A wind disc failure monitoring method and system

Abstract

The present application belongs to the technical field of data processing, and particularly relates to a kind of wind disc fault monitoring method and system, its method includes: through real-time collection wind disc operating data and environmental parameter, constructs environmental response sensitivity tensor, calculates and eliminates environmental induced supply and return water temperature difference deviation, obtains the operating characteristic vector of environmentalization.The dynamic weight between wind disc is generated by combining static hydraulic topology and dynamic behavior difference, the self-evolution covariance matrix is constructed, and the dynamic optimization calculation of Mahalanobis distance is realized.Based on the spatial topological relationship of abnormal wind disc, the fault type is distinguished, the single blockage and system blockage are accurately distinguished, and intelligent early warning is realized.The present application can effectively inhibit environmental disturbance false alarm, and improve fault positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology. More specifically, this invention relates to a method and system for monitoring fan coil unit failures. Background Technology

[0002] In modern intelligent buildings, fan coil units, as terminal equipment of central air conditioning, play a crucial role in regulating indoor thermal comfort. Their operating status directly affects user comfort, energy efficiency, and system lifespan. With the development of building intelligence, data-driven fault monitoring technology is gradually replacing traditional manual inspection methods, becoming a core means to improve operation and maintenance efficiency.

[0003] Currently, for common faults in fan coil systems (such as clogged return air filters and clogged water filters), the industry widely employs multivariate statistical analysis methods for anomaly detection. Among these, the Mahalanobis distance-based multivariate anomaly detection algorithm is widely used in equipment health assessment and fault early warning scenarios because it can effectively measure the degree of deviation between equipment status and the normal distribution of the group. This method constructs a covariance matrix of operating data among fan coil units, calculates the Mahalanobis distance between the state vector of an individual unit and its group mean, and then identifies abnormal individuals.

[0004] In the detection of anomalies in fan coil systems, although the traditional Mahalanobis distance method can effectively identify the trend of equipment deviating from the group behavior, when the outdoor temperature changes suddenly or the solar radiation increases, all fan coils will experience a general increase in the supply and return water temperature difference due to the synchronous increase in load. Mahalanobis distance is prone to misjudging such normal responses as system-level blockages. At the same time, the Mahalanobis distance static covariance model cannot adapt to the operational drift caused by seasonal changes or changes in usage patterns, resulting in poor robustness of diagnostic results and easy to cause false or missed detection of air conditioning fan coil system faults. Summary of the Invention

[0005] To address the technical problems of the Mahalanobis distance method being unable to distinguish between normal group responses and actual faults caused by environmental disturbances, and being difficult to adapt to the long-term evolution trend of the system, thus easily leading to false detections or missed detections, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides a method for monitoring fan coil unit failures, comprising:

[0007] The system collects real-time operating data and environmental parameters of the air conditioning fan coil system, including supply and return water temperatures. An environmental response sensitivity tensor for the fan coil system is constructed based on the dynamic response relationship between the operating data and environmental parameters. The environmentally induced offset of the supply and return water temperature difference is determined based on the deviation between the current environmental parameters and historical baseline environmental parameters, as well as the environmental response sensitivity tensor. The supply and return water temperature difference is corrected based on this offset to obtain a de-environmentally optimized supply and return water temperature difference. An operating feature vector for the fan coil system is constructed by combining this with the rate of change of the supply and return water temperature difference. Dynamic weights between the fan coil systems are calculated based on the static hydraulic topology relationship and the differences in the operating feature vectors. An evolutionary covariance matrix is ​​constructed using the dynamic weights, the operating feature vectors, and the historical covariance matrix. Mahalanobis distance is calculated based on the evolutionary covariance matrix and the operating feature vectors of the fan coil systems to filter abnormal fan coil systems. Based on the static hydraulic topology relationship between abnormal fan coil systems, the system identifies the type of fan coil system fault and outputs an early warning.

