, compared with prior art, at least there is following beneficial effect: based on the interconnection network relation between room and air-conditioning equipment, according to the unusual condition in room to each peer distribution Outlier factor of air-conditioning equipment, judge by the size of Outlier factor the possibility that each node device is made mistakes, thus can realize fast, expeditiously unit exception investigation.
The majority of air-conditioning equipment is extremely because progressively decaying of performance causes, in a very long time before equipment thoroughly cannot work, air-conditioning equipment has been be in abnormality, but due to this exception be progressively occur (such as refrigeration progressively weakens; Heat intermittent dwell time progressively to increase), thus, often not easily found by user or air-conditioning maintainer, and until just noted and repair when equipment thoroughly cannot work, like this, long abnormality affects user health and experience on the one hand, on the other hand, also may bring the wastings of resources such as high energy consumption or cause equipment finally cannot repair and thoroughly scrap.
In preferred version of the present invention, based on the wind blower coil tube temperature controller be connected with server, utilize Forecasting Methodology of the present invention, by the analysis to room temperature data, obtain the conclusion of each room cooling or heating effect, by the data analysis to refrigeration, heating effect, can detect people cannot perception or easily by exception that is trickle, that progressively occur that people ignores, and then to be noted abnormalities in advance equipment by the method that Outlier factor distributes, overhauled in time, exactly before equipment thoroughly damages.Avoid the wasting of resources phenomenon that Traditional Man detection method may cause, meanwhile, this preferred version also significantly reduces and manually investigates cost.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of prediction central air-conditioning exception provided by the invention;
Fig. 2 is that the central air-conditioning cold in the specific embodiment of the invention under refrigeration mode transmits schematic diagram;
Fig. 3 is the central air-conditioning part system Organization Chart of a building in the specific embodiment of the invention;
Fig. 4 is the Outlier factor mark schematic diagram of each equipment and building inner room in Fig. 3 system architecture.
Detailed description of the invention
Below contrast accompanying drawing and combine preferred embodiment the invention will be further described.
Embodiment 1
The present embodiment provides a kind of method predicting central air-conditioning exception, by analyzing the temperature data in room each in the organizational structure of central air-conditioning, judge the abnormal conditions in room, thus calculate the unit exception factor of central air-conditioning, carry out the exception of predict device.The wind blower coil tube temperature controller that the execution of the method is networked based on conditioner terminal air (generally referring in room) and the temperature sensor that accurately can sense room temperature, the flow process of the method as shown in Figure 1, comprises the following steps:
S1, obtain the operating state data of temperature data in each room and each fan coil.Wherein room temperature data are the sequence (t of temperature and time, T), T represents the temperature in t room, the operating state data of each fan coil is the sequence (t of duty and time, U), U represents the duty of t fan coil, for common third gear wind speed, U=0 represents that fan coil stops, U=1 represents that the low wind of fan coil runs, U=2 represents that fan coil apoplexy is run, and U=3 represents that fan coil height wind runs, and namely during U ≠ 0, fan coil is in running status.Aforesaid temperature data and fan coil operating state data are sent to server end, to carry out analysis and the computing of following step.
S2, the room temperature delta data be extracted under described fan coil running status, judge whether the variations in temperature in each room meets desired value according to temperature variation data, thus judge room refrigeration or heat whether extremely also mark with room Outlier factor: if abnormal, then room Outlier factor is 1, if normal, then room Outlier factor is 0.For this step S2, can (be only citing obtaining one day herein, the time interval of extracting data and analysis data can be arranged as required, also it can be three days, one week, one month etc., be not construed as limiting herein) data after, filter out the temperature data of m time period under described fan coil running status, such as in certain sky, fan coil section running time has 9:00 ~ 9:30, 9:40 ~ 9:50, 12:00 ~ 13:00, 20 time periods such as 15:15 ~ 16:30, i.e. m=20, no matter be that fan coil with which kind of wind speed runs, as long as running status (U ≠ 0) all extracts, obtain 20 temperature data set, temperature data (the t having several discrete in each set, T), for each temperature data set, all carry out linear regression, thus the relevant linear equation T of 20 temperature and times can be obtained
i=a
i* t
i+ b
i, wherein i=1,2 ... m, a
iand b
ibe constant, the m=20 herein in citing, namely obtains 20 a
i, 20 b
i:
When central air-conditioning runs in a chiller mode, calculate 20 a
iin be a of negative
iaccount for total a
ithe ratio P1 of number, and the threshold value that this ratio P1 and presets is compared, this threshold value can between 70% ~ 90%, and preferably this threshold value is 80% herein, if Pa>=80%, illustrate that this room refrigeration is normal, the Outlier factor marking this room is 0, if Pa < 80%, then represents that this room refrigeration is abnormal, the Outlier factor marking this room is 1, represents that the air-conditioning equipment connecting this room may be abnormal;
When central air-conditioning runs with heating mode, calculate 20 a
iin be a of positive number
iaccount for total a
ithe ratio P2 of number, and this ratio is compared with aforesaid threshold value 80% (this threshold value under heating mode and the threshold value under refrigeration mode consistent or inconsistent all right, preferably consistent herein), if P2>=80%, illustrate that this room heats normally, the Outlier factor marking this room is 0, otherwise, the Outlier factor marking this room is 1, represents that the air-conditioning equipment connecting this room may be abnormal.
