Network equipment fault prediction system based on AI

Abstract

The embodiment of the invention provides an AI-based network equipment fault prediction system. The method comprises the following steps: acquiring SMART information, temperature data and a power supply state of equipment in real time through a hardware sensor; extracting a hardware error log and performance index data from the system log; inputting the SMART information, the temperature data, the power state, the hardware error log and the performance index data into a pre-trained dynamic model cluster, and outputting a fault probability and a residual life prediction value; and when the fault probability exceeds a preset threshold value, the grading early warning module is triggered to generate a maintenance instruction. According to the system, a holographic portrait for dynamically sensing the health state of equipment is constructed through multi-modal data fusion of a hardware sensor and a system log, different equipment models and service stages are adaptively matched by adopting a pre-training model cluster, and end-to-end intelligent analysis from original data acquisition to residual life prediction is realized.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of equipment operation and maintenance technology, and in particular to an AI-based network equipment fault prediction system. Background Technology

[0002] Currently, network equipment operation and maintenance generally adopts alarm mechanisms based on fixed thresholds. These rely on manual experience to set static rules, which cannot adapt to dynamic changes such as hardware aging and load fluctuations, resulting in a high false alarm rate and difficulty in predicting hidden faults. Traditional methods separate sensor data from system logs, creating information silos. Fault feature extraction is limited to single-dimensional statistical analysis and lacks the ability to quantitatively model equipment degradation trends. Existing prediction systems mostly use single machine learning models, which have insufficient generalization ability when facing heterogeneous device clusters, and model updates require manual intervention, making it difficult to meet real-time requirements.

[0003] Therefore, a better solution is urgently needed. Summary of the Invention

[0004] In view of this, embodiments of this specification provide an AI-based network device fault prediction system to address the technical deficiencies existing in the prior art.

[0005] According to a first aspect of the embodiments of this specification, an AI-based network device fault prediction system is provided, comprising: The device's SMART information, temperature data, and power status are collected in real time using hardware sensors. Extract hardware error logs and performance metrics data from the system logs; Input SMART information, temperature data, power status, hardware error logs and performance metrics into a pre-trained dynamic model cluster, and output failure probability and remaining lifetime prediction values. When the probability of failure exceeds a preset threshold, the graded early warning module is triggered to generate maintenance instructions.

[0006] In one possible implementation, the construction of the dynamic model cluster includes: Match the initial model combination according to the equipment model and service duration; The parameters in the initial model combination are updated through an online incremental learning mechanism; Generative adversarial networks are used to verify the degree of model drift and trigger retraining.

[0007] In one possible implementation, performance metrics include IOPS, latency, and memory usage, and are processed through the following steps: Establish a dynamic baseline library for load patterns to store the normal fluctuation range under different business scenarios; The deviation between real-time data and the dynamic baseline database is calculated using a sliding window algorithm.

[0008] In one possible implementation, the tiered early warning module performs the following: The time window for predicting failures is divided into three categories: short-term, medium-term, and long-term. Work orders are automatically generated and prioritized based on the inventory status of maintenance resources.

[0009] In one possible implementation, the calculation of the remaining lifetime prediction includes: Obtain the time series of failure probabilities output by the dynamic model cluster ; Establish the Weibull distribution function Fit the equipment reliability curve; Solving shape parameters using maximum likelihood estimation and scale parameters ,in Characterizes the trend of failure rate changes. Characteristic lifespan cycle.

[0010] In one possible implementation, the parameters of the Weibull distribution function are determined through the following steps: Collect historical fault intervals Construct a sample set; Construct the log-likelihood function ; The gradient descent method is used to iteratively solve the problem. Maximize the combination of parameters .

[0011] In one possible implementation, the formula for calculating the critical component replacement urgency index is:

[0012] in The cumulative failure probability of component k within period T is derived from the integral result of the Weibull distribution function; The remaining useful life is obtained through dynamic model cluster regression. and These are respectively derived from downtime losses and spare parts replacement costs recorded in the operations and maintenance cost database; and These are preset weighting coefficients.

[0013] In one possible implementation, the calculation process for the cumulative failure probability includes: Numerical integration of the failure probability time series:

[0014] in The instantaneous failure rate function is obtained by differentiating the Weibull distribution function.

