Artificial Intelligence-Based Fault Early Warning Analysis Method and System for Electrical Equipment in Stainless Steel Product Forming

Abstract

This invention relates to the field of artificial intelligence technology, specifically disclosing a fault early warning analysis method and system for electrical equipment in stainless steel product forming based on artificial intelligence. The method includes: normalizing and denoising multi-source operating data of the electrical equipment to obtain an operating data sequence; extracting high-dimensional features from the operating data sequence to obtain a deep feature vector; dynamically defining early warning thresholds based on historical normal operation records to obtain an early warning threshold range; comparing the deviation between the deep feature vector and the early warning threshold range to obtain an initial early warning signal; searching and matching the initial early warning signal, and encoding and encapsulating it into an early warning maintenance instruction according to the matched maintenance guidance scheme; monitoring the response to the early warning maintenance instruction, and updating the response feedback data and the updated equipment status data to the maintenance knowledge base. This invention can improve the efficiency of fault early warning analysis for electrical equipment in stainless steel product forming.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a fault early warning analysis method and system for electrical equipment in stainless steel product forming based on artificial intelligence. Background Technology

[0002] During the stainless steel product forming process, the operating data of electrical equipment comes from diverse sources and contains a large amount of noise interference. Existing technologies struggle to achieve accurate data purification and time alignment when normalizing and filtering multi-source operating data. This results in the inability to obtain effective equipment operating characteristics in subsequent feature extraction stages, thus affecting the accuracy of fault warnings. Furthermore, existing technologies often employ fixed warning thresholds, failing to dynamically adjust them based on the process parameters of stainless steel products, changes in load conditions, and the aging and deterioration of the equipment itself. This mismatch between the warning thresholds and the actual operating status of the equipment easily leads to false or missed warnings.

[0003] Existing technologies, after acquiring initial warning signals, lack an efficient retrieval and matching mechanism with the equipment maintenance knowledge base. This makes it difficult to quickly locate suitable maintenance guidance solutions, resulting in delayed and insufficiently targeted maintenance responses, and an inability to promptly resolve potential equipment faults. Furthermore, existing technologies lack a robust knowledge base update mechanism, failing to effectively integrate response feedback data from warning maintenance commands and updated equipment operating status data into the knowledge base. This prevents the knowledge base from iteratively optimizing as equipment operating time increases, leading to a continuous decline in the effectiveness of fault warnings and maintenance guidance over the long term, making it difficult to meet the requirements for efficient and stable operation of electrical equipment used in stainless steel product forming. Summary of the Invention

[0004] This invention provides a method and system for fault early warning analysis of electrical equipment in stainless steel product forming based on artificial intelligence, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an artificial intelligence-based fault early warning analysis method for electrical equipment in stainless steel product forming, comprising: S1. Normalize and denoise the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment; S2. Perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment; S3. Based on the process stage parameters and load condition data of stainless steel products, a dynamic early warning threshold is defined for the historical normal operation records of the electrical equipment to obtain the early warning threshold range of the electrical equipment. S4. Compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment; S5. Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is retrieved and matched, and the matched maintenance guidance scheme is encoded and encapsulated into a warning maintenance instruction for the electrical equipment. S6. Monitor the response to the early warning maintenance command, and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

[0006] In a preferred embodiment, the step of normalizing and denoising the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment includes: The monitoring sensors integrated into the electrical equipment are used to synchronously collect multi-source operating data of the electrical equipment during its operation. The multi-source operating data are timestamped according to a unified time base to obtain the aligned operating data of the electrical equipment. Fill in the missing data points in the alignment operation data and remove the abnormal data points in the alignment operation data to obtain the complete operation data of the electrical equipment; Frequency domain noise is filtered out from the complete operating data to obtain the noise-reduced operating data of the electrical equipment; Based on the historical benchmark data of the electrical equipment, the noise-reduced operating data is linearly normalized and mapped to obtain the operating data sequence of the electrical equipment.

