CN106289376A - A kind of Industrial Boiler intelligent detection device - Google Patents

Abstract

The invention discloses a kind of Industrial Boiler intelligent detection device, including detection module, data processing module, analysis module, monitoring display module, healthy trend prediction module and life appraisal module；Described detection module is for detecting the various health datas of industrial boiler operation；Described analysis module is for being analyzed the health data processed by data processing module, and forms pivot chart and be transferred to monitoring display module；Described healthy trend prediction module is used for using fuzzy neural network method to carry out following operation trend analysis according to off-line and online data；Described life appraisal module is used for determining the residual life of Industrial Boiler, and reversely the trend analysis of the healthy trend prediction module of checking is the most correct, and long-life suggestion operation reserve is prolonged in formulation, and suggestion operation reserve is transferred to monitoring display module.The present invention has monitoring and warning function in real time, additionally it is possible to carry out the operational application of boiler, formulates different operation reserve according to different operation conditions.

Description

Intelligent detection device for industrial boiler

Technical Field

The invention relates to the field of boiler detection, in particular to an intelligent detection device for an industrial boiler.

Background

The operation condition of the boiler not only relates to the production efficiency, but also relates to the production safety. In the related technology, the service life of the boiler is evaluated mainly by a service life loss fraction method and a parameter method, the evaluation result cannot be verified, a corresponding control strategy for prolonging the service life is not specified, and the problem of service life of equipment cannot be well solved.

Disclosure of Invention

In order to solve the problems, the invention aims to provide an intelligent detection device for an industrial boiler.

The purpose of the invention is realized by adopting the following technical scheme:

an intelligent detection device for an industrial boiler comprises a detection module, a data processing module, an analysis module, a monitoring display module, a health trend prediction module and a service life evaluation module; the detection module is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module and the analysis module; the analysis module is used for analyzing the health data processed by the data processing module, forming a data perspective and transmitting the data perspective to the monitoring display module; the health trend prediction module is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module is used for determining the residual service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module is correct, making a recommended operation strategy for prolonging the service life and transmitting the recommended operation strategy to the monitoring display module.

The invention has the beneficial effects that: the method can not only carry out real-time monitoring and early warning according to detected health data, but also carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously, carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technicians to select, thereby solving the technical problems.

Drawings

The invention is further described by using the drawings, but the application scenarios in the drawings do not limit the invention in any way, and for those skilled in the art, other drawings can be obtained according to the following drawings without creative efforts.

FIG. 1 is a schematic diagram of the structure of the intelligent monitoring device of the present invention;

fig. 2 is a schematic structural diagram of a life evaluation module according to the present invention.

Reference numerals:

the system comprises a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, a life evaluation module 6, a data preparation sub-module 61 and a life analysis prediction sub-module 62.

Detailed Description

The invention is further described in connection with the following application scenarios.

Application scenario 1

Referring to fig. 1 and fig. 2, the intelligent detection device for an industrial boiler according to an embodiment of the application scenario includes a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, and a life evaluation module 6; the detection module 1 is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module 2 and the analysis module 3; the analysis module 3 is used for analyzing the health data processed by the data processing module 2 and forming a data perspective view to be transmitted to the monitoring display module 4; the health trend prediction module 5 is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module 6 is used for determining the remaining service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module 5 is correct, making a suggested operation strategy for prolonging the service life and transmitting the suggested operation strategy to the monitoring display module 4.

Preferably, the health data comprises boiler wall temperature, boiler wall thickness, creep, metallographic phase.

The embodiment of the invention not only can carry out real-time monitoring and early warning according to the detected health data, but also can carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously can carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technical personnel to select, thereby solving the technical problems.

Preferably, the data processing module 2 is configured to perform AD conversion on the health data, filter the health data, and remove useless health data.

In the preferred embodiment, the data processing module 2 is arranged to screen the health data, so that the speed and the precision of data analysis are improved.

Preferably, the service life evaluation module 6 comprises a data preparation module 61 and a service life analysis and prediction submodule 62, wherein the data preparation submodule 61 is used for determining an actually measured typical load spectrum of the boiler, crack positions and sizes of actual cracks on the boiler, and performing geometric simplification classification on the various cracks; the life analysis and prediction submodule 62 is configured to perform a fatigue test on a material of the boiler, obtain a fatigue crack propagation rate curve of the material corresponding to each crack, perform crack propagation analysis on the actually measured typical load spectrum, the crack position and size of each actual crack, and the fatigue crack propagation rate curve of each crack, determine a crack propagation life cycle number corresponding to each crack, determine an estimated value of a remaining fatigue life of the corresponding crack according to the crack propagation life cycle number, and finally determine an estimated value of the remaining fatigue life of the boiler.

The present preferred embodiment builds the structural framework of the life assessment module 6.