[0015] In one possible implementation, the process of constructing a multimodal data coupling index includes: Extract fan speed and power supply ripple coefficient Real-time measurement values; Calculate joint outlier:

[0016] in and Statistical feature library derived from historical data of normal equipment operation These are the modal weighting coefficients.

[0017] In one possible implementation, the optimization formula for the modal weighting coefficients is:

[0018] The denominator counts all modes in the historical fault sample set. Total number of occurrences in the numerator, numerator statistics of specific modes exist The number of times it appears in the text.

[0019] This specification provides an AI-based network device fault prediction system, comprising: real-time acquisition of SMART information, temperature data, and power status of the device via hardware sensors; extraction of hardware error logs and performance index data from system logs; inputting the SMART information, temperature data, power status, hardware error logs, and performance index data into a pre-trained dynamic model cluster, and outputting fault probability and remaining life prediction values; when the fault probability exceeds a preset threshold, triggering a tiered early warning module to generate maintenance instructions. This system constructs a holographic profile of the dynamically perceived device health status through multimodal data fusion from hardware sensors and system logs, and uses a pre-trained model cluster to adaptively match different device models and service stages, achieving end-to-end intelligent analysis from raw data acquisition to remaining life prediction. Attached Figure Description

[0020] Figure 1 This is a system schematic diagram of an AI-based network device fault prediction system provided in one embodiment of this specification. Detailed Implementation

[0021] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0022] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0023] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0024] This specification provides an AI-based network device fault prediction system, which will be described in detail in the following embodiments.

[0025] See Figure 1 , Figure 1 The diagram illustrates a system schematic of an AI-based network device fault prediction system according to an embodiment of this specification. Specifically, it includes real-time acquisition of SMART information, temperature data, and power status of the device via hardware sensors; extraction of hardware error logs and performance indicator data from system logs; input of SMART information, temperature data, power status, hardware error logs, and performance indicator data into a pre-trained dynamic model cluster, outputting fault probability and remaining life prediction values; and triggering a tiered early warning module to generate maintenance instructions when the fault probability exceeds a preset threshold.

[0026] Among these, SMART information refers to the parameter set generated by hard drive self-monitoring and analysis reporting technology, used to reflect the health status of the storage medium. Temperature data refers to real-time thermodynamic indicators of chips such as CPUs / GPUs, which can provide early warnings of heat dissipation system failure risks. Power status describes electrical characteristics such as input voltage and current ripple, and can identify power supply instability. Hardware error logs refer to low-level records such as memory ECC errors and PCIe link errors, used to locate the root cause of physical layer faults. Performance indicator data includes service load parameters such as IOPS and latency, which can quantify the degradation of equipment service capabilities. Dynamic model clusters are used to integrate prediction engines of multiple machine learning algorithms, enabling parallel detection of multiple fault modes. Failure probability refers to the estimated likelihood of equipment failure in a specific future period, used to trigger preventative maintenance. Remaining life prediction values ​​characterize the sustainable operating time of critical components, which can optimize spare parts procurement cycles. The graded early warning module generates differentiated response strategies based on risk levels, enabling precise resource scheduling. Preset thresholds refer to manually set risk thresholds, used to distinguish between normal and abnormal states. Maintenance instructions include standardized operating guidelines with specific repair steps, which can guide on-site engineers in troubleshooting.

[0027] As a concrete example: After deploying this system in a data center, CPU core temperature data is collected in real time via temperature sensors installed on the servers (sampling frequency 10Hz), while the system periodically reads the reallocation sector count from the hard drive's SMART (every 5 minutes). The system log analysis engine continuously monitors PCIe error information output by dmesg, combined with memory usage metrics collected by Prometheus (accuracy 0.1%). All data is standardized and then input into a dynamic model cluster. This cluster contains three dedicated sub-models: an XGBoost-based hard drive failure prediction model (accuracy 92%), an LSTM temperature trend prediction model (MAE less than 3℃), and a survival analysis model for power modules. When the SSD failure probability of a server exceeds the 0.65 threshold, the system automatically generates an L2-level work order containing the rack location and spare part model, and pushes it synchronously to the mobile terminal of the maintenance personnel.

