Heat pump system fault prediction method based on time sequence chain graph

Abstract

The invention relates to the technical field of heat pump fault detection and diagnosis, and discloses a heat pump system fault prediction method based on a time sequence chain diagram, which comprises the following steps of: configuring and deploying a sensor group, acquiring monitoring variable data of a heat pump system from normal operation to fault occurrence time, and recording a fault type of the heat pump system at the same time; constructing a degeneration time sequence data set of the heat pump system; a time sequence chain graph degradation model of the heat pump system from normal operation to the fault occurrence moment is constructed; constructing a hierarchical graph model of the monitoring variables, the residual life and the fault type; constructing a competition risk time sequence chain graph degradation model, and calculating a competition risk cumulative generation function; obtaining time sequence data monitored in the operation process of the heat pump system, and predicting the residual life and the fault type of the heat pump system based on the competitive risk cumulative occurrence function; according to the method, the accuracy and efficiency of heat pump fault detection and diagnosis are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat pump fault detection and diagnosis, and in particular to a heat pump system fault prediction method based on a time series chain diagram. Background Art

[0002] Heat pumps provide a low-carbon energy solution for district heating and industrial production. If a failure in the operation of a heat pump is not identified in time, it will cause system downtime, affect the continuity of heating or industrial processes, increase energy consumption and lead to uneven heating, increase maintenance and repair costs, and have a negative impact on the availability, performance and operation and maintenance costs of the heat pump system. Studies have shown that heat pumps may have 48 types and more than 120 different faults. The causes of different types of faults are varied and correlated. For example, one of the causes of refrigerant leakage is a defective shaft seal, which may also be caused by excessive vibration caused by a screw compressor slide valve failure. Given that faults may be interconnected and propagated between components, predicting the type and time of fault occurrence is a complex task.

[0003] At present, the commonly used heat pump fault detection and diagnosis methods include causal analysis based on prior knowledge, expert systems, physical models, and data-driven statistical analysis, machine learning, fuzzy logic, etc. The performance coefficient of the heat pump system is highly dependent on its operating conditions, and the heat pump system will encounter a variety of working conditions and uncertain environmental conditions during actual operation. The heat pump fault detection and diagnosis method based on prior knowledge has good prediction accuracy, but because it requires a comprehensive understanding of the equipment, components, processes, and operating conditions in the heat pump system, this type of method has a long modeling time and high cost, which makes it very limited in practical applications. In contrast, data-driven heat pump fault detection and diagnosis methods are more efficient, but existing methods usually regard the heat pump system as a "black box", making it difficult to define the probability distribution function of the heat pump fault type and occurrence time required in practical applications, and unable to characterize the possible changes in the degradation mechanism and law of the heat pump throughout its life cycle, resulting in low accuracy of the detection results. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a heat pump system fault prediction method based on a time series chain diagram to solve the problems of low efficiency, high cost and low accuracy in heat pump system fault prediction in the prior art.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is:

[0006] A heat pump system fault prediction method based on a time series chain diagram comprises the following steps:

[0007] S1. Configure and deploy the sensor group to collect monitoring variable data of the heat pump system from normal operation to the time of failure, record the failure type of the heat pump system, and construct a degradation time series data set of the heat pump system;

[0008] Among them, the monitoring variable data includes operation status data and operation environment data;

[0009] S2. Based on the heat pump system degradation time series data set, a time series chain graph degradation model of the heat pump system from normal operation to the moment of failure is constructed;

[0010] S3. Based on the time series chain graph degradation model, a hierarchical graph model of monitoring variables, remaining life and fault types is constructed;

[0011] S4. Based on the hierarchical graph model and the time series chain graph degradation model, a competing risk time series chain graph degradation model is constructed, and the competing risk cumulative occurrence function is calculated;

[0012] S5. Obtain the time series data monitored during the operation of the heat pump system, and predict the remaining life and fault type of the heat pump system based on the competing risk cumulative occurrence function.

[0013] The present invention has the following beneficial effects:

[0014] 1. The present invention proposes a heat pump system fault prediction method based on a time series chain graph. Based on the time series chain graph, a competitive risk time series chain graph degradation model is constructed. It has flexible structural properties and can accurately characterize dynamics, improves the accuracy and precision of heat pump fault detection and diagnosis, and reduces the instability of prediction ability caused by the complexity and diversity of heat pump system fault types, the interconnection and propagation between faults, and the high correlation between faults and dynamically changing working conditions;

[0015] 2. Modeling based on time series chain graphs reduces modeling costs and improves modeling efficiency. At the same time, the constructed competitive risk time series chain graph degradation model is used to present the degradation mechanism and regular changes of the heat pump system throughout its life cycle, which can effectively predict the remaining life and fault type of the heat pump system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic flow chart of a heat pump system fault prediction method based on a time series chain diagram proposed by the present invention;

[0017] Figure 2 It is a schematic diagram of the hierarchical graph model;

[0018] Figure 3 Schematic diagram of the competing risk time series chain graph degradation model. DETAILED DESCRIPTION

[0019] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0020] like Figure 1 As shown, a heat pump system fault prediction method based on a time series chain diagram includes the following steps S1-S5:

[0021] S1. Configure and deploy a sensor group to collect monitoring variable data of the heat pump system from normal operation to the time when the fault occurs, record the fault type of the heat pump system, and construct a heat pump system degradation time series data set; the monitoring variable data includes operating status data and operating environment data.

[0022] In this embodiment, according to the user's monitoring needs for the heat pump system, with the goal of maximizing observability, detectability, and separability, and taking into account sensor failures and costs, temperature, pressure, flow, vibration and other sensors are configured and deployed on the main equipment, components, and parts of the heat pump system to collect the operating status data of the heat pump system (such as the inlet and outlet water temperature of the heat pump, the inlet and outlet water temperature of the buried pipe, the system pressure, etc.); temperature, humidity and other sensors are configured and deployed in the operating environment of the heat pump system to collect the operating environment data of the heat pump system (such as soil temperature, soil humidity, indoor and outdoor temperature, etc.); and the operating environment data and operating status data of the heat pump system are sampled through sensors to monitor the process from normal operation to failure of the heat pump, record the failure type, and thus generate a degradation time series data set of the heat pump system, so that the time series chain diagram can be introduced later to predict the failure type and remaining life of the heat pump system.

