CN117267861A - Air conditioner outdoor fan fault prediction system based on Internet of things - Google Patents
Air conditioner outdoor fan fault prediction system based on Internet of things Download PDFInfo
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- CN117267861A CN117267861A CN202311308126.0A CN202311308126A CN117267861A CN 117267861 A CN117267861 A CN 117267861A CN 202311308126 A CN202311308126 A CN 202311308126A CN 117267861 A CN117267861 A CN 117267861A
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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
- F24F1/00—Room units for air-conditioning, e.g. separate or self-contained units or units receiving primary air from a central station
- F24F1/06—Separate outdoor units, e.g. outdoor unit to be linked to a separate room comprising a compressor and a heat exchanger
- F24F1/38—Fan details of outdoor units, e.g. bell-mouth shaped inlets or fan mountings
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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
- F24F11/52—Indication arrangements, e.g. displays
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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
- F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
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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
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- Mechanical Engineering (AREA)
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- Human Computer Interaction (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
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- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention belongs to the field of fan fault analysis, relates to a data analysis technology, and is used for solving the problem that an air conditioner outdoor fan fault monitoring system in the prior art cannot conduct fault prediction analysis on a fan before a fault occurs, in particular to an air conditioner outdoor fan fault prediction system based on the Internet of things, which comprises a fault prediction platform, wherein the fault prediction platform is in communication connection with an operation monitoring module, an environment analysis module, a fault prediction module and a storage module; the operation monitoring module is used for monitoring and analyzing the operation state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, and marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object; the invention can monitor and analyze the running state of the air conditioner outdoor fan, and feed back the running state of the fan through the running coefficient, thereby marking whether the analysis object has fault hidden trouble or not.
Description
Technical Field
The invention belongs to the field of fan fault analysis, relates to a data analysis technology, and particularly relates to an air conditioner outdoor fan fault prediction system based on the Internet of things.
Background
The air conditioner external unit is an important component part of cold and heat exchange, and the fan is a guarantee of normal operation. If the air conditioner outdoor unit fan does not work, the air conditioner outdoor unit fan cannot normally operate, so that the indoor temperature is too high or too low, and the life quality is affected.
The fault monitoring system of the air conditioner outdoor fan in the prior art can only conduct fault analysis according to the operation parameters of the fan, judge whether the fan breaks down according to analysis results, only can alarm after the fault occurs, but cannot conduct fault prediction analysis on the fan before the fault occurs, and therefore the fault is avoided through exception handling.
The present application provides a solution to the above technical problem.
Disclosure of Invention
The invention aims to provide an air conditioner outdoor fan fault prediction system based on the Internet of things, which is used for solving the problem that an air conditioner outdoor fan fault monitoring system in the prior art cannot perform fault prediction analysis on a fan before a fault occurs;
the technical problems to be solved by the invention are as follows: how to provide an air conditioner outdoor fan fault prediction system based on the Internet of things, which can perform fault prediction analysis on fans before faults occur.
The aim of the invention can be achieved by the following technical scheme:
the air conditioner outdoor fan fault prediction system based on the Internet of things comprises a fault prediction platform, wherein the fault prediction platform is in communication connection with an operation monitoring module, an environment analysis module, a fault prediction module and a storage module;
the operation monitoring module is used for monitoring and analyzing the operation state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, and acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period; obtaining an operation coefficient YX of an analysis object in an analysis period by carrying out numerical calculation on vibration data ZD, abnormal sound data YX and rotation speed data ZS; judging whether the fault hidden danger exists in the analysis period of the analysis object through the operation coefficient YX;
the environment analysis module is used for carrying out outdoor environment analysis when the analysis object has fault hidden danger: acquiring external temperature data WW, external wind data WF and external rain data WY in an analysis period; numerical calculation is carried out on external temperature data WW, external wind data WF and external rain data WY to obtain a ring difference coefficient HY of an analysis object in an analysis period; judging whether the analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY;
the fault prediction module is used for performing fault prediction analysis on the air conditioner outdoor fan.
As a preferred embodiment of the present invention, the vibration data ZD is a maximum value of vibration amplitude of the enclosure of the analysis object in the analysis period, the abnormal sound data YX is a maximum value of noise decibel value generated by the analysis object in the analysis period, and the rotation speed data ZS is a minimum value of fan rotation speed of the analysis object in the analysis period.
