A multi-motor coupling vibration intelligent diagnosis method and system

Abstract

The present application relates to the technical field of predictive maintenance and fault diagnosis of industrial equipment, and particularly relates to a multi-motor coupled vibration intelligent diagnosis method and system. The technical scheme comprises the following steps: based on the collected vibration data of the multi-motor under the normal operation state and the physical parameters of the motor equipment, a parameterized fault simulation module is used to generate a coupled vibration simulation data set with accurate fault labels. The present application realizes the cold start of the diagnosis system under the condition of no historical fault samples; the deep reinforcement learning agent is used to process the space-time coupled signal, and the layered reward mechanism is combined to significantly improve the decoupling diagnosis accuracy and early warning capability; through the online adaptive learning module, the system can be fine-tuned in real time according to the field feedback, and quickly adapt to the individual equipment state; the simulation-model evolution module uses the accumulated real data to continuously optimize the simulator and the diagnosis model, and forms a self-iterating closed loop.

Description

Technical Field

[0001] This invention relates to the field of predictive maintenance and fault diagnosis technology for industrial equipment, and in particular to a method and system for intelligent diagnosis of multi-motor coupled vibration. Background Technology

[0002] Multi-motor cooperative drive systems are a core power component of modern industrial production lines, high-end equipment, and automated production units. Through the precise coordination of multiple motors, the system can achieve complex motion control and high-load continuous operation, significantly improving production efficiency and process capabilities. However, the complexity and coupling of this system also bring significant challenges to fault diagnosis, mainly in the following aspects:

[0003] Vibration signals are heavily coupled, making feature extraction and decoupling difficult. In multi-motor mechanical systems, each drive unit is tightly connected by rigid or flexible components such as couplings, sliders, guide rails, and lead screws, forming a complex dynamic coupling network. When a single motor or transmission component fails, the vibration excitation it generates is transmitted, superimposed, and modulated through the structural path, resulting in a strongly coupled vibration signal that is ultimately acquired at the sensor. This coupling effect severely masks the essential characteristics of the fault, significantly reducing the accuracy of traditional methods based on single-point signal analysis or simple spectrum diagnosis, leading to persistently high false alarm and false negative rates.

[0004] The scarcity of fault sample data poses a cold-start challenge for model training. Critical equipment in industrial settings is typically expensive, structurally sophisticated, and requires continuous and stable operation. Obtaining sufficient and diverse fault vibration data through proactive fault injection (destructive testing) is often impractical or uneconomical in reality. Therefore, the extreme scarcity of historical fault samples prevents effective training and initialization of AI diagnostic models (such as deep learning models) that rely on large amounts of labeled data, resulting in a "cold start" dilemma. This severe data scarcity significantly limits the practical application and performance ceiling of data-driven methods in industrial fault diagnosis.

[0005] Existing diagnostic methods suffer from weak adaptability, making it difficult to cope with equipment state drift and changes in operating conditions. The operating status of industrial equipment changes slowly due to wear, aging, load variations, and environmental disturbances. Traditional diagnostic models, once trained offline, often have fixed parameters, lacking the ability to learn online and self-adjust. When the equipment state deviates from the range covered by the training data, the model performance degrades significantly, failing to achieve long-term reliable diagnosis. Early warning and real-time decision-making capabilities are also insufficient. Many faults exhibit gradual development characteristics, with their early features being weak and easily masked by noise. Existing methods primarily focus on classification and identification after faults occur, while their ability to provide early warning and real-time maintenance decision support during the fault's nascent stage is weak, missing the optimal opportunity for preventative maintenance. Summary of the Invention

[0006] The purpose of this invention is to address the problem that most existing diagnostic systems in the background technology are static models, which are difficult to adapt online once deployed according to equipment aging, operating condition drift or new fault modes, and lack continuous learning and evolution capabilities. This invention proposes a multi-motor coupled vibration intelligent diagnostic method and system.

[0007] In a first aspect, this application provides a method for intelligent diagnosis of multi-motor coupled vibration, comprising the following steps:

[0008] Based on the vibration data collected under normal operating conditions of multiple motors and the physical parameters of the motor equipment, a coupled vibration simulation dataset with accurate fault labels is generated through the parameterized fault simulation module.