[0023] Specifically, step S1 includes S11-S12:

[0024] S11. Configure and deploy a first sensor group on the main equipment, components, and parts of the heat pump system, and configure and deploy a second sensor group in the operating environment of the heat pump system; the first sensor group is used to monitor operating status data composed of several monitoring variables including temperature, pressure, flow, and vibration; the second sensor group is used to monitor operating environment data composed of several monitoring variables including temperature and humidity.

[0025] S12, set the sampling frequency and sampling times, use the first sensor group and the second sensor group to collect the monitoring variable data of the heat pump system from normal operation to the time when the fault occurs, and record the fault type of the heat pump system to construct the heat pump system degradation time series data set, that is:

[0026]

[0027] in, represents the heat pump system degradation time series data set, N represents the total number of sampling times, i represents the number of sampling times, X i (τ) represents the monitoring variable data sampled at the i-th time, Z i represents the heat pump system fault type sampled at the i-th time, τ represents the sampling time point, A i (τ) represents the operating environment data sampled for the i-th time at the sampling time point τ, B i (τ) represents the operating status data of the ith sampling at the sampling time point τ, k represents the heat pump system fault type, K represents the total number of heat pump system fault types, A i1 (τ) represents the data of the first operating environment monitoring variable at the sampling time point τ during the i-th sampling, A i2 (τ) represents the data of the second operating environment monitoring variable at the sampling time point τ during the i-th sampling, A ip (τ) represents the data of the pth operating environment monitoring variable at the τ sampling time point during the i-th sampling, B i1 (τ) represents the data of the first operating status monitoring variable at the sampling time point τ during the i-th sampling, B i2 (τ) represents the data of the second operating status monitoring variable at the sampling time point τ during the i-th sampling, B iq (τ) represents the data of the qth operating status monitoring variable at the τ sampling time point during the i-th sampling.

[0028] S2. Based on the heat pump system degradation time series data set, a time series chain graph degradation model of the heat pump system from normal operation to the moment of failure is constructed.

[0029] In this embodiment, a probabilistic graphical model structure learning method is used to construct an undirected graphical model of each monitoring variable, that is, an undirected graph of variable correlation, based on the degradation time series data set. According to the process from normal operation to failure of the heat pump and the characteristics of the time series chain graph, the undirected graphical model is converted into a time series chain graph degradation model. That is, the dynamic degradation process of the operating state of the heat pump system and the dynamic influence of the operating environment on the degradation process are determined by using the historical operating environment data and operating status data of the heat pump system, so that in the subsequent steps, a competing risk time series chain graph degradation model that associates the time series chain graph degradation model with the heat pump failure type, remaining life and probability distribution parameters is established, thereby estimating the parameters of the competing risk time series chain graph degradation model.

[0030] Specifically, step S2 specifically includes S21-S22:

[0031] S21. Based on the heat pump system degradation time series data set, a probabilistic graphical model structure learning method is used to establish an undirected graphical model of each monitoring variable; wherein the undirected graphical model of each monitoring variable is composed of all variable nodes and edges connecting the variable nodes, and the variable nodes are nodes of the monitoring variables.

[0032] S22. According to the undirected graph model of each monitoring variable, a time series chain graph degradation model of the heat pump system from normal operation to the time of failure is constructed, which specifically includes S221-S223:

[0033] S221. Convert the undirected edges of the variable nodes connecting different sampling time points in the undirected graph model into directed edges pointing from an earlier sampling time point to a later sampling time point.

[0034] S222. Convert the undirected edges connecting the variable nodes of different fault types at the same sampling time point in the undirected graph model into directed edges pointing from the operating environment data to the operating status data.

[0035] S223, keeping the undirected edges connecting the variable nodes of the same fault type at the same sampling time point in the undirected graph model unchanged, and obtaining the time series chain graph degradation model of the heat pump system from normal operation to the time when the fault occurs, that is:

[0036]

[0037] in, represents the time series chain graph degradation model, V TS The variable node set representing the time series chain graph degradation model, E TS represents the set of directed and undirected edges in the time series chain graph degradation model, X(τ) represents the monitoring variable data at the sampling time point τ, A(τ) represents the operating environment data at the sampling time point τ, B(τ) represents the operating status data at the sampling time point τ, A1(τ) represents the data of the first operating environment monitoring variable at the sampling time point τ, A2(τ) represents the data of the second operating environment monitoring variable at the sampling time point τ, A p (τ) represents the data of the pth operating environment monitoring variable at the sampling time point τ, B1(τ) represents the data of the first operating state monitoring variable at the sampling time point τ, B2(τ) represents the data of the second operating state monitoring variable at the sampling time point τ, and B q (τ) represents the data of the qth operating status monitoring variable at the τ sampling time point.

[0038] S3. Based on the time series chain graph degradation model, a hierarchical graph model of monitoring variables, remaining life and fault types is constructed.

[0039] In this embodiment, the established hierarchical graph model is as follows: Figure 2 As shown, Figure 2 The schematic diagram of establishing the correlation between monitoring variables and the remaining life and fault type of the heat pump system at each sampling time point in the hierarchical graph model is shown. The construction process is as follows:

[0040] Specifically, step S3 specifically includes S31-S33:

[0041] S31. Based on the time series chain graph degradation model, for each time sampling point τ=-T,…,-2,-1,0, add node π τ , β τ , σ τ , τ and {RUL,Z}(τ);

[0042] Among them, π τ ={π 1,τ ,π 2,τ ,…,π K,τ}、β τ ={β 1,τ ,β 2,τ ,…,β K,τ}、σ τ ={σ 1,τ ,σ 2,τ ,…,σ K,τ} and λ τ ={λ 1,τ ,λ 2,τ ,…,λ K,τ}, π τ represents the parameter of the probability distribution of the heat pump system failure type at the sampling time τ, β τ , σ τ , τ They represent the scale, shape and location parameters of the probability distribution of the remaining life of the heat pump system for a given fault type at the sampling time point τ, π 1,τ , π 2,τ , π K,τ They represent the probability of failure type 1, 2 and K if the heat pump system fails at the sampling time τ, β 1,τ , β 2,τ , β K,τ They represent the scale parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ, σ 1,τ , σ 2,τ , σ K,τ They represent the shape parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ, respectively, and λ 1,τ , 2,τ , K,τThey represent the location parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ respectively.

