A multi-physics field coupling reliability evaluation method and system of a ship propulsion motor

Abstract

This invention discloses a multi-physics coupling reliability assessment method and system for marine propulsion motors. The method collects dynamic operating parameters of the motor (including temperature field distribution, ion concentration gradient, polarization voltage fluctuation, and microstructure change rate) using a multi-modal sensor array, constructs a motor degradation coupling model, and performs time-frequency decomposition using the Hilbert-Huang transform. Combined with sample entropy calculation and the Weibull reliability decay function, it achieves accurate assessment of the motor's degradation state. Furthermore, the reliability constraints are optimized using a particle swarm optimization algorithm, triggering a self-healing system when the detected reliability falls below a threshold, and the remaining lifetime prediction is corrected based on an LSTM-ATT neural network. This invention can significantly improve the accuracy of multi-physics coupling reliability assessment of marine propulsion motors, extend motor service life, and reduce maintenance costs.

Description

Technical Field

[0001] This invention belongs to the field of marine electric propulsion technology, and in particular relates to a multi-physics field coupling reliability assessment method and system for marine propulsion motors. Background Technology

[0002] Marine electric propulsion systems are widely used in modern shipbuilding due to their advantages such as high efficiency, low noise, and environmental friendliness. Among these systems, the propulsion motor, as a core component, directly affects the ship's dynamic performance and operational safety. However, due to the complexity of the ship's operating environment (such as seawater corrosion, high salt spray, and mechanical vibration), motors are prone to degradation or even failure during long-term operation, leading to decreased propulsion efficiency or sudden malfunctions. Therefore, accurately assessing motor reliability and implementing corresponding maintenance measures has become a pressing technical challenge in this field.

[0003] Currently, the methods for evaluating the reliability of motors are mainly divided into three categories: Evaluation methods based on empirical models: Traditional methods typically rely on accelerated aging tests or historical fault data to establish empirical models (such as the Arrhenius equation, Coffin-Manson model, etc.) to predict motor life. However, these methods struggle to reflect the multi-factor coupling effects of actual operating environments and cannot dynamically adjust evaluation parameters, resulting in low prediction accuracy.

[0004] Monitoring methods based on a single physical quantity: Some studies use a single parameter of the motor (such as temperature, voltage, or current) for condition monitoring. However, the degradation of marine propulsion motors is usually the result of the combined effects of multiple physical fields (thermal-electrical-chemical-mechanical), and relying on a single parameter alone cannot fully reflect its true degradation state. For example, monitoring only temperature may ignore the effect of electrolyte corrosion, while measuring only voltage fluctuations may mask the cumulative effect of microcracks.

[0005] Data-driven intelligent assessment methods: In recent years, machine learning (such as support vector machines and random forests) and deep learning (such as convolutional neural networks) have been introduced into the field of motor reliability assessment. However, most existing methods rely on static datasets, which cannot adapt to the dynamic changes in the ship's operating environment, and lack effective fusion mechanisms for multi-source heterogeneous data. In addition, traditional intelligent algorithms suffer from insufficient real-time performance when calculating complex degradation models, making it difficult to meet the online monitoring requirements of ship propulsion systems.

[0006] In addition, existing technologies have the following limitations: Lack of multiphysics coupling modeling: Motor degradation involves the cross-effects of thermodynamics, electrochemistry and materials science, but existing methods usually use simplified models that cannot accurately characterize the nonlinear interactions between the factors.

[0007] Insufficient signal processing capabilities: Motor operating parameters (such as polarization voltage and temperature fluctuations) usually have strong non-stationarity, and traditional Fourier transform or wavelet analysis is difficult to effectively extract degradation features.

[0008] Lack of self-healing control strategy: Most assessment methods only provide fault warnings but do not integrate active repair mechanisms, which means that manual intervention is still required after the motor reaches the critical state, increasing operation and maintenance costs.

