CN103807980A - Method for predicting abnormities of central air conditioner - Google Patents
Method for predicting abnormities of central air conditioner Download PDFInfo
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- CN103807980A CN103807980A CN201410077544.8A CN201410077544A CN103807980A CN 103807980 A CN103807980 A CN 103807980A CN 201410077544 A CN201410077544 A CN 201410077544A CN 103807980 A CN103807980 A CN 103807980A
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Abstract
The invention discloses a method for predicting abnormities of a central air conditioner. The method comprises the following steps: S1,monitoring whether refrigeration or heating of each room is abnormal or not, marking with a room abnormal factor; if yes, marking the room abnormal factor as 1, and if not, marking the room abnormal factor as 0, S2, calculating equipment abnormal factors F of all equipment in a cooling capacity or heat transfer pipeline of the central air conditioner, wherein F is equal to r/R, r represents the abnormal room number controlled by the equipment, and R represents the total room number controlled by the equipment, and S3, marking the equipment of which F is not smaller than n and not more than 1 as abnormal nodes to carry out manual detection and screening, wherein n is a predetermined value and n is more than o and not more than 1. A maintenance staff only needs to carry out detection when abnormal nodes in the equipment are found through the method, the detection number is less, and furthermore, the problem of energy waste caused by that the abnormalities are not found in time is solved.
Description
Technical field
The present invention relates to the abnormality detection of air-conditioning equipment, relate in particular to a kind of method of predicting that central air-conditioning is abnormal.
Background technology
Central air-conditioning has been widely used in various heavy constructions, and still a lot of central air-conditioning because be negligent of safeguarding, can produce unit exception after moving several years, cause cooling or heating effect undesirable, affect user's comfort level, also can waste energy.At present, the maintenance of central air-conditioning detects and mainly still relies on traditional mode: made regular check on and safeguarded by air-conditioning attendant, but due to the complicated complexity of central air-conditioning installation and structure, in addition conditioner terminal air blower fan quantity is many, and this mode needs regularly all air-conditioning equipments to be checked, comprise normal operation and abnormal running, detection efficiency is low and cost is high.Another kind of mode is in the time that user feels that air-conditioning has problem (such as freezing), and just to attendant's inspection declaration, now abnormal conditions are often more serious, caused energy waste.
Summary of the invention
The present invention is directed to existing method for detecting abnormality efficiency present situation low, that cost is high and proposed a kind of analytical method being completed by computer, can be than the unusual circumstance more effectively in time of traditional method, thereby remind air-conditioning attendant to safeguard, reduce energy waste.
The invention provides following technical scheme and solve above-mentioned technical problem:
Predict the method that central air-conditioning is abnormal, comprise the following steps:
S1, monitor the refrigeration in each room or whether extremely heat, and carry out mark by the abnormal factor in room: if abnormal, the abnormal factor in room is labeled as 1, if normal, the abnormal factor in room is labeled as 0;
S2, the cold that calculates central air-conditioning or heat transmit the unit exception factor F:F=r/R of each equipment on pipeline, and wherein r represents the abnormal room number of this equipment control, and R represents total room number of this equipment control;
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 the 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 collecting and fan coil; Described step S1 comprises:
S11, obtain temperature data in each room and the operating state data of fan coil;
S12, be extracted in the room temperature delta data under described fan coil running status, whether the variations in temperature that judges each room according to variations in temperature data meets desired value, thereby judge room refrigeration or heat whether abnormal and carry out mark by the abnormal factor in room: if abnormal, the abnormal factor in room is 1, if normal, the abnormal factor in room is 0.
