A method for predicting the failure probability of HVAC equipment suitable for multiple working conditions

Abstract

This invention belongs to the field of HVAC equipment failure prediction and provides a method for predicting HVAC equipment failure probabilities that is adaptable to multiple operating conditions. The method includes: defining equipment types and equipment parameters, collecting recorded data, constructing data-fault mapping relationships, constructing equipment failure probability formulas, data reconstruction, data cleaning, acquiring equipment operation and maintenance parameters, adjusting equipment operation and maintenance parameters, predicting equipment operation and maintenance parameters, and classifying predicted operation and maintenance parameters. By predicting operation and maintenance data and fault diagnosis in real time, the present invention overcomes the shortcomings of existing methods that rely on a single monitoring point, upper and lower limit alarms, and manual tracking, thereby enabling the prediction and early warning of equipment failures. By constructing an environmental parameter matrix and a set of operating conditions, reliable predictions are achieved for different operating conditions. By adjusting equipment operation and maintenance parameters, the impact of manual operation on equipment operation and maintenance parameters is reduced, thereby improving the reliability and accuracy of the final prediction results.

Description

Technical Field

[0001] The present invention relates to the field of HVAC equipment failure prediction, and in particular to a HVAC equipment failure probability prediction method adaptable to multiple working conditions. Background Art

[0002] With the advancement of contemporary science and technology, the overall trend in the development of modern equipment is towards complexity, intelligence, and automation. The operational safety and reliability of equipment have a significant impact on national economy and people's livelihoods, social stability, and national resources and the environment. Ensuring the safe and reliable operation of equipment is becoming increasingly urgent, and the safety and maintenance of equipment service are receiving increasing attention. As an essential component of buildings, the stability and reliability of HVAC equipment significantly impact the comfort of building occupants. The development of equipment failure probability prediction methods is primarily intended to assist equipment operators and maintenance personnel in ensuring stable operation and on-demand maintenance of HVAC equipment, thereby preventing economic losses caused by equipment failures. HVAC equipment failures are primarily categorized into two types: hard failures and soft failures. Hard failures are characterized by complete failures of equipment or devices, such as sudden fan shutdown, belt breakage, or sensor output failure. These types of failures are relatively easy to monitor. Soft failures are characterized by performance degradation or partial failure of equipment or devices, such as gradual blockage of fan coil units due to scaling. These types of failures are generally gradual, with subtle pre-existing symptoms and often difficult to detect using sensors. In fact, soft faults are caused by the gradual deterioration of system parameters and are more harmful than hard faults.

[0003] Currently, there are no mature prediction methods for HVAC equipment failures. Traditional solutions rely on physical models and threshold settings, but these solutions have certain drawbacks: they are not adaptable to all equipment models; they cannot adapt to a wide range of operating conditions; and they overly rely on thresholds for fault prediction, resulting in insufficient accuracy. In-service equipment often operates in harsh, diverse conditions, with high power consumption, heavy loads, and continuous operation. Serious accidents caused by early failures are common. To eliminate these potential failures and prevent safety incidents, modern industry urgently needs to adopt relevant monitoring technologies and the inherent analysis techniques that ensure the safe operation of in-service equipment. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the purpose of the present invention is to provide a HVAC equipment failure probability prediction method that is adaptable to multiple working conditions. By real-time prediction of operation and maintenance data and fault judgment, the defects of the existing method that relies on a single monitoring point, upper and lower limit alarms and manual tracking are solved, and the prediction and early warning of equipment failures are realized; by constructing an environmental parameter matrix and an operating condition set, reliable prediction of different operating conditions is achieved; by adjusting the equipment operation and maintenance parameters, the impact of manual operation on the equipment operation and maintenance parameters is reduced, and the reliability and accuracy of the final prediction results are improved.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for predicting the failure probability of HVAC equipment suitable for multiple working conditions includes:

[0007] Define device type and device parameters;

[0008] Collecting recorded data for all of the aforementioned equipment types;

[0009] Building a data-fault mapping relationship based on the device type, the device parameters, and the recorded data;

[0010] Constructing a device failure probability formula based on the data-fault mapping relationship and the recorded data;

[0011] reconstructing the recorded data in chronological order to obtain reconstructed data;