As a preferred embodiment of the present invention, the specific process for determining whether the analysis object has a fault hidden trouble in the analysis period includes: the operation thresholds YXmin and YXmax are obtained through the storage module, and the operation coefficient YX of the analysis object in the analysis period is compared with the operation thresholds YXmin and YXmax: if YX is less than or equal to YXmin, judging that the running state of the analysis object in the analysis period meets the requirement; if YXmin is less than YX and less than YXmax, judging that the operation state of the analysis object in the analysis period has fault hidden danger, generating an environment analysis signal and sending the environment analysis signal to a fault prediction platform, and sending the environment analysis signal to an environment analysis module after the fault prediction platform receives the environment analysis signal; if YX is more than or equal to YXmax, judging that the running state of the analysis object in the analysis period does not meet the requirement, generating a fault alarm signal and sending the fault alarm signal to a fault prediction platform, and sending the fault alarm signal to a mobile phone terminal of a manager after the fault prediction platform receives the fault alarm signal.
As a preferred embodiment of the present invention, the outside temperature data WW is a maximum value of the air temperature value of the analysis object operation environment within the analysis period; the external wind data WF is the maximum value of the wind power level of the operation environment of the analysis object in the analysis period; the outside rain data WY is the rainfall of the region where the analysis object is located in the analysis period.
As a preferred embodiment of the present invention, the specific process for determining whether an analysis object has an extreme environmental risk includes: the method comprises the steps that a storage module obtains a ring difference threshold HYmax, and a ring difference coefficient HY of an analysis object in an analysis period is compared with the ring difference threshold HYmax: if the ring difference coefficient HY is smaller than the ring difference threshold HYmax, judging that the analysis object does not have extreme environmental hidden trouble, generating a prediction analysis signal and sending the prediction analysis signal to a fault prediction platform, and sending the prediction analysis signal to a fault prediction module after the fault prediction platform receives the prediction analysis signal; if the ring difference coefficient HY is greater than or equal to the ring difference threshold HYmax, judging that the analysis object has extreme environmental hidden danger, generating an environmental early warning signal and sending the environmental early warning signal to a fault prediction platform, and sending the environmental early warning signal to a mobile phone terminal of a manager after the fault prediction platform receives the environmental early warning signal.
As a preferred embodiment of the invention, the specific process of the fault prediction module for performing fault prediction analysis on the outdoor fan of the air conditioner comprises the following steps: arranging analysis time periods in the analysis period according to the sequence from small to large of the ring difference coefficient HY to obtain a ring difference sequence, arranging the analysis time periods in the analysis period according to the sequence from first to last of the execution time to obtain an execution sequence, arranging the analysis time periods in the analysis period according to the sequence from small to large of the operation coefficient YX to obtain an operation sequence, marking the absolute value of the difference value of the sequence number of the analysis time periods in the ring difference sequence and the sequence number of the operation sequence as the ring difference value of the analysis time periods, and summing and averaging the ring difference values of all the analysis time periods to obtain the ring difference coefficient HC; marking the absolute value of the difference value between the sequence number of the analysis period in the execution sequence and the sequence number of the operation sequence as an execution difference value of the analysis period, and summing the execution difference values of all the analysis periods to obtain an execution difference coefficient ZC; obtaining a prediction coefficient YC of an analysis object by carrying out numerical calculation on the ring difference coefficient HC and the difference execution coefficient ZC; the method comprises the steps of obtaining a prediction threshold YCmin through a storage module, comparing a prediction coefficient YC of an analysis object with the prediction threshold YCmin, and judging whether the analysis object has a fault hidden danger or not through a comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the prediction coefficient YC of the analysis object with the prediction threshold YCmin includes: if the prediction coefficient YC is smaller than the prediction threshold YCmin, judging that the analysis object has a fault hidden trouble, generating a fault prediction signal and sending the fault prediction signal to a mobile phone terminal of a manager; if the prediction coefficient YC is larger than or equal to the prediction threshold YCmin, judging that the analysis object has no fault hidden trouble, generating a normal operation signal and sending the normal operation signal to a fault prediction platform.
