Air conditioner outdoor unit fault detection method and system based on power and pressure correlation

Abstract

The application belongs to the technical field of equipment performance detection, and discloses an air conditioner outdoor unit fault detection method and system based on power and pressure correlation. The air conditioner outdoor unit fault detection method comprises the following steps: performing normalization processing on a data pair composed of online power data and pressure data of an air conditioner outdoor unit to be detected, so as to obtain a correlation data vector; obtaining a vector distance of the correlation data vector, obtaining an approximation degree between vectors based on the vector distance, and obtaining a real-time state evaluation value of the air conditioner outdoor unit based on the approximation degree; and comparing the real-time state evaluation value with a preset threshold value, and judging the real-time state of the air conditioner outdoor unit to be detected during operation. The application can accurately judge whether the air conditioner is in a fault state during real-time operation.

Description

Technical Field

[0001] This invention belongs to the field of equipment performance testing technology, and more specifically, relates to a method and system for detecting faults in air conditioning outdoor units based on the correlation between power and pressure. Background Technology

[0002] With the increasing number of household air conditioners, major air conditioning manufacturers are incurring rising costs in terms of land, time, and manpower for online testing of outdoor units. Improving the efficiency, reducing costs, and increasing the success rate of online testing for air conditioner outdoor units has become a crucial issue for air conditioning manufacturers to enhance product production efficiency and competitiveness. Methods that rely solely on compressor power, medium pressure, or inlet / outlet air temperature to evaluate the health of air conditioner operation are becoming increasingly inaccurate, and are also time-consuming and inefficient. Air conditioner factory operation testing is gradually shifting towards a more accurate and efficient model that integrates multiple data points and enables rapid testing.

[0003] Currently, the factory operation test of a single air conditioner outdoor unit is the longest cycle time on the air conditioner outdoor unit assembly line, requiring approximately 250-300 seconds, which is far longer than the assembly time cycle (approximately 8 seconds). Furthermore, there is relatively little research on the health (or "operating status") assessment of air conditioner outdoor units at the time of leaving the factory. Existing methods are mainly based on the fault status information of air conditioner operation, such as cumulative usage time, failure rate, and parts replacement rate, and use weighted and statistical methods to build models to evaluate real-time operating data. However, the overall accuracy of the assessment is not high, and the testing speed is slow. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method and system for detecting faults in air conditioner outdoor units based on the correlation between power and pressure, which is mainly used to solve the problem of low accuracy in existing air conditioner outdoor unit detection methods.

[0005] To achieve the above objectives, the present invention provides a fault detection method for air conditioner outdoor units based on power and pressure correlation, the fault detection method for air conditioner outdoor units comprising:

[0006] S1 normalizes the data pair consisting of online power data and pressure data of the outdoor unit of the air conditioner under test to obtain a correlated data vector;

[0007] S2 obtains the vector distance between data vectors in the associated data vector, obtains the approximation between vectors based on the vector distance, and obtains the real-time status evaluation value of the air conditioner outdoor unit based on the approximation;

[0008] S3 compares the real-time status evaluation value with a preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test during operation.

[0009] Furthermore, in step S3, within a certain period of time, when the number of times the real-time status evaluation value is continuously less than the preset threshold is greater than a preset number, the outdoor unit of the air conditioner under test is determined to be faulty and an alarm is triggered; when the number of times the real-time status evaluation value is continuously less than the preset threshold is less than or equal to the preset number, the outdoor unit of the air conditioner under test is determined to be normal; when the real-time status evaluation value is greater than or equal to the preset threshold, the outdoor unit of the air conditioner under test is determined to be normal.

[0010] Furthermore, the vector distance is inversely proportional to the real-time state evaluation value.

[0011] Furthermore, prior to step S1, the power and pressure data are acquired at a frequency range of 0.5s / time to 1s / time.

[0012] Furthermore, prior to step S1, the power data and pressure data are collected during the continuous operating time of the outdoor unit of the air conditioner under test to obtain data pairs that correspond one-to-one in time sequence.