[0043] Among them, {RUL,Z}(τ) represents the remaining life RUL of the heat pump system and the fault type Z at the sampling time point τ, and the fault type is subject to the parameter π 1,τ , π 2,τ , π K,τ Classification distribution of

[0044] At the same time, according to the correlation between the remaining life of the heat pump system and the fault type, at the sampling time point τ, if the fault type Z = k is given, the remaining life of the heat pump system RUL|Z = k is subject to the scale parameter β k,τ , shape parameter is σ k,τ and the position parameter is λ k,τ The generalized gamma distribution of

[0045] In this embodiment, the generalized gamma distribution is used to simulate the common probability distributions for fault remaining life prediction, such as Weibull distribution, gamma distribution, and lognormal distribution, thereby increasing the versatility of the model.

[0046] S32, add from each variable node {A1(τ), A2(τ), ..., A p (τ),B1(τ),B2(τ),…,B q (τ)} point to the node π τ , β τ , σ τ , τ The directed edge of .

[0047] S33. Add slave node π τ , β τ , σ τ , τ The directed edges pointing to the node {RUL, Z}(τ) respectively obtain the hierarchical graph model of monitoring variables, remaining life and fault types.

[0048] S4. Based on the hierarchical graph model and the time series chain graph degradation model, a competing risk time series chain graph degradation model is constructed, and the competing risk cumulative occurrence function is calculated.

[0049] In this embodiment, the historical operating environment data and operating status data of the heat pump system are used to quantify the dynamic relationship between the remaining life of the heat pump system, the fault type, the operating environment and the system status. The steps of constructing the competitive risk time series chain graph degradation model are as follows: Figure 3As shown in the figure, it specifically shows how to use the historical operating environment data and operating status data of a given heat pump system to construct a competing risk time series chain graph degradation model, as follows:

[0050] Specifically, step S4 includes S41-S45:

[0051] S41. Based on the hierarchical graph model and the time series chain graph degradation model, a competitive risk time series chain graph degradation model is constructed, namely:

[0052]

[0053] in, represents the competing risk time series chain graph degradation model, and π, β, σ, and λ all represent hidden variables that associate the monitoring variables with the remaining life of the heat pump system under different fault types.

[0054] In this embodiment, the purpose of constructing a competing risk time series chain graph degradation model is to define the correlation between the remaining life of the heat pump system under different fault types and latent variables, latent variables and monitoring variables, and the correlation between latent variables over time, that is, to use the historical operating environment data and operating status data of the heat pump system to determine the dynamic correlation between the fault type of the heat pump system and the time of occurrence of the fault, the operating environment and the degradation process.

[0055] S42. Calculate the model parameters of the competing risk time series chain graph degradation model, and obtain the joint probability distribution function of all monitoring variables of the competing risk time series chain graph degradation model in each time period.

[0056] In this embodiment, the function implemented is: quantifying the dynamic correlation between the remaining life of the heat pump system and the operating environment and the equipment degradation process.

[0057] Specifically, step S42 specifically includes S421-S426:

[0058] S421, remove the time series chain graph degradation model All directed edges in the graph are removed. Among them, Figure Each maximal connected subgraph connected by undirected edges in Each chain component From the picture The variable node v in Represents a graph with directed edges removed The collection of all chain components in .

[0059] S422, if the chain parts There is no parent node, that is Then the joint probability distribution function of the variable nodes contained in the chain component is:

[0060]

[0061] in, Represents chain components The parent node set of represents the empty set, δ1 represents the chain component The set of parameters of the joint probability distribution function of the variable nodes included, Represents chain components The joint probability distribution function of the included variable nodes.

[0062] S423, if the chain parts If there is a parent node, then for a variable node v∈V with a parent node TS and Then the conditional probability distribution function of the variable node with a parent node is:

[0063]

[0064] Among them, pa(v) represents the graph The parent node set of the variable node, δ2 represents the parameter set of the conditional probability distribution function of the variable node v with a parent node, Represents the conditional probability distribution function of a variable node v with a parent node.

[0065] S424, using the Bayesian parameter estimation method, calculate the parameter set of the joint probability distribution function of the variable nodes included in the chain component and the parameter set of the conditional probability distribution function of the variable nodes with the parent node, that is:

[0066] δ={δ1,δ2}

[0067]

[0068] Where δ represents the chain component The parameter set of the joint probability distribution function of the contained variable nodes or the parameter set of the conditional probability distribution function of the variable node v with a parent node, Represents a heat pump system degradation time series dataset The probability distribution function of the parameter set δ is, Indicates the generation of a heat pump system degradation time series dataset under the condition of parameter set δ The probability of represents the probability distribution of the empirically initialized parameter set δ, Indicates that the heat pump system degradation time series data set is collected probability.

[0069] S425, time series chain graph degradation model Decompose it into a subgraph consisting of each variable node and the parent node of the variable node, and calculate the posterior distribution and probability distribution of the parameters of the time series chain graph degradation model, that is:

[0070]

[0071] Among them, δ v|pa(v) Represents the subgraph parameter consisting of the variable node v and the parent node of the variable node v. Represents a heat pump system degradation time series dataset The values ​​of the monitoring variables corresponding to the variable node v and the parent node set pa(v) of the variable node v, represents the posterior distribution of the subgraph parameters consisting of the variable node v and the parent node of the variable node v, Represents the probability distribution of the subgraph parameters consisting of the variable node v and the parent node of the variable node v.

[0072] S426, conditional probability distribution function of variable nodes with parent nodes The joint probability distribution function of the variable nodes contained in the chain component Computational time series chain graph degradation model The joint probability distribution function of all monitored variables in each time period is:

[0073]

[0074] Among them, X(-T) represents the monitoring variable data at the -T sampling time point, X(-1) represents the monitoring variable data at the -1 sampling time point, and X(0) represents the monitoring variable data at the 0 sampling time point. Represents the time series chain graph degradation model The joint probability distribution function of all monitored variables (X(-T),…,X(-1),X(0)) in each time period, Represents chain components The joint probability distribution function of .

