CN106066252A - The health state evaluation method of equipment subsystem level destroyed by a kind of dangerous materials - Google Patents

Abstract

The health state evaluation method of equipment subsystem level destroyed by a kind of dangerous materials of the present invention, including following step: step one: health status classification；Step 2: the health degree of all parts level equipment that computing subsystem comprises；Step 3: the holistic health degree that the component-level cluster tool that computing subsystem comprises couples；Step 4: calculate the profust reliability characterized by subsystem irrespective of size status data；Step 5: the holistic health degree of subsystem irrespective of size calculates；Step 6: according to health degree difference numerical range, carry out health state evaluation.The method considers health degree, the connected mode of each parts that subsystem comprises parts, and the health degree of the status data of subsystem, and utilizes profust reliability preferably to merge the change impact of each state.Reflect health degree by profust reliability, the slight change of the holistic health state of subsystem can be described, contribute to realizing, overall monitor real-time to equipment health status and early warning, method highly versatile, it is simple to Project Realization.

Description

Health state evaluation method for subsystem level of dangerous goods destruction equipment

[ technical field ] A method for producing a semiconductor device

The invention relates to a combined health state evaluation method for fusion state data of a hazardous article destruction equipment subsystem level. The method is mainly based on rate-mode reliability, integrates the health degree of each part contained in the subsystem with the rate-mode reliability of the state data of the subsystem, and solves the problem of health state evaluation of multi-information fusion of small data samples, equipment self parameters, operation state parameters and the like. Belonging to the field of health state evaluation of complex equipment.

[ background of the invention ]

Dangerous goods destruction equipment utilizes technologies such as high temperature incineration and tail gas purification to handle all kinds of dangerous goods, includes: various toxic chemicals, smoke agents, explosive charges containing stimulants, and the like. The destroying equipment has a complex structure and high technical content. The destruction operation is in a high-risk state, the requirements on the completeness rate and the safety are extremely high, health state evaluation on the destruction equipment is urgently needed, and possible faults are judged and alarmed in advance to avoid accidents.

The complex devices or systems are divided according to the architectural features and can be generally divided into three levels, namely component level-subsystem level-system level. The subsystem may also be referred to as a subsystem, and is composed of a plurality of devices at a component level of a next level. In a flow system such as a destruction device, the subsystem includes a component level device at the next level, and a new "device" with a specific function is formed, and the new "device", that is, the function of the subsystem itself, has parameters representing its performance. These parameters are specific to the subsystem itself, rather than to the component-level devices that the subsystem contains, and the measurable state parameters of the newly added functionality specific to the subsystem itself are referred to as the operating state parameters of the subsystem.

At present, there are many methods for evaluating the health status of a system, and the common methods include: a model method, a hierarchical and fuzzy evaluation method, an artificial neural network method, a Bayesian network-based method and the like. Each of these methods has its advantages and range of applicability. In general, only the components that make up a subsystem and their structural issues are considered in assessing the health of the subsystem. The components of the combustion subsystem of the destruction device are combined together to form a new function besides the component function, and the composition is complex. For subsystems formed of multiple components that generate new state parameters, a single evaluation approach may be difficult to better address the state of health evaluation of such systems. How to better evaluate the health state evaluation of the fusion of multiple information such as the self parameters of the equipment components, the operation state parameters of the subsystems and the like becomes a problem to be solved.

The rate-mode reliability theory was proposed by the professor of north navigation zai kaiyuan in the 90 s of the 20 th century, is a theory combining probability hypothesis in the classical reliability theory and fuzzy state hypothesis in the fuzzy reliability theory, and belongs to the fuzzy reliability theory. The theory has been successfully applied to different systems such as an ACT (active compact verification) machine, a lithium battery, a computer communication network and the like, can better provide a theoretical basis for analyzing various states of equipment, and meets the requirement of health state evaluation of the equipment. The rate-mode reliability index mainly reflects the performance and reliability characteristics of the system, such as the rate-mode reliability, the average fuzzy fault time, the rate-mode fault rate and the like of the system, and can be regarded as an evaluation index related to the performance.

A measurable state value reflecting the newly added functions of the subsystem level, included components and mutual relations are comprehensively considered, a combined health state assessment method of the subsystem of the destruction equipment is provided based on multiple states of a rate-mode reliability theory, and the health state assessment problem of fusion of multiple information such as small data samples, equipment parameters and operation state parameters is solved.