Preferably, the set of estimated values of residual fatigue life corresponding to the crack i ═ 1,2, … m is defined as { P }₁,P₂,…,P_iH, an estimated value P of the residual fatigue life of the boiler_ZThen it is:

P_z＝min_i＝1,2,...m{P₁,P₂,…,P_i}。

the optimal embodiment determines the relationship between the residual fatigue life of the boiler and the residual fatigue life of each actual crack of the boiler, adopts the minimum fatigue life of the actual crack as the residual fatigue life of the boiler, accords with the wooden barrel theory, and has high accuracy.

Preferably, the performing a fatigue test on the material of the boiler to obtain a fatigue crack propagation rate curve of the material corresponding to various cracks includes:

(1) calculating stress intensity factor amplitude of various cracks, considering that a plastic deformation area of a crack tip point has a decisive influence on fatigue fracture of a material, enabling the plastic area of the crack tip to be equivalent to a homogeneous inclusion containing phase change strain, and defining the stress intensity factor amplitude delta K_pcThe calculation formula of (2) is as follows:

${\mathrm{\ΔK}}_{pc}=\left\{\begin{array}{cc}{K}_{pc}^{\max }-K_{yc}-{\mathrm{\ΔK}}_{sc},& R\≤0\\ K_{pc}^{\max }-K_{pc}^{\min },& R>0\end{array}\right.$

in the formula

$\begin{array}{c}{\mathrm{\ΔK}}_{sc}=\frac{1}{2\sqrt{2\mathrm{\π}}}{\∫}_A{r}^{-3/2}\[\frac{K_{yc}}{\sqrt{2\mathrm{\π}r}}(3{\sin }^2\mathrm{\α}\cos \mathrm{\α}+2\cos \frac{\mathrm{\α}}{2}\cos \frac{3\mathrm{\α}}{2})\\ +3({\mathrm{\σ}}_{11}-{\mathrm{\σ}}_{22})\sin \mathrm{\α}\sin \frac{5\mathrm{\α}}{2}-6{\mathrm{\σ}}_{12}\sin \mathrm{\α}\cos \frac{5\mathrm{\α}}{2}-({\mathrm{\σ}}_{11}+{\mathrm{\σ}}_{22})\cos \frac{3\mathrm{\α}}{2}\]dA\end{array}$

Wherein,for the plastically corrected stress intensity factor value calculated from the maximum load in the fatigue cycle load,for plastically corrected stress intensity factor value, K, calculated from minimum load in fatigue cyclic loading_ycCalculated from the load at which the crack opens completely, Δ K, is the stress intensity factor under far field action_scRepresenting the increase in the stress intensity factor induced in the plastic zone of the crack tip, A being the area of the plastic zone surrounding the crack tip, which includes the plastic deformation tail zone generated during crack propagation, σ₁₁、σ₁₂、σ₂₂The stress in the plastic zone of the crack tip is obtained by finite element calculation and analysis of a stress field of the plastic zone of the crack tip, and R is the ratio of tensile load to compressive load;

(2) constructing fatigue crack propagation rate curves of various cracks, taking a Paris formula as a basis, considering the influence of temperature on the fatigue crack propagation rate, and defining a correction calculation formula of the fatigue crack propagation rate as follows:

T＜0℃OR T>T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M$

0℃≤T≤T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{\left({\mathrm{\ΔK}}_{pc}\right)}^M$

wherein T is the test temperature, T_maxTo a set maximum temperature, T_maxThe value range of (A) is [35 ℃,40 DEG C]A is the crack propagation length, N is the cycle number, C and M are the material constants, Δ K_TThe influence of temperature on the propagation rate is reflected for the abnormal temperature fracture threshold value obtained by analysis after fitting the crack propagation performance curved surface at the abnormal temperature, and the delta K_TThe value range of (A) should satisfy [0, Δ K ]_pc)。

The preferred embodiment defines a calculation formula of the stress intensity factor amplitude delta K _ pc, considers that a plastic deformation area of a crack tip point has a decisive influence on the fatigue fracture of a material, and enables the crack tip plastic area to be equivalent to a homogeneous inclusion containing phase change strain, so that the defined stress intensity factor amplitude delta K _ pc can be well used as a reasonable mechanical parameter to quantitatively analyze the influence of the crack tip plastic area on the stress intensity factor; on the basis of the Paris formula, the influence of temperature on the fatigue crack propagation rate is considered, a correction calculation formula of the fatigue crack propagation rate is defined, the calculation precision is improved, and the method is simple and practical.

Preferably, the calculation formula of the crack propagation life cycle number N is as follows:

$N={\∫}_{a_0}^{a_c}\frac{1}{C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M}$

the preferred embodiment determines a calculation formula of the crack propagation life cycle number N, and improves the speed of life prediction.

Maximum temperature T of the above-described embodiment of this application scenario_maxThe set temperature is 35 ℃, and the accuracy of the boiler fatigue life prediction is relatively improved by 15%.