[0075] S43. Establish a multivariate random forest regression model of latent variables of the competing risk time series chain graph degradation model, and calculate the probability distribution function of the remaining life and fault type of the heat pump system.

[0076] In this embodiment, the function implemented is: quantifying the dynamic correlation between the remaining life of the heat pump system under various fault types and the operating environment and the equipment degradation process.

[0077] Specifically, step S43 specifically includes S431-S435:

[0078] S431. Initialize the initial points and initial covariance matrix of latent variables π, β, σ, and λ, and perform several iterations using a multivariate random forest regression model.

[0079] S432, at the nth iteration, according to the current parameter sample π (n) , β (n) , σ (n) , (n) With the current covariance matrix Σ (n) , generate candidate parameter samples π′, β′, σ′, λ′, and use the Metropolis-Hastings method to calculate the probability of receiving the candidate parameter samples, that is:

[0080]

[0081] Among them, π (n) , β (n) , σ (n) , (n) They represent the hidden variables of the nth iteration, π′, β′, σ′, λ′ represent the candidate hidden variables generated by the nth iteration, α represents the probability of receiving the candidate parameter sample, and min represents the minimum value. Represents a heat pump system degradation time series dataset The probability of the inferred candidate parameter samples π′, β′, σ′, λ′, Represents a heat pump system degradation time series dataset Infer the current parameter sample π (n) , β (n) , σ (n) , (n) probability.

[0082] S433: If the probability of receiving the candidate parameter sample is greater than the set critical value, the candidate parameter samples π′, β′, σ′, λ′ are generated when the nth iteration is accepted, and the parameter sample of the n+1th iteration is updated to π (n+1) =π ′ ,β (n+1) =β ′ ,σ (n+1) =σ ′ ,λ (n+1) =λ′, otherwise, keep the parameter sample of the n+1th iteration unchanged, which is π (n+1) =π (n) ,β (n+1) =β (n) ,σ (n+1) =σ (n) ,λ (n+1) =λ (n) .

[0083] Among them, π(n+1) , β (n+1) , σ (n+1) , (n+1) They represent the hidden variables of the n+1th iteration respectively.

[0084] S434, based on the parameter samples generated in the first n+1 iterations, update the covariance matrix Σ of the n+1th iteration (n+1) ;

[0085] Among them, the parameter samples generated by the first n+1 iterations are π (1) ,β (1) ,σ (1) ,λ (1) ,…,π (n) ,β (n) ,σ (n) ,λ (n) ,π (n+1) ,β (n+1) ,σ (n+1) ,λ (n+1) .

[0086] Among them, π (1) , β (1) , σ (1) , (1) They represent the hidden variables of the first iteration respectively.

[0087] S435, repeat the iterative process of steps S432-S434, when the difference between the parameter samples of two adjacent iterations is less than the set minimum critical value, By calculating the hidden variables π, β, σ, and λ, we can obtain the probability distribution function of the remaining life and fault type of the heat pump system, namely:

[0088]

[0089] in, Represents a heat pump system degradation time series dataset The probability distribution of the latent variables π, β, σ, and λ is inferred. Indicates that the heat pump system degradation time series data set is generated under the condition that the latent variables are π, β, σ, and λ The probability of They represent the probability distribution of the latent variables initialized to π, β, σ, and λ based on experience, respectively. represents the conditional probability distribution of the remaining life of the heat pump system when the fault type is Z at the sampling time point τ, represents the generalized gamma distribution function with latent variables β, σ, and λ, represents the probability distribution of the heat pump system failure type Z, Represents the categorical distribution function with latent variable π.

[0090] S44. According to the probability distribution function of the remaining life of the heat pump system and the failure type, and the joint probability distribution function of all monitoring variables in the competitive risk time series chain graph degradation model in each time period, the joint probability distribution function of the monitoring variables and the remaining life of the heat pump system and the failure type is calculated, that is:

[0091]

[0092] in, represents the joint probability distribution function of the monitoring variables {X(τ),τ=-T,…,-2,-1,0} and the remaining life of the heat pump system RUL and the fault type Z at the sampling time point τ, s represents the specific sampling time point, represents the joint probability distribution of the remaining life RUL of the heat pump system and the fault type Z at the sampling time point τ, represents the conditional probability distribution of the remaining life RUL and fault type Z of the heat pump system at the sampling time point τ when the hidden variables are given as π, β, σ, and λ, represents the joint probability distribution when the latent variables are σ and λ, represents the conditional probability distribution of latent variables π and β when the monitoring variable group X(τ) is observed, represents the probability distribution of observing the monitoring variable group X(s), Indicates that the jth monitoring variable X is observed j The probability distribution of (τ).

[0093] In this embodiment, the function implemented is: quantifying the dynamic association between the remaining life of the heat pump system, the fault type, the operating environment and the equipment degradation process.

[0094] S45. Calculate the cumulative occurrence function of competitive risk based on the joint probability distribution function of the monitored variables, the remaining life of the heat pump system, and the fault type, that is:

[0095]

[0096] in,

[0097] Where t represents time, x represents the time series data value monitored during the operation of the heat pump system, and F(t,k|x) represents the cumulative probability distribution function of a set of time series data values ​​x, remaining life t, and fault type k monitored during the operation of the heat pump system. It represents the conditional probability that the remaining life of the heat pump system is less than t and the fault type is k, given the observed time series data value x of the monitoring variable during the operation of the heat pump system. represents the joint probability that the remaining life of the heat pump system is less than t, the fault type is k, and the time series data of the monitoring variables in the operation of the heat pump system are observed to take the value x, represents the probability of observing the time series data of the monitoring variable taking the value x during the operation of the heat pump system, Represents the joint probability distribution of time series data, remaining life and fault type monitored during the operation of the heat pump system.

[0098] In this embodiment, the function implemented is: based on a set of collected operating environment and operating status data, the remaining life of the heat pump system and the probability of failure type are calculated.

[0099] S5. Obtain the time series data monitored during the operation of the heat pump system, and predict the remaining life and fault type of the heat pump system based on the competing risk cumulative occurrence function.