As a preferred embodiment of the invention, the working method of the air conditioner outdoor fan fault prediction system based on the Internet of things comprises the following steps:
step one: monitoring and analyzing the running state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period, performing numerical calculation to obtain a running coefficient YX, and judging whether the running state of the analysis object meets the requirement or not through the running coefficient YX;
step two: outdoor environment analysis is carried out when the analysis object has fault hidden trouble: obtaining external temperature data WW, external wind data WF and external rain data WY in an analysis period, performing numerical calculation to obtain a ring difference coefficient HY, and judging whether an analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY;
step three: and carrying out fault prediction analysis on the outdoor fan of the air conditioner, obtaining a prediction coefficient YC of an analysis object, and judging whether the analysis object has fault hidden danger or not through the prediction coefficient YC.
The invention has the following beneficial effects:
1. the operation monitoring module can monitor and analyze the operation state of the air conditioner outdoor fan, comprehensively analyze and calculate each operation parameter in the analysis period in a time-division analysis mode to obtain an operation coefficient, and feed back the operation state of the fan through the operation coefficient so as to mark whether an analysis object has a fault hidden danger or not;
2. the environment analysis module can be used for carrying out outdoor environment analysis when the fault hidden danger exists on the analysis object, comprehensively analyzing various parameters of the outdoor environment to obtain a ring abnormal coefficient, monitoring the environment abnormality degree through the ring abnormal coefficient, and timely carrying out early warning when the extreme abnormal environment appears while the fault hidden danger exists on the analysis object;
3. the fault prediction module can be used for carrying out fault prediction analysis on the air conditioner outdoor fan, comparing the generated circular abnormal sequence, the execution sequence and the operation sequence, and feeding back the relevance between the operation coefficient of the air conditioner outdoor fan, the environment and the operation time length according to the comparison result, so that the fault hidden danger judgment is carried out according to the calculated prediction coefficient.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a flowchart of a method according to a second embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in FIG. 1, the air conditioner outdoor fan fault prediction system based on the Internet of things comprises a fault prediction platform, wherein the fault prediction platform is in communication connection with an operation monitoring module, an environment analysis module, a fault prediction module and a storage module.
The operation monitoring module is used for monitoring and analyzing the operation state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, and acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period, wherein the vibration data ZD is the maximum value of vibration amplitude of a shell of the analysis object in the analysis period, the abnormal sound data YX is the maximum value of noise decibel value generated by the analysis object in the analysis period, and the rotating speed data ZS is the minimum value of the rotating speed of the fan of the analysis object in the analysis period; obtaining an operation coefficient YX of an analysis object in an analysis period through a formula yx=α1×zd+α2×yx- α3×zs, wherein α1, α2 and α3 are proportionality coefficients, and α1 > α2 > α3 > 1; the operation thresholds YXmin and YXmax are obtained through the storage module, and the operation coefficient YX of the analysis object in the analysis period is compared with the operation thresholds YXmin and YXmax: if YX is less than or equal to YXmin, judging that the running state of the analysis object in the analysis period meets the requirement; if YXmin is less than YX and less than YXmax, judging that the operation state of the analysis object in the analysis period has fault hidden danger, generating an environment analysis signal and sending the environment analysis signal to a fault prediction platform, and sending the environment analysis signal to an environment analysis module after the fault prediction platform receives the environment analysis signal; if YX is more than or equal to YXmax, judging that the running state of the analysis object in the analysis period does not meet the requirement, generating a fault alarm signal and sending the fault alarm signal to a fault prediction platform, and sending the fault alarm signal to a mobile phone terminal of a manager after the fault prediction platform receives the fault alarm signal; the operation state of the outdoor fan of the air conditioner is monitored and analyzed, the operation parameters in the analysis period are comprehensively analyzed and calculated in a time-division analysis mode to obtain operation coefficients, and the operation state of the fan is fed back through the operation coefficients, so that whether an analysis object has a fault hidden trouble or not is marked.