[0009] The coupled vibration simulation dataset is input into the deep reinforcement learning agent module for pre-training. The state input of the deep reinforcement learning agent module is a time-series three-dimensional tensor constructed based on multi-channel vibration signals, and the reward function is a composite reward function.

[0010] The multi-channel vibration signals from the real operating environment are input into the pre-trained intelligent agent model for real-time diagnosis, outputting diagnostic suggestions and receiving feedback from maintenance personnel based on the diagnostic results.

[0011] The feedback is converted into an instant reward signal, and an online policy optimization algorithm is used to update the policy network of the agent model in gradient, so as to realize online fine-tuning and adaptive optimization of the model.

[0012] By utilizing accumulated real data-label pairs that have been validated through feedback, the model parameters of the parameterized fault simulation module are optimized to generate a simulation dataset that more closely resembles the distribution of real data. This dataset is then used to incrementally train and iteratively update the agent model, forming a self-evolving diagnostic closed loop.

[0013] Optionally, the parameterized fault simulation module performs the following sub-steps:

[0014] Based on the physical parameters of the equipment, a parametric physical fault model library is constructed, including rotor imbalance, local bearing damage, misalignment, and mechanical loosening.

[0015] Set the fault type, location, and severity parameters, and generate the corresponding fault source vibration signal through the physical fault model library;

[0016] Based on the transmission path model between the vibration source and the sensor, the vibration signal of the fault source is coupled and superimposed onto the normal baseline vibration data to generate a multi-channel simulation vibration dataset with structured fault labels.

[0017] Optionally, the dimensions of the time-series three-dimensional tensor include time step, number of sensor channels, and feature dimension; the composite reward function includes at least: a basic reward item based on diagnostic accuracy, a timeliness reward item related to the timing of fault diagnosis, and a security reward item that penalizes missed reports of serious faults.

[0018] Optionally, the online policy optimization algorithm is a proximal policy optimization algorithm, which uses an interaction data tuple containing state, action, reward and next state to update the model parameters by maximizing the pruned policy objective function.

[0019] Optionally, by utilizing accumulated, feedback-verified real-data-label pairs, the model parameters of the parameterized fault simulation module are optimized to generate a simulation dataset. This simulation dataset is then used for incremental training and iterative updates of the agent model, specifically including:

[0020] Data accumulation involves storing real vibration signals confirmed by human feedback and their corresponding high-confidence fault labels to build a database of real cases.

[0021] Distribution alignment is performed, and the probability distribution differences of key features between the simulation data generated by the parameterized fault simulation module and the data in the real case database are calculated.

[0022] Parameter optimization aims to minimize the distribution differences by automatically adjusting the fault model parameters and signal transmission path parameters in the parameterized fault simulation module.

[0023] The model is iterated by generating a new simulation dataset using the optimized simulation module, incrementally training the agent model, and replacing the online diagnostic model with a more powerful trained model.

[0024] Secondly, this application provides a multi-motor coupled vibration intelligent diagnostic system for implementing the multi-motor coupled vibration intelligent diagnostic method as described in the first aspect, the system comprising:

[0025] The parameterized fault simulation module is used to generate labeled multi-motor coupled vibration simulation data based on physical mechanisms.

[0026] The deep reinforcement learning agent module, which includes a policy network and a value network, is used to learn diagnostic policies from simulated or real data.

[0027] The online diagnosis and interaction module is used to deploy the intelligent agent module, process real-time vibration signals, output diagnostic suggestions, and collect human feedback.

[0028] An online adaptive learning module is used to calculate rewards in real time based on feedback and update the policy network parameters of the agent module;

[0029] The simulation-model evolution module is used to optimize the parameterized fault simulation module based on real feedback data and drive the iterative retraining of the agent module.

[0030] Optionally, the parameterized fault simulation module includes:

[0031] The fault model library contains various mathematical fault models defined by parameters such as unbalanced mass, eccentricity, damage size, offset, angular deviation, and clearance.

[0032] The coupled simulation engine is used to map the vibration signal output by the fault model to each sensor channel according to the transfer path matrix, and superimpose it with the normal baseline signal.