Preferably, described step S12 comprises:
The temperature data set of extracting n the time period of described fan coil operation, wherein comprises several discrete temperature data points (t, T) in each temperature data set, T represents the temperature in t moment room;
Respectively the temperature data points in each temperature data set is carried out to linear regression, obtain the time dependent linear equation T of n temperature T corresponding to a described n time period
i=a
i* t
i+ b
i, wherein i=1,2 ... n, 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 by the negative a obtaining
ipercentage and a predefined threshold value compare: if negative a
ipercentage is more than or equal to described threshold value, judges that this room refrigeration is normal, and the abnormal factor in described room is 0, otherwise the abnormal factor in described room is 1;
When air-conditioning is heating mode, be calculated as a of positive number
iaccount for a
ithe percentage of total number, and by the positive number a obtaining
ipercentage and described threshold value compare: if positive number a
ipercentage is more than or equal to described threshold value, judges that this room heats normally, and the abnormal factor in described room is 0, otherwise the abnormal factor in described room is 1.
Preferably, described n=1.
Preferably, in described step S12, when the temperature data points in described temperature data set is carried out to linear regression, 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), and U represents the duty of fan coil described in the t moment, and the value of U be 0,1,2 or 3, U=0 represent to stop, U=1 represents low wind operation, U=2 represents apoplexy operation, U=3 represents high wind operation.
The abnormal method of prediction central air-conditioning provided by the invention, compared with prior art, at least there is following beneficial effect: the interconnection network relation based between room and air-conditioning equipment, distribute the abnormal factor according to the unusual condition in room to each node of air-conditioning equipment, judge by the size of the abnormal factor possibility that each node device is made mistakes, thereby can realize fast, unit exception investigation expeditiously.
The majority of air-conditioning equipment is progressively decaying and causing due to performance extremely, in a very long time before equipment thoroughly cannot be worked, air-conditioning equipment has been in abnormality, but due to this be extremely progressively occur (for example refrigeration progressively weakens; Heating intermittent dwell time progressively increases), thereby, often be difficult for being found by user or air-conditioning maintainer, and until equipment just noted and repaired thoroughly cannot work time, like this, for a long time 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 being 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 ignored by people trickle, that progressively occur abnormal, and then the method for distributing by the abnormal factor equipment that notes abnormalities in advance, before equipment thoroughly damages in time, overhaul exactly.The wasting of resources phenomenon of having avoided Traditional Man detection method to cause, meanwhile, this preferred version has also significantly reduced artificial investigation cost.
Accompanying drawing explanation
Fig. 1 is the abnormal method flow diagram of prediction central air-conditioning provided by the invention;
Fig. 2 is that in the specific embodiment of the invention, the central air-conditioning cold under refrigeration mode transmits schematic diagram;
Fig. 3 is the central air-conditioning part system architecture diagram of a building in the specific embodiment of the invention;
Fig. 4 is each equipment and the abnormal factor mark schematic diagram of building inner room in Fig. 3 system architecture.
The specific embodiment
Preferred embodiment the invention will be further described to contrast accompanying drawing combination below.
The present embodiment provides a kind of method of predicting that central air-conditioning is abnormal, by the temperature data in each room in the organizational structure of central air-conditioning is analyzed, judge the abnormal conditions in room, thereby calculate the unit exception factor of central air-conditioning, carry out the abnormal 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 accurately temperature sensor of sensing room temperature, the flow process of the method as shown in Figure 1, comprises the following steps:
