CN110173872A - The misoperation detection method and device of air conditioner - Google Patents
The misoperation detection method and device of air conditioner Download PDFInfo
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
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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
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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
- F24F2110/00—Control inputs relating to air properties
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Abstract
The invention discloses the misoperation detection method and device of air conditioner, pass through the real time monitoring adjusted to air-conditioner temperature, construct fault feature vector model, fault degree value is calculated by fault feature vector model, whether the judgement current operating conditions of intelligence are misoperation behavior, when there are misoperation behavior, then issue the user with short message prompting, realize the intelligent decision of indoor unit unusual condition, without being judged based on directly doing comparison with existing preset condition or a reference value, it is calculated according to current temperature change and carries out quick, intelligent judgement in real time, greatly facilitate the maintenance work to air-conditioning, the service life of air-conditioning can be extended indirectly, improve the experience of user.
Description
Technical Field
The present disclosure relates to the field of air conditioner equipment information and anomaly detection technology, and more particularly, to a method and apparatus for detecting abnormal operation of an air conditioner.
Background
Under the conditions of some voltage abnormity, heating or refrigeration overload, the air conditioner sometimes has no accident abnormal condition, if the air conditioner is left to operate abnormally for a long time, the condition of damage of the air conditioner can occur, it is difficult for people to accurately judge whether the operation of the air conditioner is abnormal, in order to avoid the problem that the air conditioner starts without accident in a certain time period or operates for heating or refrigeration for a long time, the abnormal operation condition of the air conditioner needs to be detected and fed back in time, in the prior art, the device and the method for detecting the air conditioner abnormity disclosed in Chinese patent application No. CN00132208.7 detect the air conditioner abnormity by comparing the acoustic emission signal from the air conditioner compressor with respective reference values when the air conditioner operates; the air conditioner and the abnormal detection method of the indoor unit thereof disclosed by the Chinese patent application number CN201610277464.6 are characterized in that in the running process of the air conditioner, the return air temperature of an indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil pipe inlet temperature of the indoor heat exchanger and the coil pipe outlet temperature of the indoor heat exchanger are obtained; calculating a first temperature difference between the return air temperature and the outlet air temperature, and calculating a second temperature difference between the inlet temperature of the coil pipe and the outlet temperature of the coil pipe; judging whether the first temperature difference value and the second temperature difference value meet preset conditions or not; and if the preset conditions are met, judging that the indoor unit is abnormal. Both of the two prior arts are based on the judgment made by directly comparing with the existing preset condition or reference value, and when the preset condition or reference value is wrong, the erroneous judgment phenomenon is easy to occur.
Disclosure of Invention
In order to solve the problems, the disclosure provides a method and a device for detecting abnormal operation of an air conditioner, which intelligently judge whether the current operation state is an abnormal operation behavior through real-time monitoring of air conditioner temperature adjustment, and send a short message prompt to a user when the abnormal operation behavior exists, so that the intelligent judgment of the abnormal state of an indoor unit is realized, and the user experience is improved.
In order to achieve the above object, according to an aspect of the present disclosure, there is provided an abnormal operation detecting method of an air conditioner, the method including the steps of:
step 1, reading a data matrix of the temperature of an air conditioner;
step 2, decomposing the eigenvalue of the data matrix to obtain an eigenvector and an eigenvalue;
step 3, constructing a fault characteristic vector model;
step 4, calculating a fault degree value through a fault characteristic vector model;
step 5, judging whether the air conditioner operates abnormally according to the fault degree value;
and 6, sending out reminding information to the user if the air conditioner runs abnormally.
Further, in step 1, the method for reading the data matrix of the temperature of the air conditioner includes, when a compressor of the air conditioner is started, starting to collect the change of the temperature of the air conditioner to obtain the data matrix of the temperature, where the data matrix includes n × m variables, n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, m is the number of variables, the variables of the data matrix include the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, and the return air temperature is obtained through a temperature sensing bag arranged at the return air inlet of the indoor heat; acquiring the air outlet temperature through a temperature sensing bulb arranged at the air outlet of the indoor heat exchanger; acquiring the inlet temperature of a coil pipe through a temperature sensing bulb arranged at the inlet of the coil pipe of the indoor heat exchanger; and acquiring the outlet temperature of the coil pipe through a temperature sensing bulb arranged at the outlet of the coil pipe of the indoor heat exchanger.