Need to illustrate, in above-mentioned room Outlier factor calculates, when linear regression is carried out to temperature data set, can least square method be adopted, obtain an aforesaid m linear equation.
By aforementioned calculating, after obtaining room Outlier factor, analyze the abnormal conditions of each air-conditioning equipment according to the organizational structure (in other words annexation) of abnormal room and air-conditioning system:
The unit exception factor F:F=r/R of each equipment in S3, the cold calculating central air-conditioning or heat transfer piping, wherein r represents that the abnormal room number that this equipment controls, R represent total room number that this equipment controls.Continue abovementioned steps S2, for refrigeration, under normal circumstances, the central air conditioner system of heavy construction adopts water system refrigeration, namely chilled water is produced by refrigeration machine and cooling tower, chilled water is delivered to each fan coil by water pipe by water pump, cold wind is sent into each room by air outlet by fan coil again, Fig. 2 is shown in cold transmission, the part framework of central air conditioner system is illustrated in Fig. 3, though System's composition is more numerous and diverse in reality, but Computing Principle is identical, this sentences this better simply system in Fig. 3 and calculates: carry out computing equipment Outlier factor according to the aforementioned room Outlier factor obtained, if by the calculating of abovementioned steps S2, obtaining the 3rd room and the 4th room freezes abnormal, Outlier factor is 1, and the first room and the second room refrigeration are normally, Outlier factor is 0, calculated by formula F=r/R, the unit exception data in Fig. 4 can be obtained, the unit exception factor is indicated below each equipment in Fig. 4, can find out, because the 3rd room and the 4th room freeze abnormal, on the cold bang path in then abnormal room, the Outlier factor of each equipment is all greater than 0, represent that this equipment may be abnormal, and Outlier factor is more close to 1, usual abnormal possibility is larger.
S4, be abnormal nodes by the device flag of n≤F≤1, to carry out manual detection investigation, wherein n is predetermined value and 0 < n≤1, the value of n can set according to the device distribution network condition of central air-conditioning, generally preferably get n=1, that is: only the equipment of F=1 is investigated, if investigation does not find warping apparatus, then can suitably expand investigation scope, such as: the equipment of F > 0.8 is investigated.Calculated by step S3, obtain the Outlier factor of each equipment, after marking abnormal nodes, hand inspection can be carried out to abnormal nodes equipment, to determine warping apparatus further.As can be seen from Figure 4, the Outlier factor F=1 of the 3rd air outlet, the 4th air outlet, the 5th air outlet, the second blower fan, is there is the larger equipment of abnormal possibility, needs manual detection, investigation.
Though the calculating of aforementioned device Outlier factor is the citing to refrigeration mode, under heating mode, even if the equipment in air-conditioning system is different, the calculating of the unit exception factor also remains equally, does not repeat them here.
By above-mentioned analytical method, after learning the equipment that possibility is abnormal, then go to carry out manual detection, not only need the equipment detected to decrease, operating efficiency improves, and can reach the object detected in real time, decreases the waste of the energy to a great extent.
Embodiment 2
The present embodiment is a kind of reduction procedure of embodiment 1, is only with the difference of embodiment 1, and the present embodiment adopts the mode initiatively reported an error by user, reports and submits room abnormal to service end.Instead of by carrying out automatic decision to the isoparametric change of temperature as embodiment 1.The present embodiment to realize cost lower, also effectively can reduce the maintenance of maintainer, investigation scope, but those maintenance performance degradation cannot be judged in advance not easily by equipment that people finds by data.
In sum, whether the method for prediction central air-conditioning exception provided by the invention, at least has the following advantages: by detecting room refrigeration or heating abnormal, based on system architecture, the Outlier factor of computing equipment, by mark abnormal nodes, to notify that air-conditioning attendant carries out field review in time.The equipment that may go wrong in advance due to this method filters out, and can shorten attendant like this and find the real time and the abnormal cause that there is abnormal equipment.
