CN114484735A - Multi-split system fault diagnosis and energy-saving potential identification method and multi-split system - Google Patents
Multi-split system fault diagnosis and energy-saving potential identification method and multi-split system Download PDFInfo
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- CN114484735A CN114484735A CN202210241434.5A CN202210241434A CN114484735A CN 114484735 A CN114484735 A CN 114484735A CN 202210241434 A CN202210241434 A CN 202210241434A CN 114484735 A CN114484735 A CN 114484735A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/89—Arrangement or mounting of control or safety devices
Abstract
The invention provides a method for fault diagnosis and energy-saving potential identification of a multi-split system, which comprises the following steps: s1: constructing a fault diagnosis and energy-saving potential identification model of the multi-split air conditioner based on the association rule; s2: acquiring operation data of the actually used multi-split air conditioner; s3: the method comprises the steps of processing collected data, utilizing the model established in the step S1 to carry out fault pre-diagnosis and energy-saving potential analysis, utilizing actual operation data of the multi-connected air conditioner to realize multi-connected air conditioner soft fault identification, and estimating the influence of soft faults on system performance, and is reliable.
Description
Technical Field
The invention belongs to the technical field of air conditioners, and particularly relates to a fault diagnosis and energy-saving potential identification method for a multi-split system and the multi-split system.
Background
The multi-split system is widely applied to buildings, the multi-split equipment is complex to control, the actual installation and operation environment is not controllable, and faults are difficult to avoid during long-time operation, particularly the system soft faults. "Soft failure" refers to a certain degree of equipment degradation, performance degradation, such as heat exchanger fouling, refrigerant leakage, and compressor wear, due to some reason. Such failures are generally caused by gradual aging or wear of system components, so that no serious problems such as shutdown, damage, etc. occur at the initial stage of the failure, and direct detection and diagnosis are difficult. However, gradual accumulation of system "soft failures" can lead to reduced component life, reduced system performance, and other problems. Therefore, how to solve the problem of system 'soft fault' pre-diagnosis is a research hotspot of the industry. When carrying out fault judgment in the current industry, the vast majority judges through whether the operating parameters of the identification system exceed the preset range, if: when the maximum value of the suction superheat degree of the system exceeds the limit value, the refrigerant shortage of the system is judged, the judgment method has low misjudgment rate, but the judgment can be realized after the system has serious faults, and the system performance is seriously reduced at the moment, even abnormal shutdown and the like occur, so the method is not feasible.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. For this purpose,
according to the embodiment of the disclosure, a method for diagnosing the fault and identifying the energy-saving potential of a multi-split system is provided, which comprises the following steps:
s1: constructing a fault diagnosis and energy-saving potential identification model of the multi-split air conditioner based on the association rule;
s2: acquiring operation data of the actually used multi-split air conditioner;
s3: and processing the acquired data, and performing fault pre-diagnosis and energy-saving potential analysis by using the model established in the step S1.
The method can realize the multi-split soft fault identification by utilizing the multi-split actual operation data, and estimate the influence of the soft fault on the system performance, and is reliable.
According to an embodiment of the present disclosure, step S1 specifically includes the following steps:
s11: acquiring operation data of the multi-split air conditioner under different working conditions and different fault combinations, and forming n groups of data sets E, wherein E is A, B, C and D, A is an environment characteristic and operation parameter data set, B is { refrigerating capacity Qc/heating capacity Qh }, C is { EER/COP }, and D is a { fault type };
s12: constructing an association rule I of the A set data and the B, C, D set data, wherein the association rule can be expressed as { M → N }
Where M and N are the antecedent and the consequent of the rule, respectively, M: interval to which parameter belongs in data set a, N: parameters in data set B or C or D;
s13: calculating and establishing the support degree and the confidence degree of the association rule by utilizing the acquired data;
and S14, obtaining the support degree and the confidence degree of the corresponding rule through calculation, searching the rule which is greater than the minimum support degree threshold value and the minimum confidence degree threshold value as a strong association rule, establishing a corresponding rule table, and selecting the strong association rule as a statistical model.
The method has the advantages that the fault pre-diagnosis model of the multi-split air conditioner system and the multi-split energy-saving operation potential recognition model are accurately established, and recognition of soft faults of the multi-split air conditioner at the later stage is facilitated.