The environment analysis module is used for carrying out outdoor environment analysis when the analysis object has fault hidden danger: acquiring external temperature data WW, external wind data WF and external rain data WY in an analysis period, wherein the external temperature data WW is the maximum value of the air temperature value of the operation environment of the analysis object in the analysis period; the external wind data WF is the maximum value of the wind power level of the operation environment of the analysis object in the analysis period; the external rain data WY is the rainfall of the area where the analysis object is located in the analysis period; obtaining a ring hetero coefficient HY of an analysis object in an analysis period through a formula HY=β1×WW+β2×WF+β3×WY, wherein β1, β2 and β3 are all proportional coefficients, and β1 > β2 > β3 > 1; the method comprises the steps that a storage module obtains a ring difference threshold HYmax, and a ring difference coefficient HY of an analysis object in an analysis period is compared with the ring difference threshold HYmax: if the ring difference coefficient HY is smaller than the ring difference threshold HYmax, judging that the analysis object does not have extreme environmental hidden trouble, generating a prediction analysis signal and sending the prediction analysis signal to a fault prediction platform, and sending the prediction analysis signal to a fault prediction module after the fault prediction platform receives the prediction analysis signal; if the ring difference coefficient HY is greater than or equal to the ring difference threshold HYmax, judging that the analysis object has extreme environmental hidden danger, generating an environmental early warning signal and sending the environmental early warning signal to a fault prediction platform, and sending the environmental early warning signal to a mobile phone terminal of a manager after the fault prediction platform receives the environmental early warning signal; the method comprises the steps of carrying out outdoor environment analysis when the fault hidden danger exists on an analysis object, comprehensively analyzing various parameters of the outdoor environment to obtain a ring abnormal coefficient, monitoring the environment abnormality degree through the ring abnormal coefficient, and carrying out early warning in time when the extreme abnormal environment appears while the fault hidden danger exists on the analysis object.
The failure prediction module is used for performing failure prediction analysis on the air conditioner outdoor fan: arranging analysis time periods in the analysis period according to the sequence from small to large of the ring difference coefficient HY to obtain a ring difference sequence, arranging the analysis time periods in the analysis period according to the sequence from first to last of the execution time to obtain an execution sequence, arranging the analysis time periods in the analysis period according to the sequence from small to large of the operation coefficient YX to obtain an operation sequence, marking the absolute value of the difference value of the sequence number of the analysis time periods in the ring difference sequence and the sequence number of the operation sequence as the ring difference value of the analysis time periods, and summing and averaging the ring difference values of all the analysis time periods to obtain the ring difference coefficient HC; marking the absolute value of the difference value between the sequence number of the analysis period in the execution sequence and the sequence number of the operation sequence as an execution difference value of the analysis period, and summing the execution difference values of all the analysis periods to obtain an execution difference coefficient ZC; obtaining a prediction coefficient YC of an analysis object through a formula YC=γ1X HY/HC+γ2X SC/ZC, wherein γ1 and γ2 are both proportionality coefficients, γ1 > γ2 > 1, and SC is the continuous operation duration of the analysis object; the prediction threshold YCmin is obtained through the storage module, and the prediction coefficient YC of the analysis object is compared with the prediction threshold YCmin: if the prediction coefficient YC is smaller than the prediction threshold YCmin, judging that the analysis object has a fault hidden trouble, generating a fault prediction signal and sending the fault prediction signal to a mobile phone terminal of a manager; if the prediction coefficient YC is larger than or equal to the prediction threshold YCmin, judging that the analysis object has no fault hidden trouble, generating a normal operation signal and sending the normal operation signal to a fault prediction platform; and performing fault prediction analysis on the outdoor fan of the air conditioner, comparing the generated abnormal ring sequence, the execution sequence and the operation sequence, and feeding back the relevance between the operation coefficient of the outdoor fan of the air conditioner, the environment and the operation time length through the comparison result, so that the hidden trouble judgment is performed according to the calculated prediction coefficient.
Example two
As shown in fig. 2, the air conditioner outdoor fan fault prediction method based on the internet of things comprises the following steps:
step one: monitoring and analyzing the running state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period, performing numerical calculation to obtain a running coefficient YX, and judging whether the running state of the analysis object meets the requirement or not through the running coefficient YX;
step two: outdoor environment analysis is carried out when the analysis object has fault hidden trouble: obtaining external temperature data WW, external wind data WF and external rain data WY in an analysis period, performing numerical calculation to obtain a ring difference coefficient HY, and judging whether an analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY;
step three: and carrying out fault prediction analysis on the outdoor fan of the air conditioner, obtaining a prediction coefficient YC of an analysis object, and judging whether the analysis object has fault hidden danger or not through the prediction coefficient YC.
