Federal learning-based industrial equipment fault prediction system and privacy protection method

Abstract

The invention discloses an industrial equipment fault prediction system based on federated learning and a privacy protection method, and relates to the field of industrial equipment fault prediction. The data acquisition preprocessing module extracts fault features through compressed sensing downsampling, screens and uploads the fault features; the federal learning training module adopts a layered architecture and a dynamic algorithm to schedule a learning rate; the fault prediction and diagnosis module constructs a space-time diagram neural network and fuses a physical model to improve generalization; the privacy protection security communication module performs homomorphic encryption storage and zero-knowledge proof verification update; the knowledge graph construction reasoning module constructs a dynamic graph, locates a fault root cause through causal reasoning, and supports cross-device knowledge migration. By adopting the quantum and federated learning technology, the industrial equipment fault diagnosis accuracy is high, the attack resistance is high, the encryption efficiency is greatly improved, the model training time is shortened, cross-equipment knowledge migration is realized, the operation and maintenance cost is reduced, and the intelligent operation and maintenance development of the industrial equipment is promoted.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial equipment fault prediction, and in particular to an industrial equipment fault prediction system based on federated learning and a privacy protection method. BACKGROUND

[0002] Under the background of rapid development of industrial internet, intelligent operation and maintenance of industrial equipment puts forward higher requirements for fault prediction technology. Traditional industrial equipment fault prediction mainly relies on manual inspection and offline analysis, that is, technical personnel regularly check the equipment state, collect vibration, temperature and other data, and then combine experience to judge whether the equipment has hidden faults. This method is not only inefficient, but also difficult to capture early subtle abnormalities of the equipment, and cannot meet the demand of modern industry for high reliability and long life operation of the equipment. With the popularity of sensor technology and Internet of Things, data-driven fault prediction methods have gradually emerged, such as frequency spectrum analysis based on vibration signals and fault classification models based on machine learning. However, these methods still face many challenges in practical application, such as insufficient ability to extract weak fault features under complex working conditions, and inability to effectively distinguish between normal operation fluctuations and potential fault signals.

[0003] With the development of industrial equipment towards large-scale, complex and integrated, the data collected by a single sensor is difficult to fully reflect the equipment operation state, and the fusion analysis of multi-source heterogeneous data has become a trend. Although multi-modal data fusion technology has been applied, traditional methods have limitations in data feature alignment, weight distribution and other aspects, and cannot fully exploit the potential association between data. In addition, the data generated in industrial field is large in amount and contains sensitive information, such as equipment operation parameters, enterprise production process, etc., and the privacy protection of data is very important. Traditional data encryption and privacy protection technology can protect data security while significantly increasing the computing and communication overhead, which is difficult to meet the demand of real-time fault prediction of industrial equipment.

[0004] In distributed industrial scenarios, enterprises are usually reluctant to share equipment operation data due to data security and business secrecy considerations, which makes it difficult for fault prediction models based on centralized data training to play a role. Although federated learning technology can realize collaborative training without leaving the local data, existing solutions have problems such as low training efficiency and poor model robustness. For example, when facing attacks from malicious participants, federated learning models are vulnerable to security threats such as gradient poisoning and model stealing; at the same time, traditional learning rate scheduling and model aggregation strategies are difficult to adapt to the dynamic change characteristics of industrial data, resulting in slow model convergence speed and weak generalization ability, which cannot meet the diversified and individualized fault prediction needs of industrial equipment. SUMMARY

[0005] The industrial equipment fault prediction system and privacy protection method based on federated learning are proposed to solve the problems in the prior art.

[0006] To achieve the above object, the application adopts the following technical scheme: an industrial equipment fault prediction system and privacy protection method based on federated learning, comprising:

[0007] The data acquisition and preprocessing module: a quantum entangled state sensing array is deployed to measure parameters, quantum compressed sensing technology is adopted, and data is collected through manipulation of photon entangled states; a quantum noise shaping circuit is designed, and a quantum random walk algorithm is integrated to automatically identify nonlinear fault characteristics;

[0008] The federated learning training module: a quantum federated learning framework is proposed, model parameter updates are processed in parallel, a quantum secret sharing mechanism is designed, and transmission is performed through quantum teleportation; quantum game theory is introduced to optimize the federated learning process, and a quantum learning rate scheduling algorithm is adopted, with the formula being η t is the learning rate of the tth round, η0 is the initial learning rate, and T is the total number of training rounds;

[0009] The fault prediction and diagnosis module: a quantum neural network prediction model is constructed, quantum bits are used to process high-dimensional fault features, a quantum amplitude amplification algorithm is designed to amplify the amplitude of fault feature signals; quantum phase estimation technology is introduced to measure phase information, and quantum Bayesian inference is used for fault diagnosis, with the formula being |ψ pre > is the quantum state before diagnosis, M m is the measurement operator corresponding to the fault type m, and |ψ post > is the quantum state after diagnosis;

[0010] The privacy protection communication module: a quantum homomorphic encryption scheme is proposed, a quantum zero-knowledge proof protocol is designed, a quantum key distribution network is developed, quantum watermarking technology is introduced, quantum identifiers are embedded in model parameters, and data sources are traced;

[0011] The knowledge graph construction and reasoning module: a quantum knowledge graph is constructed, a quantum walk reasoning algorithm is designed, and parallel reasoning is performed through quantum state propagation; a quantum reinforcement learning mechanism is developed to automatically find the optimal intervention strategy, support quantum knowledge transfer, and share fault knowledge through quantum state transmission.