Application scenario 2

Referring to fig. 1 and fig. 2, the intelligent detection device for an industrial boiler according to an embodiment of the application scenario includes a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, and a life evaluation module 6; the detection module 1 is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module 2 and the analysis module 3; the analysis module 3 is used for analyzing the health data processed by the data processing module 2 and forming a data perspective view to be transmitted to the monitoring display module 4; the health trend prediction module 5 is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module 6 is used for determining the remaining service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module 5 is correct, making a suggested operation strategy for prolonging the service life and transmitting the suggested operation strategy to the monitoring display module 4.

Preferably, the health data comprises boiler wall temperature, boiler wall thickness, creep, metallographic phase.

The embodiment of the invention not only can carry out real-time monitoring and early warning according to the detected health data, but also can carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously can carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technical personnel to select, thereby solving the technical problems.

Preferably, the data processing module 2 is configured to perform AD conversion on the health data, filter the health data, and remove useless health data.

In the preferred embodiment, the data processing module 2 is arranged to screen the health data, so that the speed and the precision of data analysis are improved.

Preferably, the service life evaluation module 6 comprises a data preparation module 61 and a service life analysis and prediction submodule 62, wherein the data preparation submodule 61 is used for determining an actually measured typical load spectrum of the boiler, crack positions and sizes of actual cracks on the boiler, and performing geometric simplification classification on the various cracks; the life analysis and prediction submodule 62 is configured to perform a fatigue test on a material of the boiler, obtain a fatigue crack propagation rate curve of the material corresponding to each crack, perform crack propagation analysis on the actually measured typical load spectrum, the crack position and size of each actual crack, and the fatigue crack propagation rate curve of each crack, determine a crack propagation life cycle number corresponding to each crack, determine an estimated value of a remaining fatigue life of the corresponding crack according to the crack propagation life cycle number, and finally determine an estimated value of the remaining fatigue life of the boiler.

The present preferred embodiment builds the structural framework of the life assessment module 6.

Preferably, the set of estimated values of residual fatigue life corresponding to the crack i ═ 1,2, … m is defined as { P }₁,P₂,…,P_iH, an estimated value P of the residual fatigue life of the boiler_ZThen it is:

P_Z＝min_i＝1,2,…m{P₁,P₂,…,P_i}。

the optimal embodiment determines the relationship between the residual fatigue life of the boiler and the residual fatigue life of each actual crack of the boiler, adopts the minimum fatigue life of the actual crack as the residual fatigue life of the boiler, accords with the wooden barrel theory, and has high accuracy.

Preferably, the performing a fatigue test on the material of the boiler to obtain a fatigue crack propagation rate curve of the material corresponding to various cracks includes:

(1) calculating stress intensity factor amplitude of various cracks, considering that a plastic deformation area of a crack tip point has a decisive influence on fatigue fracture of a material, enabling the plastic area of the crack tip to be equivalent to a homogeneous inclusion containing phase change strain, and defining the stress intensity factor amplitudeΔK_pcThe calculation formula of (2) is as follows:

${\mathrm{\ΔK}}_{pc}=\left\{\begin{array}{cc}{K}_{pc}^{\max }-K_{yc}-{\mathrm{\ΔK}}_{sc},& R\≤0\\ K_{pc}^{\max }-K_{pc}^{\min },& R>0\end{array}\right.$

in the formula

$\begin{array}{c}{\mathrm{\ΔK}}_{sc}=\frac{1}{2\sqrt{2\mathrm{\π}}}{\∫}_A{r}^{-3/2}\[\frac{K_{yc}}{\sqrt{2\mathrm{\π}r}}(3{\sin }^2\mathrm{\α}\cos \mathrm{\α}+2\cos \frac{\mathrm{\α}}{2}\cos \frac{3\mathrm{\α}}{2})\\ +3({\mathrm{\σ}}_{11}-{\mathrm{\σ}}_{22})\sin \mathrm{\α}\sin \frac{5\mathrm{\α}}{2}-6{\mathrm{\σ}}_{12}\sin \mathrm{\α}\cos \frac{5\mathrm{\α}}{2}-({\mathrm{\σ}}_{11}+{\mathrm{\σ}}_{22})\cos \frac{3\mathrm{\α}}{2}\]dA\end{array}$

Wherein,for the plastically corrected stress intensity factor value calculated from the maximum load in the fatigue cycle load,for plastically corrected stress intensity factor value, K, calculated from minimum load in fatigue cyclic loading_ycCalculated from the load at which the crack opens completely, Δ K, is the stress intensity factor under far field action_scRepresenting the increase in the stress intensity factor induced in the plastic zone of the crack tip, A being the area of the plastic zone surrounding the crack tip, which includes the plastic deformation tail zone generated during crack propagation, σ₁₁、σ₁₂、σ₂₂The stress in the plastic zone of the crack tip is obtained by finite element calculation and analysis of a stress field of the plastic zone of the crack tip, and R is the ratio of tensile load to compressive load;