[0013] Furthermore, in step S2, the vector distance is obtained using the following formula:

[0014]

[0015] In the formula, m i m j Let i and j be data vectors containing power and pressure data at two different time points, i = 1 to N, j = 1 to N; D is a 2... N ×2 N The matrix is ​​denoted by N, where N is the number of data logs consisting of power and pressure data collected within a continuous time period; a factor of 1 / 2 is used to standardize the vector distance, 0 ≤ d(m i ,m j )≤1.

[0016] Furthermore, in step S2, the approximation degree is calculated using the following formula:

[0017]

[0018] Among them, S i，j For every two data vectors m i and m j The approximation value between them.

[0019] Furthermore, in step S2, the real-time state evaluation value is obtained using the following formula:

[0020]

[0021] In the formula, i, j = 1, 2, ..., N, P(m i) represents the state evaluation value, Sim ij Indicates the degree of approximation.

[0022] According to one aspect of the present invention, an air conditioner outdoor unit fault detection system based on power and pressure correlation is also disclosed, comprising:

[0023] The detection unit is used to collect online power and pressure data of the outdoor unit of the air conditioner;

[0024] The processing unit is used to normalize the data pairs consisting of online power data and pressure data of the outdoor unit of the air conditioner under test to obtain a correlated data vector; it is also used to obtain the vector distance between the data vectors in the correlated data vector, obtain the approximation between the vectors based on the vector distance, and obtain the real-time status evaluation value of the outdoor unit of the air conditioner based on the approximation.

[0025] The judgment output unit is used to compare the real-time status evaluation value with a preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test during operation, and output the judgment information.

[0026] According to another aspect of the invention, a computer device is also disclosed, comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the detection method as described in any of the preceding claims.

[0027] Compared with the prior art, the above technical solutions conceived by this invention have the following main advantages:

[0028] 1. The detection method provided by the present invention collects power and pressure data during a continuous period of operation of the outdoor unit of the air conditioner in real time, and uses the data pairs associated with the power and pressure data to obtain the real-time status evaluation value of the outdoor unit of the air conditioner. The real-time status evaluation value is then compared with a preset threshold to determine the fault status of the outdoor unit of the air conditioner under test during operation. Since the entire calculation and judgment process utilizes a large amount of process data during the operation of the outdoor unit of the air conditioner, the final fault judgment result is more accurate.

[0029] 2. The detection method of the present invention calculates the vector distance between the associated data vectors composed of power and pressure data, thereby quickly and accurately discovering the differences between the outdoor unit of the air conditioner under test in different operating states, and the fault diagnosis results are more intuitive.

[0030] 3. In the detection method of the present invention, when obtaining approximate values, an approximate matrix is ​​constructed, and the correlation between the real-time power data and pressure data of the outdoor unit of the air conditioner under test is utilized to objectively evaluate the state characteristics of the air conditioner's qualified state, resulting in a more realistic judgment. Attached Figure Description

[0031] Figure 1This is a flowchart illustrating the air conditioner outdoor unit fault detection method based on power and pressure correlation provided by the present invention.

[0032] Figure 2 This is a schematic diagram of the data processing process of the air conditioner outdoor unit fault detection method based on power and pressure correlation provided by the present invention;

[0033] Figure 3 This is a schematic diagram of the structure of the air conditioner outdoor unit fault detection system based on online power and pressure data correlation provided by the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0035] Combination Figure 1 and 2 As shown, one embodiment of the present invention provides a fault detection method for an air conditioner outdoor unit based on power and pressure correlation. This fault detection method includes:

[0036] S1 normalizes the data pair consisting of online power data and pressure data of the outdoor unit of the air conditioner under test to obtain a correlated data vector;

[0037] S2 obtains the vector distance between data vectors in the associated data vector, obtains the approximation between vectors based on the vector distance, and obtains the real-time status evaluation value of the air conditioner outdoor unit based on the approximation.

[0038] S3 compares the real-time status assessment value with the preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test during operation.

[0039] Specifically, the online operation status of the outdoor unit of the air conditioner under test includes several stages such as startup, heating detection, shutdown switching, cooling detection, and refrigerant recovery; during the operation time of the heating and cooling stages, the operating data of the outdoor unit of the air conditioner under test during the current time period is collected.