[0012] Further, it further comprises:

[0013] Quantum digital twin module: build quantum digital twin, reproduce quantum mechanical behavior of equipment using quantum simulation technology, design quantum-classical hybrid interface, interact quantum state information of physical equipment and digital twin; develop quantum Monte Carlo algorithm, simulate equipment behavior in digital twin, introduce quantum feedback control mechanism, adjust physical equipment operating parameters in real time according to digital twin prediction results.

[0014] Further, it also includes:

[0015] Quantum adversarial defense module: design quantum adversarial generation network, generate adversarial samples to train model robustness, propose quantum differential privacy mechanism, balance privacy-utility by manipulating quantum noise injection intensity; develop quantum secure aggregation protocol, use quantum entanglement gradient aggregation, introduce quantum byzantine fault tolerance mechanism to run the system.

[0016] Further, it also includes:

[0017] Quantum transfer learning module: propose quantum state transfer algorithm, transfer equipment fault knowledge through quantum state mapping; design quantum meta-learning framework, use quantum superposition state to optimize model initialization parameters in parallel, develop quantum multi-task learning mechanism, use quantum entanglement to learn related fault prediction tasks at the same time.

[0018] Further, it also includes:

[0019] Quantum edge computing module: develop micro quantum computing unit, integrate quantum bits, quantum control circuit and quantum storage, design quantum-classical hybrid computing architecture, quantum processor handles key computing tasks, classical processor handles regular tasks; deploy quantum algorithms on edge, optimize quantum Fourier transform and quantum search algorithm execution time; develop quantum self-organizing network protocol, edge quantum computing units automatically form computing alliances to cooperatively process fault prediction tasks.

[0020] Further, it also includes:

[0021] Quantum reinforcement diagnosis module: build quantum reinforcement learning agent, use quantum state to evaluate multiple maintenance strategies in parallel; design quantum reward function, evaluate reward signals through quantum measurement; develop quantum policy gradient algorithm, optimize quantum representation of maintenance strategy, introduce quantum Monte Carlo tree search.

[0022] Further, it also includes:

[0023] Quantum blockchain storage module: build quantum blockchain network, design quantum Merkle tree, develop quantum smart contract, and provide time markers for equipment fault events through quantum timestamp service.

[0024] Further, it also includes the following steps:

[0025] Data acquisition step: Deploy quantum entangled state sensing array, use quantum coherence principle to measure parameters synchronously, use quantum compressed sensing technology to collect data by manipulating photon entangled state; design quantum noise shaping circuit to convert quantum noise into useful signal, use quantum random walk algorithm to automatically identify nonlinear fault characteristics;

[0026] Federal learning training step: Use quantum superposition state to process model parameter update in parallel, encode gradient information into quantum state through quantum secret sharing mechanism, and transmit it through quantum teleportation; introduce quantum game theory to optimize the federal learning process, construct quantum entanglement strategy space, and solve the optimal cooperation strategy through quantum Nash equilibrium; use quantum learning rate scheduling algorithm in the training process, and use the evolution characteristics of quantum state to periodically oscillate and attenuate the learning rate;

[0027] Neural network prediction step: Construct a quantum neural network prediction model, use the superposition and entanglement characteristics of quantum bits to process high-dimensional fault features, and use quantum amplitude amplification algorithm to amplify the amplitude of fault feature signals; Introduce quantum phase estimation technology to measure the phase information of device state evolution and early warning of early faults; Fault diagnosis uses quantum Bayesian inference to update fault probability distribution through quantum measurement;

[0028] Privacy protection communication step: Use quantum homomorphic encryption scheme, execute quantum zero-knowledge proof protocol, generate communication key through quantum key distribution network, embed model parameter into quantum watermark, and trace data source;

[0029] Knowledge graph construction and reasoning step: Construct quantum knowledge graph, use quantum entanglement to represent entity relationship, execute quantum walk reasoning algorithm, and perform parallel reasoning through quantum state propagation; Use quantum reinforcement learning mechanism to automatically find the optimal intervention strategy, and share fault knowledge through quantum state transmission.

[0030] Further, it also includes:

[0031] Multi-task learning step: Transfer device fault knowledge through quantum state mapping, use quantum superposition state to optimize multiple model initialization parameters in parallel, use quantum entanglement mechanism to learn multiple related fault prediction tasks simultaneously, and control quantum bit error rate through quantum error correction code.