[0008] Preferably, the environmental parameters include outdoor temperature, solar radiation intensity, and outdoor humidity.

[0009] Preferably, when constructing the environmental response sensitivity tensor of the fan coil unit, historical operating data and environmental parameters are used to achieve this through a linear regression model. The input of the linear regression model is an environmental variable vector, and the output is the supply and return water temperature difference of the fan coil unit as the response variable.

[0010] Preferably, determining the offset of the environmentally induced supply and return water temperature difference of the fan coil unit includes: taking the difference between the current environmental variable vector and the mean of the historical environmental variable vector as the environmental deviation at the current moment; and taking the product of the environmental response sensitivity tensor of the fan coil unit and the environmental deviation at the current moment as the offset of the environmentally induced supply and return water temperature difference of the fan coil unit at the current moment.

[0011] Preferably, the running feature vector is ,in, Indicates the current time. The operational characteristic vector of a typhoon disk. Indicates the current time. The temperature difference between the supply and return water after the typhoon shield is de-environmentally treated: , Indicates the current time. The temperature difference between the supply and return water of the typhoon control unit. Indicates the environmentally induced first moment at the current time. The offset of the supply and return water temperature difference of the typhoon plate; Indicates the current time. The rate of change of supply and return water temperature difference in the typhoon control unit: , Indicates the previous sampling time No. The temperature difference between the supply and return water of the typhoon control unit. This indicates a fixed sampling period.

[0012] Preferably, the dynamic weights satisfy the expression: ;in, Indicates the current moment. Indicates the current time. Typhoon Pan and the first Dynamic weights among typhoon disks; This indicates the static hydraulic connection weight. The static hydraulic connection weight between fan coil units on the same branch is set to 1, the static hydraulic connection weight between fan coil units that are on different branches but in the same unit is set to 0.3, and the static hydraulic connection weight between fan coil units in different units is set to 0. Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; This is the attenuation coefficient.

[0013] Preferably, the dynamic behavior distance satisfies the expression: ;in, Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; Indicates time Time The operational characteristic vector of the typhoon disk; Indicates time Time The operational characteristic vector of the typhoon disk; Represents the L2 norm; Indicates the length of the time window.

[0014] Preferably, the self-evolutionary covariance matrix satisfies the expression: ;in, Indicates the current moment. This represents the self-evolutionary covariance matrix at the current moment; Indicates the previous sampling time The self-evolutionary covariance matrix; Indicates the current time. Typhoon Pan and the first Dynamic weights among typhoon disks; Indicates the current time. The operational characteristic vector of the typhoon disk; Indicates the current time. The operational characteristic vector of the typhoon disk; This represents the mean of the operating characteristic vectors of all wind turbines at the current moment; This represents the matrix transpose operation; Indicates the learning rate; This indicates a fixed sampling period.

[0015] Preferably, the screening of abnormal fan coil units involves identifying the fault type and outputting an early warning based on the static hydraulic topology relationship between the abnormal fan coil units. This includes: determining that a fan coil unit has a potential abnormality if its Mahalanobis distance is greater than a preset threshold; determining that the main filter of the fan coil unit is clogged if only a single fan coil unit has a potential abnormality while the other fan coil units in the same branch are normal; determining that the filter of the branch is clogged if three or more fan coil units in the same branch have potential abnormalities simultaneously; and determining that the main filter of the unit is clogged if fan coil units in multiple branches have potential abnormalities simultaneously. Structured early warning information is generated based on the identification results and pushed to the operation and maintenance platform.

[0016] Secondly, the present invention provides a fan coil unit fault monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned fan coil unit fault monitoring method is implemented.

[0017] By adopting the above technical solution, a computer program for the above-mentioned fan coil unit fault monitoring method is generated and stored in a memory so that it can be loaded and executed by a processor. A terminal device can then be made based on the memory and processor for convenient use.