[0100] In this embodiment, the cumulative probability of observing the time series data to be predicted under each fault type is calculated by the calculation model, and for each fault type, the empirical distribution function of the time from the heat pump to the occurrence of the fault is calculated, and the time period and fault type that achieve the maximum value are found, so as to obtain the predicted fault type and the remaining life under the fault type.

[0101] Specifically, step S5 specifically includes S51-S57:

[0102] S51. Obtain the time series data {x0(t), z0} monitored during the operation of the heat pump system, wherein x0(t) represents the time series data of the monitoring variables during the operation of the heat pump system observed at the tth moment, and z0 represents the type of failure when a failure of the heat pump system is observed.

[0103] If the time length of the monitoring variable time series data x0(t) is l, and the actual start time of the monitoring variable time series data x0(t) in the degradation stage is S, when S = -T, the model fragment of the competing risk time series chain graph degradation model is:

[0104]

[0105] Among them, when S=-T, the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is: -T+l-1.

[0106] S52. Based on the competition risk cumulative occurrence function, the competition risk cumulative probability of each fault type when the model time slice E is -T+l-1 is calculated, that is:

[0107] F(-T+l-1,k=1|x0(t)),F(-T+l-1,k=2|x0(t)),…,F(-T+l-1,k

[0108] =K|x0(t))

[0109] Among them, F(-T+l-1,k=1|x0(t)) represents the cumulative probability of competitive risk of fault type k being 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1, F(-T+l-1,k=2|x0(t)) represents the cumulative probability of competitive risk of fault type k being 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1, and F(-T+l-1,k=K|x0(t)) represents the cumulative probability of competitive risk of fault type k being K when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1.

[0110] S53, the model fragment of the competing risk time series chain graph degradation model is shifted backward by one time period, that is, when S=-T+1, the model fragment of the competing risk time series chain graph degradation model is:

[0111]

[0112] Among them, when S=-T+1, the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is: -T+l.

[0113] S54. Based on the competition risk cumulative occurrence function, the competition risk cumulative probability of each fault type when the model time slice E is -T+1 is calculated, that is:

[0114] F(-T+l,k=1|x0(t)),F(-T+l,k=2|x0(t)),…,F(-T+l,k=K|x0(t))

[0115] Among them, F(-T+l,k=1|x0(t)) represents the cumulative probability of competitive risk when the fault type k is 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l, F(-T+l,k=2|x0(t)) represents the cumulative probability of competitive risk when the fault type k is 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l, and F(-T+l,k=K|x0(t)) represents the cumulative probability of competitive risk when the fault type k is K when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l.

[0116] S55, repeat steps S51-S54, each time shifting the model segment of the competitive risk time series chain graph degradation model backward by one time period, until the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, and calculate the competitive risk cumulative probability of each fault type when the model time slice E is 0, that is:

[0117] F(0,k=1|x0(t)),F(0,k=2|x0(t)),…,F(0,k=K|x0(t))

[0118] Among them, F(0,k=1|x0(t)) represents the cumulative probability of competitive risk of fault type k being 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, F(0,k=2|x0(t)) represents the cumulative probability of competitive risk of fault type k being 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, and F(0,k=K|x0(t)) represents the cumulative probability of competitive risk of fault type k being K when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0.

[0119] S56. According to the cumulative probability of competitive risk of each fault type under different model time slices, the empirical distribution function of the remaining life of the heat pump system is calculated, that is:

[0120]

[0121] in, represents the empirical distribution function of the fault type k of the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t), F(E+1,k|x0(t)) represents the cumulative probability of competing risks of the fault type k of the model time slice E+1 corresponding to the end of the time series of the monitoring variable time series data x0(t), and F(E,k|x0(t)) represents the cumulative probability of competing risks of the fault type k of the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t).

[0122] S57. Based on the empirical distribution function of the remaining life of the heat pump system, by comparing the empirical distribution functions of different remaining lifespans of the heat pump system, find the time period and fault type corresponding to the maximum value, and record it as e * and k * , then: The predicted value of the remaining life of the heat pump system is The prediction value of the corresponding fault type is

[0123] In summary, the method for predicting heat pump system faults based on time series chain graph proposed in the present invention constructs a competitive risk time series chain graph degradation model based on the time series chain graph, which has flexible structural properties and can accurately characterize dynamics; at the same time, it improves the accuracy and precision of heat pump fault detection and diagnosis, and reduces the instability of prediction capabilities caused by the complexity and diversity of heat pump system fault types, the interconnection and propagation between faults, and the high correlation between faults and dynamically changing working conditions; in addition, while reducing modeling costs and improving modeling efficiency, the model can also be used to present the degradation mechanism and regular changes of the heat pump system throughout its life cycle; finally, through data-driven combined with mechanism models, it can provide a basis for the predictive maintenance function of the heat pump system of the digital intelligent energy operation and maintenance management system.

[0124] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0125] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A heat pump system fault prediction method based on a time series chain diagram, characterized in that: The following steps are involved: S1. Configure and deploy the sensor group to collect monitoring variable data of the heat pump system from normal operation to the time of failure, record the failure type of the heat pump system, and construct a degradation time series data set of the heat pump system; Among them, the monitoring variable data includes operation status data and operation environment data; S2. Based on the heat pump system degradation time series data set, a time series chain graph degradation model of the heat pump system from normal operation to the moment of failure is constructed; S3. Based on the time series chain graph degradation model, a hierarchical graph model of monitoring variables, remaining life and fault types is constructed; S4. Based on the hierarchical graph model and the time series chain graph degradation model, a competing risk time series chain graph degradation model is constructed, and the competing risk cumulative occurrence function is calculated; S5. Obtain the time series data monitored during the operation of the heat pump system, and predict the remaining life and fault type of the heat pump system based on the competing risk cumulative occurrence function.