[ summary of the invention ]

1. The purpose is as follows: the invention aims to provide a health state evaluation method of a subsystem level of a dangerous article destruction device, which comprehensively considers the health state of the subsystem containing components, the connection mode of the components and the rate-mode reliability of state data specific to the subsystem, solves the health state evaluation problem of multi-information fusion of complex equipment subsystems such as the subsystem of the dangerous article destruction device, better fuses the running states of the component level device and the subsystem, can realize the differentiation of the slight change of the overall health state of the subsystem, and provides a new method for the health state evaluation of the subsystem level of the complex equipment.

2. The technical scheme is as follows: the invention designs a health state evaluation method for a subsystem level of dangerous goods destruction equipment. The design idea is as follows: firstly, dividing a component level and a subsystem level from the perspective of a system architecture, and then calculating the health degree of each component of the subsystem; then, calculating the health degree of the component level set according to the connection mode of each component level device included in the subsystem; secondly, calculating the rate-mode reliability of the self running state of the subsystem level to reflect the rate-mode reliability as the health degree of the subsystem state; and finally, the integral rate-mode reliability of the subsystem is comprehensively calculated by combining the health degree of the component level set and the rate-mode reliability of the running state of the subsystem level. The health state of the subsystem is reflected by the rate-mode reliability.

The following specifically describes the technical scheme of the method with reference to the steps in the flowchart of fig. 2.

The method comprises the following steps: health status grading

To some extent, the health status of the system refers to the ability of the system device to maintain a certain level of reliability, and is a confidence level that the reliability of the system is maintained within a certain range (on the premise that the system is guaranteed to complete the intended function) in the use status. The health may be used to quantitatively measure the health of a system, device or component. Let R denote the health of the system, i.e. the probability of a healthy state, with a value range of [0, 1 ]. When R is 0, the system is very unhealthy, and equipment or parts are in a serious fault state; when R is 1, the system is completely healthy, and the equipment or parts are in a 100% healthy state. In general, the health degree of the system in use is between the two.

The health status grade is determined according to the health degree R, and the system is divided into n different health status grades according to different numerical value ranges of the R.

The health degree R of the system is obtained through calculation, and the range of the health degree R is compared, so that the health state grade of the current system can be determined.

Step two: calculating health of each component-level device included in a subsystem

The complex equipment or system is divided according to the architectural characteristics and can be divided into three levels, namely component level-subsystem level-system level. The component-level equipment has the characteristics of simple structure, and can be subjected to model-based health state assessment by establishing a relatively accurate model through various ways. The general health status assessment method comprises: a model method, a hierarchical and fuzzy evaluation method, an artificial neural network method, a Bayesian network-based method and the like. In the model method, the common health status assessment method is as follows: (1) a physical model, (2) a knowledge base-based model, (3) a data mining model, and (4) a combined model.

And aiming at the characteristics of the components, selecting a corresponding method to calculate the health degree H of each component.

Step three: calculating overall health of a component-level device set connection included in a subsystem

Device set S for component level₀＝{A₁,A₂,…,A_nAnd the health degree R of the equipment is related to the connection form of the equipment, and typical connection forms of the equipment comprise series connection, parallel connection, series-parallel connection, voter connection modes and the like. As shown in fig. 3.

1. Series connection (the equipment of n component levels are connected together in series to form a subsystem, the subsystem can work normally only if the equipment of n component levels works normally, and the subsystem can not work when the equipment of one component level fails)

If A₁,A₂,…,A_nIn series (shown in FIG. 3 (a)), the set of devices S₀The health degree of (A) is:

$R_{S_0}=\underset{i=1}{\overset{n}{\mathrm{\Π}}}{R}_{A_i}---\left(1\right)$

2. parallel connection (the equipment of n component levels are connected together in parallel to form a subsystem, wherein the subsystem can work normally when the equipment of any component level works normally, and the subsystem functions only when the equipment of n component levels fails)

If A₁,A₂,…,A_nParallel connection (shown in fig. 3 (b)), the device set S₀The health degree of (A) is:

$R_{S_0}=1-\underset{i=1}{\overset{n}{\mathrm{\Π}}}(1-R_{A_i})---\left(2\right)$

3. series-parallel connection (the equipment of its component level is formed by series and parallel connection, forming mixed connection)

If A₁,A₂,…,A_nHybrid (shown in fig. 3 (c)), the device set S₀The health degree of (A) is:

$R_{S_0}=\underset{i=1}{\overset{k}{\mathrm{\Π}}}{R}_{A_i}\·\[1-\underset{i=k+1}{\overset{n}{\mathrm{\Π}}}(1-R_{A_i})\]---\left(3\right)$

4. voter form (a subsystem consisting of n component-level devices, at least K of which are working properly, the subsystem is not working properly, and a failure of a device greater than (n-K) component-level devices causes the subsystem to fail)

If A₁,A₂,…,A_nK models out of n (shown in FIG. 4), the device set S₀The health degree of (A) is:

$R_{S_0}=\underset{i=k}{\overset{n}{\mathrm{\Σ}}}\left(\begin{array}{c}n\\ i\end{array}\right)\underset{k}{\overset{i}{\mathrm{\Π}}}{R}_{A_k}\·\underset{k}{\overset{n-i}{\mathrm{\Π}}}(1-R_{A_k})---\left(4\right)$

5. complex form (the equipment connection of the component level is not the above four forms, the structure is complex and connected together to form a subsystem)

The reliability of the complex system formed by typical systems in a series connection mode, a parallel connection mode, a voter mode and the like can be calculated by gradually evaluating the lowest layer upwards and the one layer upwards, so that the health result of the complex subsystem formed by the typical systems can be obtained.

Step four: computing rate-mode reliability characterized by subsystem-level state data

The method is independent of a system model, only needs real-time measurement values of the state parameters of the subsystem, and is suitable for health state evaluation of an unknown system model₁,a₂,…,a_nThe collection of state parameters can be regarded as a new device, having

R_α＝f(w,a) (5)

Wherein w represents the weight of the influence of the subsystem state parameter set on the subsystem health state, and f represents the rate-modulo reliability algorithm.

The specific calculation steps are divided into four steps:

1. determining status data reflecting subsystem operation

And selecting a parameter capable of reflecting the key parameter of the subsystem or representing the performance change of the subsystem as a health index of the subsystem, and analyzing the state parameter to obtain the health state reflecting the system.

2. Collected data preprocessing

In the process of collecting original data, the phenomena of non-uniform collection frequency, over-collection or missing-collection often occur. Therefore, preprocessing of the data is required. For the over-sampling phenomenon, a resampling method is often adopted; for the phenomenon of missing mining or the small amount of collected data, the collected data needs to be interpolated, and a cubic Hermite interpolation method can be selected for interpolation.

3. Calculation of rate-mode reliability for each state parameter

From the theory of the health state related to the rate-mode reliability, a fuzzy event B is defined and is arranged at an initial time t₀The system is in the fuzzy success state. Here, the system is at an initial time t₀Is considered.

First, assume that two blur times are as follows:

A＝{T_SFat time [ t₀,t_o+t]Does not occur inside }

B ═ system at t₀Moment in fuzzy success state }

Then, the rate-mode reliability r (t), also called rate-mode health, at time t is defined as:

$R\left(t\right)=1-P\left(\stackrel{\‾}{A}\right|B)\·P\left(B\right)-P\left(\stackrel{\‾}{B}\right)$

wherein:

$p_{ij}(t_0,t)=\frac{n_{ij}(t_0,t)}{N_i(t_0,t)}=\frac{n_{ij}(t_0,t)}{\underset{j=1}{\overset{n}{\mathrm{\Σ}}}{n}_{ij}(t_0,t)}---\left(7\right)$

$\mathrm{\φ}\left(t\right)=\left({\mathrm{\φ}}_{S_1}\right(t),{\mathrm{\φ}}_{S_2}(t),\mathrm{...}{\mathrm{\φ}}_{S_n}(t\left)\right)\∈R^{n\×1}$

φ(t)＝P(t₀,t)·φ(t₀) (8)

parameters in the above formula, wherein P_ij(t₀And t) represents the system at time (t)₀,t_oWithin + t), the system is from S_iState transition to S_jThe size of the probability of (c);representing the degree of membership of the system;the representative system is at t₀Is in state S at the moment_iThe state probability of (2); mu.s_SAnd mu_FRepresenting the degree of membership in fuzzy success or fuzzy fault. n is_ij(t₀T) represents [ t₀,t]Time interval from S_iState transition to S_jThe number of states; phi (t) represents the system state probability vector at time t;(i-1, 2, … …, n) represents the system state S at time t_iThe probability of (c).