[0018] The beneficial effects of this invention are as follows: By constructing an environmental response sensitivity tensor and calculating environmental induced offsets, this invention effectively eliminates the interference of environmental factors on the operation data of the air handling units, avoiding misjudgments caused by changes in outdoor temperature, humidity, and sunlight, and significantly improving the environmental adaptability of anomaly detection; by integrating static hydraulic topology relationships and dynamic behavioral distances to calculate dynamic weights, and constructing a self-evolving covariance matrix based on this, this invention enables the model to perceive the physical relationships between air handling units and adapt to the long-term evolution trend of the system, solving the problem that traditional static models are unable to cope with seasonal changes and changes in usage patterns; by combining the self-evolving covariance matrix to calculate Mahalanobis distance, this invention can more accurately measure the deviation of the air handling unit's operating status from the normal distribution of the group, greatly improving the accuracy of anomaly identification; finally, this invention uses static hydraulic topology relationships to identify the fault type of abnormal air handling units, accurately locating faults such as blockage of the main filter, branch filter, or unit main filter, providing clear guidance for operation and maintenance, reducing manual troubleshooting costs, and improving the intelligent operation and maintenance level and energy utilization efficiency of the air conditioning air handling unit system. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a fan coil unit failure monitoring method according to the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0022] This invention discloses a method for monitoring fan coil unit failures, referring to... Figure 1 This includes steps S1-S8:

[0023] S1. Real-time acquisition of operating data and environmental parameters of the air conditioning fan coil system.

[0024] It should be noted that the operating status of an air conditioning fan coil system is affected by various factors, exhibiting different characteristics under different environmental conditions and operating periods. For example, fluctuations in outdoor temperature can cause changes in the supply and return water temperature difference of the fan coil unit, and adjustments to the fan speed setting can also affect the heat exchange efficiency of the fan coil unit. Therefore, this invention provides basic data support for subsequent anomaly detection by real-time collection of the fan coil system's operating data and environmental parameters.

[0025] Specifically, from the start-up of the ventilation coil system, operational data is continuously collected through intelligent controllers and sensor networks deployed in each ventilation coil, while environmental parameters are obtained through rooftop weather stations or city meteorological interfaces. The data collection frequency is set to once every 5 minutes, and the implementation personnel can adjust the collection frequency according to the actual situation.

[0026] The operational data includes supply water temperature and return water temperature. The supply water temperature is the water temperature at the inlet of the fan coil unit, and the return water temperature is the water temperature at the outlet of the fan coil unit. The environmental parameters include outdoor temperature, solar radiation intensity, and outdoor humidity.

[0027] Furthermore, based on the collected supply and return water temperatures, the supply and return water temperature difference can be calculated. ,in, Indicates the water supply temperature. This indicates the return water temperature; the temperature difference between the supply and return water reflects the heat exchange intensity of the fan coil unit.

[0028] Furthermore, in order to eliminate the impact of sensor noise, communication jitter, and transient interference on subsequent data analysis, this invention employs a Kalman filter algorithm to denoise the collected operational data and environmental parameters respectively.

[0029] In other embodiments, implementers may select other denoising methods, such as wavelet transform, moving average filtering, etc., based on the quality of the on-site data.

[0030] S2. Construct the environmental response sensitivity tensor of the fan coil unit based on the dynamic response relationship between the fan coil unit's operating data and environmental parameters.

[0031] It should be noted that different fan coil units respond differently to changes in environmental parameters. For example, fan coil units in south-facing rooms are more sensitive to changes in sunlight intensity, and in high humidity environments, the fan may increase airflow to dehumidify, thus affecting the operating status of the fan coil unit. Therefore, this invention quantifies the response characteristics of fan coil units to multi-dimensional environmental parameters by constructing an environmental response sensitivity tensor.