Further, in step 2, the method for decomposing the eigenvalues of the data matrix to obtain eigenvectors and eigenvalues includes: let X be a data matrix of nxm, wherein n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, m is the variable number, the variable is the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, and the matrix X can be decomposed into the sum of the outer products of m vectors, namelyWherein, ti∈RnCalled principal vector, i.e. extracting correlation information between data samples, pi∈RmExtracting correlation information among variables for the subvectors, i is 1 to m, and if each column is zero mean, the covariance array isWherein X is a data matrix; g is the number of data sampling points; performing eigenvalue decomposition cov (X) pi＝λipiWherein p isiFeature vectors of the covariance matrix; lambda [ alpha ]iIs the eigenvalue of the covariance matrix.
Further, in step 3, the method for constructing the fault feature vector model includes:
constructing a principal vector extraction model according to the eigenvectors and the eigenvalues,wherein, tiThe vector is a principal vector and is used for extracting the correlation information between the sampling data; p is a radical ofiIs a subvector and is used for extracting the association information between variables(ii) a For any i and j, t is satisfied when i ≠ ji Ttj0, while the length of each subvector is 1, and p is multiplied by the principal vector extraction model at the same timeiObtaining ti＝XpiWherein, tiFor the data matrix X at piDegree of coverage in the direction, t1When the variance of (a) takes a maximum value, p1Corresponding characteristic value lambda1The maximum value should also be taken; if the vector is scored, | t1||>||t2||>…>||tmI, then the subvector piRepresents the maximum direction of change of X, p2And p1Perpendicular and representing the second largest direction of change of X, pmRepresents the direction in which X changes minimally; constructing a fault feature vector model by a principal vector extraction model, wherein X is as follows:
further, in step 4, the method for calculating the fault degree value through the fault feature vector model includes: for new data matrix X by fault feature vector modelnewDetecting faults, and calculating sample principal vector tnewAnd XnewIs estimated value ofAnd residual error enew，Passing through tnew、And enewCalculating the fault degree value fault of the fault state,
further, in step 5, the method for determining whether the air conditioner is abnormally operated according to the fault degree value includes: according to the residual error judgment criterion, when fault<QαTimely judging normal operation of air conditionerWherein the fault value at the time i is XiFault degree value of (2) when fault>＝QαAnd (3) judging the air conditioner is abnormally operated, wherein the fault threshold value Q is set when the checking level is ααThe calculation formula is as follows:wherein,lambda is an eigenvalue of the X covariance matrix; cαThe statistical confidence limit of the confidence coefficient of normal distribution is (1- α)%, k is the number of main vectors reserved in the fault characteristic vector model, and m is the number of all variables.
Further, in step 6, if the air conditioner is abnormally operated, the method for sending the reminding information to the user includes: when the air conditioner operates abnormally, preset prompt information in a text format is sent to a user in any one of a short message mode, an instant messaging tool mode and an electronic mail mode.
The present invention also provides an abnormal operation detecting apparatus of an air conditioner, the apparatus including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in the units of:
the data matrix reading unit is used for reading a data matrix of the temperature of the air conditioner;
the eigenvalue decomposition unit is used for decomposing the eigenvalues of the data matrix to obtain eigenvectors and eigenvalues;
the vector model construction unit is used for constructing a fault feature vector model;
the fault degree value calculating unit is used for calculating a fault degree value through a fault characteristic vector model;
the abnormality judgment unit is used for judging whether the air conditioner operates abnormally according to the fault degree value;
and the reminding information unit is used for sending reminding information to the user if the air conditioner runs abnormally.
The beneficial effect of this disclosure does: the method and the device for detecting the abnormal operation of the air conditioner do not need to make judgment based on direct comparison with the existing preset conditions or reference values, carry out calculation according to the current temperature change and carry out quick and intelligent judgment in real time, greatly facilitate the maintenance work of the air conditioner, indirectly prolong the service life of the air conditioner and improve the user experience.