[0012] performing data cleaning on the reconstructed data to obtain historical data;

[0013] Constructing an environmental parameter matrix, using the environmental parameter matrix to obtain a set of operating conditions, and querying equipment operation and maintenance parameters based on the set of operating conditions;

[0014] The equipment operation and maintenance parameters are adjusted based on the regression model to obtain adjusted operation and maintenance data; the calculation formula of the adjusted operation and maintenance data is: ′ t =∑ p∈L [(p(t+δ)-p(t-δ))-(β0+β1I(t)+∈(t))]; where, O ′ t is the adjustment operation and maintenance data; p is the equipment operation and maintenance parameter; L is the total number of equipment operation and maintenance parameters; t is the prediction time; δ is the time window before and after the intervention; β0 is the intercept term; β1 is the regression coefficient; I(t) is the manual operation indicator function; ∈(t) is the random error term;

[0015] Predicting the equipment operation and maintenance parameters for the next time period based on the adjusted operation and maintenance data to obtain predicted operation and maintenance parameters;

[0016] The equipment failure probability formula is used to classify the predicted operation and maintenance parameters to obtain equipment failure probability data.

[0017] Preferably, the equipment failure probability formula is:

[0018]

[0019] Among them, Pf t is the equipment failure probability data at the predicted time t; s is the equipment state; N is the total number of equipment states; A is the equipment state transition probability; B is the occurrence probability of operation and maintenance parameters; S f Fault state; Pf t-1 (s) is the probability that the device is in state s at time t-1.

[0020] Preferably, the environmental parameter matrix is:

[0021]

[0022] Among them, P x is the xth environmental parameter value; T i is the time node of the i-th row data; P ij is the parameter value of the j-th environment at the i-th time node.

[0023] Preferably, the calculation formula of the working condition set is:

[0024]

[0025] Wherein, K is the working condition set; K k is the set of environmental parameters for the kth working condition; is the e-th environmental parameter in the k-th working condition; n k is the total number of environmental parameters under the kth working condition.

[0026] Preferably, the calculation formula for the predicted operation and maintenance parameters is:

[0027]

[0028] Among them, O t+1 (O ′ t ) is the predicted operation and maintenance parameter; T is the time step of the equipment operation and maintenance parameter; Softmax is the normalization operation; σ is the activation function; For the i t The weight matrix under the time step index; For the i t Bias under time step index; m(j t ) is the jth tThe attention score under the time step index; m(i t ) is the i-th t The attention score at the time step index.

[0029] Preferably, the data cleaning includes: deleting duplicate values, deleting null values, and deleting outliers.

[0030] The present invention discloses the following technical effects:

[0031] The present invention provides a method for predicting the failure probability of HVAC equipment that is adaptable to multiple working conditions. By predicting operation and maintenance data and fault judgment in real time, it solves the defects of existing methods that rely on a single monitoring point, upper and lower limit alarms, and manual tracking, and realizes the prediction and early warning of equipment failures; by constructing an environmental parameter matrix and an operating condition set, it solves the problem of different degrees of equipment parameter changes under different operating conditions, and realizes reliable prediction of different operating conditions; by adjusting the equipment operation and maintenance parameters, it solves the impact of manual operation on the equipment operation and maintenance parameters, and realizes the prediction of the failure probability of HVAC equipment that takes human factors into consideration. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of a HVAC equipment failure probability prediction process adapted to multiple working conditions provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0035] The purpose of the present invention is to provide a method for predicting the failure probability of HVAC equipment that is adaptable to multiple working conditions. By real-time prediction of operation and maintenance data and fault judgment, the defects of existing methods that rely on a single monitoring point, upper and lower limit alarms, and manual tracking are solved, and the prediction and early warning of equipment failures are realized; by constructing an environmental parameter matrix and an operating condition set, reliable prediction of different operating conditions is achieved; by adjusting the equipment operation and maintenance parameters, the impact of manual operation on the equipment operation and maintenance parameters is reduced, and the reliability and accuracy of the final prediction results are improved.