[0028] This system significantly improves fault identification accuracy by integrating multi-dimensional device status data, and is particularly adept at detecting progressive degradation faults that are difficult to detect using traditional threshold methods. The adaptive nature of the dynamic model automatically adapts to the operating characteristics of different brands of equipment, avoiding subjective biases caused by manual parameter tuning. The tiered early warning mechanism effectively distinguishes between urgent faults and potential risks, preventing resource waste caused by over-maintenance and enabling early intervention for high-risk faults. The remaining life prediction function provides a scientific basis for spare parts inventory management, significantly reducing the risk of business interruption due to sudden hardware failures. The entire system forms a closed-loop management system from status monitoring to maintenance execution, transforming network equipment operation and maintenance from a passive response model to a proactive prevention model.

[0029] In one possible implementation, the construction of the dynamic model cluster includes: matching an initial model combination based on the equipment model and service duration; updating the parameters in the initial model combination through an online incremental learning mechanism; and using a generative adversarial network to verify the degree of model drift and trigger retraining.

[0030] Among these, the equipment model can refer to a manufacturer-defined unique hardware identifier used to match the device's specific prediction model. Service life refers to the cumulative operating time of the equipment since its commissioning, which can assess the degree of component aging. The initial model set is a pre-loaded set of machine learning algorithms that can accelerate the prediction process in specific scenarios. The online incremental learning mechanism refers to a parameter update method that continuously absorbs new data to maintain the timeliness of model predictions. Generative adversarial networks can simulate anomalous data distributions to detect model performance degradation boundaries. Model drift refers to the deviation between the predicted results and the actual situation, triggering an adaptive retraining process.

[0031] As a concrete example: When deploying a dynamic model cluster in a cloud computing center, the initial combination is first retrieved from the model library based on the device model and service life (in days): an LSTM degenerate model is loaded for servers with a service life of 10 days, and a random forest baseline model is enabled for new devices. Incremental learning is triggered every time the data pipeline receives a new SMART record, and the parameters are updated using an FTRL optimizer (learning rate of 0.01). Each week, GAN-generated power anomaly data (voltage fluctuation of 15% plus temperature increase of 10 degrees Celsius) is used to validate the model. When the drift rate exceeds 5%, a retraining task is automatically initiated. The entire process takes less than 30 minutes.

[0032] This solution significantly reduces the false alarm rate during the cold start phase by accurately matching the initial model with device characteristics. The incremental learning mechanism allows the model to continuously adapt to hardware degradation curves, avoiding the resource waste of traditional periodic full-scale training. Adversarial verification technology can keenly detect early signs of model failure, ensuring consistently reliable prediction results. The automated retraining process greatly reduces the need for manual intervention, forming an intelligent prediction system with self-healing capabilities. Ultimately, this achieves a dual improvement in fault prediction accuracy and operational efficiency, providing uninterrupted protection for critical infrastructure.

[0033] In one possible implementation, performance metrics data include IOPS, latency, and memory utilization, and are processed through the following steps: establishing a dynamic baseline library for load patterns to store normal fluctuation ranges under different business scenarios; and using a sliding window algorithm to calculate the deviation between real-time data and the dynamic baseline library.

[0034] IOPS refers to the number of input / output operations per second, used to quantify the throughput capacity of storage devices. Latency refers to the time interval between a data request and a response, reflecting the system's real-time performance. Memory utilization describes the percentage of memory used relative to total memory, providing early warning of resource exhaustion risks. The dynamic baseline library for load patterns stores performance indicator ranges for typical business scenarios, establishing reference standards for normal behavior. The sliding window algorithm segments real-time data streams by time series, used to calculate short-term fluctuation characteristics. Deviation refers to the degree of difference between the current indicator and the baseline library reference value, triggering anomaly detection mechanisms.

[0035] As a concrete example: When establishing a dynamic baseline database for a distributed database system, baseline values ​​for SSD IOPS (average 3,500 IOPS, fluctuation range ±20%) under OLTP business scenarios and baseline values ​​for memory utilization (peak 75%) under OLAP scenarios are collected in advance. Real-time monitoring uses a sliding window of five minutes (30-second step). When a node's IOPS deviates from the baseline by more than 40% for three consecutive windows and its latency exceeds 50 milliseconds, it is automatically marked as a potentially faulty node and load migration is initiated.