[0032] Specifically, using historical operating data and environmental parameters from the past 7 days, an environmental response sensitivity tensor is constructed for each wind turbine:

[0033] Construct the environment variable vector for each moment using all environmental parameters: ,in, For a moment outdoor temperature at that time For a moment Solar radiation intensity at that time For a moment The outdoor humidity at any given time. The supply and return water temperature difference of each fan coil unit at each moment is used as the response variable: ,in, For the first Typhoon Pan at all times The temperature difference between the supply and return water.

[0034] For any given fan coil unit, a linear regression model is established based on the environmental variable vector at each moment and the response variable of that fan coil unit:

[0035]

[0036] in, Indicates the first The environmental response sensitivity tensor of the typhoon disk. , Each element in the table represents the strength of the influence of a certain environmental parameter on the response variable, for example... Indicates the outdoor temperature for the first The impact of the temperature difference between the supply and return water of the typhoon control unit. Indicates the solar radiation intensity for the first The impact of the temperature difference between the supply and return water of the typhoon control unit; For bias terms; A vector of environment variables; For the first The response variable vector of the typhoon disk.

[0037] It should be noted that the environmental response sensitivity tensor can accurately characterize the response characteristics of the wind turbine to multi-dimensional environmental inputs, laying the foundation for subsequent removal of environmental interference.

[0038] S3. Based on the deviation between the current environmental parameters and the historical baseline environmental parameters, as well as the environmental response sensitivity tensor of the fan coil unit, determine the offset of the environmentally induced supply and return water temperature difference of the fan coil unit.

[0039] It should be noted that changes in environmental parameters can cause deviations in the fan coil unit's operating data, which may be misjudged as anomalies. For example, an increase in outdoor temperature will increase the indoor cooling load, naturally leading to a larger temperature difference between the fan coil unit's supply and return water. Therefore, this invention calculates the environmentally induced deviation in the supply and return water temperature difference to isolate the influence of environmental factors on the supply and return water temperature difference.

[0040] Specifically, based on the deviation between current environmental parameters and historical baseline environmental parameters, and combined with the environmental response sensitivity tensor constructed in step S2, the offset of the environmentally induced supply and return water temperature difference of the fan coil unit is calculated, including:

[0041] Calculate the environmental deviation at the current moment based on the difference between the current environmental variable vector and the historical mean environmental variable vector:

[0042]

[0043] in, Indicates the current moment; Indicates the environmental deviation at the current moment; This represents the vector of environment variables at the current moment. This represents the mean of the environmental variable vector over the past 7 days.

[0044] Furthermore, based on the environmental response sensitivity tensor and the environmental deviation at the current moment, the offset of the response variable is predicted:

[0045]

[0046] in, Indicates the current moment; Indicates the environmentally induced first moment at the current time. The offset of the supply and return water temperature difference of the typhoon plate; Indicates the first The environmental response sensitivity tensor of the typhoon disk; This indicates the environmental deviation at the current moment.

[0047] It should be noted that by calculating the offset of the supply and return water temperature difference of the fan coil unit induced by the environment, this invention can accurately distinguish between changes in operating data caused by environmental factors and changes in operating data caused by anomalies, thereby improving the accuracy of subsequent anomaly detection.

[0048] S4. Correct the supply and return water temperature difference of the fan coil unit according to the offset of the supply and return water temperature difference induced by the environment, and obtain the supply and return water temperature difference of the fan coil unit after environmentalization. Construct the operating characteristic vector of the fan coil unit based on the supply and return water temperature difference after environmentalization and the rate of change of the supply and return water temperature difference.

[0049] It should be noted that after obtaining the environmentally induced supply and return water temperature difference offset, it needs to be removed from the actual measured supply and return water temperature difference in order to obtain the "net heat exchange behavior" characteristics that are not affected by environmental factors, thus providing clean input data for subsequent anomaly detection.

[0050] Specifically, based on the current temperature difference between the supply and return water of each fan unit and the environmental factors induced at the current moment... The offset of the supply and return water temperature difference in the typhoon control panel is used to determine the current supply and return water temperature difference after environmental degradation.