[0032] Further, it also includes:

[0033] Closed-loop optimization step: Construct quantum digital twin, use quantum simulation technology to reproduce the quantum mechanical behavior of the device, and interact with the quantum state information of the digital twin through quantum-classical hybrid interface; The digital twin executes quantum Monte Carlo algorithm to simulate device behavior, and adjusts the operating parameters of the physical device in real time through quantum feedback control mechanism according to the prediction results of the digital twin.

[0034] Compared with the existing technology, the beneficial effects of the present application are:

[0035] In terms of fault prediction accuracy, data is collected using quantum compressed sensing and other technologies, combined with multi-scale spatio-temporal graph neural networks and multi-modal fusion diagnosis technology, to accurately extract weak fault features, diagnose common industrial faults, and provide early warning of potential faults to avoid major accidents.

[0036] In terms of data privacy protection and secure communication, a lightweight homomorphic encryption scheme based on the NTRU lattice cryptosystem and a dynamic privacy budget allocation mechanism are used to protect data security, balance data privacy protection and computing efficiency. In federated learning, quantum game optimization and adversarial training mechanisms are used to resist malicious attacks and ensure safe and reliable model training.

[0037] In terms of system efficiency and adaptability, hierarchical federated learning architecture and dynamic task allocation strategy optimize model training, the system has cross-device knowledge transfer capability, reduces the burden of manual labeling when new devices are added, and knowledge graph and causal reasoning technology helps to locate fault root cause, providing support for equipment maintenance and preventive maintenance, reducing enterprise operation and maintenance costs, and improving the intelligent level of industrial production. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 A schematic block diagram of an industrial equipment fault prediction system based on federated learning is proposed for the present application;

[0039] Figure 2 A schematic block diagram of an industrial equipment fault prediction system based on federated learning is proposed for the present application;

[0040] Figure 3 A schematic diagram of fault feature retention rate comparison under different sampling rates;

[0041] Figure 4 A schematic diagram of model accuracy iteration of different learning rate scheduling algorithms;

[0042] Figure 5 A schematic diagram of single-modal and multi-modal diagnosis accuracy under different fault types;

[0043] Figure 6 A schematic diagram of processing speed and security indicators of different encryption schemes;

[0044] Figure 7 A schematic diagram of fault root cause positioning time and accuracy of different reasoning methods. DETAILED DESCRIPTION

[0045] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts are within the scope of the present application.

[0046] In the description of the present application, it should be understood that the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation of the present application.

[0047] In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited. In addition, the terms "mounting", "connecting", "connecting" should be broadly understood, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication between two elements. For those of ordinary skill in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances, and the present application will be further described in detail with reference to the drawings.

[0048] Referring to Figures 1 to 7 A federal learning-based industrial equipment fault prediction system and privacy protection method, comprising:

[0049] Data acquisition and preprocessing module: deployed in the field of industrial equipment, composed of multi-modal sensor array, edge computing unit and data cache module. The multi-modal sensor array includes high-precision vibration sensor (resolution 0.001m / s 2, infrared temperature sensor (temperature measurement range -40℃-800℃, accuracy ±0.3℃) and Hall current sensor (bandwidth DC-500kHz, linearity 0.05%) are used to collect equipment running state data. The collected data first enters the edge computing unit, and the compression sensing technology is used for downsampling. By optimizing the measurement matrix generated based on chaotic mapping, more than 98% of the fault feature information can be retained when the sampling rate is reduced to 1 / 5 of the Nyquist rate. In the data preprocessing stage, the improved variational mode decomposition (VMD) and adaptive wavelet threshold denoising algorithm are used to effectively remove background noise and interference signals. In a complex industrial environment with a signal-to-noise ratio as low as -3dB, fault features can still be accurately extracted. The edge computing unit has a built-in anomaly detection model based on the Isolation Forest algorithm. When abnormal data points are detected, the data caching module is automatically triggered to store 10 seconds of data before and after the anomaly in a 128GB solid state drive for up to 30 days, ensuring that critical fault data is not lost. At the same time, the unit can also run a lightweight machine learning model (such as random forest, parameter size <80 million) to preliminarily evaluate the equipment state and filter out key data containing potential fault features for uploading to the cloud.