2. The heat pump system fault prediction method based on time series chain diagram according to claim 1 is characterized in that: Step S1 specifically includes: S11, configuring and deploying a first sensor group on the main equipment, components, and parts of the heat pump system, and configuring and deploying a second sensor group in the operating environment of the heat pump system; The first sensor group is used to monitor the operating status data composed of several monitoring variables including temperature, pressure, flow rate and vibration; The second sensor group is used to monitor operating environment data composed of several monitoring variables including temperature and humidity; S12, set the sampling frequency and sampling times, use the first sensor group and the second sensor group to collect the monitoring variable data of the heat pump system from normal operation to the time when the fault occurs, and record the fault type of the heat pump system to construct the heat pump system degradation time series data set, that is: in, represents the heat pump system degradation time series data set, N represents the total number of sampling times, i represents the number of sampling times, X i (τ) represents the monitoring variable data sampled at the i-th time, Z i represents the heat pump system fault type sampled at the i-th time, τ represents the sampling time point, A i (τ) represents the operating environment data sampled for the i-th time at the sampling time point τ, B i (τ) represents the operating status data of the ith sampling at the sampling time point τ, k represents the heat pump system fault type, K represents the total number of heat pump system fault types, A i1 (τ) represents the data of the first operating environment monitoring variable at the sampling time point τ during the i-th sampling, A i2 (τ) represents the data of the second operating environment monitoring variable at the sampling time point τ during the i-th sampling, A ip (τ) represents the data of the pth operating environment monitoring variable at the τ sampling time point during the i-th sampling, B i1 (τ) represents the data of the first operating status monitoring variable at the sampling time point τ during the i-th sampling, B i2 (τ) represents the data of the second operating status monitoring variable at the sampling time point τ during the i-th sampling, B iq (τ) represents the data of the qth operating status monitoring variable at the τ sampling time point during the i-th sampling.

3. The heat pump system fault prediction method based on time series chain diagram according to claim 2 is characterized in that: Step S2 specifically includes: S21. Based on the heat pump system degradation time series data set, the probabilistic graphical model structure learning method is used to establish an undirected graphical model of each monitoring variable; The undirected graph model of each monitoring variable is composed of all variable nodes and edges connecting the variable nodes, and the variable nodes are nodes of the monitoring variables; S22. Based on the undirected graph model of each monitoring variable, a time series chain graph degradation model of the heat pump system from normal operation to the time of failure is constructed, which specifically includes: S221, converting the undirected edges of the variable nodes connecting different sampling time points in the undirected graph model into directed edges pointing from an earlier sampling time point to a later sampling time point; S222, converting the undirected edges connecting the variable nodes of different fault types at the same sampling time point in the undirected graph model into directed edges pointing from the operating environment data to the operating status data; S223, the undirected edges connecting the variable nodes of the same fault type at the same sampling time point in the undirected graph model are kept unchanged, and the time series chain graph degradation model of the heat pump system from normal operation to the time when the fault occurs is obtained, that is: in, represents the time series chain graph degradation model, V TS The variable node set representing the time series chain graph degradation model, E TS represents the set of directed and undirected edges in the time series chain graph degradation model, X(τ) represents the monitoring variable data at the sampling time point τ, A(τ) represents the operating environment data at the sampling time point τ, B(τ) represents the operating status data at the sampling time point τ, A1(τ) represents the data of the first operating environment monitoring variable at the sampling time point τ, A2(τ) represents the data of the second operating environment monitoring variable at the sampling time point τ, A p (τ) represents the data of the pth operating environment monitoring variable at the sampling time point τ, B1(τ) represents the data of the first operating state monitoring variable at the sampling time point τ, B2(τ) represents the data of the second operating state monitoring variable at the sampling time point τ, and B q (τ) represents the data of the qth operating status monitoring variable at the τ sampling time point.

4. The heat pump system fault prediction method based on time series chain diagram according to claim 3 is characterized in that: Step S3 specifically includes: S31. Based on the time series chain graph degradation model, for each time sampling point τ=-T,…,-2,-1,0, add node π τ , β τ , σ τ , τ and {RUL,Z}(τ); Among them, π τ ={π 1,τ ,π 2,τ ,…,π K,τ }、β τ ={β 1,τ ,β 2,τ ,…,β K,τ }、σ τ ={σ 1,τ ,σ 2,τ ,…,σ K,τ } and λ τ ={λ 1,τ ,λ 2,τ ,…,λ K,τ }, π τ represents the parameter of the probability distribution of the heat pump system failure type at the sampling time τ, β τ , σ τ , τ They represent the scale, shape and location parameters of the probability distribution of the remaining life of the heat pump system for a given fault type at the sampling time point τ, π 1,τ , π 2,τ , π K,τ They represent the probability of failure type 1, 2 and K if the heat pump system fails at the sampling time τ, β 1,τ , β 2,τ , β K,τ They represent the scale parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ, σ 1,τ , σ 2,τ , σ K,τ They represent the shape parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ, respectively, and λ 1,τ , 2,τ , K,τ They represent the position parameters of the probability distribution of the remaining life of the heat pump system when the fault types are 1, 2 and K at the sampling time point τ respectively; Among them, {RUL,Z}(τ) represents the remaining life RUL of the heat pump system and the fault type Z at the sampling time point τ, and the fault type is subject to the parameter π 1,τ , π 2,τ , π K,τ Classification distribution of At the same time, according to the correlation between the remaining life of the heat pump system and the fault type, at the sampling time point τ, if the fault type Z = k is given, the remaining life of the heat pump system RUL|Z = k is subject to the scale parameter β k,τ , shape parameter is σ k,τ and the position parameter is λ k,τ The generalized gamma distribution of S32, add from each variable node {A1(τ), A2(τ), ..., A p (τ),B1(τ),B2(τ),…,B q (τ)} point to the node π τ , β τ , σ τ , τ The directed edges of S33. Add slave node π τ , β τ , σ τ , τ The directed edges pointing to the node {RUL, Z}(τ) respectively obtain the hierarchical graph model of monitoring variables, remaining life and fault types.