[0036] This solution accurately captures performance characteristics under different business scenarios through a dynamic baseline library, avoiding misjudgments caused by fixed thresholds. The sliding window algorithm effectively identifies short-term abnormal fluctuations, balancing detection sensitivity and anti-interference capabilities. Deviation metric evaluation significantly improves the accuracy of perceiving gradual performance degradation, providing an intuitive basis for operational decisions. Ultimately, it achieves an upgrade from extensive monitoring to intelligent diagnosis, ensuring the stable operation of critical business systems.

[0037] In one possible implementation, the hierarchical early warning module performs the following: it divides the predicted fault time window into three categories: short-term, medium-term, and long-term; and it automatically generates work orders based on the status of maintenance resource inventory.

[0038] The predicted failure time window refers to the estimated time interval for a failure to occur, used to classify urgency levels. Short-term refers to a high-risk period within the next 24 hours, triggering immediate emergency repair procedures. Medium-term describes the potential failure period of three days to one week, enabling the coordination of planned maintenance resources. Long-term refers to a low-probability warning period exceeding one week, guiding spare parts procurement cycle planning. The operational resource inventory status reflects the availability of spare parts, manpower, and other resources, used for dynamically adjusting response strategies. Work order priority ranking refers to a task sequence based on failure urgency and resource matching, optimizing operational efficiency.

[0039] As a concrete example: A system divides the predicted failure time window into three categories: short-term (e.g., a base station power module has an 85% probability of failure within the next twelve hours), medium-term (a core switch fan is expected to reach the end of its lifespan in five days), and long-term (a data center air conditioning system experiences a 30% performance degradation after three months). The system synchronizes its inventory database in real time (current spare parts: three power modules remaining, fan inventory critically low), and automatically generates a work order queue: prioritizing base station power supply issues (Level 1), coordinating with third-party repair providers to handle switch fans (Level 2), and including the air conditioning system in the next quarter's procurement plan (Level 3).

[0040] This solution achieves tiered fault response management through refined time window classification, ensuring resources are precisely allocated to the highest-risk scenarios. The dynamic prioritization mechanism effectively addresses resource mismatch issues caused by the traditional first-come, first-served model, maximizing the efficiency of the operations and maintenance team, especially during spare parts shortages. Long-term early warning systems, integrated with the procurement system, significantly reduce the risk of business interruption due to supply chain delays, forming a closed-loop management system covering the entire lifecycle from emergency response to strategic planning. Ultimately, this achieves an optimal balance between operational costs and system availability, providing sustainable assurance capabilities for critical infrastructure.

[0041] In one possible implementation, the calculation of the remaining lifetime prediction includes: obtaining the failure probability time series output by the dynamic model cluster. Establish the Weibull distribution function. Fit the equipment reliability curve; solve the shape parameters using maximum likelihood estimation. and scale parameters ,in Characterizes the trend of failure rate changes. Characteristic lifespan cycle.

[0042] The failure probability time series P(t) refers to a time-ordered sequence of equipment failure probabilities, used to quantify the risk level at different time periods. The Weibull distribution function R(t) describes the mathematical model of how equipment reliability changes over time, revealing the evolution of failure modes. The shape parameter β controls the slope of the distribution curve, distinguishing between early failures, random failures, and wear-out failures. The scaling parameter η characterizes the time point at which the equipment reaches its characteristic failure probability, calibrating the time base of the prediction model. Maximum likelihood estimation can infer the optimal parameter combination from historical data, improving the fitting accuracy of the distribution function.

[0043] As a concrete example: A wind turbine prediction system receives a P(t) sequence (e.g., daily failure probabilities for the next 30 days: [0.01, 0.015...0.08]) from a dynamic model cluster. Using a Weibull distribution fit, the initial parameters β are obtained as 2.3 and η as 60 days. After iterative optimization using maximum likelihood estimation, the final parameter β is corrected to 2.1 (indicating a slowly increasing wear-and-tear failure rate), and η is corrected to 65 days. Based on this, the system generates a remaining lifespan report: if the failure probability exceeds 20% after 30 days, gearbox replacement is recommended; if the probability exceeds 50% after 90 days, a red alert is triggered.

[0044] This solution transforms discrete failure probability sequences into continuous reliability curves using the Weibull distribution, significantly improving the smoothness and interpretability of lifespan predictions. Co-optimization of shape and dimensional parameters accurately captures the full lifecycle characteristics of equipment from break-in to aging, avoiding the mechanical biases of traditional linear prediction models. Maximum likelihood estimation endows the model with adaptive adjustment capabilities, ensuring that prediction results dynamically evolve with the actual state of the equipment. Ultimately, this achieves a transformation from extensive, periodic maintenance to precise predictive maintenance, providing a quantitative basis for decision-making in the full lifecycle management of high-value equipment.