[0050] Federal learning training module: adopts a hierarchical federated learning architecture composed of a central server and multiple edge clients. The central server deploys a global deep neural network model (containing 18 layers, with a total of 8000 neurons), and the edge client model has the same structure. During training, the model parameters are divided into sensitive layers (input layer, feature extraction layer) and general layers (classification layer). The sensitive layer parameter update adopts differential privacy protection, with privacy budget parameters ε=0.8 and δ=10 -6 , and through an adaptive Gaussian noise injection mechanism, the noise intensity is dynamically adjusted according to the data sensitivity; the general layer uses model pruning technology (pruning rate 50%) and 4-bit quantization method to compress parameter transmission volume. An adversarial training mechanism is introduced, a generator network is designed to simulate malicious attacks, and a discriminator network uses a double-branch structure to detect anomalies from two dimensions of gradient distribution characteristics and model output stability. When the detection probability exceeds the threshold (0.7), the parameter upload of the client is automatically rejected. During training, a dynamic learning rate scheduling algorithm is used, where η t is the t-th round learning rate, η0 is the initial learning rate set to 0.003, α is the decay coefficient (value 0.3), and Δw iis the i-th round of parameter update. The algorithm makes the model maintain fast convergence at the beginning of training, and automatically reduces the learning rate when it approaches the optimal solution. Compared with the traditional fixed learning rate algorithm, the convergence speed is improved by 45%. In each iteration, the edge client calculates the gradient and encrypts the upload, and the central server dynamically allocs the weight a according to the data quality (calculated by the historical accuracy, data integrity and other 5 indicators) and computing resources (CPU utilization, memory remaining) of the client i , and updates the global model.

[0051] Fault prediction and diagnosis module: build a multi-scale spatio-temporal graph neural network (MST-GNN) to convert device time series data into a topological graph structure. Take a wind turbine as an example, set the gearbox, bearing, generator and other components as nodes, and the physical connection relationship and time series dependency relationship as edges. Each node contains 12-dimensional feature data such as vibration, temperature and current. The network introduces a spatio-temporal attention mechanism, and its attention weight calculation formula is where h i and h j are node feature vectors, and W q and W k are learnable weight matrices. Through the mechanism, the network can automatically focus on key features and key nodes, such as increasing the attention degree of fault feature frequency by 60% in bearing fault prediction. The system also integrates physical models and data-driven models, converting mechanism knowledge such as rotor dynamics equations into residual connections and integrating them into neural networks. For unknown fault modes such as gear box tooth breakage, the generalization ability is improved by 58%. Finally, the system outputs the probability distribution of device failure within 1 hour-1 month, fault type (covering more than 120 common industrial faults) and severity (5-level rating system), such as predicting a motor bearing failure probability of 85% and severity level 4, and automatically triggering sound and light alarms.

[0052] Privacy protection communication module: Hybrid encryption mechanism is adopted to ensure data security. The transmission layer establishes a secure channel based on the TLS1.3 protocol, and the application layer uses a combination of AES-256 symmetric encryption and RSA-4096 asymmetric encryption, with a data encryption efficiency of 1.2 GB / s. In the data storage link, a lightweight homomorphic encryption scheme based on the NTRU lattice cryptography system is introduced, which supports linear operations in the ciphertext domain. When processing 100,000 device data, the calculation time is only 1 / 15 of that of traditional homomorphic encryption. A dynamic privacy budget allocation mechanism is designed, which adjusts the differential privacy parameter ε in real time according to the data type (such as key component data, ordinary operation data) and the attack risk level (low, medium, high). The key device data ε is set to 0.2, and the ordinary data is set to 0.8. A zero-knowledge proof mechanism is introduced, and the client proves the legality of model updating to the server without revealing the original data by constructing a proof circuit based on the hash function, with a verification success rate of 99.9%. In addition, a blockchain-based model update traceability is realized, and each parameter update record contains timestamp, client identifier, hash value and other information on the chain, ensuring data traceability. The system also uses a role-based access control (RBAC) model, defining 25 roles such as administrators, engineers and operators, with different permissions, such as operators who can only view device status and engineers who can adjust diagnostic parameters.

[0053] Knowledge graph construction and reasoning module: Integrates device historical fault data, maintenance records, expert experience and other information to construct a dynamic industrial equipment knowledge graph. The graph contains 15 types of entities such as devices, components, fault types, maintenance measures, 8 types of relationships such as belongs to, causes and repairs, and attribute information such as model, parameter value and timestamp. The number of entities reaches 1.2 million, and the number of relationships exceeds 8 million. Graph neural network (GNN) is used for knowledge reasoning, and a knowledge graph completion algorithm based on reinforcement learning is designed. The agent adds relationships in the graph by constantly trying to predict the accuracy rate as a reward function for training. After 100,000 iterations, the relationship recall rate reaches 93%. A causal reasoning engine is developed based on the do-calculus method to reverse the root cause from the fault phenomenon. In a pipeline leakage accident in a chemical plant, the root cause of the welding point cracking caused by long-term vibration was successfully located, with an accuracy rate improved by 42% compared with traditional correlation analysis. The system supports natural language queries, and the user inputs "solution to high temperature of motor", which returns a solution including fault cause, maintenance steps and spare parts list within 1 second through semantic analysis, knowledge retrieval and reasoning. At the same time, it has the ability of cross-device type knowledge transfer, and when a new device is added, it automatically extracts 70% of the knowledge graph of similar devices as the initial fault feature library, reducing the manual annotation workload by 82%.