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The foregoing and other features of the present disclosure will become more apparent from the detailed description of the embodiments shown in conjunction with the drawings in which like reference characters designate the same or similar elements throughout the several views, and it is apparent that the drawings in the following description are merely some examples of the present disclosure and that other drawings may be derived therefrom by those skilled in the art without the benefit of any inventive faculty, and in which:
FIG. 1 is a flow chart showing a method for detecting an abnormal operation of an air conditioner;
fig. 2 is a diagram showing an abnormal operation detection device of an air conditioner.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, a flow chart of an abnormal operation detection method of an air conditioner according to the present disclosure is shown, and the abnormal operation detection method of the air conditioner according to an embodiment of the present disclosure will be described with reference to fig. 1.
The disclosure provides an abnormal operation detection method of an air conditioner, which specifically comprises the following steps:
step 1, reading a data matrix of the temperature of an air conditioner;
step 2, decomposing the eigenvalue of the data matrix to obtain an eigenvector and an eigenvalue;
step 3, constructing a fault characteristic vector model;
step 4, calculating a fault degree value through a fault characteristic vector model;
step 5, judging whether the air conditioner operates abnormally according to the fault degree value;
and 6, sending out reminding information to the user if the air conditioner runs abnormally.
Further, in step 1, the method for reading the data matrix of the temperature of the air conditioner includes, when a compressor of the air conditioner is started, starting to collect the change of the temperature of the air conditioner to obtain the data matrix of the temperature, where the data matrix includes n × m variables, n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, m is the number of variables, the variables of the data matrix include the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, and the return air temperature is obtained through a temperature sensing bag arranged at the return air inlet of the indoor heat; acquiring the air outlet temperature through a temperature sensing bulb arranged at the air outlet of the indoor heat exchanger; acquiring the inlet temperature of a coil pipe through a temperature sensing bulb arranged at the inlet of the coil pipe of the indoor heat exchanger; and acquiring the outlet temperature of the coil pipe through a temperature sensing bulb arranged at the outlet of the coil pipe of the indoor heat exchanger.
Further, in step 2, the eigenvalue of the data matrix is decomposed intoThe method for obtaining the feature vector and the feature value comprises the following steps: let X be a data matrix of nxm, wherein n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, m is the variable number, the variable is the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, and the matrix X can be decomposed into the sum of the outer products of m vectors, namelyWherein, ti∈RnCalled principal vector, i.e. extracting correlation information between data samples, pi∈RmExtracting correlation information among variables for the subvectors, i is 1 to m, and if each column is zero mean, the covariance array isWherein X is a data matrix; g is the number of data sampling points; performing eigenvalue decomposition cov (X) pi＝λipiWherein p isiFeature vectors of the covariance matrix; lambda [ alpha ]iIs the eigenvalue of the covariance matrix.
Further, in step 3, the method for constructing the fault feature vector model includes:
constructing a principal vector extraction model according to the eigenvectors and the eigenvalues,wherein, tiThe vector is a principal vector and is used for extracting the correlation information between the sampling data; p is a radical ofiThe subvectors are used for extracting the correlation information between the variables; for any i and j, t is satisfied when i ≠ ji Ttj0, while the length of each subvector is 1, and p is multiplied by the principal vector extraction model at the same timeiObtaining ti＝XpiWherein, tiFor the data matrix X at piDegree of coverage in the direction, t1When the variance of (a) takes a maximum value, p1Corresponding characteristic value lambda1The maximum value should also be taken; if the vector is scored, | t1||>||t2||>…>||tmI, then the subvector piRepresents the maximum direction of change of X, p2And p1Perpendicular and representing the second largest direction of change of X, pmRepresents the direction in which X changes minimally; constructing a fault feature vector model by a principal vector extraction model, wherein X is as follows:
further, in step 4, the method for calculating the fault degree value through the fault feature vector model includes: for new data matrix X by fault feature vector modelnewDetecting faults, and calculating sample principal vector tnewAnd XnewIs estimated value ofAnd residual error enew，Passing through tnew、And enewCalculating the fault degree value fault of the fault state,
further, in step 5, the method for determining whether the air conditioner is abnormally operated according to the fault degree value includes: according to the residual error judgment criterion, when fault<QαJudging the air conditioner to normally operate, wherein the fault value at the moment i is XiFault degree value of (2) when fault>＝QαAnd (3) judging the air conditioner is abnormally operated, wherein the fault threshold value Q is set when the checking level is ααThe calculation formula is as follows:wherein,lambda is an eigenvalue of the X covariance matrix; cαThe statistical confidence limit of the confidence coefficient of normal distribution is (1- α)%, k is the number of main vectors reserved in the fault characteristic vector model, and m is the number of all variables.