Above content is in conjunction with concrete preferred embodiment further description made for the present invention, can not assert that specific embodiment of the invention is confined to these explanations.For those skilled in the art, without departing from the inventive concept of the premise, some equivalent to substitute or obvious modification can also be made, and performance or purposes identical, all should be considered as belonging to protection scope of the present invention.
Claims (8)
1. predict a method for central air-conditioning exception, it is characterized in that, comprise the following steps:
S1, monitor each room refrigeration or heat whether abnormal, and to mark with room Outlier factor: if exception, then room Outlier factor is labeled as 1, if normally, then room Outlier factor is labeled as 0;
The unit exception factor F:F=r/R of each equipment in S2, the cold calculating central air-conditioning or heat transfer piping, wherein r represents that the abnormal room number that this equipment controls, R represent total room number that this equipment controls;
S3, be abnormal nodes by the device flag of n≤F≤1, to carry out manual detection investigation, wherein, n is predetermined value, and 0 < n≤1.
2. the method for claim 1, it is characterized in that: described central air-conditioning has the wind blower coil tube temperature controller and temperature sensor that are connected with server end, described temperature sensor is in order to gather indoor temperature, and described wind blower coil tube temperature controller is in order to be sent to described server end by the duty of the described indoor temperature collected and fan coil;
Described step S1 comprises:
S11, obtain the operating state data of temperature data in each room and fan coil;
S12, the room temperature delta data be extracted under described fan coil running status, judge whether the variations in temperature in each room meets desired value according to temperature variation data, thus judge room refrigeration or heat whether extremely also mark with room Outlier factor: if abnormal, then room Outlier factor is 1, if normal, then room Outlier factor is 0.
3. method as claimed in claim 2, is characterized in that: described step S12 comprises:
Extract the temperature data set of m the time period that described fan coil runs, wherein comprise several discrete temperature data points (t, T) in each temperature data set, T represents the temperature in t room;
Respectively linear regression is carried out to the temperature data points in each temperature data set, obtain the time dependent linear equation T of m temperature T corresponding to a described m time period
i=a
i* t
i+ b
i, wherein i=1,2 ... m, a
iand b
ibe constant;
When air-conditioning is refrigeration mode, be calculated as a of negative
iaccount for a
ithe percentage of total number, and the negative a that will obtain
ipercentage and the threshold value preset compare: if negative a
ipercentage is more than or equal to described threshold value, then judge that this room refrigeration is normal, described room Outlier factor is 0, otherwise described room Outlier factor is 1;
When air-conditioning is heating mode, be calculated as a of positive number
iaccount for a
ithe percentage of total number, and the positive number a that will obtain
ipercentage and described threshold value compare: if positive number a
ipercentage is more than or equal to described threshold value, then judge that this room heats normally, described room Outlier factor is 0, otherwise described room Outlier factor is 1.
4. method as claimed in claim 2 or claim 3, is characterized in that: described n=1.
5. method as claimed in claim 3, is characterized in that: in described step S12, when carrying out linear regression to the temperature data points in described temperature data set, adopts least square method.
6. method as claimed in claim 3, is characterized in that: described threshold value is 70% ~ 90%.
7. method as claimed in claim 6, is characterized in that: described threshold value is 80%.
8. method as claimed in claim 2, it is characterized in that: the operating state data of described fan coil is (U, t), U represents the duty of fan coil described in t, and the value of U be 0,1,2 or 3, U=0 represent stopping, U=1 represents that low wind runs, U=2 represents that apoplexy is run, and U=3 represents that high wind runs.
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| JP6947478B2 (en) * | 2015-10-09 | 2021-10-13 | フィッシャー−ローズマウント システムズ，インコーポレイテッド | Systems and methods for creating a set of monitoring and effect blocks from the cause-effect matrix |
| CN110986312B (en) * | 2019-11-25 | 2021-05-11 | 青岛聚好联科技有限公司 | Method and device for determining and analyzing refrigeration index of air conditioner |
| CN112556087B (en) * | 2020-11-20 | 2021-12-10 | 珠海格力电器股份有限公司 | Unit fault diagnosis method and device and controller |
| CN113531981B (en) * | 2021-07-20 | 2022-08-02 | 四川虹美智能科技有限公司 | Refrigerator refrigeration abnormity detection method and device based on big data |
| CN116151037B (en) * | 2023-04-18 | 2023-08-01 | 中汽研新能源汽车检验中心(天津)有限公司 | Battery pack storage and transportation space temperature prediction model construction and danger source positioning method |
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