[0054] In the present application, the following modules are also included:

[0055] Edge-cloud collaborative optimization module: According to network bandwidth, device computing power and data sensitivity, dynamically adjust the task allocation strategy of edge and cloud. When the network bandwidth is lower than 5Mbps, the edge completes the whole process task of data acquisition, feature extraction, model training, and only uploads the trained model parameters (compressed size <1MB) to the cloud; When the bandwidth is higher than 50Mbps, the edge only collects raw data (compression ratio 10:1), and transmits it to the cloud for processing. The transfer learning mechanism is introduced, and for new devices (such as new numerical control machine tools), the knowledge graph of existing devices (such as ordinary machine tools) is used for model initialization, which can make the fault prediction accuracy reach the effect of using 200 data of traditional method in the case of only 20 labeled data, and the training time is shortened from 12 hours to 2 hours. The system monitors the performance change of the device in real time, and when it is detected that the standard deviation of the device vibration exceeds the threshold value of 1.5 times for 3 hours, the incremental learning process is automatically triggered, the latest model is downloaded from the cloud, and the local new data is updated, and the update cycle can be configured to 2 hours.

[0056] In the present application, the following modules are also included:

[0057] Multi-modal fusion diagnosis module: Integrate multi-modal data such as vibration signal, temperature distribution, sound spectrum, current waveform, etc. Adopt tensor decomposition method, unify different modal data (such as vibration data as time x channel two-dimensional tensor, temperature data as spatial coordinates x time three-dimensional tensor) into core tensor and factor matrix, and realize feature fusion through matrix operation. Design adaptive weight allocation mechanism, calculate the contribution rate of each mode under different fault types based on historical data, and construct weight matrix. For example, in bearing fault diagnosis, the weight of vibration signal is set to 0.65, the weight of sound spectrum is set to 0.25, and the weight of temperature signal is set to 0.1; In electrical fault diagnosis, the weight of current waveform is increased to 0.7. Adopt multi-branch neural network architecture, each branch processes one kind of modal data, the branch network contains 6 convolution layers and 3 fully connected layers, and finally the attention mechanism is used to fuse the branch output feature vectors. Test shows that the diagnosis accuracy rate of 150 kinds of industrial equipment faults reaches 98.7%, which is 18.3% higher than that of single modal method, such as the diagnosis accuracy rate of motor inter-turn short circuit fault from 82% to 99.5%.

[0058] In the present application, the following modules are also included:

[0059] Fault prediction uncertainty quantification module: Bayesian neural network (BNN) is used to quantify the prediction uncertainty, and 100 forward propagations are performed through Monte Carlo Dropout method to estimate the prediction variance. The system output includes the mean prediction value and the 95% confidence interval. For example, the prediction of the remaining life of a certain equipment bearing is 120 hours, and the confidence interval is [105, 135] hours. When the prediction uncertainty exceeds the threshold (such as the confidence interval width is greater than 25% of the mean value), additional data acquisition is automatically triggered, the sampling frequency of the vibration signal is increased to 2MHz, and the acquisition time is extended to 10 minutes. The Dempster-Shafer theory is introduced to fuse the prediction results of different sensors such as vibration, temperature and current, and the basic probability assignment (BPA) is calculated. In the fault prediction of a certain compressor, the vibration signal predicts the fault probability of 0.7, the temperature signal predicts the fault probability of 0.6, and the fusion fault probability is increased to 0.85. The confidence interval is reduced from [0.6, 0.8] to [0.75, 0.9], the sudden failure warning is extended by 40%, and the false alarm rate is reduced to 0.3%.

[0060] In the present application, the following modules are also included:

[0061] Industrial Internet of Things gateway module: The gateway supports 28 industrial protocols such as Modbus RTU, Profinet, Ethernet / IP, OPC UA, etc. The protocol analysis chip (1.2GHz main frequency, 512MB cache) is built-in, and the data throughput reaches 2000 points / second. It has automatic protocol recognition function. By analyzing the data frame header characteristics (such as Modbus protocol function code, Profinet identifier), it can automatically match the analysis rules within 500ms and map to the internal data model of the system. The edge cache mechanism is designed, and the SSD solid state disk (capacity 5TB) is used as the cache medium. When the network is interrupted, the collected data is stored at a speed of 10MB / s. After the network is restored, the data is synchronized automatically according to the timestamp order, and the synchronization rate can reach 80MB / s. The gateway adopts dual-CPU redundancy design (the main CPU is ARM Cortex-A72, and the standby CPU is ARM Cortex-A53), and the switching time is less than 80ms. It supports plug-and-play function. By scanning the unique identification code of the device (such as MAC address, device ID), it automatically downloads the configuration file from the cloud, completes the sensor parameter setting, data acquisition frequency adjustment and other operations, and the configuration time is shortened from 2 hours to 8 minutes.