[0033] Optionally, the deep reinforcement learning agent module includes:

[0034] The feature extraction and state construction unit is used to extract time-domain, frequency-domain, and time-frequency-domain features from the original vibration signal and construct a time-series three-dimensional state tensor.

[0035] The core network of the intelligent agent adopts an Actor-Critic-based architecture, in which the Actor network outputs the probability distribution of diagnostic actions, and the Critic network evaluates the state value.

[0036] Optionally, the online adaptive learning module includes:

[0037] The reward calculation unit calculates the real-time scalar reward based on the preset composite reward function and human feedback labels;

[0038] An experience replay pool is used to cache interactive experience data;

[0039] An online optimizer is used to sample data from the experience replay pool and execute a near-end policy optimization algorithm to update network parameters.

[0040] Optionally, the simulation-model evolution module includes:

[0041] A high-confidence database of real-world cases;

[0042] Distribution difference measurement unit, used to quantify the difference in feature distribution between simulated data and real data;

[0043] The simulation parameter optimizer uses the gradient descent method to automatically adjust the fault simulation parameters with the goal of minimizing the distribution differences.

[0044] Compared with the prior art, this application includes at least one of the following beneficial technical effects:

[0045] The parameterized fault simulation module generates a fault simulation dataset with precise labels based on the equipment's physical parameters and normal operation data, enabling the diagnostic system to complete initial training and deployment without relying on any actual historical fault samples.

[0046] Deep reinforcement learning agents can effectively decouple mixed vibration signals by directly processing temporal three-dimensional state tensors containing spatiotemporal information and training them in combination with hierarchical composite reward functions that include accuracy, timeliness and safety. This can improve the accuracy of fault location and type identification and enable early warning.

[0047] The online adaptive learning module instantly transforms maintenance personnel feedback into reward signals and uses online strategy optimization algorithms to fine-tune the model, enabling the diagnostic system to quickly adapt to individual state differences and slow performance drift of specific equipment.

[0048] The simulation-model evolution module automatically optimizes fault simulation parameters by comparing and minimizing the differences in feature distribution between simulation data and real feedback data, thereby generating more realistic training data, driving the diagnostic model to perform iterative incremental training, and forming a closed loop of continuous self-optimization.

[0049] In summary, this invention generates labeled data through a parameterized fault simulation module, enabling the diagnostic system to cold start under conditions where there are no historical fault samples; it utilizes a deep reinforcement learning agent to process spatiotemporally coupled signals, combined with a hierarchical reward mechanism, significantly improving the accuracy of decoupled diagnosis and early warning capabilities; through an online adaptive learning module, the system can fine-tune in real time based on on-site feedback, quickly adapting to the individual equipment status; and the simulation-model evolution module continuously optimizes the simulator and diagnostic model using accumulated real data, forming a self-iteratory closed loop. Attached Figure Description

[0050] Figure 1 A flowchart of a multi-motor coupled vibration intelligent diagnosis method;

[0051] Figure 2 This is a schematic diagram of the working principle of the parameterized fault simulation module.

[0052] Figure 3 This is a schematic diagram of the structure of a multi-motor driven slider assembly unit.

[0053] Reference numerals in the attached drawings: 1. Drive motor M3; 2. Gripper; 3. Vibration sensor; 4. Drive motor M1; 5. Coupling; 6. Slider; 7. Guide rail; 8. Lead screw; 9. Drive motor M2; 10. Base; 11. First vibration sensor Ch1; 12. Second vibration sensor Ch2; 13. Third vibration sensor Ch3; ​​14. Fourth vibration sensor Ch4; 15. Fifth vibration sensor Ch5. Detailed Implementation

[0054] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0055] Example: To make the objectives, technical solutions, and advantages of the present invention clearer, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The present invention provides a multi-motor coupled vibration fault diagnosis method and system based on deep reinforcement learning and simulation generation, aiming to solve the problems of difficult cold start, low accuracy, and poor adaptability of diagnostic models in the prior art due to severe vibration signal coupling and scarcity of historical fault data. This embodiment uses the multi-motor driven slider assembly unit shown in the accompanying drawings as an application scenario, but the application of the present invention is not limited to this.