[0045] In one possible implementation, the parameters of the Weibull distribution function are determined through the following steps: collecting historical fault intervals. Construct a sample set; construct the log-likelihood function. Iterative solution using gradient descent method makes Maximize the combination of parameters .

[0046] Historical failure intervals refer to the time difference sequence between various equipment failures, serving as the data foundation for reliability analysis. The sample set contains all failure interval records within a specific time period, supporting parameter learning for statistical models. The log-likelihood function transforms probability products into logarithmic summations, simplifying computational complexity during parameter optimization. Gradient descent iteratively updates parameters along the negative gradient of the function to gradually approximate the maximum value of the likelihood function.

[0047] As a concrete example: A device monitoring system collects the intervals (in hours) of thirty faults over the past five years to form a sample set: [872, 905...1230]. After initializing the Weibull parameters β=1.5 and η=1000, a log-likelihood function is constructed and its initial value is calculated to be -120.6. Iteratively using gradient descent (learning rate 0.01), the gradient direction is recalculated after each parameter update. After fifty iterations, it converges to the optimal solution β=1.8 and η=1150. Based on this, the system generates a maintenance recommendation: when the engine has been running for 950 hours, it should enter a critical monitoring phase.

[0048] This scheme transforms the complex probability product into a differentiable convex optimization problem through logarithmic transformation, greatly improving the numerical stability of parameter solving. The adaptive iterative nature of gradient descent effectively avoids the sensitivity of traditional analytical methods to initial values, maintaining robustness even with insufficient sample size. Full utilization of historical fault data enables the prediction model to adapt to the actual wear characteristics of different equipment, extracting individualized prediction parameters from collective experience data. Ultimately, this achieves a leap from experience-driven maintenance decisions to data-driven prediction, providing mathematical tools to support the precise operation and maintenance of high-value equipment.

[0049] In one possible implementation, the formula for calculating the critical component replacement urgency index is:

[0050] in The cumulative failure probability of component k within period T is derived from the integral result of the Weibull distribution function; The remaining useful life is obtained through dynamic model cluster regression. and These are respectively derived from downtime losses and spare parts replacement costs recorded in the operations and maintenance cost database; and These are preset weighting coefficients.

[0051] The critical component replacement urgency index is a comprehensive indicator that quantifies the urgency of component replacement, guiding preventative maintenance decisions. The cumulative failure probability reflects the total failure risk of a component within a specific period, assessing the degree of stability degradation. Remaining useful life predicts the operational time of a component after period T, allowing for dynamic adjustment of the maintenance time window. Downtime loss refers to the direct and indirect economic losses caused by equipment failure, measuring the cost of maintenance delays. Spare parts replacement cost includes all costs of purchasing and installing new components, evaluating the economics of maintenance actions. Weighting coefficients adjust the contribution ratio of failure risk factors to cost factors, catering to decision-making preferences in different scenarios.

[0052] As a concrete example: a certain system calculates hour: The next thirty days can be obtained by integrating the Weibull distribution. 0.18; Dynamic model prediction Forty-five days; this was obtained by calling the database. It costs 20,000 yuan per hour. Set at 120,000 yuan; set weights 0.7 It is 0.3; ultimately A value of 1.26 (exceeding the threshold of 1.0) triggers an orange alert and automatically generates a purchase order, taking priority over other alerts. Equipment components smaller than one.

[0053] This solution effectively addresses the limitations of traditional single-indicator decision-making by constructing an urgency index through the fusion of multi-dimensional parameters. The dynamic balance between failure probability and remaining lifespan identifies high-risk but short-lived "critical components," preventing the neglect of long-term losses due to excessive focus on short-term risks. The introduction of cost factors ensures that decisions consider both technical feasibility and economic efficiency, making it particularly suitable for resource optimization in budget-constrained scenarios. The flexible adjustment of weighting coefficients adapts to the differentiated safety and economic needs of various industries, providing a quantitative decision-making tool for equipment lifecycle management. Ultimately, this upgrades the operation and maintenance model from reactive emergency repairs to proactive prevention, significantly improving the overall availability of the production system.