5. The heat pump system fault prediction method based on time series chain diagram according to claim 4 is characterized in that: Step S4 specifically includes: S41. Based on the hierarchical graph model and the time series chain graph degradation model, a competitive risk time series chain graph degradation model is constructed, namely: in, represents the competing risk time series chain graph degradation model, π, β, σ, and λ all represent hidden variables that associate the monitoring variables with the remaining life of the heat pump system under different fault types; S42, calculating the model parameters of the competing risk time series chain graph degradation model, and obtaining the joint probability distribution function of all monitoring variables of the competing risk time series chain graph degradation model in each time period; S43. Establish a multivariate random forest regression model of latent variables of the competing risk time series chain graph degradation model, and calculate the probability distribution function of the remaining life and failure type of the heat pump system; S44, calculating the joint probability distribution function of the monitoring variables, the remaining life of the heat pump system, and the failure type according to the probability distribution function of the remaining life of the heat pump system and the failure type and the joint probability distribution function of all monitoring variables of the competitive risk time series chain graph degradation model in each time period; S45. Calculate the cumulative occurrence function of competitive risk based on the joint probability distribution function of the monitored variables, the remaining life of the heat pump system, and the failure type.

6. The heat pump system fault prediction method based on time series chain diagram according to claim 5 is characterized in that: Step S42 specifically includes: S421, remove the time series chain graph degradation model All directed edges in the graph are removed. Among them, Figure Each maximal connected subgraph connected by undirected edges in Each chain component From the picture The variable node v in Represents a graph with directed edges removed The set of all chain components in; S422, if the chain parts There is no parent node, that is Then the joint probability distribution function of the variable nodes contained in the chain component is: in, Represents chain components The parent node set of represents the empty set, δ1 represents the chain component The set of parameters of the joint probability distribution function of the variable nodes included, Represents chain components The joint probability distribution function of the included variable nodes; S423, if the chain parts If there is a parent node, then for a variable node v∈V with a parent node TS and Then the conditional probability distribution function of the variable node with a parent node is: Among them, pa(v) represents the graph The parent node set of the variable node, δ2 represents the parameter set of the conditional probability distribution function of the variable node v with a parent node, Represents the conditional probability distribution function of the variable node v with a parent node; S424, using the Bayesian parameter estimation method, calculate the parameter set of the joint probability distribution function of the variable nodes included in the chain component and the parameter set of the conditional probability distribution function of the variable nodes with the parent node, that is: δ={δ1,δ2} Among them, δ represents the chain component The parameter set of the joint probability distribution function of the contained variable nodes or the parameter set of the conditional probability distribution function of the variable node v with a parent node, Represents a heat pump system degradation time series dataset The probability distribution function of the parameter set δ is, Indicates the generation of a heat pump system degradation time series dataset under the condition of parameter set δ The probability of represents the probability distribution of the empirically initialized parameter set δ, Indicates that the heat pump system degradation time series data set is collected probability; S425, time series chain graph degradation model Decompose it into a subgraph consisting of each variable node and the parent node of the variable node, and calculate the posterior distribution and probability distribution of the parameters of the time series chain graph degradation model, that is: Among them, δ v|pa(v) Represents the subgraph parameter consisting of the variable node v and the parent node of the variable node v. Represents a heat pump system degradation time series dataset The values ​​of the monitoring variables corresponding to the variable node v and the parent node set pa(v) of the variable node v, represents the posterior distribution of the subgraph parameters consisting of the variable node v and the parent node of the variable node v, Represents the probability distribution of the subgraph parameters consisting of the variable node v and the parent node of the variable node v; S426, conditional probability distribution function of variable nodes with parent nodes The joint probability distribution function of the variable nodes contained in the chain component Computational time series chain graph degradation model The joint probability distribution function of all monitored variables in each time period is: Among them, X(-T) represents the monitoring variable data at the -T sampling time point, X(-1) represents the monitoring variable data at the -1 sampling time point, and X(0) represents the monitoring variable data at the 0 sampling time point. Represents the time series chain graph degradation model The joint probability distribution function of all monitored variables (X(-T),…,X(-1),X(0)) in each time period, Represents chain components The joint probability distribution function of .

7. The heat pump system fault prediction method based on time series chain diagram according to claim 6 is characterized in that: Step S43 specifically includes: S431, initializing the initial points and initial covariance matrix of latent variables π, β, σ, λ, and performing several iterations using a multivariate random forest regression model; S432, at the nth iteration, according to the current parameter sample π (n) , β (n) , σ (n) , (n) With the current covariance matrix Σ (n) , generate candidate parameter samples π′, β′, σ′, λ′, and use the Metropolis-Hastings method to calculate the probability of receiving the candidate parameter samples, that is: Among them, π (n) , β (n) , σ (n) , (n) They represent the hidden variables of the nth iteration, π′, β′, σ′, λ′ represent the candidate hidden variables generated by the nth iteration, α represents the probability of receiving the candidate parameter sample, and min represents the minimum value. Represents a heat pump system degradation time series dataset The probability of the inferred candidate parameter samples π′, β′, σ′, λ′, Represents a heat pump system degradation time series dataset Infer the current parameter sample π (n) , β (n) , σ (n) , (n) probability; S433: If the probability of receiving the candidate parameter sample is greater than the set critical value, the candidate parameter samples π′, β′, σ′, λ′ are generated when the nth iteration is accepted, and the parameter sample of the n+1th iteration is updated to π (n+1) =π ′ ,β (n+1) =β ′ ,σ (n+1) =σ ′ ,λ (n+1) =λ′, otherwise, keep the parameter sample of the n+1th iteration unchanged, which is π (n+1) =π (n) ,β (n+1) =β (n) ,σ (n+1) =σ (n) ,λ (n+1) =λ (n) ; Among them, π (n+1) , β (n+1) , σ (n+1) , (n+1) They represent the hidden variables of the n+1th iteration respectively; S434, based on the parameter samples generated in the first n+1 iterations, update the covariance matrix Σ of the n+1th iteration (n+1) ; Among them, the parameter samples generated by the first n+1 iterations are π (1) ,β (1) ,σ (1) ,λ (1) ,…,π (n) ,β (n) ,σ (n) ,λ (n) ,π (n+1) ,β (n+1) ,σ (n+1) ,λ (n+1) ; Among them, π (1) , β (1) , σ (1) , (1) Respectively represent the hidden variables of the first iteration; S435, repeat the iterative process of steps S432-S434, when the difference between the parameter samples of two adjacent iterations is less than the set minimum critical value, By calculating the hidden variables π, β, σ, and λ, we can obtain the probability distribution function of the remaining life and fault type of the heat pump system, namely: in, Represents a heat pump system degradation time series dataset The probability distribution of the latent variables π, β, σ, and λ is inferred. Indicates that the degradation time series data set of the heat pump system is generated under the condition that the latent variables are π, β, σ, and λ The probability of They represent the probability distribution of the latent variables initialized to π, β, σ, and λ based on experience, respectively. represents the conditional probability distribution of the remaining life of the heat pump system when the fault type is Z at the sampling time point τ, represents the generalized gamma distribution function with latent variables β, σ, and λ, represents the probability distribution of the heat pump system failure type Z, Represents the categorical distribution function with latent variable π.