[0051]

[0052] in, Indicates the current moment; Indicates the current time. The temperature difference between the supply and return water after the typhoon shield is de-environmentally modified; Indicates the environmentally induced first moment at the current time. The offset of the supply and return water temperature difference of the typhoon plate; Indicates the current time. The temperature difference between the supply and return water of the typhoon control unit.

[0053] Furthermore, the rate of change of the supply and return water temperature difference is calculated:

[0054]

[0055] in, Indicates the current moment. Indicates the current time. The rate of change of the supply and return water temperature difference of the typhoon fan is used to characterize the dynamic trend of the heat exchange process of the fan fan. Indicates the current time. The temperature difference between the supply and return water of the typhoon control unit; Indicates the previous sampling time No. The temperature difference between the supply and return water of the typhoon control unit; This indicates a fixed sampling period, as shown in this embodiment. Minutes are kept consistent with the system's data acquisition frequency to ensure the alignment of time-series data and the stability of calculations.

[0056] Furthermore, the current temperature difference between the supply and return water of each fan coil unit after removing environmental factors, and the rate of change of the supply and return water temperature difference of each fan coil unit at the current moment, constitute the operating characteristic vector of each fan coil unit at the current moment: ,in, Indicates the current time. The operational characteristic vector of a typhoon disk. Indicates the current time. The temperature difference between the supply and return water after the typhoon shield is de-environmentally treated. Indicates the current time. The rate of change in the supply and return water temperature difference of the typhoon control unit.

[0057] It should be noted that the operating characteristic vector of each fan coil unit can reflect the actual heat exchange behavior of the fan coil unit, eliminating the interference of environmental factors, and providing a reliable basis for subsequent anomaly detection based on the population.

[0058] S5. Calculate the dynamic weights between the wind turbines based on the static hydraulic topology relationship and the difference in operating characteristic vectors between the wind turbines.

[0059] It should be noted that there are hydraulic coupling characteristics among the fan coil units in the system, and the functional similarity between the fan coil units changes dynamically with the operating mode. For example, during low-load periods at night, the fan coil units on the east and west wings may exhibit a stronger correlation. Therefore, this invention constructs dynamic weights between the fan coil units based on their static hydraulic topological relationship and dynamic behavioral distance, in order to quantify the degree of correlation between different fan coil units.

[0060] Specifically, the dynamic behavioral distance between different fan coil units is determined based on the differences in their operational feature vectors:

[0061]

[0062] in, Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; Indicates time Time The operational characteristic vector of the typhoon disk; Indicates time Time The operational characteristic vector of the typhoon disk; This represents the L2 norm, used to calculate the spatial distance between two eigenvectors; Indicates the length of the time window. (Corresponding to 24-hour data, with 5-minute intervals, i.e., 12 5-minute sampling points per hour), used to limit the time range for calculating dynamic behavior distance.

[0063] Furthermore, based on the dynamic behavioral distance between different fan coil units and the static hydraulic connection weights between them, the dynamic weights between different fan coil units are determined:

[0064]

[0065] in, Indicates the current moment. Indicates the current time. Typhoon Pan and the first Dynamic weights among typhoon disks; The static hydraulic connection weight is indicated by the following: In this embodiment, the static hydraulic connection weight between fan coil units on the same branch is set to 1, the static hydraulic connection weight between fan coil units on different branches but in the same unit is set to 0.3, and the static hydraulic connection weight between fan coil units in different units is set to 0. Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; The attenuation coefficient is used to control the intensity of the influence of dynamic behavior distance on dynamic weights. In this embodiment... In other embodiments, implementers may set the parameters according to the actual implementation situation.

[0066] It should be noted that dynamic weighting integrates the static hydraulic topology and behavioral similarity of the wind turbines, and can more accurately reflect the degree of correlation between the wind turbines.

[0067] S6. Based on the dynamic weights between the wind turbines and the operating characteristic vectors of the wind turbines, construct an evolutionary covariance matrix by combining the historical covariance matrix.