[0056] I. Overall System Implementation Framework

[0057] refer to Figure 1 The overall flowchart shown illustrates that the diagnostic system of this invention, during implementation, primarily follows a closed-loop process of "simulation-generated pre-training, online interactive diagnosis, and feedback-driven evolution." In the initial stage, the system requires no historical fault data and can be started solely based on normal equipment operation data and physical parameters. Its core implementation modules include:

[0058] Parametric Fault Simulation Module: Responsible for generating simulated fault datasets with precise labels.

[0059] Deep reinforcement learning agent module: As the core diagnostic decision-making unit, it performs pre-training and online inference.

[0060] Online diagnosis and interaction module: responsible for on-site data collection, display of diagnostic results and human-computer interaction.

[0061] Online adaptive learning module: Uses on-site feedback to fine-tune the agent in real time.

[0062] Simulation-Model Evolution Module: Utilizes accumulated real data to optimize the simulator and drive iterative upgrades of the model.

[0063] II. Specific Implementation of the Parametric Fault Simulation Module

[0064] The purpose of this module is to generate realistic coupled vibration data with multiple fault labels, providing sufficient pre-training samples for deep reinforcement learning agents. The implementation steps are as follows, and the working principle can be found in [reference needed]. Figure 2 :

[0065] Baseline data and physical parameter acquisition: at the target device (e.g., Figure 3 The multi-motor driven slider assembly unit shown has been running stably for a sufficient period of time under healthy conditions, and multi-channel normal vibration data has been synchronously collected by multiple installed vibration sensors (such as Ch1-Ch5). ,in, Indicates the first One sample, Number of sensor channels The number of sampling points. It represents the real number field. Simultaneously, it acquires the physical parameters of the equipment, including the rated speed of each motor, bearing type, and the relative position coordinates of the sensors and each motor.

[0066] Constructing a parametric physical fault model library: Based on common mechanical fault mechanisms, establish fault models that can be controlled by key parameters. Specifically, this includes:

[0067] Rotor imbalance model: composed of unbalanced mass and eccentricity Parameterization. The generated simulated fault signal is... ,in, Indicates the first Taiwan motor in time The generated simulated vibration signal, For the transmission coefficient, This represents the time-domain function of the centrifugal force caused by rotor imbalance, where m represents the equivalent unbalanced mass on the rotor, and r is the eccentricity. It is the first The rotational angular frequency of the trolley motor, It is the initial phase angle.

[0068] Bearing local damage model: based on damage size The position is parameterized, and the simulation is a periodic impact sequence, with the waveform of a single impact being a decaying sinusoidal model. ,in, It is a vibration signal caused by a single impact response due to localized damage to the bearing. It is the impact amplitude coefficient, which is positively correlated with the damage size s. The attenuation coefficient is... This is the structure's natural frequency.

[0069] Misalignment model: determined by offset and angle deviation Parameterization, the simulated signal is ,in The simulated vibration signal is caused by misalignment. It is the second harmonic amplitude coefficient. ,in The system sensitivity coefficient, This is twice the rotational speed frequency, i.e., the characteristic frequency of misalignment. This indicates the phase angle.

[0070] Mechanical loosening model: based on gaps Parameterization; the simulated signal includes subharmonics and amplified noise. . This represents a simulated vibration signal caused by mechanical loosening. , The amplitudes are the 1 / 2 harmonic and 1 / 3 harmonic amplitudes, respectively. It is the rotational angular frequency. These represent the initial phases of the 1 / 2 and 1 / 3 harmonic components, respectively. express, It is a Gaussian white noise sequence. This is the noise amplification factor. This is the noise gain factor;