(2) constructing fatigue crack propagation rate curves of various cracks, taking a Paris formula as a basis, considering the influence of temperature on the fatigue crack propagation rate, and defining a correction calculation formula of the fatigue crack propagation rate as follows:

T＜0℃OR T>T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M$

0℃≤T≤T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{\left({\mathrm{\ΔK}}_{pc}\right)}^M$

wherein T is the test temperature, T_maxTo a set maximum temperature, T_maxThe value range of (A) is [35 ℃,40 DEG C]A is the crack propagation length, N is the cycle number, C and M are the material constants, Δ K_TThe influence of temperature on the propagation rate is reflected for the abnormal temperature fracture threshold value obtained by analysis after fitting the crack propagation performance curved surface at the abnormal temperature, and the delta K_TThe value range of (A) should satisfy [0, Δ K ]_pc)。

The preferred embodiment defines a calculation formula of the stress intensity factor amplitude delta K _ pc, considers that a plastic deformation area of a crack tip point has a decisive influence on the fatigue fracture of a material, and enables the crack tip plastic area to be equivalent to a homogeneous inclusion containing phase change strain, so that the defined stress intensity factor amplitude delta K _ pc can be well used as a reasonable mechanical parameter to quantitatively analyze the influence of the crack tip plastic area on the stress intensity factor; on the basis of the Paris formula, the influence of temperature on the fatigue crack propagation rate is considered, a correction calculation formula of the fatigue crack propagation rate is defined, the calculation precision is improved, and the method is simple and practical.

Preferably, the calculation formula of the crack propagation life cycle number N is as follows:

$N={\∫}_{a_0}^{a_c}\frac{1}{C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M}$

the preferred embodiment determines a calculation formula of the crack propagation life cycle number N, and improves the speed of life prediction.

Maximum temperature T of the above-described embodiment of this application scenario_maxThe set temperature is 36 ℃, and the accuracy of the boiler fatigue life prediction is relatively improved by 14 percent.

Application scenario 3

Referring to fig. 1 and fig. 2, the intelligent detection device for an industrial boiler according to an embodiment of the application scenario includes a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, and a life evaluation module 6; the detection module 1 is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module 2 and the analysis module 3; the analysis module 3 is used for analyzing the health data processed by the data processing module 2 and forming a data perspective view to be transmitted to the monitoring display module 4; the health trend prediction module 5 is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module 6 is used for determining the remaining service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module 5 is correct, making a suggested operation strategy for prolonging the service life and transmitting the suggested operation strategy to the monitoring display module 4.

Preferably, the health data comprises boiler wall temperature, boiler wall thickness, creep, metallographic phase.

The embodiment of the invention not only can carry out real-time monitoring and early warning according to the detected health data, but also can carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously can carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technical personnel to select, thereby solving the technical problems.

Preferably, the data processing module 2 is configured to perform AD conversion on the health data, filter the health data, and remove useless health data.

In the preferred embodiment, the data processing module 2 is arranged to screen the health data, so that the speed and the precision of data analysis are improved.

Preferably, the service life evaluation module 6 comprises a data preparation module 61 and a service life analysis and prediction submodule 62, wherein the data preparation submodule 61 is used for determining an actually measured typical load spectrum of the boiler, crack positions and sizes of actual cracks on the boiler, and performing geometric simplification classification on the various cracks; the life analysis and prediction submodule 62 is configured to perform a fatigue test on a material of the boiler, obtain a fatigue crack propagation rate curve of the material corresponding to each crack, perform crack propagation analysis on the actually measured typical load spectrum, the crack position and size of each actual crack, and the fatigue crack propagation rate curve of each crack, determine a crack propagation life cycle number corresponding to each crack, determine an estimated value of a remaining fatigue life of the corresponding crack according to the crack propagation life cycle number, and finally determine an estimated value of the remaining fatigue life of the boiler.

The present preferred embodiment builds the structural framework of the life assessment module 6.

Preferably, the set of estimated values of residual fatigue life corresponding to the crack i ═ 1,2, … m is defined as { P }₁,P₂,…,P_iH, an estimated value P of the residual fatigue life of the boiler_ZThen it is:

P_Z＝min_i＝1,2,…m{P₁,P₂,…,P_i}。

the optimal embodiment determines the relationship between the residual fatigue life of the boiler and the residual fatigue life of each actual crack of the boiler, adopts the minimum fatigue life of the actual crack as the residual fatigue life of the boiler, accords with the wooden barrel theory, and has high accuracy.