[0068] It should be noted that the operating behavior of the wind turbine system exhibits significant time-varying and non-stationarity, and static statistical models cannot adapt to long-term evolutionary trends. Therefore, this invention employs a sliding window weighting and online learning mechanism to construct a self-evolving covariance matrix, enabling the model to adapt to the dynamic changes of the system.

[0069] Specifically, the update formula for the self-evolutionary covariance matrix is:

[0070]

[0071] in, Indicates the current moment. This represents the self-evolutionary covariance matrix at the current moment, used to describe the joint distribution characteristics of de-environmentalized eigenvectors in the population; Indicates the previous sampling time The self-evolutionary covariance matrix is ​​used to preserve the statistical properties of historical operating patterns; Indicates the current time. Typhoon Pan and the first The dynamic weights between typhoon disks reflect the strength of their association in terms of structural topology and behavioral similarity; Indicates the current time. The operational characteristic vector of the typhoon disk; Indicates the current time. The operational characteristic vector of the typhoon disk; This represents the mean of the operating characteristic vectors of all wind turbines at the current moment, reflecting the average operating state of the group as a whole; This represents the matrix transpose operation; The learning rate, ranging from [0,1], is used to control the fusion ratio of historical information and current data. In this embodiment... In other embodiments, implementers can set the learning rate according to the actual implementation situation. When the learning rate The larger the value, the more adaptable the model is to the latest behavior; This indicates a fixed sampling period, as shown in this embodiment. Minutes, consistent with the system's data acquisition frequency.

[0072] It should be noted that the present invention is achieved through... By preserving stable patterns in the historical covariance matrix and avoiding interference from short-term fluctuations on the model, while also leveraging... By introducing weighted statistical information at the current moment, the latest operating trends can be tracked, achieving a fusion of historical and real-time data. Simultaneously, this invention utilizes the dynamic weights among the fan coil units. Deviation items between fan coil units By performing weighted summation, the contribution of physically related and behaviorally similar wind turbine devices to the covariance matrix is ​​enhanced, improving the model's ability to perceive topological structures. This allows the self-evolving covariance matrix to retain the stability of historical data while incorporating the latest behavioral patterns, effectively resisting short-term noise interference, adapting to long-term changes such as seasonal changes and usage pattern changes, and ensuring that the model maintains high detection accuracy at different times. This provides a reliable distribution model foundation for subsequent Mahalanobis distance calculations.

[0073] It is important to note that in the actual deployment of air conditioning fan coil systems, there must be at least one fan coil combination with a non-zero static weight (i.e., at least one pair of fan coils must satisfy the topological relationship of being on the same branch or in the same unit across branches). This deployment characteristic ensures that the denominator in the formula... The value is not zero, thus ensuring the validity of the calculation of the self-evolutionary covariance matrix.

[0074] S7. Based on the self-evolutionary covariance matrix and the operating feature vector of the wind turbine, calculate the Mahalanobis distance and filter out abnormal wind turbines.

[0075] It should be noted that Mahalanobis distance can measure the degree of deviation of a single sample from the sample set. Therefore, this invention uses Mahalanobis distance to identify wind turbine anomalies.

[0076] Specifically, the Mahalanobis distance of each wind turbine is determined based on its operational characteristic vector and self-evolutionary covariance matrix.

[0077]

[0078] in, Indicates the current moment. Indicates the current time. The Mahalanobis distance of the typhoon disk reflects the current moment. The degree to which the typhoon's operating status deviates from the normal operating status of the group is used to measure the... Is the typhoon warning system abnormal? Indicates the current time. The operational characteristic vector of the typhoon disk; This represents the mean of the operating characteristic vectors of all wind turbines at the current moment; This represents the self-evolutionary covariance matrix at the current moment; This represents the matrix transpose operation.