[0071] Coupled simulation data generation: Define a transfer path matrix Where K is the number of sensor channels and M is the number of potential fault sources. The initial value of matrix element T[k][m] can be estimated based on the physical distance between the sensor and the fault source using an attenuation model. Estimate, of which Here, is the attenuation coefficient of the vibration signal during transmission, and is the distance between the vibration source and the sensor. By setting different fault types, locations (such as motors M1, M2, and M3) and severity parameters, the corresponding fault model is invoked to generate the fault source signal. , The specific fault model mentioned above (m=1, representing) m=2 represents m=3 represents m=4 represents Then follow the formula ,in, It is the final generated number Simulated vibration data of each channel at time t. This is the first vibration sensor that synchronously collects data. Normal vibration data for each channel at time t, where M is the total number of fault types and m is the fault type number, m = 1, 2, ..., M. For matrix elements, For the simulation of fault signals in a specific fault model, For the k-th channel, all relevant fault signals are weighted and superimposed. This is then coupled to the baseline data to generate the final signal. Each data sample is labeled with a structured tag such as {Fault Source: Drive Motor M2 9, Type: Bearing Outer Ring Damage}, forming a simulation dataset. .

[0072] III. Specific Implementation of the Deep Reinforcement Learning Agent Module

[0073] This module is the core of the system's intelligent decision-making. It receives vibration signals, analyzes them, and outputs diagnostic actions.

[0074] State space construction: The input multi-channel vibration signal is segmented into non-overlapping time windows of fixed length. For each time window sample, the following features are extracted in parallel:

[0075] Temporal characteristics: such as root mean square (RMS), kurtosis, and crease factor.

[0076] Frequency domain characteristics: The frequency spectrum is calculated by FFT to extract the energy proportion of the frequency band where the rotation frequency and its harmonics of each motor and the bearing fault characteristic frequency are located.

[0077] Time-frequency domain features: The energy of the node with the highest energy distribution entropy value is selected as the feature through wavelet packet transform.

[0078] The features of the most recent T consecutive time windows are stacked in the format of [time step T, number of sensor channels K, feature dimension F] to construct a three-dimensional temporal state tensor. , as input to the intelligent agent.

[0079] Network architecture and training: The agent adopts an Actor-Critic architecture.

[0080] Policy Network (Actor): Input is the state tensor The algorithm sequentially passes through a one-dimensional convolutional layer (for extracting local spatial feature patterns) and a long short-term memory network layer (for capturing long temporal dependencies), ultimately outputting the probability distribution of all predefined diagnostic actions. ,in The discrete motion space includes fault types such as normal, rotor imbalance, misalignment, local bearing damage, and mechanical loosening.

[0081] Value Network (Critic): Has a feature extraction layer similar to the policy network, outputting the long-term value of the current state. Scalar estimation.

[0082] Pre-training: using a simulated dataset The agent is pre-trained using a proximal strategy optimization algorithm.

[0083] Action space and reward function design:

[0084] Action space: defined as a composite action, including fault location (specific motor number), fault type (such as imbalance, bearing damage, etc.), confidence level, and suggested operation (such as no action, warning, shutdown for inspection).

[0085] Hierarchical composite reward function It guides agent optimization during the training and online learning phases.

[0086] Accuracy Rewards When the diagnosis is correct, administer Rewards are given for mistakes. punish, This represents the base reward, which is a positive value.

[0087] Timely rewards To encourage early warning, if the fault characteristics are fully manifested within the reference time window... If the diagnosis is correct, an additional timely reward will be given. ,in As a timeliness reward coefficient, This indicates the actual duration from the occurrence of the fault to the current moment. The reference time window indicating when the fault characteristics are fully manifested, and This reward is only activated at that time to encourage early warnings and improve the timeliness of system warnings.

[0088] Security rewards Severe penalties will be imposed for failure to report serious faults to ensure system reliability. To ensure system reliability It indicates the severity of the punishment.

[0089] IV. Specific Implementation of the Online Diagnosis and Interaction Module

[0090] This module is responsible for deploying the trained agent to the actual industrial site for real-time diagnostics.

[0091] Hardware deployment and signal acquisition: Figure 3 The multi-motor driven slider assembly unit shown is an example. The multi-motor driven slider assembly unit will be described in detail below.

[0092] 1. Overall structure and installation foundation

[0093] The base 10 serves as the rigid foundation and mounting platform for the entire assembly unit. All core moving parts and sensors are directly or indirectly fixed to it, and the fifth vibration sensor Ch5 15 is installed at its geometric center.