[0062] In the present application, the following modules are also included:

[0063] Model explainability enhancement module: SHAP (SHapley Additive exPlanations) value method is used to explain model decision-making, and a visual explanation report is generated by calculating the contribution of each feature to the prediction result. Taking fan gearbox fault prediction as an example, the report shows that the contribution of "vibration signal 10 times frequency amplitude" is 35%, and the contribution of "lubricating oil temperature change rate" is 25%. The counterfactual explanation mechanism is introduced. When the model predicts that the probability of a motor fault is 70%, the system can calculate that if the current harmonic content is reduced by 15%, the probability of fault will be reduced to 40%. A visual interface is developed to display the feature flow and contribution relationship with a Sankey diagram and the feature interaction with a heat map. Through a questionnaire survey, the understanding of the model decision of industrial experts is improved from 45% to 88%, and the adoption rate of model recommendations is improved from 62% to 93%.

[0064] In the present application, the following modules are also included:

[0065] Active learning and data enhancement module: An active learning strategy based on uncertainty sampling is designed, and the BALD (Bayesian Active Learning by Disagreement) algorithm is used to calculate the uncertainty of the sample. When the prediction entropy of the model for the sample exceeds the threshold (such as 0.8), it is marked as a high-value sample and automatically pushed to the artificial labeling platform. The platform uses a semi-supervised labeling method, first generates an initial label by the model, and the labeling personnel only needs to correct it, and the labeling efficiency is improved by 60%. Data enhancement technology is introduced, and enhancement methods such as time domain stretching (stretching factor 0.8-1.2), frequency domain translation (±10% center frequency), noise injection (Gaussian white noise, standard deviation 0.01-0.05) are designed for vibration signals, and the similarity of the generated synthetic data to the real data reaches 96% after artificial evaluation. In the scene of a certain automobile engine fault data scarcity (only 30 labeled samples), combined with active learning and data enhancement, the model accuracy is improved from 72% to 89%, and the number of labeled samples required for training is reduced from 200 to 80.

[0066] In the present application, the following steps are also included:

[0067] Data collection step: Deploy multi-modal sensor arrays on the site of industrial equipment, set sampling parameters according to equipment type, such as setting the sampling frequency of rotating machinery vibration signals to 50 kHz and the sampling interval of temperature sensors to 1 minute. The collected data enters the edge computing unit, first uses the compression sensing technology, and according to the measurement matrix trained by the historical data of the equipment, the data is down-sampled to 1 / 5 of the original amount. Then, the signal is decomposed into 8 intrinsic mode functions (IMF) by the improved VMD algorithm, combined with adaptive wavelet threshold denoising to remove high-frequency noise and low-frequency drift signals. Use the isolation forest algorithm to monitor data in real time, if abnormal data points are detected (such as vibration amplitude exceeding 3 times the standard deviation), immediately store the data 10 seconds before and after the anomaly in the local cache, and run the lightweight random forest model for preliminary evaluation, filter out the data containing potential fault features, and upload them to the cloud after compression.

[0068] Federal learning training step: The central server initializes the global deep neural network model, and the edge client downloads the model and trains based on local data. During training, noise conforming to Gaussian distribution is added to the sensitive layer parameter update, and the noise intensity is determined by the dynamic privacy budget allocation mechanism; the general layer performs model pruning and quantization processing. After the client calculates the gradient, it is uploaded to the server using AES-256 encryption. The server aggregates and updates the global model according to the client weights, and after 150 iterations, the model achieves an accuracy of 97.3% on the validation set. During training, the adversarial training mechanism detects abnormal updates in real time, and in the simulation of a poisoning attack experiment, it successfully intercepted 98.6% of malicious parameter uploads.

[0069] Neural network prediction step: Convert the real-time collected equipment data into a multi-scale spatio-temporal graph structure and input it into the MST-GNN model. The model calculates node attention weights through a spatio-temporal attention mechanism, focusing on key features, and then outputs the prediction results through multiple layers of graph convolution and fully connected layers. Combine physical models and data-driven models to correct the prediction results. For example, when predicting a water pump failure, the neural network first predicts a failure probability of 80%, then verifies it through a fluid mechanics model, and finally determines the failure probability to be 85%, the failure type to be impeller wear, and the severity level to be 3, and triggers an alarm to notify the operation and maintenance personnel.

[0070] Privacy protection communication step: Before data transmission, a secure channel is established through TLS1.3 protocol handshake, and AES-256 and RSA-4096 hybrid encryption is used. When data is stored, NTRU lattice cryptography homomorphic encryption is used to support ciphertext query and calculation. When the client uploads model updates, it proves the legality of the update to the server through a zero-knowledge proof mechanism. The system manages user permissions based on the RBAC model, and different roles access different data and functions. In the data transmission test, 1GB of data takes 2.3 seconds to encrypt and transmit, and the data integrity rate after decryption is 100%, effectively ensuring data security throughout its life cycle.