[0079] when The larger the value, the more significant the [value]. The greater the deviation between the operating status of a typhoon manipulator and the normal operating status of the group, the higher the probability that the manipulator is abnormal; conversely, when The smaller the value, the closer the operation of the wind turbine is to the average level of the group, and the more normal its operation. This invention calculates the deviation between the individual wind turbine's operational characteristic vector and the group's mean vector, and then weights this deviation using the inverse of the self-evolutionary covariance matrix, thus obtaining a quantitative indicator that reflects the difference between the individual and the overall group's operational patterns. (Self-evolutionary covariance matrix) It incorporates the topological relationships and dynamic behavior characteristics of the wind turbine system. Its inverse matrix can reasonably scale the differences in features of different dimensions, avoiding the interference of features of different dimensions on the distance calculation. This makes the obtained Mahalanobis distance consider both the absolute differences between individual wind turbines and the group, as well as the distribution pattern of features within the group, and can more accurately identify the abnormal operating state of the wind turbine.

[0080] Furthermore, in response to Mahalanobis distance Greater than the preset threshold The system determines that the fan coil unit has a potential malfunction and proceeds to the fault type identification stage. In this embodiment, the threshold... The empirical value is 3.2. In other embodiments, implementers can set the threshold according to the actual implementation situation. .

[0081] S8. Based on the static hydraulic topology relationship between abnormal fan coil units, determine the type of fan coil unit failure and output early warning.

[0082] It should be noted that for fan coil units marked as potentially abnormal, this invention combines their topological location in the system with the abnormal conditions of other fan coil units to determine the fault type, thereby identifying the specific fault location and type and providing accurate guidance for operation and maintenance.

[0083] Specifically, if only a single fan coil unit has a potential abnormality while the other fan coil units in the same branch are normal, it is determined that the filter of that fan coil unit is blocked; if three or more fan coil units in the same branch have potential abnormalities simultaneously, it is determined that the filter of that branch is blocked; if fan coil units in multiple branches have potential abnormalities simultaneously, it is determined that the main filter of the unit is blocked.

[0084] Furthermore, structured early warning information is generated based on the judgment results and pushed to the operation and maintenance platform.

[0085] This invention also discloses a fan coil unit fault monitoring system, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a fan coil unit fault monitoring method according to the present invention.

[0086] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0087] In the description of this specification, "multiple" or "several" means at least two, such as two, three or more, unless otherwise expressly and specifically defined.

[0088] While this specification has shown and described numerous embodiments of the invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in the practice of this invention.

Claims

1. A method for monitoring fan coil unit failures, characterized in that, include: Real-time acquisition of operating data and environmental parameters of the air conditioning fan coil system, including water supply temperature and return water temperature; An environmental response sensitivity tensor for the fan is constructed based on the dynamic response relationship between the fan operating data and environmental parameters. The environmentally induced offset of the fan supply and return water temperature difference is determined based on the deviation between the current environmental parameters and historical baseline environmental parameters, as well as the environmental response sensitivity tensor of the fan. The fan supply and return water temperature difference is corrected based on the offset to obtain the de-environmentalized fan supply and return water temperature difference. The operating feature vector of the fan is constructed by combining the rate of change of the fan supply and return water temperature difference. The dynamic weights between the wind turbines are calculated based on the static hydraulic topology relationship and the difference in their operating feature vectors. An evolutionary covariance matrix is ​​constructed using the dynamic weights, operating feature vectors, and historical covariance matrices. Based on the evolutionary covariance matrix and the operating feature vectors of the wind turbines, Mahalanobis distance is calculated to screen abnormal wind turbines. Based on the static hydraulic topology relationship between the abnormal wind turbines, the fault type of the wind turbines is identified and an early warning is output.

2. The method for monitoring fan coil unit failure according to claim 1, characterized in that, The environmental parameters include outdoor temperature, solar radiation intensity, and outdoor humidity.