[0094] 2. Horizontal drive and motion module

[0095] Drive motors M1 4 and M2 9 serve as the power source for horizontal movement, and are arranged in parallel and fixed on the base 10.

[0096] Transmission and guiding components: The output shafts of the two horizontal motors are connected to parallel lead screws 8 via couplings 5. Guide rails 7 and lead screws 8 are installed parallel to each other, together forming a motion guiding system.

[0097] The slider 6 is the core load-bearing component for horizontal movement. It engages with the lead screw 8 through a nut, and its bottom cooperates with the groove of the guide rail 7, thereby converting the rotational motion of the motor into precise linear reciprocating motion.

[0098] 3. Vertical Drive and Execution Module

[0099] The drive motor M31 is mounted on the slider 6, and its output shaft drives vertical movement.

[0100] The gripper 2, as the end effector, is driven directly by the drive motor M31 or through a transmission mechanism to realize the gripping and releasing of the workpiece.

[0101] 4. Vibration monitoring sensor network layout

[0102] Five vibration sensors 3 (general schematic) constitute a monitoring network designed to decouple multi-source vibration signals, and their arrangement strategy is as follows:

[0103] Direct monitoring points:

[0104] The first vibration sensor Ch1 11 is installed on the non-drive end housing of the horizontal drive motor M1 4 to directly monitor its body vibration.

[0105] The second vibration sensor Ch2 12 is installed on the non-drive end housing of the horizontal drive motor M2 9 to directly monitor its vibration.

[0106] The fourth vibration sensor Ch4 14 is installed at the mounting flange of the vertical drive motor M3 1 to directly monitor its vibration status.

[0107] Key points of coupling monitoring:

[0108] The third vibration sensor, Ch3 13, is installed at the center of slider 6. This is a key node where the motion of the three motors is coupled, and it can comprehensively reflect the vibration generated by the interaction of horizontal and vertical motion.

[0109] Environmental reference points:

[0110] The fifth vibration sensor Ch5 15 is mounted at the geometric center of the base 10 to monitor background vibration and environmental noise, providing a reference for the system vibration signal and helping to improve the signal-to-noise ratio.

[0111] A monitoring network is formed by three drive motors (M1, M2, M3) and five vibration sensors (Ch1-Ch5). Sensor signals are input to the industrial control computer in real time via a synchronous acquisition card.

[0112] Real-time diagnostic process: The acquired raw vibration signal is first preprocessed (denoising and normalization). Then, the state tensor for the current moment is constructed following the same process as in the pre-training phase. The data is then input into the deployed agent model. After inference, the model outputs an action probability distribution. The system selects the action with the highest probability as a diagnostic suggestion. For example, location: M2, fault type: bearing wear, confidence level: 85%, suggestion: warning.

[0113] Human-Computer Interaction: Diagnostic suggestions are presented to maintenance personnel through a graphical human-computer interface. Based on the actual situation during on-site inspections, maintenance personnel provide feedback by clicking the "Correct," "False Alarm," or "Missing Alarm" buttons. This feedback is a key data source driving subsequent online learning and model evolution.

[0114] V. Specific Implementation of the Online Adaptive Learning Module

[0115] This module enables the system to quickly adapt to the unique characteristics of specific equipment based on on-site feedback.

[0116] Feedback Conversion: Convert a single piece of feedback from maintenance personnel (such as clicking "Correct") into an immediate scalar reward that conforms to the definition of the reward function. (For example, to obtain) ).

[0117] Online policy update: The system uses the online PPO algorithm for policy fine-tuning. Each interaction generates a quadruple (...). , , , The data is stored in a fixed-capacity experience replay pool. When the amount of data in the pool reaches a small batch size, a batch of data is randomly sampled, and the dominance function is calculated. And perform a PPO gradient update to optimize the policy network parameters. Its objective function is:

[0118]

[0119] in, For loss function, For the current policy network parameters, For old strategy parameters, For the first The action to be performed step by step For the first The state tensor of the step, Let the probability of the action be given by the current policy. The probability of an action under the old strategy. This is the hyperparameter pruning coefficient (usually set to 0.2), used to constrain the step size of policy updates and ensure training stability. This process continues, causing the model policy to converge towards the optimal diagnostic policy for the current device.