8. The heat pump system fault prediction method based on time series chain diagram according to claim 7 is characterized in that: The calculation formula of the joint probability distribution function of the monitoring variable, the remaining life of the heat pump system, and the fault type in step S44 is: in, represents the joint probability distribution function of the monitoring variables {X(τ),τ=-T,…,-2,-1,0} and the remaining life of the heat pump system RUL and the fault type Z at the sampling time point τ, s represents the specific sampling time point, represents the joint probability distribution of the remaining life RUL of the heat pump system and the fault type Z at the sampling time point τ, represents the conditional probability distribution of the remaining life RUL and fault type Z of the heat pump system at the sampling time point τ when the hidden variables are given as π, β, σ, and λ, represents the joint probability distribution when the latent variables are σ and λ, represents the conditional probability distribution of latent variables π and β when the monitoring variable group X(τ) is observed, represents the probability distribution of observing the monitoring variable group X(s), Indicates that the jth monitoring variable X is observed j The probability distribution of (τ).

9. The heat pump system fault prediction method based on time series chain diagram according to claim 8 is characterized in that: The calculation formula of the competition risk cumulative occurrence function in step S45 is: in, Where t represents time, x represents the time series data value monitored during the operation of the heat pump system, and F(t,k|x) represents the cumulative probability distribution function of a set of time series data values ​​x, remaining life t, and fault type k monitored during the operation of the heat pump system. It represents the conditional probability that the remaining life of the heat pump system is less than t and the fault type is k, given the observed time series data value x of the monitoring variable during the operation of the heat pump system. represents the joint probability that the remaining life of the heat pump system is less than t, the fault type is k, and the time series data of the monitoring variables in the operation of the heat pump system are observed to take the value x, represents the probability of observing the time series data of the monitoring variable taking the value x during the operation of the heat pump system, Represents the joint probability distribution of time series data, remaining life and fault type monitored during the operation of the heat pump system.

10. The heat pump system fault prediction method based on time series chain diagram according to claim 9 is characterized in that: Step S5 specifically includes: S51, obtaining time series data {x0(t), z0} monitored during the operation of the heat pump system, wherein x0(t) represents the time series data of the monitoring variables during the operation of the heat pump system observed at the tth moment, and z0 represents the type of failure when the heat pump system fails; If the time length of the monitoring variable time series data x0(t) is l, and the actual start time of the monitoring variable time series data x0(t) in the degradation stage is S, when S = -T, the model fragment of the competing risk time series chain graph degradation model is: Among them, when S = -T, the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is: -T+l-1; S52. Based on the competition risk cumulative occurrence function, the competition risk cumulative probability of each fault type when the model time slice E is -T+l-1 is calculated, that is: F(-T+l-1,k=1|x0(t)),F(-T+l-1,k=2|x0(t)),…,F(-T+l-1,k =K|x0(t)) Among them, F(-T+l-1, k=1|x0(t)) represents the cumulative probability of competitive risk of fault type k being 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1, F(-T+l-1, k=2|x0(t)) represents the cumulative probability of competitive risk of fault type k being 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1, F(-T+l-1, k=K|x0(t)) represents the cumulative probability of competitive risk of fault type k being K when the model time slice K corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l-1; S53, the model fragment of the competing risk time series chain graph degradation model is shifted backward by one time period, that is, when S=-T+1, the model fragment of the competing risk time series chain graph degradation model is: Among them, when S=-T+1, the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is: -T+1; S54. Based on the competition risk cumulative occurrence function, the competition risk cumulative probability of each fault type when the model time slice E is -T+1 is calculated, that is: F(-T+l,k=1|x0(t)),F(-T+l,k=2|x0(t)),…,F(-T+l,k=K|x0(t)) Among them, F(-T+l,k=1|x0(t)) represents the cumulative probability of competitive risk of fault type k being 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l, F(-T+l,k=2|x0(t)) represents the cumulative probability of competitive risk of fault type k being 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l, and F(-T+l,k=K|x0(t)) represents the cumulative probability of competitive risk of fault type k being K when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is -T+l; S55, repeat steps S51-S54, each time shifting the model segment of the competitive risk time series chain graph degradation model backward by one time period, until the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, and calculate the competitive risk cumulative probability of each fault type when the model time slice E is 0, that is: F(0,k=1|x0(t)),F(0,k=2|x0(t)),…,F(0,k=K|x0(t)) Among them, F(0,k=1|x0(t)) represents the cumulative probability of competitive risk of fault type k being 1 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, F(0,k=2|x0(t)) represents the cumulative probability of competitive risk of fault type k being 2 when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0, and F(0,k=K|x0(t)) represents the cumulative probability of competitive risk of fault type k being K when the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t) is 0; S56. According to the cumulative probability of competitive risk of each fault type under different model time slices, the empirical distribution function of the remaining life of the heat pump system is calculated, that is: in, represents the empirical distribution function of the fault type k of the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t), F(E+1,k|x0(t)) represents the cumulative probability of competitive risk of the fault type k of the model time slice E+1 corresponding to the end of the time series of the monitoring variable time series data x0(t), and F(E,k|x0(t)) represents the cumulative probability of competitive risk of the fault type k of the model time slice E corresponding to the end of the time series of the monitoring variable time series data x0(t); S57. Based on the empirical distribution function of the remaining life of the heat pump system, by comparing the empirical distribution functions of different remaining lifespans of the heat pump system, find the time period and fault type corresponding to the maximum value, and record it as e * and k * , then: The predicted value of the remaining life of the heat pump system is The prediction value of the corresponding fault type is

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