4. Multi-state parameter health calculation based on state data

The state data based rate mode reliability of the subsystem is calculated using equation (5).

Step five: sub-system level overall health calculation

Suppose that the subsystem S comprises a device S of n component levels₀＝{A₁,A₂,…,A_nAnd a parameter α ═ a indicative of the operating state of the subsystem₁,a₂,…,a_nAnd the health state of the subsystem is determined by the health state combination of equipment health state of the next level component level contained in the subsystem and the health state of the running state parameter of the subsystem. In this case, the structure of the subsystem is S ═ S₀α, the health of the available subsystems is:

$R_S=-w_1{R}_{S_0}+w_2{R}_{\mathrm{\α}}---\left(9\right)$

wherein,health of a collection of devices representing a component level encompassed by a subsystem; r_aA rate-mode reliability representing a state parameter of the subsystem; w is a₁,w₂And respectively representing the influence weight of the component level equipment set and the subsystem state parameter set of the subsystem on the health state of the subsystem, specifically, the influence weight can be obtained according to the analysis of safety, reliability and the like, and the influence weight needs to be determined by combining with the practical engineering application.

Step six: according to different value ranges of the health degree, the health state is evaluated

And comparing the range of the R by the R obtained by the last step, and further determining the health state grade state of the current subsystem. According to the health degree value and the predetermined health state grade (step one), the calculated value of the overall health degree of the subsystem level is compared with the health state interval, and the health state grade of the subsystem level is obtained.

Therefore, when the system runs, the health state of the system is represented by the value of the health degree R, and the classification limit is determined by combining with the actual engineering, so that the health state grade of the system is determined.

3. The advantages and the effects are as follows: the invention provides a combined health state evaluation method for fusion state data of a hazardous article destruction equipment subsystem level. The method has the advantages that the health degree of the components, the connection mode of the components and the state data health degree of the subsystems are comprehensively considered, and the utilization rate model reliability is better fused with the change influence of each state. The health degree is reflected through the reliability of the rate model, the slight change of the whole health state of the subsystem can be described, the real-time and comprehensive monitoring and early warning of the health state of the equipment are facilitated, the method is strong in universality and convenient for engineering realization.

[ description of the drawings ]

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:

fig. 1 is a combustion subsystem component of a hazardous material destruction facility.

FIG. 2 is a block flow diagram of the method of the present invention.

Figure 3a is a series arrangement of devices.

Fig. 3b is a device parallel form.

Fig. 3c is a device series-parallel format.

FIG. 4 is a plant voter attachment form.

FIG. 5 is sensor measurement subsystem status data.

FIG. 6 is a plot of the temperature of a combustion chamber as a function of time and interpolated data.

FIG. 7 is a membership function for a chamber temperature.

FIG. 8 is a graph of rate mode reliability for a combustion chamber temperature.

FIG. 9 is a rate-mode reliability curve for two-chamber temperature.

FIG. 10 is a graph of rate mode reliability for furnace pressure.

FIG. 11 is a graph of rate mode reliability for tank liquid level.

FIG. 12 is a state-based rate-mode state-of-health curve for a combustion subsystem.

FIG. 13 is a combustion subsystem health assessment results plot.

[ detailed description ] embodiments

The invention provides a combined rate model health state evaluation method of a dangerous goods destruction equipment subsystem based on a rate model reliability theory, which mainly designs three contents: designing a subsystem to calculate the reliability of the rate mode of connection of the component-level equipment set; designing a subsystem for calculating a rate-mode reliability of the subsystem characterized by the state data using the state data; and calculating the integral rate modulus reliability of the design subsystem.

The combustion subsystem of the destruction equipment mainly comprises a first burner, a second burner, an air supply device, an oil supply pump and an emergency cooling device, wherein the combustion temperature of the combustion subsystem is a special state parameter of the combustion subsystem, and the combustion temperature is a special parameter of the subsystem formed by the burners and the air supply device. The main structure of the subsystem is shown in fig. 1.