[0036] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0037] Figure 1 A schematic diagram of a failure probability prediction process for HVAC equipment adapted to multiple working conditions provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a method for predicting the failure probability of HVAC equipment that is adaptable to multiple working conditions, including:

[0038] Step 100: Define device type and device parameters;

[0039] Step 200: Collect recorded data of all device types;

[0040] Step 300: Constructing a data-fault mapping relationship based on device type, device parameters, and recorded data;

[0041] Step 400: Constructing a device failure probability formula based on the data-fault mapping relationship and the recorded data;

[0042] Step 500: reconstructing the recorded data in chronological order to obtain reconstructed data;

[0043] Step 600: Clean the reconstructed data to obtain historical data;

[0044] Step 700: Construct an environmental parameter matrix, use the environmental parameter matrix to obtain a set of working conditions, and query equipment operation and maintenance parameters based on the set of working conditions;

[0045] Step 800: Adjust the equipment operation and maintenance parameters based on the regression model to obtain adjusted operation and maintenance data; the calculation formula for the adjusted operation and maintenance data is: ′ t =∑ p∈L [(p(t+δ)-p(t-δ))-(β0+β1I(t)+∈(t))]; where, O ′ t is the adjustment operation and maintenance data; p is the equipment operation and maintenance parameter; L is the total number of equipment operation and maintenance parameters; t is the prediction time; δ is the time window before and after the intervention; β0 is the intercept term; β1 is the regression coefficient; I(t) is the manual operation indicator function; ∈(t) is the random error term;

[0046] Step 900: Predicting equipment operation and maintenance parameters for the next time period based on the adjusted operation and maintenance data to obtain predicted operation and maintenance parameters;

[0047] Step 1000: Classify the predicted operation and maintenance parameters using the equipment failure probability formula to obtain equipment failure probability data.

[0048] Specifically, the equipment failure probability formula is:

[0049]

[0050] Among them, Pf t is the equipment failure probability data at the prediction time t; s is the equipment state; N is the total number of equipment states; A is the equipment state transition probability; B is the occurrence probability of operation and maintenance parameters; S f Fault state; Pf t-1 (s) is the probability that the device is in state s at time t-1.

[0051] Furthermore, the environmental parameter matrix is:

[0052]

[0053] Among them, P x is the xth environmental parameter value; T i is the time node of the i-th row data; P ij is the parameter value of the j-th environment at the i-th time node.

[0054] Specifically, the calculation formula of the working condition set is:

[0055]

[0056] Among them, K is the working condition set; K k is the set of environmental parameters for the kth working condition; is the e-th environmental parameter in the k-th working condition; n k is the total number of environmental parameters under the kth working condition.

[0057] Specifically, the calculation formula for predicting operation and maintenance parameters is:

[0058]

[0059] Among them, O t+1 (O ′ t ) is the predicted operation and maintenance parameter; T is the time step of the equipment operation and maintenance parameter; Softmax is the normalization operation; σ is the activation operation; For the i t The weight matrix under the time step index; For the i t Bias under time step index; m(j t ) is the jth t The attention score under the time step index; m(i t ) is the i-th t The attention score at the time step index.

[0060] Furthermore, data cleaning includes: deleting duplicate values, deleting null values, and deleting outliers.

[0061] Specifically, the calculation formula for the device state transition probability is: The calculation formula for the occurrence probability of operation and maintenance parameters is: Among them, N ij N is the number of times the i-th device state is transferred to the j-th device state; i is the number of times the i-th device state appears; N j is the number of times the j-th device state occurs.

[0062] Specifically, the detailed process of HVAC equipment failure probability prediction includes:

[0063] S1 defines the monitored equipment types and corresponding parameters. Equipment types include chillers, fresh air units, air conditioning units, cooling water pumps, chilled water pumps, and cooling towers. Environmental parameters include temperature, relative humidity, heat load, number of people in the building, and indoor / outdoor air quality. Environmental parameters are represented by the symbol P.

[0064] S2, collects historical operation and maintenance data O, environmental parameters P, equipment failure data F (operation and maintenance data when equipment fails), equipment rated parameters, equipment factory operation curves, and manual operation records I (such as equipment maintenance, spare parts replacement, etc.) of various types of equipment.