Preferably, the performing a fatigue test on the material of the boiler to obtain a fatigue crack propagation rate curve of the material corresponding to various cracks includes:

(1) calculating stress intensity factor amplitude of various cracks, considering that a plastic deformation area of a crack tip point has a decisive influence on fatigue fracture of a material, enabling the plastic area of the crack tip to be equivalent to a homogeneous inclusion containing phase change strain, and defining the stress intensity factor amplitude delta K_pcThe calculation formula of (2) is as follows:

${\mathrm{\ΔK}}_{pc}=\left\{\begin{array}{cc}{K}_{pc}^{\max }-K_{yc}-{\mathrm{\ΔK}}_{sc},& R\≤0\\ K_{pc}^{\max }-K_{pc}^{\min },& R>0\end{array}\right.$

in the formula

$\begin{array}{c}{\mathrm{\ΔK}}_{sc}=\frac{1}{2\sqrt{2\mathrm{\π}}}{\∫}_A{r}^{-3/2}\[\frac{K_{yc}}{\sqrt{2\mathrm{\π}r}}(3{\sin }^2\mathrm{\α}\cos \mathrm{\α}+2\cos \frac{\mathrm{\α}}{2}\cos \frac{3\mathrm{\α}}{2})\\ +3({\mathrm{\σ}}_{11}-{\mathrm{\σ}}_{22})\sin \mathrm{\α}\sin \frac{5\mathrm{\α}}{2}-6{\mathrm{\σ}}_{12}\sin \mathrm{\α}\cos \frac{5\mathrm{\α}}{2}-({\mathrm{\σ}}_{11}+{\mathrm{\σ}}_{22})\cos \frac{3\mathrm{\α}}{2}\]dA\end{array}$

Wherein,for the plastically corrected stress intensity factor value calculated from the maximum load in the fatigue cycle load,for plastically corrected stress intensity factor value, K, calculated from minimum load in fatigue cyclic loading_ycCalculated from the load at which the crack opens completely, Δ K, is the stress intensity factor under far field action_scRepresenting the increase in the stress intensity factor induced in the plastic zone of the crack tip, A being the area of the plastic zone surrounding the crack tip, which includes the plastic deformation tail zone generated during crack propagation, σ₁₁、σ₁₂、σ₂₂For stresses in the plastic zone of the crack tip, by aligning the crack tipObtaining the stress field of the plastic zone by finite element calculation and analysis, wherein R is the ratio of tensile load to compressive load;

(2) constructing fatigue crack propagation rate curves of various cracks, taking a Paris formula as a basis, considering the influence of temperature on the fatigue crack propagation rate, and defining a correction calculation formula of the fatigue crack propagation rate as follows:

T＜0℃OR T>T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M$

0℃≤T≤T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{\left({\mathrm{\ΔK}}_{pc}\right)}^M$

wherein T is the test temperature, T_maxTo a set maximum temperature, T_maxThe value range of (A) is [35 ℃,40 DEG C]A is the crack propagation length, N is the cycle number, C and M are the material constants, Δ K_TTo fit a non-positiveThe abnormal temperature fracture threshold value obtained by analyzing the crack propagation performance curved surface at normal temperature reflects the influence of temperature on the propagation rate, and delta K_TThe value range of (A) should satisfy [0, Δ K ]_pc)。

The preferred embodiment defines a calculation formula of the stress intensity factor amplitude delta K _ pc, considers that a plastic deformation area of a crack tip point has a decisive influence on the fatigue fracture of a material, and enables the crack tip plastic area to be equivalent to a homogeneous inclusion containing phase change strain, so that the defined stress intensity factor amplitude delta K _ pc can be well used as a reasonable mechanical parameter to quantitatively analyze the influence of the crack tip plastic area on the stress intensity factor; on the basis of the Paris formula, the influence of temperature on the fatigue crack propagation rate is considered, a correction calculation formula of the fatigue crack propagation rate is defined, the calculation precision is improved, and the method is simple and practical.

Preferably, the calculation formula of the crack propagation life cycle number N is as follows:

$N={\∫}_{a_0}^{a_c}\frac{1}{C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M}$

the preferred embodiment determines a calculation formula of the crack propagation life cycle number N, and improves the speed of life prediction.

Maximum temperature T of the above-described embodiment of this application scenario_maxThe set temperature is 38 ℃, and the accuracy of the boiler fatigue life prediction is relatively improved by 12 percent.

Application scenario 4

Referring to fig. 1 and fig. 2, the intelligent detection device for an industrial boiler according to an embodiment of the application scenario includes a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, and a life evaluation module 6; the detection module 1 is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module 2 and the analysis module 3; the analysis module 3 is used for analyzing the health data processed by the data processing module 2 and forming a data perspective view to be transmitted to the monitoring display module 4; the health trend prediction module 5 is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module 6 is used for determining the remaining service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module 5 is correct, making a suggested operation strategy for prolonging the service life and transmitting the suggested operation strategy to the monitoring display module 4.

Preferably, the health data comprises boiler wall temperature, boiler wall thickness, creep, metallographic phase.

The embodiment of the invention not only can carry out real-time monitoring and early warning according to the detected health data, but also can carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously can carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technical personnel to select, thereby solving the technical problems.

Preferably, the data processing module 2 is configured to perform AD conversion on the health data, filter the health data, and remove useless health data.

