CN114611424A - Large-scale fan blade service life prediction method fusing voiceprint data and CAE algorithm - Google Patents

Large-scale fan blade service life prediction method fusing voiceprint data and CAE algorithm Download PDF

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CN114611424A
CN114611424A CN202210208681.5A CN202210208681A CN114611424A CN 114611424 A CN114611424 A CN 114611424A CN 202210208681 A CN202210208681 A CN 202210208681A CN 114611424 A CN114611424 A CN 114611424A
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谢文锋
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Haifang Shanghai Technology Co ltd
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Abstract

The invention belongs to the technical field of wind power generation, and particularly relates to a method for predicting the service life of a large fan blade by fusing voiceprint data and a CAE algorithm, which specifically comprises the following steps: establishing a fluidics simulation model comprising an offshore complex wind field numerical model and a fan blade aerodynamic model; establishing a structural mechanics simulation model, performing statics and dynamics simulation of the fan blade based on a structural mechanics mechanism, and analyzing structural stress distribution and fatigue of the marine large-scale fan blade; establishing a coupling mechanism model, a coupling fluid mechanics model and a structural mechanics model; establishing a voiceprint data model, and training a fault prediction model after extracting the characteristics of voiceprint data by collecting the voice data in the running process of the fan; and the mechanism model and the data model are coupled to realize online real-time fault prediction and diagnosis of the large-scale offshore wind turbine blade. Through a coupling mechanism model simulation result database and a data model based on sound data analysis, the accuracy and consistency of prediction are greatly improved.