[0120] VI. Specific Implementation of the Simulation-Model Evolution Module

[0121] This module is the core of the system's lifelong learning and continuous performance improvement, indirectly enhancing the diagnostic model by optimizing the simulator.

[0122] Construction of a high-confidence case library: Real-time vibration data and their actual fault labels corresponding to all manually confirmed "correct" diagnoses are stored as high-confidence data-label pairs in a dedicated database.

[0123] Simulator parameter optimization:

[0124] Distribution difference assessment: Periodically sample a set of real data from the case library and use the current parametric fault simulation module to generate simulation data with the same fault parameters.

[0125] Difference Measurement and Optimization: Calculate the difference in probability distributions between simulated data and real data on key features. The calculation formula is as follows:

[0126]

[0127] Where P(x) is the empirical probability of feature x appearing in the real data, and Q(x) is the model probability of the same feature x appearing in the simulation data. It is the set of all possible eigenvalues, using KL divergence. Measurement is performed. Among them... For the true data distribution, This is the distribution of simulation data.

[0128] Parameter tuning: to minimize With the goal of improving the simulation model, optimization algorithms such as gradient descent are used to automatically adjust the parameters of the fault model (such as m, r, s, d, etc.) and the parameters of the transfer path matrix T in the fault simulation module, so that the distribution of the simulation data continuously approximates the distribution of the real data.

[0129] Model Iterative Update: Using the optimized fault simulation module, a new batch of simulation datasets that more closely resemble real-world scenarios is generated. Using simulation datasets Incremental training is performed on the current online diagnostic model (which can incorporate historical real-world data). Once training is complete, a more powerful next-generation diagnostic model is obtained, replacing the original online model and completing one iterative evolution. This closed-loop process can be executed periodically or triggered automatically, allowing the system's diagnostic capabilities to continuously improve with device uptime.

[0130] It is worth noting that, through the specific implementation methods described above, the system implemented by this invention requires no historical fault data. Training is initiated by generating data through physical mechanism simulation, thus solving the initial data bottleneck in industrial scenarios. This invention utilizes deep reinforcement learning to directly process coupled spatiotemporal state tensors, combined with a targeted reward mechanism, effectively identifying and locating faults from mixed signals. Through an online learning module, it rapidly adapts to individual equipment differences, and through a simulation-model evolution closed loop, it uses continuously generated field data to drive a spiral improvement in the overall system performance, possessing true intelligent evolutionary characteristics. This invention features a clear modular design, forming a complete technical closed loop from simulation, training, deployment to evolution, making it suitable for complex industrial environments with high requirements for reliability, accuracy, and long-term maintenance costs.

[0131] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A method for intelligent diagnosis of multi-motor coupled vibration, characterized in that, Includes the following steps: Based on the vibration data collected under normal operating conditions of multiple motors and the physical parameters of the motor equipment, a coupled vibration simulation dataset with accurate fault labels is generated through the parameterized fault simulation module. The coupled vibration simulation dataset is input into the deep reinforcement learning agent module for pre-training. The state input of the deep reinforcement learning agent module is a time-series three-dimensional tensor constructed based on multi-channel vibration signals, and the reward function is a composite reward function. The multi-channel vibration signals from the real operating environment are input into the pre-trained intelligent agent model for real-time diagnosis, outputting diagnostic suggestions and receiving feedback from maintenance personnel based on the diagnostic results. The feedback is transformed into an instant reward signal, and an online policy optimization algorithm is used to update the policy network of the agent model using gradients. By utilizing accumulated, feedback-verified real-data-label pairs, the model parameters of the parameterized fault simulation module are optimized, a simulation dataset is generated, and the agent model is incrementally trained and iteratively updated using the simulation dataset.

2. The intelligent diagnostic method for multi-motor coupled vibration according to claim 1, characterized in that, The parameterized fault simulation module performs the following sub-steps: Based on the physical parameters of the equipment, a parametric physical fault model library is constructed, including rotor imbalance, local bearing damage, misalignment, and mechanical loosening. Set the fault type, location, and severity parameters, and generate the corresponding fault source vibration signal through the physical fault model library; Based on the transmission path model between the vibration source and the sensor, the vibration signal of the fault source is coupled and superimposed onto the normal baseline vibration data to generate a multi-channel simulation vibration dataset with structured fault labels.

