CN-121983200-A - Flame-retardant-early warning-damage identification integrated intelligent coating system and construction method thereof

CN121983200ACN 121983200 ACN121983200 ACN 121983200ACN-121983200-A

Abstract

The invention provides a flame-retardant-early warning-damage identification integrated intelligent coating system and a construction method thereof, and belongs to the technical field of intelligent protection of high-end equipment and structural health monitoring. According to the invention, through collaborative design, functional components such as a copolymer matrix, thermoelectric fillers, piezoelectric fillers and the like of HEA and VS are integrated, and an intelligent recognition system is constructed by combining a K-means clustering algorithm and a convolutional neural network model, so that the functions of flame retardance, fire early warning and damage recognition are integrated. The principle is that the thermoelectric filler senses temperature change in the functional layer to generate a voltage signal, the piezoelectric filler captures a stress signal caused by structural deformation, the early warning and the accurate judgment of damage of a fire disaster are realized through intelligent model analysis, and meanwhile, flame propagation is inhibited by flame retardant components through a carbon layer blocking and free radical capturing mechanism. The invention realizes the cooperative optimization of the safety protection and the state monitoring of the high-end equipment structural member, obviously improves the safety reliability under complex working conditions, and provides a brand new technical scheme for the high-end equipment protection.

Inventors

YU TAO
WEI ZHIBIAO
ZHANG QIHANG
WANG XUANYE

Assignees

同济大学

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (10)

1. The construction method of the flame-retardant, early-warning and damage-identifying integrated intelligent coating system is characterized by comprising the following steps of: S10, uniformly mixing, dissolving and randomly copolymerizing hydroxyethyl acrylate, sodium vinylsulfonate and an initiator to obtain a flame-retardant coating solution; S20, adding thermoelectric filler and piezoelectric filler into the flame-retardant coating solution, and uniformly mixing to obtain functional coating slurry; s30, coating the functional coating slurry on a substrate, and drying to obtain a functional integrated coating; s40, setting electrodes on the functional integrated coating and electrically connecting the electrodes with a signal acquisition module, wherein the signal acquisition module is used for acquiring original electric signals generated by the functional integrated coating, The original electric signal at least comprises a thermoelectric signal generated by the thermoelectric filler due to temperature change and a piezoelectric signal generated by the piezoelectric filler due to structural deformation of the functional integrated coating; s50, constructing an intelligent recognition module and connecting the intelligent recognition module with the signal acquisition module, wherein the intelligent recognition module is configured to: firstly, the original electric signal acquired by the signal acquisition module is received, The raw electrical signals are then analyzed by an unsupervised clustering algorithm to establish a baseline of signal distribution under normal conditions and identify signals of abnormal events deviating from the baseline, Finally, carrying out depth feature extraction and pattern recognition on the signals of the abnormal events through a pre-trained deep learning model so as to distinguish the categories of the abnormal events and output a judging result of fire risk levels and/or structural damage types and corresponding degrees thereof; S60, the substrate, the function integrated coating, the signal acquisition module and the intelligent identification module are used as the flame-retardant, early-warning and damage identification integrated intelligent coating system.
2. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: in step S10, the molar ratio of hydroxyethyl acrylate to sodium vinylsulfonate is 1:1.
3. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: Wherein in step S10, the initiator comprises potassium persulfate, The mixing mode is as follows: 200W to 400W of power ultrasonic dispersion is carried out for 30min to 60min,600rpm to 1500rpm is stirred for 60min to 120min, The random copolymerization mode is water bath heating reflux.
4. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: Wherein in step S20, the thermoelectric filler comprises any one or more of bismuth telluride, MXene nano sheet or modified carbon nano tube, The piezoelectric filler comprises lead zirconate titanate.
5. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 4, wherein the method comprises the following steps: In the step S20, the mass ratio of the copolymer of hydroxyethyl acrylate and sodium vinylsulfonate to the thermoelectric filler to the piezoelectric filler is (10-60): 5-15): 8-20.
6. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: in the step S20, the uniform mixing mode is 600-1500 rpm stirring for 60min, and then ultrasonic dispersing for 30min.
7. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: in step S30, the functional integrated coating layer has a random copolymer generated by copolymerizing hydroxyethyl acrylate and sodium vinylsulfonate, and the random copolymer acts through a gas-solid phase synergistic flame retardant mechanism under a fire condition: in the gas phase, the decomposition products of sodium vinylsulfonate are used to trap free radicals required for the combustion chain reaction to provide intrinsic flame retardancy, In the solid phase, hydroxyethyl acrylate is dehydrated at high temperature, absorbs heat and reduces temperature to form a carbonization layer which is firmly attached and has a compact structure, and the carbonization layer is used for isolating heat and oxygen, so that the fireproof protection of the functional integrated coating is realized.
8. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: wherein, in step S30: The matrix comprises any one or more of fabric, foam and fiber reinforced composite, The thickness of the functional integrated coating is controlled to be 0.5-100 mu m, The drying mode is that the vacuum drying is carried out for 0.5 to 12 hours at the temperature of 60 to 90 ℃.
9. The method for constructing the flame retardant-early warning-damage identification integrated intelligent coating system according to claim 1, wherein the method comprises the following steps: wherein, in step S50, the unsupervised clustering algorithm comprises a K-means clustering algorithm, The deep learning model includes a convolutional neural network.
10. The flame-retardant, early-warning and damage-recognition integrated intelligent coating system is characterized by being constructed by the construction method of the flame-retardant, early-warning and damage-recognition integrated intelligent coating system according to any one of claims 1-9, and comprises the following steps: a base; the functional integrated coating is arranged on the substrate and comprises a flame-retardant coating and functional fillers dispersed in the flame-retardant coating, wherein the flame-retardant coating is a random copolymer of hydroxyethyl acrylate and sodium vinylsulfonate, and the functional fillers comprise the thermoelectric fillers and the piezoelectric fillers; the signal acquisition module is electrically connected with the functional integrated coating and is used for acquiring an original electric signal generated by the functional integrated coating; And the intelligent identification module is connected with the signal acquisition module and is used for processing the original electric signal so as to output a fire risk level and/or a structural damage type and a judgment result of the corresponding degree thereof.