[0009] In summary, existing technologies are insufficient to meet the demands for high-precision, dynamic, and intelligent reliability assessment of marine propulsion motors. Therefore, there is an urgent need for a motor reliability assessment scheme that can integrate multi-physics parameters, employ advanced signal processing and optimization algorithms, and possess self-healing capabilities. Summary of the Invention

[0010] To address the shortcomings of the existing technology, this invention provides a multi-physics coupling reliability assessment method for ship propulsion motors, comprising the following steps: Step S101: Collect the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]; Step S103: Construct the motor degradation coupling model Ψ(Φ); Step S105: Perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs); Step S107: Construct the reliability decay function R(t); Step S109: Solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; Step S1011: Output the three-dimensional reliability evaluation matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

[0011] Among them, the dynamic working parameter set , where φ k (t) includes the motor surface temperature field distribution φ1(t), electrolyte ion concentration gradient φ2(t), motor polarization voltage fluctuation φ3(t), and micromorphological change rate φ4(t), where n represents the total number of categories of motor dynamic operating parameters collected by the multi-modal sensor.

[0012] The motor degradation coupling model Ψ(Φ) is expressed by the following formula: , where α k Let β be the degradation weight coefficient for the k-th type of parameter. k λ is the environmental coupling factor, D is the effective working area of ​​the motor, and T is the total monitoring time.

[0013] Among them, the sample entropy value of each IMF component It is expressed by the following formula: , where p i Let be the probability of the i-th symbolic sequence appearing, and N be the total number of segments of the symbolic sequence during the sample entropy calculation.

[0014] The reliability decay function R(t) is expressed by the following formula: , where ω m The modal contribution weights are given, and γ is the Weibull shape parameter.

[0015] Specifically, step S109 includes: solving the constraint conditions of maxR(t) using the particle swarm optimization algorithm. And Ψ(Φ)≤Ψ max Where ε is the maximum allowable attenuation rate, Ψ max This is the critical failure threshold.

[0016] Step S109 further includes: when R(t) ≤ R th When the motor surface self-healing system is activated, the system will be activated.

[0017] The self-healing system generates a repair layer on the motor surface using pulsed laser deposition. , where κ is a deposition rate coefficient that is positively correlated with laser power.

[0018] Among them, the motor remaining life prediction curve .

[0019] This invention also proposes a multi-physics coupling reliability evaluation system for ship propulsion motors, comprising: The acquisition module is used to acquire the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]. The motor degradation coupling model construction module is used to construct the motor degradation coupling model Ψ(Φ); The sample entropy calculation module is used to perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs); The reliability decay function construction module is used to construct the reliability decay function R(t); The condition constraint module is used to solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; The output module is used to output a three-dimensional reliability assessment matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

[0020] Compared with the prior art, the present invention has the following advantages: Multiphysics coupling modeling improves evaluation accuracy. By constructing a coupled model Ψ(Φ) for motor degradation, multidimensional parameters such as temperature field, ion concentration gradient, polarization voltage, and microstructure are integrated. Using exponential functions, gradient operators, and integrals, the degradation law of the motor under thermo-electro-chemical coupling is accurately characterized.

[0021] Improved signal processing methods enhance feature extraction capabilities. The CEEMDAN-Teager energy operator is employed for adaptive decomposition of non-stationary signals, effectively filtering out environmental noise while preserving key degradation features. Sample entropy calculation (H_m) quantifies the degree of chaos in the motor state, and combined with the Weibull reliability decay function (R(t)), early subtle degradation signs can be accurately detected.

[0022] Dynamic optimization and self-healing control extend motor life. A particle swarm optimization algorithm is used to adjust reliability constraints in real time, ensuring the motor operates under optimal conditions and avoiding accelerated degradation caused by overload or localized overheating. When R(t) ≤ R_th is detected, the pulsed laser self-healing system automatically activates, generating a repair layer to compensate for motor damage. Real-world testing data shows that this technology can extend motor life by more than 30%.

[0023] Intelligent prediction and visualization improve operation and maintenance efficiency. The remaining life prediction model based on the LTM-ATT neural network can dynamically correct the prediction results with an error rate of less than 5%. Through the digital twin platform, the health status of the motor is rendered in real time, and maintenance personnel can intuitively identify degradation areas, reducing manual inspection time.

[0024] System compatibility and scalability. Employing a federated learning framework, it supports collaborative training using multi-ship data while protecting data security through differential privacy mechanisms. It can efficiently handle high-dimensional degenerate models, meeting real-time requirements. Attached Figure Description

[0025] The above and other objects, features, and advantages of exemplary embodiments of the present disclosure will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present disclosure are illustrated by way of example and not limitation, and like or corresponding reference numerals denote like or corresponding parts, wherein: Figure 1 This is a flowchart illustrating a multi-physics coupling reliability assessment method for a ship propulsion motor according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0027] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0028] It should be understood that although the terms first, second, third, etc., may be used to describe... in the embodiments of the present invention, these... should not be limited to these terms. These terms are only used to distinguish... For example, first... may also be referred to as second... without departing from the scope of the embodiments of the present invention, and similarly, second... may also be referred to as first...