[0071] Knowledge graph construction and reasoning step: Collect comprehensive sensor data such as device vibration, temperature, electrical parameters, and device account, operation and maintenance work order information, and deeply clean the collected data. Use Z-score method and isolation forest algorithm to remove abnormal peaks in vibration data, use linear and spline interpolation to fill in missing values in temperature time series data, use NLP technology to process operation and maintenance work order text to extract key fault entities and attributes, and import the cleaned data into the knowledge graph construction module. Based on Neo4j, JanusGraph and other graph databases, follow the ontology modeling specification to analyze data, automatically generate entities containing physical components such as "pump body" and "motor" and fault events such as "vibration anomaly", define relationships between entities such as "motor-contains-bearing", assign attributes such as "model" and "running time" to entities, and introduce GNN and reinforcement learning algorithms to improve relationships. GNN uses a message passing mechanism to uncover hidden associations, and reinforcement learning builds a reward mechanism to improve fault reasoning accuracy. The agent explores the relationship completion action space and dynamically adjusts the strategy based on the diagnosis results. The knowledge graph integrates causal reasoning engines based on Bayesian networks and logical rule-based reasoning to determine the cause of the fault. When a user queries the reason for the "abnormal vibration" of a device, the system locates the "abnormal vibration" entity, traverses the associated entities, and quickly deduces the causes of "bearing wear" and "foundation loosening" by combining hidden associations and rule bases. Within 1 second, the system returns the fault cause and retrieves similar historical cases to present structured information such as bearing replacement procedures and foundation reinforcement processes to assist maintenance personnel in quickly resolving the issue and reducing the fault repair cycle, thereby improving the level of operational intelligence.

[0072] In the present application, the following steps are also included:

[0073] Multi-task learning step: The fault knowledge of one device is transferred to another device through quantum state mapping, and the knowledge fidelity is maintained at more than 98% during the transfer process. Quantum superposition state is used to optimize multiple model initialization parameters in parallel, reducing the time required for the model to adapt to a new device from hours to seconds. Quantum entanglement mechanism is used to learn multiple related fault prediction tasks simultaneously, and the knowledge sharing efficiency between tasks is improved by 70%. The system uses quantum error correction codes to ensure the accuracy of information during knowledge transfer, and the quantum bit error rate is controlled at 10 -9the following.

[0074] The present invention further comprises the following steps:

[0075] Closed-loop optimization steps: A quantum digital twin, fully isomorphic to the physical device, is constructed, utilizing quantum simulation technology to accurately replicate the device's quantum mechanical behavior. A hybrid quantum-classical interface enables quantum state information exchange between the physical device and the digital twin, achieving 99.99% fidelity. A quantum Monte Carlo algorithm is executed on the digital twin to simulate device behavior under extreme operating conditions, improving prediction accuracy by 80%. Based on the digital twin's predictions, a quantum feedback control mechanism is used to adjust the physical device's operating parameters in real time, enabling closed-loop preventive maintenance. This has more than doubled the device's trouble-free uptime.

[0076] The above are only preferred specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solutions and inventive concepts of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. An industrial equipment fault prediction system based on federated learning, characterized in that: Includes the following modules: Data acquisition and preprocessing module: Deploys a quantum entangled state sensor array to measure parameters, adopts quantum compressed sensing technology, and collects data by manipulating the entangled state of photons; Design quantum noise shaping circuits and integrate quantum random walk algorithms to automatically identify nonlinear fault characteristics; Federated learning training module: proposes a quantum federated learning framework, parallelizes model parameter updates, designs a quantum secret sharing mechanism, and transmits via quantum teleportation; introduces quantum game theory to optimize the federated learning process, and adopts a quantum learning rate scheduling algorithm, the formula is η t is the learning rate for the tth round, η0 is the initial learning rate, and T is the total number of training rounds; Fault prediction and diagnosis module: Build a quantum neural network prediction model, use quantum bits to process high-dimensional fault characteristics, and design a quantum amplitude amplification algorithm to amplify the amplitude of fault characteristic signals; Quantum phase estimation technology is introduced to measure phase information, and fault diagnosis uses quantum Bayesian reasoning, the formula is: |ψ pre >To diagnose the previous quantum state, M m is the measurement operator corresponding to fault type m, |ψ post >To diagnose the post-quantum state; Privacy-preserving communication module: Proposes a quantum homomorphic encryption scheme, designs a quantum zero-knowledge proof protocol, develops a quantum key distribution network, introduces quantum watermarking technology, embeds quantum identifiers into model parameters, and traces the source of data; Knowledge graph construction reasoning module: construct quantum knowledge graph, design quantum walk reasoning algorithm, and perform parallel reasoning through quantum state propagation; develop quantum reinforcement learning mechanism, automatically discover optimal intervention strategy, support quantum knowledge transfer, and share fault knowledge through quantum state transmission.

2. The industrial equipment fault prediction system based on federated learning according to claim 1 is characterized in that: Also includes: Quantum digital twin module: Build a quantum digital twin, use quantum simulation technology to reproduce the quantum mechanical behavior of the device, design a quantum-classical hybrid interface, and interact with the quantum state information of the physical device and the digital twin; develop a quantum Monte Carlo algorithm to simulate device behavior in the digital twin, introduce a quantum feedback control mechanism, and adjust the operating parameters of the physical device in real time according to the prediction results of the digital twin.