The data of the embodiment is derived from sensor measurement data of a marketing device in 7 months and 9 days in 2009, and the data form is shown in fig. 5. The data calculation process is carried out in a Matlab2010a environment on a computer with a CPU main frequency of 2.93GHz and a memory of 3.00 GB. The implementation steps are as follows:

the method comprises the following steps: calculating health of each component of a combustion subsystem

Respectively calculating the health degree R of each component of the combustion subsystem by using the measured data of the sensor and a health state evaluation method based on a model_Ai。

Step two: computing health of a subsystem including component-level device set connections

The connection form of each component stage device contained in the combustion subsystem is in series connection, and then the formula (1) is used for calculating

Step three: using state data to calculate rate-mode reliability of subsystems characterized by state data

1. Determining a state parameter reflecting operation of a system

The combustion subsystem of the destruction equipment is used as a core subsystem of the equipment, and has the function of decomposing substances through high-temperature combustion, and the state parameters reflecting the specific performance of the system are the temperature of a first combustion chamber, the temperature of a second combustion chamber, the pressure of a hearth and the liquid level of an oil tank.

2. Data pre-processing

The reliability of the calculation rate module needs to meet the law of majority, and a large amount of data is needed to evaluate the online health state of the system. Therefore, interpolation processing of raw data is required. Taking a combustion chamber temperature as an example, the original data is interpolated by using three interpolation methods of linear interpolation, cubic spline interpolation and cubic Hermite interpolation respectively, and the interval between data points after interpolation is 5s, as shown in FIG. 6.

3. Determination of health membership of each parameter

Taking a combustion chamber temperature as an example, the normal range of the combustion chamber temperature is 700 ℃ and 900 ℃, and can be higher than 900 ℃ and cannot be lower than 700 ℃. Here, the membership functions as shown in the following figure are selected. The main reason for setting the lower threshold of the normal range to 720 is to consider that if it is set to 700, then when the algorithm calculates that the health has decreased, it has serious consequences. Setting the lower limit of the normal range to 720 can detect the trend of temperature decrease, i.e., the trend of the system gradually getting out of the healthy state, and can find problems in advance and prevent the problems in the future. The membership function is shown in fig. 7.

4. Each parameter rate modulo reliability calculation

Taking the rate-mode reliability of the temperature of the combustion chamber as an example for calculation, the sampling period T is taken as 5s, the temperature is taken every 5s from the data curve chart of FIG. 5, and the obtained temperature value T is taken₀,t₁,…t_nSubstituting into formula (7) to obtain the state transition probability p of every 5s temperature_ij(t₀T); substituting the obtained temperature value into a combustion chamber temperature membership function to obtain the corresponding phi (t)_i) Then, phi (t) can be obtained according to equation (8). Root of herbaceous plantFrom equation (6), the rate-mode reliability of a combustion chamber temperature can be calculated, and the curve is plotted as shown in FIG. 8. Similarly, the reliability values of the rate modes of other parameters can be obtained, and the curves are plotted as shown in fig. 9, 10 and 11.

5. Multi-parameter health state calculation based on state data

According to equation (5), the combustion subsystem rate-mode reliability R based on the state data is:

R＝w₁·R_{one combustion chamber}+w₂·R_{Second combustion chamber}+w₃·R_{Pressure of furnace}+w₄·R_{Oil tank liquid level}

Get w₁＝0.3,w₂＝0.3,w₃＝0.2,w₄At 0.2, the state of health curve for the combustion subsystem is shown in fig. 12. The black solid line represents the change of the health state of the combustion subsystem, the liquid level of the fuel tank is always 1, and the other lines represent the change of the health state of each state data. The embodiment shows that the health state evaluation based on the state data can acquire the system performance change information from each state parameter of the system, further give the health state change of the system in a mode of rate-mode reliability, and better fuse the change information of each parameter.

Step four: overall rate-mode reliability calculation for combustion subsystems

According to the formula (9), the health state of the subsystem depends not only on the state parameters, but also on the component level equipment under the subsystem, and w is taken₁＝w₂When the value is 0.5, the final health assessment result of the combustion subsystem is shown in fig. 13, in which the line containing the mark a (the uppermost line) represents the rate mode reliability after the components are combined, the middle dotted line represents the rate mode reliability of the subsystem, and the black solid line (the lowermost line) represents the rate mode reliability of the state parameter. The detailed data are shown in Table 1.