Further, in step 6, if the air conditioner is abnormally operated, the method for sending the reminding information to the user includes: when the air conditioner operates abnormally, preset prompt information in a text format is sent to a user in any one of a short message mode, an instant messaging tool mode and an electronic mail mode.
An abnormal operation detection device of an air conditioner according to an embodiment of the present disclosure is a diagram of the abnormal operation detection device of the air conditioner according to the present disclosure as shown in fig. 2, and includes: the processor executes the computer program to realize the steps in the embodiment of the abnormal operation detection device of the air conditioner.
The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in the units of:
the data matrix reading unit is used for reading a data matrix of the temperature of the air conditioner;
the eigenvalue decomposition unit is used for decomposing the eigenvalues of the data matrix to obtain eigenvectors and eigenvalues;
the vector model construction unit is used for constructing a fault feature vector model;
the fault degree value calculating unit is used for calculating a fault degree value through a fault characteristic vector model;
the abnormality judgment unit is used for judging whether the air conditioner operates abnormally according to the fault degree value;
and the reminding information unit is used for sending reminding information to the user if the air conditioner runs abnormally.
The abnormal operation detection device of the air conditioner can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The abnormal operation detection device of the air conditioner can be operated by devices including but not limited to a processor and a memory. It will be understood by those skilled in the art that the example is only an example of the abnormal operation detecting device of the air conditioner, and does not constitute a limitation to the abnormal operation detecting device of the air conditioner, and may include more or less components than the above, or some components in combination, or different components, for example, the abnormal operation detecting device of the air conditioner may further include an input/output device, a network access device, a bus, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general processor may be a microprocessor or the processor may be any conventional processor, etc., and the processor is a control center of the abnormal operation detecting device operation apparatus of the air conditioner, and various interfaces and lines are used to connect various parts of the entire abnormal operation detecting device operable apparatus of the air conditioner.
The memory may be used to store the computer program and/or the module, and the processor may implement various functions of the abnormal operation detecting apparatus of the air conditioner by operating or executing the computer program and/or the module stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the present disclosure has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the disclosure by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (8)
1. An abnormal operation detection method of an air conditioner, characterized by comprising the steps of:
step 1, reading a data matrix of the temperature of an air conditioner;
step 2, decomposing the eigenvalue of the data matrix to obtain an eigenvector and an eigenvalue;
step 3, constructing a fault characteristic vector model;
step 4, calculating a fault degree value through a fault characteristic vector model;
step 5, judging whether the air conditioner operates abnormally according to the fault degree value;
and 6, sending out reminding information to the user if the air conditioner runs abnormally.
2. The method for detecting the abnormal operation of the air conditioner according to claim 1, wherein in the step 1, the method for reading the data matrix of the temperature of the air conditioner is that when a compressor of the air conditioner is started, the change of the temperature of the air conditioner is started to acquire the data matrix of the temperature, the data matrix comprises n × m variables, n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil pipe inlet temperature of the indoor heat exchanger and the coil pipe outlet temperature of the indoor heat exchanger, m is the number of variables, the variables of the data matrix comprise the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil pipe inlet temperature of the indoor heat exchanger and the coil pipe outlet temperature of the indoor heat exchanger, and the temperature sensing pack arranged at the return air inlet of the indoor heat exchanger is; acquiring the air outlet temperature through a temperature sensing bulb arranged at the air outlet of the indoor heat exchanger; acquiring the inlet temperature of a coil pipe through a temperature sensing bulb arranged at the inlet of the coil pipe of the indoor heat exchanger; and acquiring the outlet temperature of the coil pipe through a temperature sensing bulb arranged at the outlet of the coil pipe of the indoor heat exchanger.