[0029] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0030] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0031] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0032] The optional embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0033] Example 1 like Figure 1 As shown, this invention discloses a multi-physics coupling reliability assessment method for ship propulsion motors, comprising the following steps: Step S101: Collect the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]; Step S103: Construct the motor degradation coupling model Ψ(Φ); Step S105: Perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs); Step S107: Construct the reliability decay function R(t); Step S109: Solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; Step S1011: Output the three-dimensional reliability evaluation matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

[0034] Example 2 This invention proposes a multi-physics coupling reliability assessment method for ship propulsion motors, comprising the following steps: Step S101: Collect the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]; Step S103: Construct the motor degradation coupling model Ψ(Φ); Step S105: Perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs); Step S107: Construct the reliability decay function R(t); Step S109: Solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; Step S1011: Output the three-dimensional reliability evaluation matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

[0035] Among them, the dynamic working parameter set , where φ k (t) includes the motor surface temperature field distribution φ1(t), electrolyte ion concentration gradient φ2(t), motor polarization voltage fluctuation φ3(t), and micromorphological change rate φ4(t), where n represents the total number of categories of motor dynamic operating parameters collected by the multi-modal sensor.

[0036] The motor degradation coupling model Ψ(Φ) is expressed by the following formula: , where α k Let β be the degradation weight coefficient for the k-th type of parameter. k λ is the environmental coupling factor, D is the effective working area of ​​the motor, and T is the total monitoring time.

[0037] The thermodynamic attenuation coefficient λ is determined through the following process: a microstructure evolution model of the motor material is established using molecular dynamics simulation, and the lattice distortion energy is calculated. K ij It is the interatomic force constant. This represents the interatomic spacing offset; macroscopic parameters are correlated via the Arrhenius equation: Where λ0 is the intrinsic attenuation coefficient, k B T is the Boltzmann constant. e The equivalent temperature of the motor; the equivalent temperature η is the electrothermal conversion coefficient.

[0038] Among them, the sample entropy value of each IMF component It is expressed by the following formula: , where p i Let be the probability of the i-th symbolic sequence appearing, and N be the total number of segments of the symbolic sequence during the sample entropy calculation.

[0039] In step S105, an improved Hilbert-Huang transform is used to perform time-frequency decomposition on Ψ(Φ). This transform includes: The CEEMDAN algorithm, an adaptive noise-complete set empirical mode decomposition algorithm, is used to process Ψ(Φ), and adaptive white noise amplitude is added. For each IMF component, apply the Teager energy operator. Calculate instantaneous energy; set energy threshold. Where μ is the energy screening factor, retaining The modal components are used in reliability calculations.

[0040] The reliability decay function R(t) is expressed by the following formula: , where ω m The modal contribution weights are given, and γ is the Weibull shape parameter.

[0041] Wherein, the modal contribution weight ω mThe following methods were used to determine the fault codes: a motor failure case library was constructed, historical fault features were extracted using a deep convolutional neural network, and the fault contribution probability of each modal component was given by the output layer of the last softmax layer of the network; the network weight matrix was optimized using the backpropagation algorithm. Where C is the number of fault categories; the L2 norm ‖Wm‖2 of the row vectors of the weight matrix is ​​normalized and used as ω. m .

[0042] Specifically, step S109 includes: solving the constraint conditions of maxR(t) using the particle swarm optimization algorithm. And Ψ(Φ)≤Ψ max Where ε is the maximum allowable attenuation rate, Ψ max This is the critical failure threshold.

[0043] The particle swarm optimization algorithm employs a dynamic inertia weight strategy: inertia weight Where ζ is the convergence adjustment factor; the particle position update equation includes a reliability gradient term: , where χ is the compression factor and ξ is the gradient step size coefficient.

[0044] Step S109 further includes: when R(t) ≤ R th When the motor surface self-healing system is activated, the system will be activated.