Description

Large fan blade service life prediction method fusing voiceprint data and CAE algorithm
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a method for predicting the service life of a large fan blade by fusing voiceprint data and a CAE algorithm.
Background
Since offshore wind farms are far from the continent, many remote health monitoring systems are applied to reduce the risk of failure, identify the occurrence of failure and improve the performance by analyzing the data measured by the wind turbines. Supervisory control and data acquisition (SCADA) systems and Condition Monitoring Systems (CMSs) are considered to be the two most common structural health monitoring approaches.
SCADA systems are commonly used to monitor several parameters associated with different wind turbine components (e.g., structural vibration levels, temperature, bearings, tower and drive train accelerations). Researchers make full use of data collected by the SCADA system to perform further analysis of parameter identification, fault diagnosis and risk prediction. The CMS is another important tool, and can continuously monitor and measure data such as vibration, load, wind speed and temperature of an Offshore Wind Turbine (OWT) structure at a higher frequency (usually exceeding 50Hz) to check whether the wind turbine is operating correctly, so as to achieve the purposes of gear tooth damage detection, fault diagnosis and alarm reporting based on time domain and frequency domain analysis.
The main technologies adopted at present are:
1) the SCADA system and CMS are considered to be the two most common health monitoring systems in the OWT architecture. However, both systems attempt to integrate with each other to significantly improve the efficiency and accuracy of OWT structural health monitoring. The two systems both take detection data as an analysis object, lack fatigue mechanism data support, and the design and optimization of the integral monitoring system combined with a mechanism model will be a future development trend.
2) Currently, most research is focused on new techniques for vibration signal feature information extraction and identification. However, few effective metrics and security assessment systems have been constructed that can integrate multiple identification techniques to assess the operational security and stability of OWTs.
The effective means for improving the service life prediction of the large-scale wind turbine generator, especially the large-scale fan blade, by the online predictive maintenance algorithm of the offshore fan based on the CAE mechanism model and the voiceprint data model developed by the patent technology.
Disclosure of Invention
The invention aims to provide a method for predicting the service life of a large fan blade by fusing voiceprint data and a CAE algorithm, overcomes the defects of the prior art, and greatly improves the accuracy and consistency of prediction by a coupling mechanism model simulation result database and a data model based on sound data analysis.
In order to solve the problems, the technical scheme adopted by the invention is as follows:
a large fan blade service life prediction method fusing voiceprint data and a CAE algorithm comprises the following steps:
and (3) fluid mechanics simulation analysis: analyzing the handling conditions of the large fan blade in different wind fields, acquiring data, and constructing a marine complex wind field data model and a fan blade aerodynamic model;
structural mechanics simulation analysis: analyzing structural stress distribution and fatigue of the marine large-scale fan blade based on statics and dynamics simulation of the fan blade, and constructing a structural mechanics simulation model to perform prediction analysis on the service life and fracture of the blade;
establishing a coupling mechanism model: coupling fluid mechanics simulation and structural mechanics simulation, simulating and analyzing blade stress, fatigue, fracture and mutual influence of the marine large-scale fan blade in different complex wind fields, and constructing a marine large-scale fan blade mechanics fault prediction mechanism model;
establishing a voiceprint data model: acquiring sound data in the operation process of the fan blade, extracting the characteristics of the voiceprint data, constructing a fault prediction data model based on the voiceprint data, and training the fault prediction data model by adopting a machine learning algorithm;
coupling mechanism model and data model: and coupling the failure prediction mechanism model with the failure prediction data model, and performing grading early warning prediction on the failure of the fan blade.
Further, the fluid mechanics simulation analysis specifically includes:
(1) constructing a complex wind field simulation model and wind field dynamic simulation based on computational fluid mechanics simulation, and simulating the forced dynamic change process of the fan blade in the complex wind field;
(2) establishing a relation between a wind field and blade aerodynamic force and a relation between blade motion and blade aerodynamic force, and analyzing working condition factors and design factors influencing blade stress;
(3) and analyzing the anisotropic characteristic and periodicity of aerodynamic force in the movement process of the fan blade.
Further, the building of the complex wind field simulation model and the wind field dynamic simulation includes:
establishing a wind field database according to climate data of an area where a large offshore wind turbine is installed;
and extracting wind field characteristic parameters from the database to be used as input of wind field simulation, and performing dynamic simulation on the wind field of the area by using a computational fluid method to simulate real severe weather conditions.
Further, the structural mechanics simulation analysis includes:
(1) analyzing the strength and deformation of the structural component of the large fan blade based on the principle of statics simulation;
(2) analyzing the strength and deformation of the structural component of the large fan blade in the movement process based on a multi-body dynamics simulation principle;
(3) analyzing vibration and stability in the operation process based on a dynamic simulation principle;
(4) analyzing the operation failure problem based on the buckling simulation principle;
(5) and analyzing the running state of the equipment based on a fatigue simulation principle, and determining the maintenance problem of the blade.
Further, to blade atress, fatigue, fracture and influence each other under different complicated wind fields of marine large-scale fan blade simulation and analysis, specifically include:
(1) obtaining the natural frequency and the vibration mode change rule of the healthy blade and the crack fault blade by using modal test and simulation analysis, and analyzing and comparing the interaction between the blade surface aerodynamic force and each order vibration mode of the blade and the interaction between the crack fault position and the aerodynamic force;
(2) analyzing the distribution rule of aerodynamic force in a fluid field under different working conditions and different wind fields, analyzing and comparing the distribution condition of the aerodynamic force on the surface of the blade, providing a load base for the dynamic analysis of the blade, and analyzing the change rule of fluid flow in the fluid field under different parameters by utilizing a flow diagram of the fluid field of the ventilator;
(4) the distribution rules of stress, strain and total deformation of healthy blades and cracked and failed blades of the fan under different wind fields are simulated, and the influence rules of cracks on the strain and the stress of the blades and the expansion trend of the cracks of the blades are researched through simulation analysis.
Further, the constructing a fault prediction data model based on voiceprint data comprises:
(1) developing a voiceprint data processing method based on a short-time Fourier transform and time-frequency transform algorithm;
(2) developing a fault sound feature extraction algorithm;