[0040] For example, within a continuous time period, the voltage, current, and refrigerant pressure sensor signal values ​​of the outdoor unit of the air conditioner under test are collected every time interval t. The specific time interval t can be determined according to the operating speed of the acquisition module, and is generally no more than 1 second.

[0041] After collecting a large amount of operational data, the power value at the corresponding moment can be obtained through voltage and current values. Then, the large amount of operational data collected is normalized to obtain a data vector time series (specifically, a data pair containing power data and pressure data) within the heating or cooling period. In this normalization process, the closer the time series value is to the reference historical data or the standard data that reflects the overall characteristics of a qualified air conditioner, the closer the normalized value will be to 1.

[0042] In a preferred embodiment, in step S3, within a certain period of time, when the number of times the real-time status evaluation value is continuously less than the preset threshold exceeds a preset number, the outdoor unit of the air conditioner under test is determined to be faulty and an alarm is triggered; when the number of times the real-time status evaluation value is continuously less than the preset threshold is less than or equal to the preset number, the outdoor unit of the air conditioner under test is determined to be normal; when the real-time status evaluation value is greater than or equal to the preset threshold, the outdoor unit of the air conditioner under test is determined to be normal.

[0043] For example, if the number of times the real-time status assessment value is continuously less than the preset threshold exceeds the preset number, it indicates that the outdoor unit of the air conditioner under test has malfunctioned during the test operation, and the fault signal will be directly output to the test personnel; if the number of times the real-time status assessment value is continuously less than the preset threshold does not exceed the preset number, it indicates that the outdoor unit of the air conditioner under test is normal, and the fault signal will be directly output to the test personnel. In this way, the air conditioner status is consistent with the actual air conditioner status, and the test accuracy is higher; if the real-time status assessment value obtained in a single instance is greater than or equal to the preset threshold, the outdoor unit of the air conditioner is determined to be normal.

[0044] In a preferred embodiment, before step S1, power data and pressure data are collected at a frequency range of 0.5s / time to 1s / time. Specifically, the collection frequency range can be selected as 0.5s / time to 1s / time, such as the time interval being selected as 0.5s, 0.8s, 1s, etc., to ensure that the amount of data collected is large enough, thereby ensuring the accuracy of fault diagnosis.

[0045] In a preferred embodiment, before step S1, power data and pressure data are collected during the continuous operating time of the outdoor unit of the air conditioner under test to obtain data pairs that correspond one-to-one in time sequence; specifically, the data collection process can be carried out in any of the following stages: startup, heating detection, shutdown switching, cooling detection, and refrigerant recovery.

[0046] In a preferred embodiment, in step S2, the vector distance is obtained using the following formula:

[0047]

[0048] In the formula, m i m j These are data vectors containing power and pressure data at two different time points, i = 1 to N, j = 1 to N; D It is a 2N ×2 N The matrix is ​​denoted by N, where N is the number of data logs consisting of power and pressure data collected within a continuous time period; a factor of 1 / 2 is used to standardize the vector distance, 0 ≤ d(m i ,m j )≤1; Optionally, when the (N+1)th new data pair is collected, steps S1-S3 can be repeated to recalculate.

[0049] Specifically, during long-term measurements, due to the influence of factors such as the quality of the air conditioner outdoor unit itself, the external environment, system real-time performance, and sensor quality, the obtained data vectors (power and pressure data pairs) values ​​differ and are distributed in different spatial areas. In this case, the system needs to determine which data vectors represent the performance of a qualified product and which represent a fault. By calculating the vector distance, qualified data vectors and faulty data vectors can be distinguished. Furthermore, the greater the support a data vector receives from other data vectors collected simultaneously, the higher its weight should be. Based on this idea, a data vector weight can be defined to reflect the correlation between each data vector and other data vectors.

[0050] In a preferred embodiment, in step S2, the approximation is calculated using the following formula:

[0051]

[0052] Among them, S i，j For every two data vectors m i and m j The approximation value between them;

[0053] Specifically, since the approximation between two data vectors is related, an "approximation matrix" (3) can be constructed based on these N data vectors. From this approximation matrix, the degree of mutual support or correlation between the two data vectors can be clearly seen:

[0054]

[0055] In a preferred embodiment, in step S2, the real-time state evaluation value is obtained using the following formula:

[0056]

[0057] In the formula, i, j = 1, 2, ..., N, P(m i ) represents the state evaluation value, Sim ij Indicates the degree of approximation.