According to the embodiment of the present disclosure, in step S13, two indexes of Support (a → B)) and Confidence (a → B)) are used as the measurement criteria of the validity of the rule, as shown below,
Support(A→B)＝P(A∪B)
Confidence(A→B)＝P(A|B)＝P(A∪B)/P(A)。
the validity of the association rule can be conveniently measured, and the method is convenient and effective.
According to an embodiment of the present disclosure, between steps S2 and S3, the following steps are further included: the collected data are transmitted to the cloud platform through the gateway, and the collected data can be effectively processed.
According to the embodiment of the disclosure, the cloud platform processes the data in step S3, which is convenient and fast.
According to the embodiment of the disclosure, the environmental characteristics include an outdoor ambient temperature Ta and an indoor ambient temperature Ti, and the environment can be better described.
According to the embodiment of the disclosure, the operation parameters include the compressor exhaust temperature Td, the compressor exhaust pressure Pd, the compressor suction temperature Ts, the compressor suction pressure Ps, the outdoor heat exchanger liquid pipe temperature Te, the indoor liquid pipe temperature Tl, the indoor air pipe temperature Tg and the number of the starting units n, and the operation condition of the air conditioner can be better described.
According to the embodiment of the disclosure, a multi-split air conditioner is further provided, the multi-split air conditioner comprises at least one indoor unit and at least one outdoor unit, the multi-split air conditioner can perform the method for fault diagnosis and energy-saving potential identification of the multi-split air conditioner, the multi-split air conditioner can identify soft faults by using actual operation data, the influence of the soft faults on system performance can be estimated, and the method is reliable.
According to the embodiment of the disclosure, the multi-split air conditioner further comprises a cloud platform, and the cloud platform is used for processing the operation data of the multi-split air conditioner, can be used for fault pre-diagnosis and energy-saving potential analysis, and is convenient to analyze.
According to the embodiment of the disclosure, the system further comprises a gateway, wherein the gateway is connected between the cloud platform and the multi-split air conditioner and is used for transmitting data between the multi-split air conditioner and the cloud platform, and the data transmission is facilitated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a step diagram of a method for diagnosing a fault and identifying an energy saving potential of a multi-split system according to an embodiment of the present disclosure;
FIG. 2 is a diagram of steps for building a multiple on-line system fault diagnosis and energy saving potential identification model based on association rules, according to an embodiment of the disclosure;
FIG. 3 is a schematic diagram of the building of a rule table according to strong association rules according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a structure of a multiple on-line system according to an embodiment of the disclosure.
Detailed Description
The invention is described in detail below by way of exemplary embodiments. It should be understood, however, that elements, structures and features of one embodiment may be beneficially incorporated in other embodiments without further recitation.
The invention provides a method for fault diagnosis and energy-saving potential identification of a multi-split system.
The air conditioner performs a refrigeration cycle of the air conditioner by using a compressor, a condenser, an expansion valve, and an evaporator. The refrigeration cycle includes a series of processes involving compression, condensation, expansion, and evaporation, and supplies refrigerant to the air that has been conditioned and heat-exchanged.
The compressor compresses a refrigerant gas in a high-temperature and high-pressure state and discharges the compressed refrigerant gas. The discharged refrigerant gas flows into the condenser. The condenser condenses the compressed refrigerant into a liquid phase, and heat is released to the surrounding environment through the condensation process.
The expansion valve expands the liquid-phase refrigerant in a high-temperature and high-pressure state condensed in the condenser into a low-pressure liquid-phase refrigerant. The evaporator evaporates the refrigerant expanded in the expansion valve and returns the refrigerant gas in a low-temperature and low-pressure state to the compressor. The evaporator can achieve a cooling effect by heat-exchanging with a material to be cooled using latent heat of evaporation of a refrigerant. The air conditioner can adjust the temperature of the indoor space throughout the cycle.
The multi-split air conditioner includes at least one indoor unit and at least one outdoor unit, the outdoor unit being a portion of a refrigeration cycle including a compressor and an outdoor heat exchanger, the indoor unit including an indoor heat exchanger, and an expansion valve may be provided in the indoor unit or the outdoor unit of the air conditioner. The indoor heat exchanger and the outdoor heat exchanger serve as a condenser or an evaporator.
Referring to fig. 1, the method for diagnosing the fault and identifying the energy-saving potential of the multi-split system comprises the following steps:
s1: constructing a fault diagnosis and energy-saving potential identification model of the multi-split air conditioner based on the association rule;
s2: acquiring operation data of the actually used multi-split air conditioner;
s3: and processing the acquired data, and performing fault pre-diagnosis and energy-saving potential analysis by using the model established in the step S1.