[0065] S3, based on equipment failure data, equipment rated parameters, equipment factory operation curve and historical operation and maintenance data, classify the equipment operation status of each type of equipment and build a mapping relationship between "data-state". The relationship includes: health status S h , the corresponding data are the equipment rated parameters, equipment factory operation curve, fault status S f , the state can include multiple fault states such as minor fault, serious fault, etc., corresponding to the equipment fault data. The data corresponding to the above states are arranged in chronological order to form the equipment state set S s .

[0066] S4, based on the timestamp, reconstructs the historical operation and maintenance data O, environmental parameters P, manual operation records I, and equipment failure data F. All reconstructed data of the same model equipment at the same time are arranged in chronological order. Table 1 shows the data structure of the chiller after reconstruction.

[0067] Table 1

[0068]

[0069] In Table 1, the value 0 corresponding to the manual operation record indicates no operation, and 1 indicates operation. i ∈Ss .

[0070] S5, cleans the reconstructed data in S4, removes duplicate values, null values, and abnormal values, and uses them as historical data T.

[0071] S6, constructing a working condition classification function D, the execution steps of which are as follows:

[0072] S6.1, construct the environmental parameter matrix P x , whose expression is:

[0073]

[0074] S6.2, according to the matrix P x It is classified into k working conditions. The set is represented by K. The value of k is manually set by the user. The larger the k value, the richer the working condition division. The classification method is shown in the following formula:

[0075]

[0076] S6.3, query the equipment operation and maintenance parameters O corresponding to the working condition according to the working condition set K. t .

[0077] S7, build an equipment operation and maintenance simulation model based on the perceptron network, add a pre-perception module to improve the simulation accuracy, and simulate the equipment operation and maintenance data of the next time period. t+1 The model construction process is as follows.

[0078] S7.1, construct a pre-perception module based on the regression model to enable the model, which can automatically identify and adjust the equipment operation and maintenance parameter changes caused by human intervention. The equipment operation and maintenance data filtered by the working condition classification function of the perception module is the data set O t Make adjustments, and the adjustment logic is shown in the following formula:

[0079]

[0080] Where I(t) = 1 if manual operation occurs at time t and 0 otherwise. β0 is the intercept term of the regression model, representing the baseline change in the absence of manual operation. β1 is the coefficient of the regression model, representing the degree of influence of manual operation on parameter change. ∈(t) represents the random error term when no model is established.

[0081] S7.2: Use the equipment operation and maintenance parameters obtained by the pre-sensing module to simulate the equipment operation and maintenance parameters in the future time period. The mathematical formula for the simulation logic is as follows:

[0082]

[0083] i tIndicates the index of the time step, that is, when the model simulates the equipment operation and maintenance parameters at the next moment (time step t+1), i corresponds to the current time step t. The model processes the operation and maintenance data at time step t and outputs the simulation value. t Represents the index of the activation function in the model, corresponding to the output of different layers or nodes in the perceptron network. It is used to represent the contribution of the perceptron network to calculate the operation data in multiple dimensions at each time step t. m represents a perceptron network, which is used to evaluate the attention score of the processed time step. m(i t ) refers to the perceptron network in the operation and maintenance simulation model for time step i t The attention score of m(j t ) is similar to m(i t ), represents the attention scores at different time steps or different working conditions, and aggregates the states of multiple time steps through weighted summation to simulate the equipment operation and maintenance status at the next moment.

[0084] S8, build equipment failure probability diagnosis model to infer the predicted equipment operation and maintenance data O t+1 The probability of causing failure in the construction process is as follows.

[0085] S8.1, according to the device state set S s Combined with historical data T, we can obtain the equipment state transition probability A and the occurrence probability B of the equipment operation and maintenance parameters under specified working conditions. The expression formulas are as follows:

[0086]

[0087] S8.2, determine the probability Pf of equipment failure at time t t , the formula is:

[0088]

[0089] When t=1, the probability P′ of the device's state is expressed by the following formula:

[0090]

[0091] In the above formula, N represents the total number of times the device has been in all states.

[0092] S9, execute the working condition classification function to retrieve the historical data T, input the output value as a data set into the equipment operation and maintenance simulation model for training respectively, and obtain the equipment operation and maintenance simulation model set M.

[0093] S10, obtaining environmental parameters at the prediction time t, searching the equipment operation and maintenance simulation model set M, and obtaining the simulation model M′ that best matches the environmental parameters.