3. The method for detecting an abnormal operation of an air conditioner according to claim 2, wherein in step 2, the method for decomposing the eigenvalues of the data matrix to obtain the eigenvector and the eigenvalue comprises: let X be a data matrix of nxm, wherein n is the data sampling times of the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, m is the variable number, the variable is the return air temperature of the indoor heat exchanger, the outlet air temperature of the indoor heat exchanger, the coil inlet temperature of the indoor heat exchanger and the coil outlet temperature of the indoor heat exchanger, and the matrix X can be decomposed into the sum of the outer products of m vectors, namelyWherein, ti∈RnCalled principal vector, i.e. extracting correlation information between data samples, pi∈RmExtracting correlation information among variables for the subvectors, i is 1 to m, and if each column is zero mean, the covariance array isWherein X is a data matrix; g is the number of data sampling points; performing eigenvalue decomposition cov (X) pi＝λipiWherein p isiFeature vectors of the covariance matrix; lambda [ alpha ]iIs the eigenvalue of the covariance matrix.
4. The abnormal operation detection method of an air conditioner according to claim 3, wherein in step 3, the method of constructing the fault feature vector model is:
constructing a principal vector extraction model according to the eigenvectors and the eigenvalues,wherein, tiThe vector is a principal vector and is used for extracting the correlation information between the sampling data; p is a radical ofiThe subvectors are used for extracting the correlation information between the variables; for any i and j, t is satisfied when i ≠ ji Ttj0, while the length of each subvector is 1, and p is multiplied by the principal vector extraction model at the same timeiTo obtain ti＝XpiWherein, tiFor the data matrix X at piDegree of coverage in the direction, t1When the variance of (a) takes a maximum value, p1Corresponding characteristic value lambda1The maximum value should also be taken;
if the vector is scored, | t1||>||t2||>…>||tmI, then the subvector piRepresents the maximum direction of change of X, p2And p1Perpendicular and representing the second largest direction of change of X, pmRepresents the direction in which X changes minimally; fault feature construction through principal vector extraction modelThe vector model is X:
5. the method for detecting an abnormal operation of an air conditioner according to claim 4, wherein the step 4 of calculating the fault degree value by the fault eigenvector model comprises: for new data matrix X by fault feature vector modelnewDetecting faults, and calculating sample principal vector tnewAnd XnewIs estimated value ofAnd residual error enew， Passing through tnew、And enewCalculating the fault degree value fault of the fault state,
6. an abnormal operation detection method of an air conditioner according to claim 5, wherein in the step 5, the method of determining whether the air conditioner is operating abnormally according to the failure degree value includes: according to the residual error judgment criterion, when fault<QαJudging the air conditioner to normally operate, wherein the fault value at the moment i is XiFault degree value of (2) when fault>＝QαWhen the air conditioner is abnormally operated, the fault threshold Q is determined when the test level is ααThe calculation formula is as follows: lambda is an eigenvalue of the X covariance matrix; cαThe statistical confidence limit of the confidence coefficient of normal distribution is (1- α)%, k is the number of main vectors reserved in the fault characteristic vector model, and m is the number of all variables.
7. The method for detecting an abnormal operation of an air conditioner according to claim 6, wherein in the step 6, if the air conditioner is operated abnormally, a method for sending a warning message to a user is: when the air conditioner operates abnormally, preset prompt information in a text format is sent to a user in any one of a short message mode, an instant messaging tool mode and an electronic mail mode.
8. An abnormal operation detection device for an air conditioner, characterized by comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in the units of:
the data matrix reading unit is used for reading a data matrix of the temperature of the air conditioner;
the eigenvalue decomposition unit is used for decomposing the eigenvalues of the data matrix to obtain eigenvectors and eigenvalues;
the vector model construction unit is used for constructing a fault feature vector model;
the fault degree value calculating unit is used for calculating a fault degree value through a fault characteristic vector model;
the abnormality judgment unit is used for judging whether the air conditioner operates abnormally according to the fault degree value;
and the reminding information unit is used for sending reminding information to the user if the air conditioner runs abnormally.
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| CN201910321686.7A CN110173872A (en) | 2019-04-22 | 2019-04-22 | The misoperation detection method and device of air conditioner |
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| CN201910321686.7A CN110173872A (en) | 2019-04-22 | 2019-04-22 | The misoperation detection method and device of air conditioner |
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Cited By (3)
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| CN111546854A (en) * | 2020-06-18 | 2020-08-18 | 中南大学 | A method for in-transit identification and diagnosis of intelligent train air-conditioning units |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115187155A (en) * | 2022-09-15 | 2022-10-14 | 广东银纳增材制造技术有限公司 | School laboratory equipment state data control method |
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Application publication date: 20190827 |