When the air conditioner outdoor fan fault prediction system based on the Internet of things works, an analysis period is generated and divided into a plurality of analysis periods, the air conditioner outdoor fan which is subjected to operation state monitoring analysis is marked as an analysis object, vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period are obtained, a numerical value is calculated to obtain an operation coefficient YX, and whether the operation state of the analysis object meets the requirement or not is judged through the operation coefficient YX; obtaining external temperature data WW, external wind data WF and external rain data WY in an analysis period, performing numerical calculation to obtain a ring difference coefficient HY, and judging whether an analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY; and carrying out fault prediction analysis on the outdoor fan of the air conditioner, obtaining a prediction coefficient YC of an analysis object, and judging whether the analysis object has fault hidden danger or not through the prediction coefficient YC.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
The formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to a true value, and coefficients in the formulas are set by a person skilled in the art according to actual conditions; such as: the formula yx=α1×zd+α2×yx- α3×zs; collecting a plurality of groups of sample data by a person skilled in the art and setting a corresponding operation coefficient for each group of sample data; substituting the set operation coefficient and the acquired sample data into a formula, forming a ternary one-time equation set by any three formulas, screening the calculated coefficient, and taking an average value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 3.52, 2.85 and 2.26;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and the size of the coefficient depends on the number of sample data and the corresponding operation coefficient is preliminarily set for each group of sample data by a person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected, for example, the operation coefficient is proportional to the value of the vibration data.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (8)
1. The air conditioner outdoor fan fault prediction system based on the Internet of things is characterized by comprising a fault prediction platform, wherein the fault prediction platform is in communication connection with an operation monitoring module, an environment analysis module, a fault prediction module and a storage module;
the operation monitoring module is used for monitoring and analyzing the operation state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, and acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period; obtaining an operation coefficient YX of an analysis object in an analysis period by carrying out numerical calculation on vibration data ZD, abnormal sound data YX and rotation speed data ZS; judging whether the fault hidden danger exists in the analysis period of the analysis object through the operation coefficient YX;
the environment analysis module is used for carrying out outdoor environment analysis when the analysis object has fault hidden danger: acquiring external temperature data WW, external wind data WF and external rain data WY in an analysis period; numerical calculation is carried out on external temperature data WW, external wind data WF and external rain data WY to obtain a ring difference coefficient HY of an analysis object in an analysis period; judging whether the analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY;
the fault prediction module is used for performing fault prediction analysis on the air conditioner outdoor fan.
2. The air conditioner outdoor fan fault prediction system based on the internet of things according to claim 1, wherein vibration data ZD is a maximum value of vibration amplitude of a shell of an analysis object in an analysis period, abnormal sound data YX is a maximum value of noise decibel value generated by the analysis object in the analysis period, and rotation speed data ZS is a minimum value of fan rotation speed of the analysis object in the analysis period.
3. The air conditioner outdoor fan fault prediction system based on the internet of things according to claim 2, wherein the specific process of determining whether the fault hidden danger exists in the analysis period of the analysis object comprises the following steps: the operation thresholds YXmin and YXmax are obtained through the storage module, and the operation coefficient YX of the analysis object in the analysis period is compared with the operation thresholds YXmin and YXmax: if YX is less than or equal to YXmin, judging that the running state of the analysis object in the analysis period meets the requirement; if YXmin is less than YX and less than YXmax, judging that the operation state of the analysis object in the analysis period has fault hidden danger, generating an environment analysis signal and sending the environment analysis signal to a fault prediction platform, and sending the environment analysis signal to an environment analysis module after the fault prediction platform receives the environment analysis signal; if YX is more than or equal to YXmax, judging that the running state of the analysis object in the analysis period does not meet the requirement, generating a fault alarm signal and sending the fault alarm signal to a fault prediction platform, and sending the fault alarm signal to a mobile phone terminal of a manager after the fault prediction platform receives the fault alarm signal.
4. The air conditioner outdoor fan fault prediction system based on the internet of things according to claim 3, wherein the external temperature data WW is a maximum value of an air temperature value of an analysis object operation environment in an analysis period; the external wind data WF is the maximum value of the wind power level of the operation environment of the analysis object in the analysis period; the outside rain data WY is the rainfall of the region where the analysis object is located in the analysis period.
5. The air conditioner outdoor fan fault prediction system based on the internet of things according to claim 4, wherein the specific process of determining whether the analysis object has an extreme environmental hidden trouble comprises: the method comprises the steps that a storage module obtains a ring difference threshold HYmax, and a ring difference coefficient HY of an analysis object in an analysis period is compared with the ring difference threshold HYmax: if the ring difference coefficient HY is smaller than the ring difference threshold HYmax, judging that the analysis object does not have extreme environmental hidden trouble, generating a prediction analysis signal and sending the prediction analysis signal to a fault prediction platform, and sending the prediction analysis signal to a fault prediction module after the fault prediction platform receives the prediction analysis signal; if the ring difference coefficient HY is greater than or equal to the ring difference threshold HYmax, judging that the analysis object has extreme environmental hidden danger, generating an environmental early warning signal and sending the environmental early warning signal to a fault prediction platform, and sending the environmental early warning signal to a mobile phone terminal of a manager after the fault prediction platform receives the environmental early warning signal.