3. The method for monitoring fan coil unit failure according to claim 1, characterized in that, When constructing the environmental response sensitivity tensor of the fan coil unit, historical operating data and environmental parameters are used to achieve this through a linear regression model. The input of the linear regression model is an environmental variable vector, and the output is the supply and return water temperature difference of the fan coil unit as the response variable.

4. The method for monitoring fan coil unit failure according to claim 1, characterized in that, The determination of the environmentally induced supply and return water temperature difference offset includes: The difference between the current environmental variable vector and the mean of the historical environmental variable vector is taken as the environmental deviation at the current moment; the product of the environmental response sensitivity tensor of the fan coil unit and the environmental deviation at the current moment is taken as the offset of the supply and return water temperature difference of the fan coil unit induced by the environment at the current moment.

5. The method for monitoring fan coil unit failure according to claim 1, characterized in that, The running feature vector is ,in, Indicates the current time. The operational characteristic vector of the typhoon disk. Indicates the current time. The temperature difference between the supply and return water after the typhoon shield is de-environmentally treated: , Indicates the current time. The temperature difference between the supply and return water of the typhoon control unit. Indicates the environmentally induced first moment at the current time. The offset of the temperature difference between the supply and return water of the typhoon control plate; Indicates the current time. The rate of change of supply and return water temperature difference in the typhoon control unit: , Indicates the previous sampling time No. The temperature difference between the supply and return water of the typhoon control unit. This indicates a fixed sampling period.

6. The method for monitoring fan coil unit failure according to claim 1, characterized in that, Obtain dynamic weights that satisfy the expression: ； in, Indicates the current moment. Indicates the current time. Typhoon Pan and the first Dynamic weights among typhoon disks; This indicates the static hydraulic connection weight. The static hydraulic connection weight between fan coil units on the same branch is set to 1, the static hydraulic connection weight between fan coil units that are on different branches but in the same unit is set to 0.3, and the static hydraulic connection weight between fan coil units in different units is set to 0. Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; This is the attenuation coefficient.

7. The method for monitoring fan coil unit failure according to claim 6, characterized in that, The dynamic behavior distance satisfies the expression: ； in, Indicates the first Typhoon Pan and the first The dynamic behavior and distance of the typhoon over the past 24 hours; Indicates time Time The operational characteristic vector of the typhoon disk; Indicates time Time The operational characteristic vector of the typhoon disk; Represents the L2 norm; Indicates the length of the time window.

8. The method for monitoring fan coil unit failure according to claim 1, characterized in that, The self-evolutionary covariance matrix satisfies the expression: ； in, Indicates the current moment. This represents the self-evolutionary covariance matrix at the current moment; Indicates the previous sampling time The self-evolutionary covariance matrix; Indicates the current time. Typhoon Pan and the first Dynamic weights among typhoon disks; Indicates the current time. The operational characteristic vector of the typhoon disk; Indicates the current time. The operational characteristic vector of the typhoon disk; This represents the mean of the operating characteristic vectors of all wind turbines at the current moment; This represents the matrix transpose operation; Indicates the learning rate; This indicates a fixed sampling period.

9. The method for monitoring fan coil unit failure according to claim 1, characterized in that, The process of screening abnormal fan coil units involves identifying the type of fan coil unit failure and outputting an early warning based on the static hydraulic topology relationship between the abnormal fan coil units, including: If the Mahalanobis distance of the fan coil unit is greater than a preset threshold, it is determined that the fan coil unit has a potential anomaly. If only a single fan coil unit has a potential anomaly while the rest of the fan coil units in the same branch are normal, it is determined that the filter of that fan coil unit is blocked. If three or more fan coil units in the same branch have potential anomalies simultaneously, it is determined that the filter of that branch is blocked. If fan coil units in multiple branches have potential anomalies simultaneously, it is determined that the main filter of the unit is blocked. Based on the judgment results, structured early warning information is generated and pushed to the operation and maintenance platform.

10. A fan coil unit fault monitoring system, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement a fan coil unit fault monitoring method according to any one of claims 1-9.

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