(3) and (3) training a voiceprint data model based on a machine learning algorithm and developing a fault prediction algorithm.
Further, the coupling the failure prediction mechanism model and the failure prediction data model specifically includes:
(1) based on a mechanism model, integrating dynamic simulation data analysis, developing a mechanical fault diagnosis model, and simultaneously providing a large amount of data to a data analysis model;
(2) establishing a fan blade state parameter database based on a mechanism model research result and a data model analysis method;
(3) and after the characteristics of the fan blade state data are extracted by applying a big data algorithm, training a fault prediction model by using a machine learning algorithm to realize early warning prediction of fan blade faults.
Compared with the prior art, the invention has the following beneficial effects:
according to the method, the accuracy and consistency of prediction are greatly improved through a coupling mechanism model simulation result database and a data model based on sound data analysis, short-medium-long-term 3-level early warning is realized, and a data basis is provided for safe operation and maintenance of the large fan blade.
Drawings
FIG. 1 is a fluid field of a large fan blade.
Fig. 2 is a structural mechanical analysis diagram of a large fan blade.
FIG. 3 is a simulated view of aerodynamic force distribution in a fluid field of a large fan blade.
FIG. 4 is a simulation of a voiceprint data model.
FIG. 5 is a failure rate prediction line graph for a large fan blade.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention discloses a method for predicting the service life of a large fan blade by fusing voiceprint data and a CAE algorithm.
1. Fluid mechanics simulation analysis: analyzing the handling conditions of the large fan blade in different wind fields, acquiring data, and constructing a marine complex wind field data model and a fan blade aerodynamic model;
the method specifically comprises the following steps:
(1) establishing a wind field database according to climate data of a region where a large offshore wind turbine is installed on the basis of computational fluid mechanics simulation; extracting wind field characteristic parameters from a database as input of wind field simulation, performing dynamic simulation on the wind field in the area by using a computational fluid method, simulating real severe weather conditions, constructing a complex wind field simulation model and wind field dynamic simulation, and simulating a stress dynamic change process of a fan blade in a complex wind field;
(2) simulating and analyzing a wind field and blade stress in the blade movement process by using a computational fluid mechanics method; stress distribution and dynamic force distribution conditions of the blades in various complex wind field environments are mainly analyzed; establishing a relation between a wind field and the aerodynamic force of the blade and a relation between the motion of the blade and the aerodynamic force of the blade, and analyzing working condition factors and design factors influencing the stress of the blade;
(3) and analyzing the anisotropic characteristic and periodicity of aerodynamic force in the movement process of the fan blade.
The method mainly provides boundary conditions for aerodynamic load calculation of the fan blade.
2. Structural mechanics simulation analysis: analyzing structural stress distribution and fatigue of the marine large-scale fan blade based on statics and dynamics simulation of the fan blade, and constructing a structural mechanics simulation model to perform prediction analysis on the service life and fracture of the blade;
the structural mechanics simulation analysis comprises the following steps:
(1) analyzing the strength and deformation of the structural component of the large fan blade based on the principle of statics simulation;
(2) analyzing the strength and deformation of the structural component of the large fan blade in the movement process based on a multi-body dynamics simulation principle;
(3) analyzing vibration and stability in the operation process based on a dynamic simulation principle;
(4) analyzing the operation failure problem based on the buckling simulation principle;
(5) and analyzing the running state of the equipment based on a fatigue simulation principle, and determining the maintenance problem of the blade.
3. Establishing a coupling mechanism model: coupling fluid mechanics simulation and structural mechanics simulation, simulating and analyzing blade stress, fatigue, fracture and mutual influence of the marine large-scale fan blade in different complex wind fields, and constructing a marine large-scale fan blade mechanics fault prediction mechanism model;
the method specifically comprises the following steps:
(1) obtaining the natural frequency and the vibration mode change rule of the healthy blade and the crack fault blade by using modal test and simulation analysis, and analyzing and comparing the interaction between the blade surface aerodynamic force and each order vibration mode of the blade and the interaction between the crack fault position and the aerodynamic force;
(2) analyzing the distribution rule of aerodynamic force in a fluid field under different working conditions and different wind fields, analyzing and comparing the distribution condition of the aerodynamic force on the surface of the blade, providing a load base for the dynamic analysis of the blade, and analyzing the change rule of fluid flow in the fluid field under different parameters by utilizing a flow diagram of the fluid field of the ventilator;
(4) the distribution rules of stress, strain and total deformation of healthy blades and cracked and failed blades of the fan under different wind fields are simulated, and the influence rules of cracks on the strain and the stress of the blades and the expansion trend of the cracks of the blades are researched through simulation analysis.
4. Establishing a voiceprint data model: acquiring sound data in the operation process of the fan blade, extracting the characteristics of the voiceprint data, constructing a fault prediction data model based on the voiceprint data, and training the fault prediction data model by adopting a machine learning algorithm;
the fault prediction data model based on the voiceprint data is constructed by the following steps:
(1) developing a voiceprint data processing method based on a short-time Fourier transform and time-frequency transform algorithm;
(2) developing a fault sound feature extraction algorithm;
(3) and (3) training a voiceprint data model based on a machine learning algorithm and developing a fault prediction algorithm.
5. Coupling mechanism model and data model: coupling a fault prediction mechanism model with a fault prediction data model, establishing a bidirectional fluid-solid coupling mathematical model comprising a fan blade fluid model and a solid model, and performing simulation analysis on the relationship between a fan fluid field and a blade mode and the inherent frequency of the blade; by using the built fluid-solid coupling simulation model, the change rules of aerodynamic force of a ventilator fluid field and blade cracks under the working conditions of different rotating speeds and wind resistance are simulated and analyzed, so that the fault of the fan blade is subjected to graded early warning prediction;
the coupling of the failure prediction mechanism model and the failure prediction data model specifically comprises the following steps:
(1) based on a mechanism model, integrating dynamic simulation data analysis, developing a mechanical fault diagnosis model, and simultaneously providing a large amount of data to a data analysis model;
(2) establishing a fan blade state parameter database based on a mechanism model research result and a data model analysis method;
(3) and after the characteristics of the fan blade state data are extracted by applying a big data algorithm, training a fault prediction model by using a machine learning algorithm to realize early warning prediction of fan blade faults.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (7)