3. The intelligent diagnostic method for multi-motor coupled vibration according to claim 1, characterized in that, The dimensions of the temporal three-dimensional tensor include time step, number of sensor channels, and feature dimension; The composite reward function includes at least: a basic reward based on diagnostic accuracy, a timeliness reward related to the timing of fault diagnosis, and a security reward that penalizes missed reports of serious faults.

4. The intelligent diagnostic method for multi-motor coupled vibration according to claim 1, characterized in that, The online policy optimization algorithm is a proximal policy optimization algorithm. It uses an interactive data tuple containing state, action, reward and next state to update the model parameters by maximizing the pruned policy objective function.

5. The intelligent diagnostic method for multi-motor coupled vibration according to claim 1, characterized in that, Using accumulated, feedback-verified real-world data-label pairs, the model parameters of the parameterized fault simulation module are optimized, a simulation dataset is generated, and the agent model is incrementally trained and iteratively updated using the simulation dataset. Specifically, this includes: Data accumulation involves storing real vibration signals confirmed by human feedback and their corresponding high-confidence fault labels to build a database of real cases. Distribution alignment is performed, and the probability distribution differences of key features between the simulation data generated by the parameterized fault simulation module and the data in the real case database are calculated. Parameter optimization aims to minimize the distribution differences by automatically adjusting the fault model parameters and signal transmission path parameters in the parameterized fault simulation module. The model is iterated by generating a new simulation dataset using the optimized simulation module, incrementally training the agent model, and replacing the online diagnostic model with a more powerful trained model.

6. A multi-motor coupled vibration intelligent diagnostic system, characterized in that, The system for implementing the intelligent diagnostic method for multi-motor coupled vibration as described in any one of claims 1-5 includes: The parameterized fault simulation module is used to generate labeled multi-motor coupled vibration simulation data based on physical mechanisms. The deep reinforcement learning agent module, which includes a policy network and a value network, is used to learn diagnostic policies from simulated or real data. The online diagnosis and interaction module is used to deploy the intelligent agent module, process real-time vibration signals, output diagnostic suggestions, and collect human feedback. An online adaptive learning module is used to calculate rewards in real time based on feedback and update the policy network parameters of the agent module; The simulation-model evolution module is used to optimize the parameterized fault simulation module based on real feedback data and drive the iterative retraining of the agent module.

7. The multi-motor coupled vibration intelligent diagnostic system according to claim 6, characterized in that, The parameterized fault simulation module includes: The fault model library contains various mathematical fault models defined by parameters such as unbalanced mass, eccentricity, damage size, offset, angular deviation, and clearance. The coupled simulation engine is used to map the vibration signal output by the fault model to each sensor channel according to the transfer path matrix, and superimpose it with the normal baseline signal.

8. The multi-motor coupled vibration intelligent diagnostic system according to claim 6, characterized in that, The deep reinforcement learning agent module includes: The feature extraction and state construction unit is used to extract time-domain, frequency-domain, and time-frequency-domain features from the original vibration signal and construct a time-series three-dimensional state tensor. The core network of the intelligent agent adopts an Actor-Critic-based architecture, in which the Actor network outputs the probability distribution of diagnostic actions, and the Critic network evaluates the state value.

9. The multi-motor coupled vibration intelligent diagnostic system according to claim 6, characterized in that, The online adaptive learning module includes: The reward calculation unit calculates the real-time scalar reward based on the preset composite reward function and human feedback labels; An experience replay pool is used to cache interactive experience data. An online optimizer is used to sample data from the experience replay pool and execute a near-end policy optimization algorithm to update network parameters.

10. A multi-motor coupled vibration intelligent diagnostic system according to claim 6, characterized in that, The simulation-model evolution module includes: A high-confidence database of real-world cases; Distribution difference measurement unit, used to quantify the difference in feature distribution between simulated data and real data; The simulation parameter optimizer uses the gradient descent method to automatically adjust the fault simulation parameters with the goal of minimizing the distribution differences.

Images