[0058] In a preferred embodiment, the vector distance is inversely proportional to the real-time status evaluation value, which is within the range [0,1]. Within the same time series, the shorter the distance between a data vector (i.e., the real-time power and pressure data pair of the outdoor unit of the air conditioner) and other data vectors, the greater the support it receives from other data vectors, resulting in a larger real-time status evaluation value and a closer proximity to a qualified product. Conversely, if the distance between a data vector and other data vectors is large, its status evaluation value is smaller, indicating a greater distance from a qualified product, or even a faulty product.

[0059] For example, the evaluation benchmark value (i.e., the preset threshold, which is set according to the historical normal operating conditions of the air conditioner) is set to 0.6. When the number of times the real-time status evaluation value is continuously less than 0.6 is greater than the preset number, it indicates that the air conditioner under test is currently in a fault state, and an alarm signal is issued to the testing personnel. When the number of times the real-time status evaluation value is continuously less than 0.6 is less than or equal to the preset number, it indicates that the outdoor unit of the air conditioner under test is currently in a normal state. Or, if the real-time status evaluation value is greater than or equal to 0.6 in multiple consecutive calculations, it indicates that the outdoor unit of the air conditioner is in a normal state.

[0060] For example, if the evaluation benchmark value is set to 0.7, and the number of times the real-time status evaluation value is continuously less than 0.7 exceeds the preset number, it indicates that the outdoor unit of the air conditioner under test is currently in a fault state, and an alarm signal is sent to the testing personnel; if the number of times the real-time status evaluation value is continuously less than 0.7 is less than or equal to the preset number, it indicates that the outdoor unit of the air conditioner under test is currently in a normal state; if the real-time status evaluation value is greater than or equal to 0.7, it indicates that the outdoor unit of the air conditioner is normal.

[0061] More specifically, the number of consecutive deviations of the real-time status evaluation value from the preset threshold is set to at least 3 times. This can avoid misjudgment and further improve the detection accuracy.

[0062] For example, if the preset number of times is set to 3, then if the number of times the real-time status evaluation value is less than 0.6 is greater than 3, it indicates that the air conditioner under test is currently in a fault state and an alarm signal will be issued; if the number of times the real-time status evaluation value is less than 0.6 is less than or equal to 3, such as if the real-time status evaluation value is less than 0.6 for 2 consecutive times, it indicates that the outdoor unit of the air conditioner under test is currently in a normal state.

[0063] For example, if the preset number of times is set to 5, then when the number of times the real-time status evaluation value is less than 0.7 is greater than 5, it indicates that the air conditioner under test is currently in a fault state and an alarm signal will be issued; or when the number of times the real-time status evaluation value is less than 0.7 is less than or equal to 5, it indicates that the outdoor unit of the air conditioner under test is currently in a normal state.

[0064] Combination Figure 3 As shown, according to another embodiment of the present invention, an air conditioner outdoor unit fault detection system based on power and pressure correlation is also disclosed, the system comprising:

[0065] The detection unit is used to collect online power and pressure data of the outdoor unit of the air conditioner;

[0066] The processing unit is used to acquire the associated data vectors of online power data and pressure data of the outdoor unit of the air conditioner under test within a continuous time period; it is also used to acquire the vector distance between data vectors in the associated data vector, acquire the approximation between vectors based on the vector distance, and acquire the real-time status evaluation value of the outdoor unit of the air conditioner based on the approximation.

[0067] The judgment output unit is used to compare the real-time status evaluation value with the preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test, and output the judgment information.

[0068] Specifically, the detection unit is used to collect operating data of the outdoor unit under test during the current period, including current and voltage, as well as pressure data, when the outdoor unit under test is in several operating stages such as start-up, heating detection, shutdown switching, cooling detection, and refrigerant recovery. The power and pressure data are then transmitted to the processing unit through the transmission module.

[0069] The processing unit calculates the real-time power from the received current and voltage, then processes the real-time power and pressure into a time-series data vector; then it calculates the vector distance and approximation, and finally calculates the real-time state evaluation value and transmits it to the judgment unit.