[0054] In one possible implementation, the calculation process for the cumulative failure probability includes: Numerical integration of the failure probability time series:

[0055] in The instantaneous failure rate function is obtained by differentiating the Weibull distribution function.

[0056] Numerical integration refers to calculating the numerical solution of the definite integral of a continuous function using discretization methods, and is used to handle complex integral problems that cannot be solved analytically. Instantaneous failure rate function. It can describe the intensity of equipment failure at a specific point in time and reflect the dynamic changes in the equipment aging rate.

[0057] As a specific example: When a monitoring system calculates the cumulative failure probability for the next 100 days: Based on Weibull distribution parameters =2.1、 =300 days, the derivative yields the instantaneous failure rate function. =2.1 divided by 300 multiplied by (t divided by 300) raised to the power of 1; divide the interval from 0 to 100 days into 1000 equal parts, and use the trapezoidal rule for numerical integration to calculate the failure rate of each small interval; sum the results of all small intervals to obtain The value is equal to 0.15; based on this, the system generates an early warning: the motor's failure risk exceeds the threshold of 0.12 within a 100-day cycle, and it is recommended to arrange preventive maintenance on the 80th day.

[0058] This solution transforms the theoretical failure rate function into an operable cumulative risk value through numerical integration, overcoming the limitations of traditional analytical methods on the function form. The dynamic characteristics of the instantaneous failure rate function can capture the full lifecycle features of equipment from early break-in to late aging, making it particularly suitable for accurate prediction in nonlinear loss scenarios. The discretized integration algorithm significantly reduces the performance requirements of the processor while maintaining computational accuracy, enabling edge computing devices to perform complex prediction tasks in real time. Ultimately, this achieves an upgrade from static lifetime estimation to dynamic risk quantification, providing a mathematical foundation for the precise maintenance of critical equipment.

[0059] In one possible implementation, the process of constructing a multimodal data coupling index includes: Extract fan speed and power supply ripple coefficient Real-time measurement values; Calculate joint outlier:

[0060] in and Statistical feature library derived from historical data of normal equipment operation These are the modal weighting coefficients.

[0061] Among them, multimodal data coupling index refers to a comprehensive evaluation parameter that integrates data from multiple sensors to fully reflect the operating status of equipment. Fan speed can characterize the real-time operating condition of mechanical components and can detect speed fluctuations caused by bearing wear or abnormal load. Power ripple coefficient is used to quantify the stability of power supply quality and can identify power module aging or circuit interference problems. Joint anomaly degree can integrate abnormal signals of different physical dimensions to eliminate the one-sidedness of single-modal monitoring. Modal weighting coefficient can adjust the contribution ratio of each monitoring parameter to adapt to the sensitivity differences of different fault modes.

[0062] As a specific example: A monitoring platform performs the following operations: Data from the speed sensor is collected every five minutes. =3200 RPM, power analyzer data =0.8%; Normal parameters are retrieved from the statistics library. =3000 RPM =Fifty revolutions =0.5 percent =0.2 percent; Set weight =0.6 =0.4; Calculate the AD value to 1.73 (exceeding the threshold of 1.5), trigger a level 3 alarm and automatically start the backup fan, while pushing a power module detection work order to the operation and maintenance terminal.

[0063] This solution effectively improves the accuracy of fault early warning through multi-source data fusion, avoiding resource waste caused by false alarms from a single sensor. The introduction of weighting coefficients allows the system to adapt to the varying importance of different components, automatically increasing the decision-making weight of electrical parameters in power-sensitive equipment. The real-time coupled computing mechanism significantly shortens the response delay from data acquisition to decision execution, making it particularly suitable for online health management of high-value equipment. Ultimately, it achieves a leap from isolated parameter monitoring to comprehensive condition assessment, providing a more reliable quantitative basis for predictive maintenance.

[0064] In one possible implementation, the optimization formula for the modal weighting coefficients is:

[0065] The denominator counts all modes in the historical fault sample set. Total number of occurrences in the numerator, numerator statistics of specific modes exist The number of times it appears in the text.

[0066] The historical fault sample set refers to the collection of multimodal data recorded in past equipment fault events, used to establish a benchmark database for weight optimization. Occurrence frequency statistics can quantify the correlation strength of specific modal data in fault cases, reflecting the indicative value of that modality for the fault. A modality describes a sequence of measured values ​​for a certain type of equipment operating parameter, characterizing state features in a specific dimension.