The method can realize the multi-split soft fault identification by utilizing the multi-split actual operation data, and estimate the influence of the soft fault on the system performance, and is reliable.
Referring to fig. 2 to 3, step S1 specifically includes the following steps:
s11: the method comprises the steps of collecting operation data of the multi-split air conditioner under different working conditions and different fault combinations, and forming n groups of data sets E, wherein E is A, B, C and D, A is an environment characteristic and operation parameter data set, B is { refrigerating capacity Qc/heating capacity Qh }, C is { EER/COP }, and D is { fault type }.
Specifically, the operation data comprises environmental characteristics, operation parameters, performance characteristics, operation characteristics and the like, wherein the environmental characteristics comprise outdoor environment temperature Ta and indoor environment temperature Ti, and the environment can be better described; the operation parameters comprise compressor exhaust temperature Td, compressor exhaust pressure Pd, compressor suction temperature Ts, compressor suction pressure Ps, outdoor heat exchanger liquid pipe temperature Te, indoor liquid pipe temperature Tl, indoor machine air pipe temperature Tg and starting number n, and the operation condition of the air conditioner can be better described; the performance characteristics comprise refrigerating capacity Qc/heating capacity Qh, energy efficiency ratio EER/coefficient of performance COP; the operational characteristics include a fault type.
S12: constructing an association rule I of the A set data and the B, C, D set data, wherein the association rule can be expressed as { M → N }
Where M and N are the antecedent and the consequent of the rule, respectively, M: interval to which parameter belongs in data set a, N: the parameters in data sets B or C or D refer to the intervals.
Specifically, M: the section to which the parameter belongs in the data set a, for example: ta belongs to [30 ℃, 35 ℃), Ti belongs to [26 ℃,29 ℃), Td belongs to [75 ℃, 80 ℃), Pd belongs to [3.1,3.3 ], Ts belongs to [0,3 ], Ps belongs to [0.8,1.0 ], Te belongs to [40, 43 ℃), Tl belongs to [7,9 ℃), Tg belongs to [7,9 ℃), and n belongs to [1, 2); n: the parameters in data sets B or C or D refer to the intervals, for example: qc e [2.8, 3)/Qh e [3.6, 3.8) or EER e [2.6, 2.8)/COP e [3.2, 3.5) or failure type 20% refrigerant starvation;
s13: and calculating the support degree and the confidence degree of the established association rule by using the collected data.
In particular, with reference to FIG. 3, association rule mining is an effective means of statistics and machine learning, often used to find previously hidden relationships of large amounts of data. The method is to count the occurrence probability and the proportion of different feature combinations in the whole data set so as to judge whether the correlation exists between the related features. Generally, two indexes of Support (a → B)) and Confidence (a → B)) are used as the measurement criteria of the validity of the rule, as shown in formulas (1) to (2),
Support(A→B)＝P(A∪B) (1)
Confidence(A→B)＝P(A|B)＝P(A∪B)/P(A) (2)
the validity of the association rule can be conveniently measured, and the method is convenient and effective.
And S14, obtaining the support degree and the confidence degree of the corresponding rule through calculation, searching the rule which is greater than the minimum support degree threshold value and the minimum confidence degree threshold value as a strong association rule, establishing a corresponding rule table, and selecting the strong association rule as a statistical model.
The method has the advantages that the multi-split fault pre-diagnosis model and the multi-split energy-saving operation potential identification model are accurately established, and later-period identification of soft faults of the multi-split system is facilitated.
Specifically, the operation data in S2 is the data in the data set a.
The following steps are also included between steps S2 and S3: the collected data are transmitted to the cloud platform through the gateway, the data can be effectively processed, and the cloud platform processes the collected data in the step S3, so that the method is convenient and quick.
Referring to fig. 4, the present invention further provides a multi-split air conditioner system, which includes a multi-split air conditioner, where the multi-split air conditioner includes at least one indoor unit and at least one outdoor unit, and the multi-split air conditioner is capable of performing the method for fault diagnosis and energy saving potential identification of the multi-split air conditioner. The multi-split air conditioner can identify the soft fault by using actual operation data, can estimate the influence of the soft fault on the system performance, and is reliable.