[0070] The judgment unit compares the received real-time status evaluation value with the preset threshold stored in advance to determine whether the outdoor unit of the air conditioner under test is in a fault state.

[0071] In yet another embodiment of the invention, a computer device is also disclosed, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the detection method as described in any of the preceding embodiments.

[0072] The air conditioner outdoor unit status evaluation standard is obtained by comparing and analyzing the power / pressure data of healthy and faulty air conditioner outdoor units, based on power / pressure data vectors and historical operating condition information corresponding to these data vectors.

[0073] It is understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0074] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the aforementioned prediction method can be completed by integrated logic circuits in the processor's hardware or by software instructions. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the aforementioned method.

[0075] It is understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0076] For software implementation, the techniques described herein can be implemented through modules (e.g., procedures, functions, etc.) that perform the functions described herein. The software code can be stored in memory and executed by a processor. The memory can be implemented within the processing module or external to the processing module.

[0077] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting faults in an air conditioner outdoor unit based on the correlation between power and pressure, characterized in that, The method for detecting faults in the outdoor unit of an air conditioner includes: S1. Normalize the data pairs consisting of online power data and pressure data at different times during the same operating phase of the outdoor unit of the air conditioner under test to obtain the corresponding correlation data vector at each time. S2. The vector distance between the associated data vectors corresponding to any two moments in the same running phase is obtained using the following formula: In the formula, , They are time points The corresponding associated data vector contains power and pressure data. , D is a The matrix, where, This is the logarithm of power and pressure data collected within a continuous time period during the same operational phase; a factor of 1 / 2 is used to standardize the vector distance. For associated data vectors and The vector distance between them, and ; Calculate the approximation between the associated data vectors corresponding to two time points in the same running phase based on the vector distance between them: wherein, is an approximation of the correlation data vector and is an approximation of the correlation data vector According to the degree of approximation between the correlation data vector of the time point and the correlation data vectors of all other time points, the real-time state evaluation value of the time point is obtained: In the formula, is the real-time state evaluation value at the time t; S3. Compare the real-time status evaluation values ​​of the outdoor unit of the air conditioner under test at each moment during the same operating phase with the preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test during that operating phase.

2. The outdoor unit failure detection method based on the power and pressure correlation according to claim 1, wherein In step S3, if the number of times the real-time status evaluation value is continuously less than the preset threshold exceeds the preset number within a certain period of time, the outdoor unit of the air conditioner under test is determined to be faulty and an alarm is triggered. When the number of times the real-time status evaluation value is continuously less than the preset threshold is less than or equal to the preset number, the outdoor unit of the air conditioner under test is determined to be normal. When the real-time status evaluation value is greater than or equal to the preset threshold, the outdoor unit of the air conditioner under test is determined to be normal.

3. The air conditioner outdoor unit fault detection method based on power and pressure correlation as described in claim 1, characterized in that, The vector distance is inversely proportional to the real-time state evaluation value.

4. The air conditioner outdoor unit fault detection method based on power and pressure correlation as described in claim 1, characterized in that, Before step S1, the power data and pressure data are collected at a frequency range of 0.5s / time to 1s / time.

5. The air conditioner outdoor unit fault detection method based on power and pressure correlation as described in claim 1, characterized in that, Before step S1, the power data and pressure data are collected during the continuous operating time of the outdoor unit of the air conditioner under test to obtain data pairs that correspond one-to-one in time sequence.

6. A system for implementing the air conditioner outdoor unit fault detection method based on power and pressure correlation as described in any one of claims 1-5, characterized in that, include: The detection unit is used to collect online power and pressure data of the outdoor unit of the air conditioner; The processing unit is used to normalize the data pairs consisting of online power data and pressure data of the outdoor unit of the air conditioner under test to obtain a correlated data vector; it is also used to obtain the vector distance between the data vectors in the correlated data vector, obtain the approximation between the vectors based on the vector distance, and obtain the real-time status evaluation value of the outdoor unit of the air conditioner based on the approximation. The judgment output unit is used to compare the real-time status evaluation value with a preset threshold to determine the real-time status of the outdoor unit of the air conditioner under test during operation, and output the judgment information.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the detection method according to any one of claims 1 to 5.

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