[0007] In a preferred embodiment, the step of performing high-dimensional feature extraction on the operating data sequence to obtain the depth feature vector of the electrical equipment includes: The time-series structure of the running data sequence is divided by a sliding window to obtain data segments of equal length from the running data sequence; The operating mode is identified by the equal-length data segments to obtain the steady-state operating segments and transient process segments of the electrical equipment. The steady-state characteristics of the electrical equipment are obtained by extracting features from the statistical distribution characteristics of the steady-state operation segment. The transient characteristics of the electrical equipment are obtained by performing a multi-scale description of the change trajectory morphology of the transient process segment. The steady-state features and the transient features are fused by feature dimensionality reduction to obtain the deep feature vector of the electrical equipment.

[0008] In a preferred embodiment, the dynamic early warning threshold definition based on the process stage parameters and load condition data of the stainless steel products, according to the historical normal operation records of the electrical equipment, yields the early warning threshold range for the electrical equipment, including: Based on the process stage parameters and load condition data of stainless steel products, a similarity clustering analysis is performed on the historical normal operation records of the electrical equipment to obtain a subset of historical classification data of the electrical equipment. By performing time-series aggregation statistics on the historical classification data subset, a baseline statistical distribution of the operating parameters in the electrical equipment is obtained; Based on the benchmark statistical distribution, the probability density of the parameter fluctuation boundary of the electrical equipment is evaluated to obtain the initial parameter safety boundary of the electrical equipment. Based on the real-time aging and degradation coefficient of the electrical equipment, the initial parameter safety boundary is adaptively shifted and contracted to obtain the warning threshold range of the electrical equipment.

[0009] In a preferred embodiment, the step of adaptively shifting and contracting the initial parameter safety boundary based on the real-time aging degradation coefficient of the electrical equipment to obtain the warning threshold range of the electrical equipment includes: Based on the historical cumulative equivalent operating time and wear index of key components of the electrical equipment, the performance degradation status of the electrical equipment is quantitatively evaluated to obtain the real-time aging degradation coefficient of the electrical equipment. The operating cycle normalization mapping is performed on the real-time aging degradation coefficient to obtain the standardized degradation impact factor of the electrical equipment. Based on the standardized decay impact factor, the upper and lower limits of the initial parameter safety boundary are linearly shifted and proportionally shrunk to obtain the adjusted safety boundary of the electrical equipment. The adjusted safety boundary is validated for reasonableness to obtain the warning threshold range of the electrical equipment.

[0010] In a preferred embodiment, the step of quantitatively assessing the performance degradation state of the electrical equipment based on its historical cumulative equivalent operating time and wear indicators of key components to obtain the real-time aging degradation coefficient of the electrical equipment includes: The historical cumulative equivalent operating time of the electrical equipment is obtained, as well as the wear rate data of key components periodically collected by the embedded sensors of the electrical equipment; Based on preset failure weights, the wear rate data of the key components are weighted and fused to obtain the comprehensive wear index of the electrical equipment. Based on the historical load spectrum of the stainless steel product forming process, the average load strength coefficient corresponding to the historical cumulative equivalent running time is extracted. Based on the historical cumulative equivalent operating time, the comprehensive wear index, and the average load intensity coefficient, the real-time aging degradation coefficient of the electrical equipment is calculated, wherein the calculation formula for the real-time aging degradation coefficient is as follows: ; In the formula, This represents the real-time aging and degradation coefficient. This represents the historical cumulative equivalent runtime. This represents the basic value of the rated design life of the electrical equipment. This represents the natural exponential function. This represents the comprehensive wear index. This represents the baseline wear index of the electrical equipment in a brand-new condition. This indicates the preset load impact correction factor. This represents the average load intensity coefficient.