Referring to fig. 4, the multi-split system further includes a cloud platform, and the cloud platform processes the operation data of the multi-split air conditioner and can perform fault pre-diagnosis and energy-saving potential analysis, thereby facilitating analysis. The multi-split air conditioner system further comprises a gateway, and the gateway is connected between the cloud platform and the multi-split air conditioner and used for transmitting data between the multi-split air conditioner and the cloud platform, so that data transmission is facilitated.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for fault diagnosis and energy-saving potential identification of a multi-split system is characterized by comprising the following steps:
s1: constructing a fault diagnosis and energy-saving potential identification model of the multi-split air conditioner based on the association rule;
s2: acquiring operation data of an actually used multi-split air conditioner;
s3: and processing the acquired data, and performing fault pre-diagnosis and energy-saving potential analysis by using the model established in the step S1.
2. The method for fault diagnosis and energy-saving potential identification of a multi-split system according to claim 1, wherein the step S1 specifically includes the following steps:
s11: acquiring operation data of the multi-split air conditioner under different working conditions and different fault combinations, and forming n groups of data sets E, wherein E is A, B, C and D, A is an environment characteristic and operation parameter data set, B is { refrigerating capacity Qc/heating capacity Qh }, C is { EER/COP }, and D is a { fault type };
s12: constructing A set data and B, C, D set dataThe association rule I, the association rule can be expressed as
Where M and N are the antecedent and the consequent of the rule, respectively, M: interval to which parameter belongs in data set a, N: parameters in data set B or C or D;
s13: calculating and establishing the support degree and the confidence degree of the association rule by utilizing the acquired data;
s14: and calculating to obtain the support degree and the confidence degree of the corresponding rule, searching the rule larger than the minimum support degree threshold value and the minimum confidence degree threshold value as a strong association rule, establishing a corresponding rule table, and selecting the strong association rule as a statistical model.
3. The method for diagnosing faults and identifying energy-saving potential of a multi-split system according to claim 2, wherein in step S13, two indexes of Support (a → B)) and Confidence (a → B)) are used as the measurement criteria of rule validity, as shown below,
Support(A→B)＝P(A∪B)
Confidence(A→B)＝P(A|B)＝P(A∪B)/P(A)。
4. the method for fault diagnosis and energy saving potential identification of a multi-split system as claimed in claim 1, further comprising the following steps between steps S2 and S3: and transmitting the acquired data to the cloud platform through the gateway.
5. The method for fault diagnosis and energy-saving potential identification of a multi-split system according to claim 4, wherein the cloud platform processes the collected data in step S3.
6. The method for fault diagnosis and energy-saving potential identification of a multi-split system as claimed in claim 2, wherein the environmental characteristics include outdoor ambient temperature Ta and indoor ambient temperature Ti.
7. The method for fault diagnosis and energy-saving potential identification of a multi-split system as claimed in claim 2, wherein the operation parameters include a compressor discharge temperature Td, a compressor discharge pressure Pd, a compressor suction temperature Ts, a compressor suction pressure Ps, an outdoor heat exchanger liquid pipe temperature Te, an indoor liquid pipe temperature Tl, an indoor unit air pipe temperature Tg, and a number of start-up units n.
8. A multi-split air conditioner system comprising a multi-split air conditioner capable of performing the method for diagnosing a malfunction and identifying an energy saving potential of the multi-split air conditioner as set forth in any one of claims 1 to 7, the multi-split air conditioner comprising at least one indoor unit and at least one outdoor unit.
9. The multi-split system as claimed in claim 8, further comprising a cloud platform processing operation data of the multi-split air conditioner and enabling fault pre-diagnosis and energy saving potential analysis.
10. The multi-split system as claimed in claim 9, further comprising a gateway connected between the cloud platform and the multi-split air conditioner for transmitting data between the multi-split air conditioner and the cloud platform.
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| CN202210241434.5A CN114484735B (en) | 2022-03-11 | 2022-03-11 | Multi-split system fault diagnosis and energy-saving potential identification method and multi-split system |
| PCT/CN2023/080554 WO2023169519A1 (en) | 2022-03-11 | 2023-03-09 | Multi-split air conditioning system and fault-tolerant control method therefor |
| CN202380012646.9A CN117642585A (en) | 2022-03-11 | 2023-03-09 | Multi-split air conditioning system and fault-tolerant control method thereof |
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| CN115095953A (en) * | 2022-06-16 | 2022-09-23 | 青岛海信日立空调系统有限公司 | Training method and device of fault diagnosis model |
| CN115095953B (en) * | 2022-06-16 | 2024-04-09 | 青岛海信日立空调系统有限公司 | Training method and device for fault diagnosis model |
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