[0067] As a specific example: A wind turbine intelligent diagnostic system performs weight optimization: Retrieve 72 gearbox failure records from the past three years and construct a structure including vibration spectrum. Oil temperature gradient Fault sample set F; statistical vibration modes It appears 58 times in F, oil temperature mode. It appears 34 times; calculate the optimized weights. * = 58 divided by 92 ≈ 0.63 *=34 divided by 92 ≈ 0.37; Update the weight configuration of the real-time monitoring system to increase the decision weight of vibration data by 26%.

[0068] This solution overcomes the subjectivity of traditional experience-based weighting by using dynamic weight adjustment driven by failure cases. The frequency-based optimization method automatically captures the actual correlation between different modes and failures, making it particularly suitable for large equipment with complex failure mechanisms. A data backtracking mechanism ensures that the weight coefficients continuously evolve with the accumulation of failure cases, gradually forming an optimal monitoring strategy that matches a specific equipment group. Ultimately, this achieves a shift from static weight allocation to adaptive learning, significantly improving the accuracy and reliability of multimodal fusion diagnosis.

[0069] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0070] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. An AI-based network device fault prediction system, characterized in that, include: The data acquisition module is used to collect SMART information, temperature data and power status of the device in real time through hardware sensors; The data extraction module is used to extract hardware error logs and performance indicator data from the system logs; The dynamic model module is used to input the SMART information, the temperature data, the power status, the hardware error log, and the performance index data into a pre-trained dynamic model cluster, and output the failure probability and remaining lifetime prediction values. The early warning module is used to trigger the graded early warning module to generate maintenance instructions when the probability of the fault exceeds a preset threshold.

2. The system according to claim 1, characterized in that, The construction of the dynamic model cluster includes: Match the initial model combination according to the equipment model and service duration; The parameters in the initial model combination are updated using an online incremental learning mechanism; Generative adversarial networks are used to verify the degree of model drift and trigger retraining.

3. The system according to claim 2, characterized in that, The performance metrics data include IOPS, latency, and memory utilization, and are processed through the following steps: Establish a dynamic baseline library for load patterns to store the normal fluctuation range under different business scenarios; The deviation between real-time data and the dynamic baseline library is calculated using a sliding window algorithm.

4. The system according to claim 3, characterized in that, The tiered early warning module executes: The time window for predicting failures is divided into three categories: short-term, medium-term, and long-term. Work orders are automatically generated and prioritized based on the inventory status of maintenance resources.

5. The system according to claim 4, characterized in that, The calculation of the predicted remaining lifetime includes: Obtain the fault probability time series output by the dynamic model cluster. ; Establish the Weibull distribution function Fit the equipment reliability curve; Solving shape parameters using maximum likelihood estimation and scale parameters ,in Characterizes the trend of failure rate changes. Characteristic lifespan cycle.

6. The system according to claim 5, characterized in that, The parameters of the Weibull distribution function are determined through the following steps: Collect historical fault intervals Construct a sample set; Construct the log-likelihood function ; The gradient descent method is used to iteratively solve the problem. Maximize the combination of parameters .

7. The system according to claim 6, characterized in that, The formula for calculating the critical component replacement urgency index is: in The cumulative failure probability of component k within period T is derived from the integral result of the Weibull distribution function; The remaining service life is obtained through cluster regression of the dynamic model. and These are respectively derived from downtime losses and spare parts replacement costs recorded in the operations and maintenance cost database; and These are preset weighting coefficients.

8. The system according to claim 7, characterized in that, The calculation process for the cumulative failure probability includes: Numerical integration is performed on the fault probability time series: in The instantaneous failure rate function is obtained by differentiating the Weibull distribution function.

9. The system according to claim 8, characterized in that, The process of constructing multimodal data coupling metrics includes: Extract fan speed and power supply ripple coefficient Real-time measurement values; Calculate joint outlier: in and Statistical feature library derived from historical data of normal equipment operation These are the modal weighting coefficients.

10. The system according to claim 9, characterized in that, The optimization formula for the modal weighting coefficients is as follows: The denominator counts all modes in the historical fault sample set. Total number of occurrences in the numerator, numerator statistics of specific modes exist The number of times it appears in the text.

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