[0011] In a preferred embodiment, the step of comparing the depth feature vector with the warning threshold range to obtain the initial warning signal for the electrical equipment includes: Based on the deep feature vector, the warning threshold range is mapped dimension by dimension, and the mapping results are analyzed for membership degree to obtain the mapping relationship table of the electrical equipment. The deviation metric is performed on the mapping table to obtain the real-time deviation sequence of the electrical equipment; Based on the real-time deviation sequence, multi-rule pattern identification is performed on the deviation pattern of the electrical equipment to obtain the preliminary abnormal pattern identification of the electrical equipment. The abnormal mode identifier is structured and encapsulated to obtain the initial warning signal of the electrical equipment.

[0012] In a preferred embodiment, the step of retrieving and matching the initial warning signal based on the maintenance knowledge base of the electrical equipment, and encoding and encapsulating the matched maintenance guidance scheme into a warning maintenance instruction for the electrical equipment, includes: Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is subjected to signal feature semantic analysis to obtain a standardized fault query description of the electrical equipment. The standardized fault query description is subjected to knowledge base similarity retrieval to obtain candidate maintenance guidance schemes related to historical cases in the maintenance knowledge base; Based on the current operating context information of the electrical equipment, the applicability scores of the candidate maintenance guidance schemes are evaluated, and the scheme with the highest applicability score is selected as the preferred maintenance guidance scheme for the electrical equipment. The preferred maintenance guidance scheme is deconstructed and encoded to obtain the early warning maintenance instructions for the electrical equipment.

[0013] In a preferred embodiment, the step of monitoring the response to the early warning maintenance command and updating the maintenance knowledge base with the monitored response feedback data of the early warning maintenance command and the updated equipment status data includes: The execution process of the early warning maintenance command is tracked in real time to obtain the command execution log of the electrical equipment; The key operation nodes and device response parameters of the instruction execution log are analyzed to obtain the response feedback data of the electrical equipment. Based on the response feedback data and the real-time equipment operation status data of the electrical equipment, the effectiveness of the maintenance operation of the electrical equipment is evaluated, and a maintenance effect evaluation report of the electrical equipment is obtained. The maintenance effect evaluation report is structured and integrated to obtain maintenance knowledge entries for the electrical equipment, and these maintenance knowledge entries are then updated to the maintenance knowledge base.

[0014] To address the above problems, the present invention also provides an artificial intelligence-based fault early warning and analysis system for electrical equipment in stainless steel product forming, the system comprising: The data preprocessing module is used to normalize and denoise the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment; The feature extraction module is used to perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment. The threshold definition module is used to dynamically define the warning threshold based on the process stage parameters and load condition data of the stainless steel products, and obtain the warning threshold range of the electrical equipment. The deviation comparison module is used to compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment. The instruction generation module is used to retrieve and match the initial warning signal based on the maintenance knowledge base of the electrical equipment, and encapsulate the matched maintenance guidance scheme into a warning maintenance instruction for the electrical equipment. The response monitoring module is used to monitor the response to the early warning maintenance command and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention accurately captures the essence of equipment operating status by normalizing and denoising multi-source operating data and extracting high-dimensional features through the fusion of steady-state and transient features; it dynamically defines the warning threshold by combining process stage, load condition and real-time aging and degradation coefficient, avoiding the limitations of fixed thresholds, greatly reducing false and missed warnings, and providing a reliable basis for fault prediction.