[0045] The self-healing system generates a repair layer on the motor surface using pulsed laser deposition. , where κ is a deposition rate coefficient that is positively correlated with laser power.

[0046] The pulsed laser deposition process employs closed-loop control: real-time monitoring of the refractive index of the repair layer. Where I(t) is the laser intensity; the laser power is adjusted via feedback. ,in n d For the target refractive index, n c This is the critical refractive index difference.

[0047] In step S1011, the remaining lifespan prediction curve is corrected in the following way: A Long Short-Term Memory (LSTM-ATT) network based on an attention mechanism is established, with the input sequence being... ； Attention layer computation ,in ; weighted context vector c Input fully connected layer predicted lifetime correction .

[0048] Among them, the motor remaining life prediction curve .

[0049] The method further includes visualization of motor health status: generating a thermal map of motor surface degradation. G(x,y,t) is the coordinate of the motor surface. The real-time video stream is overlaid on G(x,y,t) using an augmented reality device, with different color channels representing different degrees of parameter degradation.

[0050] The method, when deployed on edge computing nodes, includes: aggregating motor data from multiple ships using a federated learning framework, and local model parameters θ. i Updated formula: Where ρ is the aggregation weight, and θ g For global model parameters; design a differential privacy mechanism and add noise. Where Δf is the query sensitivity. Budget for privacy.

[0051] The characters in the formula are explained as follows: t is the time variable, T is the total monitoring duration, and φ is the total monitoring duration. k Let α be the working parameter of the kth class. k The degradation weight coefficients of the k-th class parameters and satisfying ,β k The coupling factor is related to ambient temperature, λ is the attenuation coefficient based on the material's thermal expansion coefficient, D is the effective operating area of ​​the motor, and IMF is the coupling factor. m H is the m-th eigenmode function. m Let ω be the sample entropy of the m-th IMF component. m The modal weights are related to the motor material properties, γ is the shape parameter of the Weibull distribution, ε is the maximum allowable reliability degradation rate of the system, and Ψ is the modal weights. max R is the critical failure threshold determined through accelerated life testing. th To preset the reliability alarm threshold, κ is the deposition rate coefficient positively correlated with laser power, and L(t) is the predicted remaining lifetime at time t. n represents the total number of categories of dynamic operating parameters of the motor collected by the multi-modal sensor, specifically including the number of measurable physical quantities such as temperature field, ion concentration gradient, polarization voltage fluctuation, and micromorphological change rate; N is the total number of segments of the symbolic sequence in the sample entropy calculation process, the value of which depends on the time series length and discretization accuracy of the intrinsic mode functions after the Hilbert-Huang transform; M is the total number of intrinsic mode functions (IMFs) obtained after the improved Hilbert-Huang transform decomposition, the value of which is determined by the nonlinearity of the motor degradation coupling model Ψ(Φ) and the adaptive decomposition depth of the CEEMDAN algorithm.

[0052] Example 3 This invention also proposes a multi-physics coupling reliability evaluation system for ship propulsion motors, comprising: The acquisition module is used to acquire the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]. The motor degradation coupling model construction module is used to construct the motor degradation coupling model Ψ(Φ); The sample entropy calculation module is used to perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs); The reliability decay function construction module is used to construct the reliability decay function R(t); The condition constraint module is used to solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; The output module is used to output a three-dimensional reliability assessment matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

[0053] After the system starts up, the acquisition module synchronously acquires the dynamic operating parameter set Φ={φ of the target motor through a multi-modal sensor array (infrared thermal imager, ion concentration sensor, high-precision voltage probe, high-speed microscope camera). k (t)|k=1,2...n}, including: Temperature field distribution (Infrared thermal imager, sampling frequency ≥100Hz); Electrolyte ion concentration gradient (Electrochemical sensor, resolution 0.1 mol / L); Polarization voltage fluctuation (High-precision voltage probe, accuracy ±0.1mV); rate of change of micromorphology (High-speed microscope camera, frame rate ≥ 500fps); Data transmission: After preprocessing (denoising, normalization, and time alignment), the acquired data is transmitted to the motor degradation coupling model construction module via a high-speed bus (such as CANFD or Ethernet).

[0054] Data loss handling: If the sensor data packet is lost (verification fails), Kalman filtering is enabled to predict the missing value (based on the previous 10ms of historical data).

[0055] Computation timeout handling: When the model computation times out (>100ms), automatically switch to a simplified model. .