Description

Flame-retardant-early warning-damage identification integrated intelligent coating system and construction method thereof Technical Field The invention belongs to the technical field of intelligent protection and structural health monitoring of high-end equipment, and particularly relates to a flame-retardant-early-warning-damage identification integrated intelligent coating system and a construction method thereof. Background With the rapid development of modern aviation, aerospace, ships, rail transit and other industries, various high-end equipment is continuously upgraded towards high speed, large-scale and intelligent directions, and the structural safety and reliability of the high-end equipment become core elements. In the aviation industry, for example, an aviation structural member of an aircraft always faces extremely complex and severe environmental tests in a long-term service process, wherein on one hand, the critical parts such as an engine cabin, a fuel oil system, a hydraulic pipeline, electrical equipment and the like have extremely high fire risks, and once a fire occurs, the fire can rapidly evolve into a catastrophic accident in a sealed cabin environment at a high altitude, and on the other hand, the aircraft inevitably generates hidden defects such as fatigue cracks, layering debonding, impact damage and the like under the action of complex loads such as a lifting cycle, airflow impact, temperature alternation, foreign object impact and the like of the aircraft, and if the damage cannot be found and evaluated in time, the structural integrity is directly threatened, and the catastrophic failure is caused. It is counted that aviation accidents caused by cabin fires and structural damages in the global world in recent years account for more than 35%, and economic losses and social effects caused by the aviation accidents are extremely serious. Therefore, the construction of a structural safety system with active protection, early warning and real-time monitoring capabilities has become a key technical bottleneck to be broken through in the aviation field. Traditional aviation protection technology has long adopted the mode of "function separation, independent deployment", namely realizes passive fire prevention through fire-retardant coating, realizes fire detection through independent sensor network, realizes the damage aassessment through periodic nondestructive test. This fragmentation solution exposes a number of systematic drawbacks in practical applications. In the aspect of flame retardation protection, the existing aviation flame retardation coating mostly adopts halogen-based, phosphorus-based or intumescent flame retardants, but the flame retardation coating still belongs to a passive response mechanism in nature, can only play a role after a fire disaster occurs, and lacks early sensing and active early warning capability on the fire disaster. More seriously, the traditional flame-retardant coating can release toxic smoke when decomposed at high temperature, which not only endangers the life safety of passengers, but also causes secondary damage to precise electronic equipment. In addition, the coating has a single function, and cannot meet structural health monitoring requirements, so that the protection system has high redundancy and high maintenance cost. In the fire early warning technology, current aircraft mainly rely on smoke detectors, infrared sensors and independent temperature sensing units. The smoke detector has the response hysteresis problem, the alarm can be triggered only by generating visible smoke after combustion, the fire is difficult to control, the infrared sensor can realize non-contact temperature measurement and is easily interfered by other heat sources in a cabin, the false alarm rate is high, the distributed temperature sensing system needs a complex wiring network, the wiring difficulty is high, the electromagnetic compatibility is poor under the application scene of compact aviation structure and sensitive weight, the interface mismatch problem exists between the sensor and the structural body, and the reliability is obviously reduced under the long-term vibration environment. Particularly, the early warning devices and the structural protective coating are mutually independent, so that extra space and load are occupied, function coordination and data fusion cannot be realized, and the development requirements of modern aviation equipment on light weight and integration are difficult to meet. Structural damage identification techniques also face significant challenges. In the current aviation field, a periodic nondestructive testing (NDI) mode is commonly adopted, such as ultrasonic C scanning, X-ray detection, eddy current flaw detection and the like, and the methods have higher precision, but have inherent limitations, namely, firstly, the detection periodicity is strong, real-time online monitoring cannot be realized, the damage generated in t