In the preferred embodiment, the data processing module 2 is arranged to screen the health data, so that the speed and the precision of data analysis are improved.

Preferably, the service life evaluation module 6 comprises a data preparation module 61 and a service life analysis and prediction submodule 62, wherein the data preparation submodule 61 is used for determining an actually measured typical load spectrum of the boiler, crack positions and sizes of actual cracks on the boiler, and performing geometric simplification classification on the various cracks; the life analysis and prediction submodule 62 is configured to perform a fatigue test on a material of the boiler, obtain a fatigue crack propagation rate curve of the material corresponding to each crack, perform crack propagation analysis on the actually measured typical load spectrum, the crack position and size of each actual crack, and the fatigue crack propagation rate curve of each crack, determine a crack propagation life cycle number corresponding to each crack, determine an estimated value of a remaining fatigue life of the corresponding crack according to the crack propagation life cycle number, and finally determine an estimated value of the remaining fatigue life of the boiler.

The present preferred embodiment builds the structural framework of the life assessment module 6.

Preferably, the set of estimated values of residual fatigue life corresponding to the crack i ═ 1,2, … m is defined as { P }₁,P₂,…,P_iH, an estimated value P of the residual fatigue life of the boiler_ZThen it is:

P_Z＝min_i＝1,2,…m{P₁,P₂,…,P_i}。

the optimal embodiment determines the relationship between the residual fatigue life of the boiler and the residual fatigue life of each actual crack of the boiler, adopts the minimum fatigue life of the actual crack as the residual fatigue life of the boiler, accords with the wooden barrel theory, and has high accuracy.

Preferably, the performing a fatigue test on the material of the boiler to obtain a fatigue crack propagation rate curve of the material corresponding to various cracks includes:

(1) calculating stress intensity factor amplitude of various cracks, considering that a plastic deformation area of a crack tip point has a decisive influence on fatigue fracture of a material, enabling the plastic area of the crack tip to be equivalent to a homogeneous inclusion containing phase change strain, and defining the stress intensity factor amplitude delta K_pcThe calculation formula of (2) is as follows:

${\mathrm{\ΔK}}_{pc}=\left\{\begin{array}{cc}{K}_{pc}^{\max }-K_{yc}-{\mathrm{\ΔK}}_{sc},& R\≤0\\ K_{pc}^{\max }-K_{pc}^{\min },& R>0\end{array}\right.$

in the formula

$\begin{array}{c}{\mathrm{\ΔK}}_{sc}=\frac{1}{2\sqrt{2\mathrm{\π}}}{\∫}_A{r}^{-3/2}\[\frac{K_{yc}}{\sqrt{2\mathrm{\π}r}}(3{\sin }^2\mathrm{\α}\cos \mathrm{\α}+2\cos \frac{\mathrm{\α}}{2}\cos \frac{3\mathrm{\α}}{2})\\ +3({\mathrm{\σ}}_{11}-{\mathrm{\σ}}_{22})\sin \mathrm{\α}\sin \frac{5\mathrm{\α}}{2}-6{\mathrm{\σ}}_{12}\sin \mathrm{\α}\cos \frac{5\mathrm{\α}}{2}-({\mathrm{\σ}}_{11}+{\mathrm{\σ}}_{22})\cos \frac{3\mathrm{\α}}{2}\]dA\end{array}$

Wherein,for the plastically corrected stress intensity factor value calculated from the maximum load in the fatigue cycle load,for plastically corrected stress intensity factor value, K, calculated from minimum load in fatigue cyclic loading_ycCalculated from the load at which the crack opens completely, Δ K, is the stress intensity factor under far field action_scRepresenting the increase in the stress intensity factor induced in the plastic zone of the crack tip, A being the area of the plastic zone surrounding the crack tip, which includes the plastic deformation tail zone generated during crack propagation, σ₁₁、σ₁₂、σ₂₂The stress in the plastic zone of the crack tip is obtained by finite element calculation and analysis of a stress field of the plastic zone of the crack tip, and R is the ratio of tensile load to compressive load;

(2) constructing fatigue crack propagation rate curves of various cracks, taking a Paris formula as a basis, considering the influence of temperature on the fatigue crack propagation rate, and defining a correction calculation formula of the fatigue crack propagation rate as follows:

T＜0℃OR T>T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M$

0℃≤T≤T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{\left({\mathrm{\ΔK}}_{pc}\right)}^M$

wherein T is the test temperature, T_maxTo a set maximum temperature, T_maxThe value range of (A) is [35 ℃,40 DEG C]A is the crack propagation length, N is the cycle number, C and M are the material constants, Δ K_TThe influence of temperature on the propagation rate is reflected for the abnormal temperature fracture threshold value obtained by analysis after fitting the crack propagation performance curved surface at the abnormal temperature, and the delta K_TThe value range of (A) should satisfy [0, Δ K ]_pc)。

The preferred embodiment defines a calculation formula of the stress intensity factor amplitude delta K _ pc, considers that a plastic deformation area of a crack tip point has a decisive influence on the fatigue fracture of a material, and enables the crack tip plastic area to be equivalent to a homogeneous inclusion containing phase change strain, so that the defined stress intensity factor amplitude delta K _ pc can be well used as a reasonable mechanical parameter to quantitatively analyze the influence of the crack tip plastic area on the stress intensity factor; on the basis of the Paris formula, the influence of temperature on the fatigue crack propagation rate is considered, a correction calculation formula of the fatigue crack propagation rate is defined, the calculation precision is improved, and the method is simple and practical.