TABLE 1 rate-mode reliability calculation results at subsystem level

Step five: health status assessment of combustion subsystems based on rate mode reliability being graded into different health levels according to different numerical ranges

TABLE 2 health status evaluation results at subsystem level

Summarizing the above design and examples, it can be concluded that: the method constructs a general method for evaluating the health state of the rate model of the subsystem level, better integrates the running states of the component level equipment and the subsystem, can distinguish the slight change of the overall health state of the subsystem, and can well evaluate and monitor the health state of the equipment.

Claims (4)

1. A health state assessment method for a hazardous material destruction equipment subsystem is characterized by comprising the following steps: the method specifically comprises the following steps:

the method comprises the following steps: health status grading

Assuming that R represents the health degree of the system, namely the probability of a health state, and the value range is [0, 1 ]; when R is 0, the system is very unhealthy, and equipment or parts are in a serious fault state; when R is 1, the system is completely healthy, and the equipment or parts are in a 100% healthy state; the health degree of the system in use is between the two;

step two: calculating health of each component-level device included in a subsystem

Aiming at the characteristics of equipment or components, selecting a corresponding method to calculate the health degree H of each component;

step three: calculating overall health of a component-level device set connection included in a subsystem

Device set S for component level₀＝{A₁,A₂,…,A_nIts health R is related to the form of coupling of the device, including the following forms:

3.1 tandem form

The equipment of n component levels is connected in series to form a subsystem, and the subsystem can normally work only if the equipment of n component levels normally works; if one of the component-level devices fails, the subsystem functions fail;

if A₁,A₂,…,A_nIn series, the devices are set S₀The health degree of (A) is:

$R_{S_0}=\underset{i=1}{\overset{n}{\mathrm{\Π}}}{R}_{A_i}---\left(1\right)$

3.2 parallel form

The equipment of n component levels is connected together in parallel to form a subsystem, wherein the subsystem can work normally when the equipment of any one component level works normally; only if n component level devices fail, the subsystem function fails;

if A₁,A₂,…,A_nParallel connection, then the equipment set S₀The health degree of (A) is:

$R_{S_0}=1-\underset{i=1}{\overset{n}{\Π}}(1-R_{A_i})---\left(2\right)$

3.3 series-parallel connection

The equipment at the component level is formed by mixing serial connection and parallel connection to form a mixed connection mode;

if A₁,A₂,…,A_nParallel-serial, then the device set S₀The health degree of (A) is:

$R_{S_0}=\underset{i=1}{\overset{k}{\Π}}{R}_{A_i}\·\[1-\underset{i=k+1}{\overset{n}{\Π}}(1-R_{A_i})\]---\left(3\right)$

3.4 voter form

The subsystem is composed of n component-level devices, at least K component-level devices normally work, the subsystem can normally work, and if the devices larger than (n-K) component levels fail, the subsystem fails; the system thus forms a voter form;

if A₁,A₂,…,A_nK models are taken from n, then the equipment set S₀The health degree of (A) is:

$R_{S_0}=\underset{i=k}{\overset{n}{\Σ}}\left(\begin{array}{c}n\\ i\end{array}\right)\underset{k}{\overset{i}{\Π}}{R}_{A_k}\·\underset{k}{\overset{n-i}{\Π}}(1-R_{A_k})---\left(4\right)$

3.5 Complex form

The equipment connection of the component level is not in the four forms, and the structure is complicated and the equipment connection is connected together to form a subsystem;

step four: computing rate-mode reliability characterized by subsystem-level state data

The method for determining the health of a multi-parameter system by using the operating state data of the subsystem is as follows, the subsystem S is assumed to contain a parameter α which represents the operating state of the subsystem (a)₁,a₂,…,a_nThe collection of state parameters is regarded as a new device, having

R_α＝f(w,a) (5)