1. A method for predicting the service life of a large fan blade by fusing voiceprint data and a CAE algorithm is characterized by comprising the following steps of: the method comprises the following steps:
fluid mechanics simulation analysis: analyzing the handling conditions of the large fan blade in different wind fields, acquiring data, and constructing a marine complex wind field data model and a fan blade aerodynamic model;
structural mechanics simulation analysis: analyzing structural stress distribution and fatigue of the marine large-scale fan blade based on statics and dynamics simulation of the fan blade, and constructing a structural mechanics simulation model to perform prediction analysis on the service life and fracture of the blade;
establishing a coupling mechanism model: coupling fluid mechanics simulation and structural mechanics simulation, simulating and analyzing blade stress, fatigue, fracture and mutual influence of the marine large-scale fan blade in different complex wind fields, and constructing a marine large-scale fan blade mechanics fault prediction mechanism model;
establishing a voiceprint data model: acquiring sound data in the running process of the fan blade, extracting the characteristics of the voiceprint data, constructing a fault prediction data model based on the voiceprint data, and training the fault prediction data model by adopting a machine learning algorithm;
coupling mechanism model and data model: and coupling the failure prediction mechanism model with the failure prediction data model, and performing grading early warning prediction on the failure of the fan blade.
2. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 1, wherein the method comprises the following steps: the fluid mechanics simulation analysis specifically comprises:
(1) constructing a complex wind field simulation model and wind field dynamic simulation based on computational fluid mechanics simulation, and simulating the forced dynamic change process of the fan blade in the complex wind field;
(2) establishing a relation between a wind field and blade aerodynamic force and a relation between blade motion and blade aerodynamic force, and analyzing working condition factors and design factors influencing blade stress;
(3) and analyzing the anisotropic characteristic and periodicity of aerodynamic force in the movement process of the fan blade.
3. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 2, wherein the method comprises the following steps: the method for constructing the complex wind field simulation model and the wind field dynamic simulation comprises the following steps:
establishing a wind field database according to climate data of an area where a large offshore wind turbine is installed;
and extracting wind field characteristic parameters from the database to be used as input of wind field simulation, and performing dynamic simulation on the wind field of the area by using a computational fluid method to simulate real severe weather conditions.
4. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 1, wherein the method comprises the following steps: the structural mechanics simulation analysis comprises:
(1) analyzing the strength and deformation of the structural component of the large fan blade based on the principle of statics simulation;
(2) analyzing the strength and deformation of the structural component of the large fan blade in the movement process based on a multi-body dynamics simulation principle;
(3) analyzing vibration and stability in the operation process based on a dynamic simulation principle;
(4) analyzing the operation failure problem based on the buckling simulation principle;
(5) and analyzing the running state of the equipment based on a fatigue simulation principle, and determining the maintenance problem of the blade.
5. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 1, wherein the method comprises the following steps: the method is characterized by simulating and analyzing blade stress, fatigue, fracture and mutual influence of the marine large-scale fan blade in different complex wind fields, and specifically comprises the following steps:
(1) obtaining the natural frequency and the vibration mode change rule of the healthy blade and the crack fault blade by using modal test and simulation analysis, and analyzing and comparing the interaction between the blade surface aerodynamic force and each order vibration mode of the blade and the interaction between the crack fault position and the aerodynamic force;
(2) analyzing the distribution rule of aerodynamic force in a fluid field under different working conditions and different wind fields, analyzing and comparing the distribution condition of the aerodynamic force on the surface of the blade, providing a load base for the dynamic analysis of the blade, and analyzing the change rule of fluid flow in the fluid field under different parameters by utilizing a flow diagram of the fluid field of the ventilator;
(4) the distribution rules of stress, strain and total deformation of healthy blades and cracked and failed blades of the fan under different wind fields are simulated, and the influence rules of cracks on the strain and the stress of the blades and the expansion trend of the cracks of the blades are researched through simulation analysis.
6. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 1, wherein the method comprises the following steps: the method for constructing the fault prediction data model based on the voiceprint data comprises the following steps:
(1) developing a voiceprint data processing method based on a short-time Fourier transform and time-frequency transform algorithm;
(2) developing a fault sound feature extraction algorithm;
(3) and (3) training a voiceprint data model based on a machine learning algorithm and developing a fault prediction algorithm.
7. The method for predicting the service life of the large fan blade by fusing the voiceprint data and the CAE algorithm according to claim 1, wherein the method comprises the following steps: the coupling of the failure prediction mechanism model and the failure prediction data model specifically comprises:
(1) based on a mechanism model, integrating dynamic simulation data analysis, developing a mechanical fault diagnosis model, and simultaneously providing a large amount of data to a data analysis model;
(2) establishing a fan blade state parameter database based on a mechanism model research result and a data model analysis method;
(3) and after the characteristics of the fan blade state data are extracted by applying a big data algorithm, training a fault prediction model by using a machine learning algorithm to realize early warning prediction of fan blade faults.
CN202210208681.5A 2022-03-04 2022-03-04 Large-scale fan blade service life prediction method fusing voiceprint data and CAE algorithm Pending CN114611424A (en)

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