Preferably, the calculation formula of the crack propagation life cycle number N is as follows:

$N={\∫}_{a_0}^{a_c}\frac{1}{C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M}$

the preferred embodiment determines a calculation formula of the crack propagation life cycle number N, and improves the speed of life prediction.

Maximum temperature T of the above-described embodiment of this application scenario_maxThe set temperature is 39 ℃, and the accuracy of the boiler fatigue life prediction is relatively improved by 11%.

Application scenario 5

Referring to fig. 1 and fig. 2, the intelligent detection device for an industrial boiler according to an embodiment of the application scenario includes a detection module 1, a data processing module 2, an analysis module 3, a monitoring display module 4, a health trend prediction module 5, and a life evaluation module 6; the detection module 1 is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module 2 and the analysis module 3; the analysis module 3 is used for analyzing the health data processed by the data processing module 2 and forming a data perspective view to be transmitted to the monitoring display module 4; the health trend prediction module 5 is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module 6 is used for determining the remaining service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module 5 is correct, making a suggested operation strategy for prolonging the service life and transmitting the suggested operation strategy to the monitoring display module 4.

Preferably, the health data comprises boiler wall temperature, boiler wall thickness, creep, metallographic phase.

The embodiment of the invention not only can carry out real-time monitoring and early warning according to the detected health data, but also can carry out operation condition analysis of the boiler, formulate different operation strategies according to different operation conditions, protect the boiler in a safe operation environment in real time, simultaneously can carry out future trend prediction according to the health data, and carry out reverse verification on the result of the future trend prediction according to the estimated residual fatigue life, thereby formulating a corresponding operation strategy for prolonging the service life and providing the operation strategy for technical personnel to select, thereby solving the technical problems.

Preferably, the data processing module 2 is configured to perform AD conversion on the health data, filter the health data, and remove useless health data.

In the preferred embodiment, the data processing module 2 is arranged to screen the health data, so that the speed and the precision of data analysis are improved.

Preferably, the service life evaluation module 6 comprises a data preparation module 61 and a service life analysis and prediction submodule 62, wherein the data preparation submodule 61 is used for determining an actually measured typical load spectrum of the boiler, crack positions and sizes of actual cracks on the boiler, and performing geometric simplification classification on the various cracks; the life analysis and prediction submodule 62 is configured to perform a fatigue test on a material of the boiler, obtain a fatigue crack propagation rate curve of the material corresponding to each crack, perform crack propagation analysis on the actually measured typical load spectrum, the crack position and size of each actual crack, and the fatigue crack propagation rate curve of each crack, determine a crack propagation life cycle number corresponding to each crack, determine an estimated value of a remaining fatigue life of the corresponding crack according to the crack propagation life cycle number, and finally determine an estimated value of the remaining fatigue life of the boiler.

The present preferred embodiment builds the structural framework of the life assessment module 6.

Preferably, the set of estimated values of residual fatigue life corresponding to the crack i ═ 1,2, … m is defined as { P }₁,P₂,…,P_iH, an estimated value P of the residual fatigue life of the boiler_zThen it is:

P_z＝min_i＝1,2,…m{P₁,P₂,…,P_i}。

the optimal embodiment determines the relationship between the residual fatigue life of the boiler and the residual fatigue life of each actual crack of the boiler, adopts the minimum fatigue life of the actual crack as the residual fatigue life of the boiler, accords with the wooden barrel theory, and has high accuracy.

Preferably, the performing a fatigue test on the material of the boiler to obtain a fatigue crack propagation rate curve of the material corresponding to various cracks includes:

(1) calculating stress intensity factor amplitude of various cracks, considering that a plastic deformation area of a crack tip point has a decisive influence on fatigue fracture of a material, enabling the plastic area of the crack tip to be equivalent to a homogeneous inclusion containing phase change strain, and defining the stress intensity factor amplitude delta K_pcThe calculation formula of (2) is as follows:

${\mathrm{\ΔK}}_{pc}=\left\{\begin{array}{cc}{K}_{pc}^{\max }-K_{yc}-{\mathrm{\ΔK}}_{sc},& R\≤0\\ K_{pc}^{\max }-K_{pc}^{\min },& R>0\end{array}\right.$

in the formula

$\begin{array}{c}{\mathrm{\ΔK}}_{sc}=\frac{1}{2\sqrt{2\mathrm{\π}}}{\∫}_A{r}^{-3/2}\[\frac{K_{yc}}{\sqrt{2\mathrm{\π}r}}(3{\sin }^2\mathrm{\α}\cos \mathrm{\α}+2\cos \frac{\mathrm{\α}}{2}\cos \frac{3\mathrm{\α}}{2})\\ +3({\mathrm{\σ}}_{11}-{\mathrm{\σ}}_{22})\sin \mathrm{\α}\sin \frac{5\mathrm{\α}}{2}-6{\mathrm{\σ}}_{12}\sin \mathrm{\α}\cos \frac{5\mathrm{\α}}{2}-({\mathrm{\σ}}_{11}+{\mathrm{\σ}}_{22})\cos \frac{3\mathrm{\α}}{2}\]dA\end{array}$

Wherein,for the plastically corrected stress intensity factor value calculated from the maximum load in the fatigue cycle load,for plastically corrected stress intensity factor value, K, calculated from minimum load in fatigue cyclic loading_ycFor strong stress under far field actionDegree factor, calculated from the load at full crack opening, Δ K_scRepresenting the increase in the stress intensity factor induced in the plastic zone of the crack tip, A being the area of the plastic zone surrounding the crack tip, which includes the plastic deformation tail zone generated during crack propagation, σ₁₁、σ₁₂、σ₂₂The stress in the plastic zone of the crack tip is obtained by finite element calculation and analysis of a stress field of the plastic zone of the crack tip, and R is the ratio of tensile load to compressive load;

(2) constructing fatigue crack propagation rate curves of various cracks, taking a Paris formula as a basis, considering the influence of temperature on the fatigue crack propagation rate, and defining a correction calculation formula of the fatigue crack propagation rate as follows:

T＜0℃OR T>T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M$

0℃≤T≤T_maxwhen the temperature of the water is higher than the set temperature,

$\frac{da}{dN}=C{\left({\mathrm{\ΔK}}_{pc}\right)}^M$

wherein T is the test temperature, T_maxTo a set maximum temperature, T_maxThe value range of (A) is [35 ℃,40 DEG C]A is the crack propagation length, N is the cycle number, C and M are the material constants, Δ K_TThe influence of temperature on the propagation rate is reflected for the abnormal temperature fracture threshold value obtained by analysis after fitting the crack propagation performance curved surface at the abnormal temperature, and the delta K_TThe value range of (A) should satisfy [0, Δ K ]_pc)。

The preferred embodiment defines a calculation formula of the stress intensity factor amplitude delta K _ pc, considers that a plastic deformation area of a crack tip point has a decisive influence on the fatigue fracture of a material, and enables the crack tip plastic area to be equivalent to a homogeneous inclusion containing phase change strain, so that the defined stress intensity factor amplitude delta K _ pc can be well used as a reasonable mechanical parameter to quantitatively analyze the influence of the crack tip plastic area on the stress intensity factor; on the basis of the Paris formula, the influence of temperature on the fatigue crack propagation rate is considered, a correction calculation formula of the fatigue crack propagation rate is defined, the calculation precision is improved, and the method is simple and practical.

Preferably, the calculation formula of the crack propagation life cycle number N is as follows:

$N={\∫}_{a_0}^{a_c}\frac{1}{C{({\mathrm{\ΔK}}_{pc}-{\mathrm{\ΔK}}_T)}^M}$

the preferred embodiment determines a calculation formula of the crack propagation life cycle number N, and improves the speed of life prediction.

Maximum temperature T of the above-described embodiment of this application scenario_maxThe set temperature is 40 ℃, and the accuracy of the boiler fatigue life prediction is relatively improved by 10 percent.

Finally, it should be noted that the above application scenarios are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred application scenarios, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims (3)

1. An intelligent detection device for an industrial boiler is characterized by comprising a detection module, a data processing module, an analysis module, a monitoring display module, a health trend prediction module and a service life evaluation module; the detection module is used for detecting various health data of the operation of the industrial boiler and is sequentially connected with the data processing module and the analysis module; the analysis module is used for analyzing the health data processed by the data processing module, forming a data perspective and transmitting the data perspective to the monitoring display module; the health trend prediction module is used for analyzing the future operation trend according to the off-line and on-line data by adopting a fuzzy neural network method; the service life evaluation module is used for determining the residual service life of the industrial boiler, reversely verifying whether the trend analysis of the health trend prediction module is correct, making a recommended operation strategy for prolonging the service life and transmitting the recommended operation strategy to the monitoring display module.

2. The intelligent detecting device for the industrial boiler as claimed in claim 1, wherein the health data comprises boiler wall temperature, boiler wall thickness, creep and metallographic phase.

3. The intelligent detection device for the industrial boiler as claimed in claim 2, wherein the data processing module is used for performing AD conversion on the health data, screening the health data and eliminating useless health data.