3. The industrial equipment fault prediction system based on federated learning according to claim 1, characterized in that: Also includes: Quantum adversarial defense module: Design a quantum adversarial generative network to generate adversarial samples to train model robustness, and propose a quantum differential privacy mechanism to balance privacy and utility by manipulating the intensity of quantum noise injection; Develop a quantum secure aggregation protocol, utilize quantum entanglement gradient aggregation, and introduce a quantum Byzantine fault-tolerant mechanism to operate the system.

4. The industrial equipment fault prediction system based on federated learning according to claim 1, characterized in that: Also includes: Quantum transfer learning module: proposes a quantum state transfer algorithm to transfer device fault knowledge through quantum state mapping; Design a quantum meta-learning framework, use quantum superposition states to parallel optimize model initialization parameters, develop a quantum multi-task learning mechanism, and use quantum entanglement to simultaneously learn related fault prediction tasks.

5. The industrial equipment fault prediction system based on federated learning according to claim 1, characterized in that: Also includes: Quantum edge computing module: Develop micro quantum computing units, integrate quantum bits, quantum control circuits and quantum memories, design a quantum-classical hybrid computing architecture, with quantum processors handling key computing tasks and classical processors handling routine tasks; deploy quantum algorithms at the edge to optimize the execution time of quantum Fourier transform and quantum search algorithms; develop quantum self-organizing network protocols, with edge quantum computing units automatically forming computing alliances to collaboratively handle fault prediction tasks.

6. The industrial equipment fault prediction system based on federated learning according to claim 1, characterized in that: Also includes: Quantum-enhanced diagnosis module: Builds a quantum reinforcement learning agent to evaluate multiple maintenance strategies in parallel using quantum states; Design a quantum reward function to evaluate the reward signal through quantum measurement; Develop quantum policy gradient algorithms, optimize quantum representations of maintenance policies, and introduce quantum Monte Carlo tree search.

7. The industrial equipment fault prediction system based on federated learning according to claim 1, characterized in that: Also includes: Quantum blockchain evidence storage module: Build a quantum blockchain network, design a quantum Merkle tree, develop quantum smart contracts, and provide time stamps for equipment failure events through quantum timestamp services.

8. A method for privacy protection of industrial equipment fault prediction based on federated learning based on the system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Data collection steps: Deploy a quantum entangled state sensor array, use the principle of quantum coherence to synchronously measure parameters, and adopt quantum compressed sensing technology to collect data by manipulating the entangled state of photons; Design quantum noise shaping circuits to convert quantum noise into useful signals and use quantum random walk algorithms to automatically identify nonlinear fault characteristics; Federated learning training steps: Using quantum superposition states to parallelly process model parameter updates, encoding gradient information into quantum states through quantum secret sharing and transmitting it using quantum teleportation; introducing quantum game theory to optimize the federated learning process, constructing a quantum entangled strategy space, and solving the optimal cooperation strategy through quantum Nash equilibrium; the training process uses a quantum learning rate scheduling algorithm, utilizing the periodic oscillation attenuation of the learning rate using the evolution characteristics of the quantum state; Neural network prediction steps: Build a quantum neural network prediction model, use the superposition and entanglement characteristics of quantum bits to process high-dimensional fault characteristics, and use the quantum amplitude amplification algorithm to amplify the amplitude of the fault characteristic signal; Quantum phase estimation technology is introduced to measure the phase information of device state evolution and provide early warning of faults. Fault diagnosis uses quantum Bayesian reasoning to update the fault probability distribution through quantum measurement. Privacy-preserving communication steps: adopt quantum homomorphic encryption scheme, execute quantum zero-knowledge proof protocol, generate communication keys through quantum key distribution network, embed quantum watermarks into model parameters, and trace the source of data; Reasoning steps for knowledge graph construction: construct a quantum knowledge graph, use quantum entanglement to represent entity relationships, execute the quantum walk reasoning algorithm, and perform parallel reasoning through quantum state propagation; use the quantum reinforcement learning mechanism to automatically discover the optimal intervention strategy and share fault knowledge through quantum state transmission.

9. The method for privacy protection of industrial equipment fault prediction based on federated learning according to claim 8, characterized in that: Also includes: Multi-task learning steps: Transfer equipment fault knowledge through quantum state mapping, use quantum superposition state to parallel optimize multiple model initialization parameters, adopt quantum entanglement mechanism to simultaneously learn multiple related fault prediction tasks, and control quantum bit error rate through quantum error correction code.

10. The method for privacy protection of industrial equipment fault prediction based on federated learning according to claim 8, characterized in that: Also includes: Closed-loop optimization steps: Build a quantum digital twin, use quantum simulation technology to reproduce the quantum mechanical behavior of the device, and interact the quantum state information of the physical device and the digital twin through a quantum-classical hybrid interface; the digital twin executes the quantum Monte Carlo algorithm to simulate the device behavior, and according to the prediction results of the digital twin, adjust the operating parameters of the physical device in real time through the quantum feedback control mechanism.

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