Wherein w represents the influence weight of the subsystem state parameter set on the subsystem health state, and f represents a rate modulus reliability algorithm;

step five: sub-system level overall health calculation

Suppose that the subsystem S comprises a device S of n component levels₀＝{A₁,A₂,…,A_nAnd a parameter α ═ a indicative of the operating state of the subsystem₁,a₂,…,a_nThe health state of the subsystem is determined by the health state combination of equipment at the next level of component level contained in the subsystem and the health state of the running state parameters of the subsystem; in this case, the structure of the subsystem is S ═ S₀α, the health of the subsystem is obtained as:

$R_S=w_1{R}_{S_0}+w_2{R}_{\mathrm{\α}}---\left(9\right)$

wherein,health of a collection of devices representing a component level encompassed by a subsystem; r_aRepresenting state parameters of subsystemsRate mode reliability; w is a₁,w₂Respectively representing the influence weight of the component level equipment set and the subsystem state parameter set of the subsystem on the health state of the subsystem;

step six: according to different value ranges of the health degree, the health state is evaluated

Comparing the range of the R through the calculated R, and further determining the health state grade state of the current subsystem; according to the health degree value and the health state grade determined in advance, the calculated value of the overall health degree of the subsystem level is compared with the health state interval, and the health state grade of the subsystem level is obtained.

2. The health status assessment method for the subsystem level of hazardous material destruction equipment according to claim 1, wherein: in the first step, the health state grade is determined according to the health degree R, and the system is divided into n different health state grades according to different numerical value ranges of the R;

and (4) obtaining the health degree R of the system through calculation, and comparing the range of the health degree R, thereby determining the health state grade of the current system.

3. The health status assessment method for the subsystem level of hazardous material destruction equipment according to claim 1, wherein: the corresponding method in the second step comprises a model method, a hierarchical and fuzzy evaluation method, an artificial neural network method and a Bayesian network-based method.

4. The health status assessment method for the subsystem level of hazardous material destruction equipment according to claim 1, wherein: the fourth step also comprises the following steps:

4.1 determining status data reflecting subsystem operation

Selecting a parameter capable of reflecting the key parameter of the subsystem or representing the performance change of the subsystem as a health index of the subsystem, and analyzing the state parameter to obtain the health state reflecting the system;

4.2 Pre-processing of acquired data

For the phenomenon of missing mining or the small amount of collected data, the collected data needs to be subjected to interpolation processing, and a three-time Hermite interpolation method is selected for interpolation;

4.3 calculation of Rate-modulo reliability of State parameters

From the theory of the health state related to the rate-mode reliability, a fuzzy event B is defined and is arranged at an initial time t₀The system is in a fuzzy success state; the system is at an initial time t₀The state of (c) is considered;

first, assume that two blur times are as follows:

A＝{T_SFat time [ t₀,t_o+t]Does not occur inside }

B ═ system at t₀Moment in fuzzy success state }

Then, the rate-mode reliability r (t), also called rate-mode health, at time t is defined as:

$R\left(t\right)=1-P\left(\stackrel{\‾}{A}\right|B)\·P\left(B\right)-P\left(\stackrel{\‾}{B}\right)$

wherein:

$p_{ij}(t_0,t)=\frac{n_{ij}(t_0,t)}{N_i(t_0,t)}=\frac{n_{ij}(t_0,t)}{\underset{j=1}{\overset{n}{\Σ}}{n}_{ij}(t_0,t)}---\left(7\right)$

$\mathrm{\φ}\left(t\right)=({\mathrm{\φ}}_{S_1}\left(t\right),{\mathrm{\φ}}_{S_2}\left(t\right),...{\mathrm{\φ}}_{S_n}\left(t\right))\∈R^{n\×1}$

φ(t)＝P(t₀,t)·φ(t₀) (8)

parameters in the above formula, wherein P_ij(t₀And t) represents the system at time (t)₀,t_oWithin + t), the system is from S_iState transition to S_jThe size of the probability of (c);representing the degree of membership of the system;the representative system is at t₀Is in state S at the moment_iThe state probability of (2); mu.s_SAnd mu_FRepresenting the degree of membership in a fuzzy success or a fuzzy fault; n is_ij(t₀T) represents [ t₀,t]Time interval from S_iState transition to S_jThe number of states; phi (t) represents the system state probability vector at time t;representing the state S of the system at time t_iThe probability of (d);

4.4 Multi-State parameter health calculation based on State data

The state data based rate mode reliability of the subsystem is calculated using equation (5).