[0016] 2. This invention relies on a maintenance knowledge base to quickly retrieve and match initial warning signals, generate targeted warning and maintenance instructions, and shorten the fault handling cycle; by monitoring instruction response feedback and equipment status updates, the knowledge base content is continuously enriched, forming a closed-loop mechanism of "early warning-maintenance-feedback-optimization" to help the equipment operate stably for a long time. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a method for fault early warning analysis of electrical equipment in stainless steel product forming based on artificial intelligence, according to an embodiment of the present invention. Figure 2 A functional block diagram of an artificial intelligence-based fault early warning and analysis system for stainless steel product forming electrical equipment provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an artificial intelligence-based method for fault early warning analysis of electrical equipment in stainless steel product forming. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the artificial intelligence-based method for fault early warning analysis of electrical equipment in stainless steel product forming can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1The diagram shown is a flowchart illustrating a fault early warning analysis method for electrical equipment in stainless steel product forming based on artificial intelligence, according to an embodiment of the present invention. In this embodiment, the fault early warning analysis method for electrical equipment in stainless steel product forming based on artificial intelligence includes: S1. Normalize and denoise the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment; In this embodiment of the invention, the step of normalizing and denoising the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment includes: The monitoring sensors integrated into the electrical equipment are used to synchronously collect multi-source operating data of the electrical equipment during its operation. The multi-source operating data are timestamped according to a unified time base to obtain the aligned operating data of the electrical equipment. Fill in the missing data points in the alignment operation data and remove the abnormal data points in the alignment operation data to obtain the complete operation data of the electrical equipment; Frequency domain noise is filtered out from the complete operating data to obtain the noise-reduced operating data of the electrical equipment; Based on the historical benchmark data of the electrical equipment, the noise-reduced operating data is linearly normalized and mapped to obtain the operating data sequence of the electrical equipment.

[0021] Relying on monitoring sensors integrated at various monitoring points of electrical equipment, during the continuous operation of the electrical equipment, each monitoring sensor carries out data collection work according to a preset fixed collection frequency. The collection actions of all monitoring sensors are kept synchronized in time through the built-in synchronous trigger module of the sensor. Different monitoring sensors collect different types of operating data such as current, voltage, temperature, vibration, and power corresponding to the electrical equipment. All the raw data of different types and different monitoring points collected are initially integrated to finally form multi-source operating data of the electrical equipment.

[0022] The system clock of the main control system of the electrical equipment is selected as the only unified time reference. The timestamp information corresponding to the system clock is matched for each original data record in the multi-source operation data. For the slight deviation of timestamps caused by the hardware response differences of different monitoring sensors, the timestamps of all multi-source operation data are uniformly adjusted to a completely consistent state with the timestamp of the main control system clock as the standard. For cases where no data of the corresponding type is collected at the same timestamp, the timestamp is retained and the corresponding data position is marked as missing. Finally, all data with completed timestamp adjustment are arranged in order according to the order of the timestamps to obtain the aligned operation data of the electrical equipment.

[0023] For missing data points in the aligned operation data, the specific value corresponding to the missing data point is calculated according to the continuous change pattern of the data, using the two adjacent valid data points as references. The calculated value is then directly filled into the marked position of the missing data point to complete the numerical filling of all missing data points. For abnormal data points in the aligned operation data, the normal fluctuation range of each type of operation data is determined based on the rated operating parameters of the electrical equipment and historical data of long-term normal operation. Data points that exceed the normal fluctuation range and discrete data points that have obvious numerical changes with adjacent valid data points are all marked as abnormal data points. All marked abnormal data points are directly removed from the aligned operation data. The dataset after completing the missing data point filling and abnormal data point removal is the complete operation data of the electrical equipment.

[0024] The complete operational data undergoes frequency domain transformation, converting the original continuous operational data in the time domain into spectral data in the frequency domain. Combining the design and operating parameters of the electrical equipment with the signal characteristics during normal operation, the frequency range corresponding to the effective signal of each type of operational data of the electrical equipment is determined. Frequency components in the spectral data that exceed the effective signal frequency range are all identified as noise frequency components and directly removed from the spectral data, retaining only the effective signal frequency components. The noise-removed spectral data is then subjected to inverse frequency domain transformation to restore the frequency domain data back to continuous data in the time domain. This restored time domain data is the noise-reduced operational data of the electrical equipment.

[0025] The various types of operational data generated by electrical equipment under long-term normal operation are collected and used as historical baseline data for the electrical equipment. The maximum and minimum values ​​corresponding to each type of operational data in the historical baseline data are extracted. Each data point of each type in the denoised operational data is treated as an independent processing object. The single data point is compared with the maximum and minimum values ​​of the corresponding type of historical baseline data. According to the linear change law, the data point is mapped to a fixed numerical range. In this way, the linear normalization mapping of all types of data in the denoised operational data is completed, eliminating the influence of differences in the units and original numerical ranges of different types of operational data. Finally, all the data after linear normalization mapping are arranged continuously and orderly according to the time sequence. The resulting ordered dataset is the operational data sequence of the electrical equipment.