S1, obtain temperature data in each room and the operating state data of each fan coil.Sequence (the t that wherein room temperature data are temperature and time, T), T represents the temperature in t moment room, and the operating state data of each fan coil is the sequence (t of duty and time, U), U represents the duty of t moment fan coil, and take common third gear wind speed as example, U=0 represents that fan coil stops, U=1 represents the low wind operation of fan coil, U=2 represents the operation of fan coil apoplexy, and U=3 represents the high wind operation of fan coil, and U ≠ 0 o'clock 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, be extracted in the room temperature delta data under described fan coil running status, whether the variations in temperature that judges each room according to variations in temperature data meets desired value, thereby judge room refrigeration or heat whether abnormal and carry out mark by the abnormal factor in room: if abnormal, the abnormal factor in room is 1, if normal, the abnormal factor in room is 0.For this step S2, can (be only for example having obtained one day herein, the time interval of extracting data and analysis data can arrange 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 n time period under described fan coil running status, for example, within certain day, 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, be n=20, no matter be that fan coil is with which kind of wind speed operation, as long as running status (U ≠ 0) all extracts, obtain 20 temperature data set, in each set, there are several discrete temperature data (t, T), for each temperature data set, all carry out linear regression, thereby can obtain 20 linear equation T that temperature and time is relevant
i=a
i* t
i+ b
i, wherein i=1,2 ... n, a
iand b
ibe constant, the n=20 in herein giving an example, obtains 20 a
i, 20 b
i:
When in the situation of central air-conditioning with refrigeration mode operation, calculate 20 a
iin be a of negative
iaccount for total a
ithe ratio P1 of number, and this ratio P1 and a predefined threshold value are compared, this threshold value can be between 70%～90%, and preferably this threshold value is 80% herein, if Pa>=80%, illustrate that this room refrigeration is normal, the abnormal factor in this room of mark is 0, if Pa < 80% represents that this room refrigeration is abnormal, the abnormal factor in this room of mark is 1, represents that the air-conditioning equipment in this room of connection may be abnormal;
When in the situation of central air-conditioning with heating mode operation, 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% to (threshold value under this threshold value and refrigeration mode under heating mode is consistent or inconsistent all right, preferably consistent herein), if P2>=80%, illustrate that this room heats normally, the abnormal factor in this room of mark is 0, otherwise, the abnormal factor in this room of mark is 1, represents that the air-conditioning equipment in this room of connection may be abnormal.
Need explanation, in the abnormal factor in above-mentioned room is calculated, when temperature data set is carried out to linear regression, can adopt least square method, obtain an aforesaid n linear equation.
By aforementioned calculating, after obtaining the abnormal factor in room, analyze the abnormal conditions of each air-conditioning equipment according to the organizational structure of abnormal room and air-conditioning system (annexation in other words):
S3, the cold that calculates central air-conditioning or heat transmit the unit exception factor F:F=r/R of each equipment on pipeline, and wherein r represents the abnormal room number of this equipment control, and R represents total room number of this equipment control.The abovementioned steps that continues S2, take refrigeration as example, under normal circumstances, the central air conditioner system of heavy construction adopts water system refrigeration, produce chilled water by refrigeration machine and cooling tower, water pump is delivered to each fan coil by chilled water by water pipe, fan coil is sent cold wind into each room by air outlet again, Fig. 2 is shown in cold transmission, in Fig. 3, show the part framework of central air conditioner system, though system forms more numerous and diverse in reality, but Computing Principle is identical, this sentences this better simply system in Fig. 3 and calculates: carry out the abnormal factor of computing equipment according to the abnormal factor in the aforementioned room obtaining, if by the calculating of abovementioned steps S2, obtain the 3rd room and the 4th room refrigeration extremely, the abnormal factor is 1, and the first room and the second room refrigeration are normal, the abnormal factor is 0, calculate by formula F=r/R, can obtain the unit exception data in Fig. 4, in Fig. 4, each equipment below indicates the unit exception factor, can find out, because the 3rd room and the 4th room refrigeration is abnormal, on the cold bang path in abnormal room, the abnormal factor of each equipment is all greater than 0, represent that this equipment may be abnormal, and the abnormal factor more approaches 1, conventionally 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 be 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 abnormal equipment, can suitably expand investigation scope, for example: the equipment to F > 0.8 is investigated.By step, S3 calculates, and has obtained the abnormal factor of each equipment, marks after abnormal nodes, can carry out hand inspection to abnormal nodes equipment, further to determine abnormal equipment.As can be seen from Figure 4, the abnormal factor F=1 of the 3rd air outlet, the 4th air outlet, the 5th air outlet, the second blower fan, is the equipment that exists abnormal possibility larger, needs manual detection, investigation.
Though the calculating of the abnormal factor of aforementioned device be to refrigeration mode for example, under heating mode, even if the equipment in air-conditioning system is different, it is the same that the calculating of the unit exception factor also remains, and do not repeat them here.