[0094] S11, use the simulation model M′ to simulate the equipment operation and maintenance parameters T of the next time period at the prediction time t t+1 .

[0095] S12, using equipment failure probability diagnosis model to process T t+1 , and obtain the probability of equipment failure in this time period.

[0096] The beneficial effects of the present invention are as follows:

[0097] The present invention solves the defects of existing methods that rely on a single monitoring point, upper and lower limit alarms, and manual tracking by predicting operation and maintenance data and fault judgment in real time, and realizes the prediction and early warning of equipment failures; by constructing an environmental parameter matrix and an operating condition set, it realizes reliable prediction of different operating conditions; by adjusting equipment operation and maintenance parameters, it reduces the impact of manual operation on equipment operation and maintenance parameters, and improves the reliability and accuracy of the final prediction results.

[0098] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0099] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for predicting the probability of failure of HVAC equipment that is adaptable to multiple working conditions, characterized in that: include: Define device type and device parameters; Collecting recorded data for all of the aforementioned equipment types; Building a data-fault mapping relationship based on the device type, the device parameters, and the recorded data; Constructing a device failure probability formula based on the data-fault mapping relationship and the recorded data; reconstructing the recorded data in chronological order to obtain reconstructed data; performing data cleaning on the reconstructed data to obtain historical data; Constructing an environmental parameter matrix, using the environmental parameter matrix to obtain a set of operating conditions, and querying equipment operation and maintenance parameters based on the set of operating conditions; The equipment operation and maintenance parameters are adjusted based on the regression model to obtain adjusted operation and maintenance data; the calculation formula of the adjusted operation and maintenance data is: ′ t =∑ p∈L [(p(t+δ)-p(t-δ))-(β0+β1I(t)+∈(t))]; where, O ′ t is the adjustment operation and maintenance data; p is the equipment operation and maintenance parameter; L is the total number of equipment operation and maintenance parameters; t is the prediction time; δ is the time window before and after the intervention; β0 is the intercept term; β1 is the regression coefficient; I(t) is the manual operation indicator function; ∈(t) is the random error term; Predicting the equipment operation and maintenance parameters for the next time period based on the adjusted operation and maintenance data to obtain predicted operation and maintenance parameters; The equipment failure probability formula is used to classify the predicted operation and maintenance parameters to obtain equipment failure probability data.

2. A method for predicting the probability of failure of HVAC equipment adaptable to multiple working conditions according to claim 1, characterized in that: The equipment failure probability formula is: Among them, Pf t is the equipment failure probability data at the predicted time t; s is the equipment state; N is the total number of equipment states; A is the equipment state transition probability; B is the occurrence probability of operation and maintenance parameters; S f Fault state; Pf t-1 (s) is the probability that the device is in state s at time t-1.

3. The method for predicting the failure probability of HVAC equipment suitable for multiple working conditions according to claim 1 is characterized in that: The environmental parameter matrix is: Among them, P x is the xth environmental parameter value; T i is the time node of the i-th row data; P ij is the parameter value of the j-th environment at the i-th time node.

4. The method for predicting the probability of failure of HVAC equipment adapting to multiple working conditions according to claim 1 is characterized in that: The calculation formula of the working condition set is: Wherein, K is the working condition set; K k is the set of environmental parameters for the kth working condition; is the e-th environmental parameter in the k-th working condition; n k is the total number of environmental parameters under the kth working condition.

5. The method for predicting the failure probability of HVAC equipment adaptable to multiple working conditions according to claim 1 is characterized in that: The calculation formula for the predicted operation and maintenance parameters is: Among them, O t+1 (O ′ t ) is the predicted operation and maintenance parameter; T is the time step of the equipment operation and maintenance parameter; Softmax is the normalization operation; σ is the activation function; For the i t The weight matrix under the time step index; For the i t Bias under time step index; m(j t ) is the jth t The attention score under the time step index; m(i t ) is the i-th t The attention score at the time step index.

6. The method for predicting the failure probability of HVAC equipment adaptable to multiple working conditions according to claim 1 is characterized in that: The data cleaning includes: deleting duplicate values, deleting null values ​​and deleting outliers.

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