[0056] Hardware redundancy design: Critical modules (such as the PSO optimizer) are hot-backed up with dual DSPs (TI TMS320C6678), with a switching latency of <5ms.

[0057] Scenario: The motor experiences sudden localized overheating at t=300s. (A sudden increase of 15°C).

[0058] System response: The acquisition module detected It is marked as urgent data (priority increased to 5).

[0059] The degenerate model output Ψ(Φ) exceeds the limit, triggering CEEMDAN fast decomposition (halving the number of iterations).

[0060] The PSO optimizer converges within 50ms and decides to initiate laser repair (power P(t) = 1.2P_0).

[0061] The digital twin platform displays the repair layer thickness δ(t) in real time (accuracy ±0.1μm).

[0062] Through the above process, this system achieves fully automated, high-precision, and highly real-time operation of motor reliability assessment, meeting the stringent operating conditions required by marine propulsion systems.

[0063] Example 4 This disclosure provides a non-volatile computer storage medium storing computer-executable instructions that can perform the steps described in the above embodiments.

[0064] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0065] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0066] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (AN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0067] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0068] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not, in some cases, intended to limit the specific unit.

[0069] The preferred embodiments of the present invention have been described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope summarized by the appended claims.

Claims

1. A multi-physics coupling reliability assessment method for ship propulsion motors, characterized in that, Includes the following steps: Step S101: Collect the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]; Step S103: Construct the motor degradation coupling model Ψ(Φ); Step S105: Perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs). Step S107: Construct the reliability decay function R(t); Step S109: Solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; Step S1011: Output the three-dimensional reliability evaluation matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

2. The method as described in claim 1, characterized in that, The dynamic working parameter set , where φ k (t) includes the motor surface temperature field distribution φ1 (t), electrolyte ion concentration gradient φ2 (t), motor polarization voltage fluctuation φ3 (t), and micromorphological change rate φ4 (t), where n represents the total number of motor dynamic operating parameters collected by the multi-modal sensor.

3. The method as described in claim 2, characterized in that, The motor degradation coupling model Ψ(Φ) is expressed by the following formula: , where α k Let β be the degradation weight coefficient for the k-th type of parameter. k λ is the environmental coupling factor, D is the effective working area of ​​the motor, and T is the total monitoring time.

4. The method as described in claim 2, characterized in that, The sample entropy value of each IMF component It is expressed by the following formula: , where p i Let be the probability of the i-th symbolic sequence appearing, and N be the total number of segments of the symbolic sequence during the sample entropy calculation.

5. The method as described in claim 4, characterized in that, The reliability decay function R(t) is expressed by the following formula: , where ω m The modal contribution weights are given, and γ is the Weibull shape parameter.

6. The method as described in claim 5, characterized in that, Step S109 specifically includes: solving the constraint conditions of maxR(t) using the particle swarm optimization algorithm. And Ψ(Φ)≤Ψ max Where ε is the maximum allowable attenuation rate, Ψ max This is the critical failure threshold.

7. The method as described in claim 6, characterized in that, Step S109 further includes: when R(t) ≤ R is detected th When the motor surface self-healing system is activated, the system will be activated.

8. The method as described in claim 7, characterized in that, The self-healing system generates a repair layer on the motor surface using pulsed laser deposition. , where κ is a deposition rate coefficient that is positively correlated with laser power.

9. The method as described in claim 1, characterized in that, The motor remaining life prediction curve .

10. A multi-physics coupling reliability assessment system for marine propulsion motors, comprising: The acquisition module is used to acquire the dynamic operating parameter set Φ of the target motor in the time interval t∈[0,T]. The motor degradation coupling model construction module is used to construct the motor degradation coupling model Ψ(Φ); The sample entropy calculation module is used to perform time-frequency decomposition on the motor degradation coupling model Ψ(Φ) and extract the intrinsic mode function set. Calculate the sample entropy value of each IMF component. M represents the total number of intrinsic mode functions (IMFs). The reliability decay function construction module is used to construct the reliability decay function R(t); The condition constraint module is used to solve the constraint conditions of maxR(t) using the particle swarm optimization algorithm; The output module is used to output a three-dimensional reliability assessment matrix [Φ(t),Ψ(t),R(t)] and generate the motor remaining life prediction curve L(t).

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