By above-mentioned analytical method, learn after the equipment that possibility is abnormal, then go to carry out manual detection, not only need the equipment detecting to reduce, operating efficiency has improved, and can reach the object of real-time detection, has reduced to a great extent the waste of the energy.
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 reporting an error by user, reports and submits room abnormal to service end.Rather than as embodiment 1 by temperature isoparametric variation carry out automatic decision.The present embodiment to realize cost lower, maintenance, the investigation scope that also can effectively dwindle maintainer, but cannot judge that in advance those maintenance performance degradation are difficult for the equipment being found by people by data.
Whether in sum, the abnormal method of prediction central air-conditioning provided by the invention, at least has the following advantages: by detecting room refrigeration or heating abnormal, based on system architecture, the abnormal factor of computing equipment, by mark abnormal nodes, to notify in time air-conditioning attendant to carry out field review.The equipment that may go wrong in advance due to this method filters out, and can shorten like this attendant and find time and the abnormal cause of the abnormal equipment of real existence.
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, can also make some being equal to substitute or obvious modification, and performance or purposes identical, all should be considered as belonging to protection scope of the present invention.
Claims (8)
1. predict the method that central air-conditioning is abnormal, it is characterized in that, comprise the following steps:
S1, monitor the refrigeration in each room or whether extremely heat, and carry out mark by the abnormal factor in room: if abnormal, the abnormal factor in room is labeled as 1, if normal, the abnormal factor in room is labeled as 0;
S2, the cold that calculates central air-conditioning or heat transmit the unit exception factor F:F=r/R of each equipment on pipeline, and wherein r represents the abnormal room number of this equipment control, and R represents total room number of this equipment control;
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 the 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 collecting and fan coil;
Described step S1 comprises:
S11, obtain temperature data in each room and the operating state data of fan coil;
S12, be extracted in the room temperature delta data under described fan coil running status, whether the variations in temperature that judges each room according to variations in temperature data meets desired value, thereby judge room refrigeration or heat whether abnormal and carry out mark by the abnormal factor in room: if abnormal, the abnormal factor in room is 1, if normal, the abnormal factor in room is 0.
3. method as claimed in claim 2, is characterized in that: described step S12 comprises:
The temperature data set of extracting n the time period of described fan coil operation, wherein comprises several discrete temperature data points (t, T) in each temperature data set, T represents the temperature in t moment room;
Respectively the temperature data points in each temperature data set is carried out to linear regression, obtain the time dependent linear equation T of n temperature T corresponding to a described n time period
i=a
i* t
i+ b
i, wherein i=1,2 ... n, 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 by the negative a obtaining
ipercentage and a predefined threshold value compare: if negative a
ipercentage is more than or equal to described threshold value, judges that this room refrigeration is normal, and the abnormal factor in described room is 0, otherwise the abnormal factor in described room is 1;
When air-conditioning is heating mode, be calculated as a of positive number
iaccount for a
ithe percentage of total number, and by the positive number a obtaining
ipercentage and described threshold value compare: if positive number a
ipercentage is more than or equal to described threshold value, judges that this room heats normally, and the abnormal factor in described room is 0, otherwise the abnormal factor in described room 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 the temperature data points in described temperature data set is carried out to linear regression, adopt 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 the t moment, and the value of U be 0,1,2 or 3, U=0 represent to stop, U=1 represents the operation of low wind, U=2 represents apoplexy operation, and U=3 represents high wind operation.
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| CN110986312A (en) * | 2019-11-25 | 2020-04-10 | 青岛聚好联科技有限公司 | Method and device for determining and analyzing refrigeration index of air conditioner |
| CN112556087A (en) * | 2020-11-20 | 2021-03-26 | 珠海格力电器股份有限公司 | Unit fault diagnosis method and device and controller |
| CN113531981A (en) * | 2021-07-20 | 2021-10-22 | 四川虹美智能科技有限公司 | Refrigerator refrigeration abnormity detection method and device based on big data |
| CN116151037A (en) * | 2023-04-18 | 2023-05-23 | 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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