6. The air conditioner outdoor fan fault prediction system based on the internet of things according to claim 5, wherein the specific process of the fault prediction module for performing the fault prediction analysis on the air conditioner outdoor fan comprises: arranging analysis time periods in the analysis period according to the sequence from small to large of the ring difference coefficient HY to obtain a ring difference sequence, arranging the analysis time periods in the analysis period according to the sequence from first to last of the execution time to obtain an execution sequence, arranging the analysis time periods in the analysis period according to the sequence from small to large of the operation coefficient YX to obtain an operation sequence, marking the absolute value of the difference value of the sequence number of the analysis time periods in the ring difference sequence and the sequence number of the operation sequence as the ring difference value of the analysis time periods, and summing and averaging the ring difference values of all the analysis time periods to obtain the ring difference coefficient HC; marking the absolute value of the difference value between the sequence number of the analysis period in the execution sequence and the sequence number of the operation sequence as an execution difference value of the analysis period, and summing the execution difference values of all the analysis periods to obtain an execution difference coefficient ZC; obtaining a prediction coefficient YC of an analysis object by carrying out numerical calculation on the ring difference coefficient HC and the difference execution coefficient ZC; the method comprises the steps of obtaining a prediction threshold YCmin through a storage module, comparing a prediction coefficient YC of an analysis object with the prediction threshold YCmin, and judging whether the analysis object has a fault hidden danger or not through a comparison result.
7. The system for predicting the failure of an air conditioner outdoor fan based on the internet of things according to claim 6, wherein the specific process of comparing the prediction coefficient YC of the analysis object with the prediction threshold YCmin comprises: if the prediction coefficient YC is smaller than the prediction threshold YCmin, judging that the analysis object has a fault hidden trouble, generating a fault prediction signal and sending the fault prediction signal to a mobile phone terminal of a manager; if the prediction coefficient YC is larger than or equal to the prediction threshold YCmin, judging that the analysis object has no fault hidden trouble, generating a normal operation signal and sending the normal operation signal to a fault prediction platform.
8. The air conditioner outdoor fan fault prediction system based on the internet of things according to any one of claims 1 to 7, wherein the working method of the air conditioner outdoor fan fault prediction system based on the internet of things comprises the following steps:
step one: monitoring and analyzing the running state of the air conditioner outdoor fan: generating an analysis period, dividing the analysis period into a plurality of analysis periods, marking an air conditioner outdoor fan for running state monitoring analysis as an analysis object, acquiring vibration data ZD, abnormal sound data YX and rotating speed data ZS of the analysis object in the analysis period, performing numerical calculation to obtain a running coefficient YX, and judging whether the running state of the analysis object meets the requirement or not through the running coefficient YX;
step two: outdoor environment analysis is carried out when the analysis object has fault hidden trouble: obtaining external temperature data WW, external wind data WF and external rain data WY in an analysis period, performing numerical calculation to obtain a ring difference coefficient HY, and judging whether an analysis object has extreme environmental hidden trouble or not through the ring difference coefficient HY;
step three: and carrying out fault prediction analysis on the outdoor fan of the air conditioner, obtaining a prediction coefficient YC of an analysis object, and judging whether the analysis object has fault hidden danger or not through the prediction coefficient YC.
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| CN117590141A (en) * | 2024-01-19 | 2024-02-23 | 青岛中微创芯电子有限公司 | IPM electrical parameter testing system based on data analysis |
| CN117804518A (en) * | 2023-12-29 | 2024-04-02 | 苏州市朗世润电子有限公司 | Test fixture is used in production of response components and parts |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117804518A (en) * | 2023-12-29 | 2024-04-02 | 苏州市朗世润电子有限公司 | Test fixture is used in production of response components and parts |
| CN117590141A (en) * | 2024-01-19 | 2024-02-23 | 青岛中微创芯电子有限公司 | IPM electrical parameter testing system based on data analysis |
| CN117590141B (en) * | 2024-01-19 | 2024-03-26 | 青岛中微创芯电子有限公司 | IPM electrical parameter testing system based on data analysis |
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