[0026] S2. Perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment; In this embodiment of the invention, the step of performing high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment includes: The time-series structure of the running data sequence is divided by a sliding window to obtain data segments of equal length from the running data sequence; The operating mode is identified by the equal-length data segments to obtain the steady-state operating segments and transient process segments of the electrical equipment. The steady-state characteristics of the electrical equipment are obtained by extracting features from the statistical distribution characteristics of the steady-state operation segment. The transient characteristics of the electrical equipment are obtained by performing a multi-scale description of the change trajectory morphology of the transient process segment. The steady-state features and the transient features are fused by feature dimensionality reduction to obtain the deep feature vector of the electrical equipment.

[0027] Based on the sequential arrangement of the electrical equipment operation data sequence, a fixed-length window is used to fully cover the sequence. The window slides non-overlapping along the continuous temporal direction of the operation data sequence. After each slide, the corresponding part of the operation data sequence within the window is precisely extracted. This process is repeated to complete the segmented extraction operation of the entire operation data sequence, ensuring that all extracted datasets have the same data length, and finally obtaining equal-length data segments of the operation data sequence.

[0028] First, it is clarified that the core criterion for judging the steady-state operation of electrical equipment is that the data values ​​do not show significant trend changes and are always within a fixed stable fluctuation range. The core criterion for judging the transient operation is that the data values ​​show obvious trend changes, sudden changes, or continuous fluctuations exceeding the steady-state fluctuation range. Then, the numerical fluctuation status and overall change characteristics of all operating data within each equal-length data segment are comprehensively checked and judged one by one. The equal-length data segments that meet the steady-state operation criterion are marked as the steady-state operation segments of the electrical equipment, and the equal-length data segments that meet the transient operation criterion are marked as the transient process segments of the electrical equipment.

[0029] Each steady-state operation segment of electrical equipment is independently analyzed, and the overall statistical distribution characteristics of all operating data within the segment are comprehensively analyzed. The core attributes that can directly and intuitively reflect the statistical distribution characteristics are accurately extracted. The core attributes extracted from each steady-state operation segment are systematically integrated and sorted to form a feature set that can comprehensively and accurately characterize the steady-state operation state of electrical equipment. This feature set is the steady-state feature of electrical equipment.

[0030] The trajectory of the change data within the transient process segment of each electrical device is analyzed and described from multiple dimensions. The changes are comprehensively sorted out and specifically depicted from different scales, such as the overall trend of data change, the rate of data change, the magnitude of data change, and the staged morphological manifestation of the change trajectory. The description of the change trajectory morphology at each scale is integrated and refined to form a feature set that can completely and accurately characterize the transient operation process of electrical equipment. This feature set is the transient feature of electrical equipment.

[0031] The extracted steady-state and transient features of the electrical equipment are integrated to form a comprehensive feature set containing both types of features. A comprehensive correlation analysis is performed on all features in the comprehensive feature set to accurately identify and eliminate features that are repetitive or redundant in representing the equipment's operating status. Only the key features that can accurately represent the overall operating status of the electrical equipment are retained. All the retained key features are arranged in a fixed logical order to form a feature sequence with high compactness and strong representation. This feature sequence is the deep feature vector of the electrical equipment.

[0032] S3. Based on the process stage parameters and load condition data of stainless steel products, a dynamic early warning threshold is defined for the historical normal operation records of the electrical equipment to obtain the early warning threshold range of the electrical equipment. In this embodiment of the invention, the dynamic early warning threshold definition of the historical normal operation records of the electrical equipment based on the process stage parameters and load condition data of the stainless steel products is used to obtain the early warning threshold range of the electrical equipment, including: Based on the process stage parameters and load condition data of stainless steel products, a similarity clustering analysis is performed on the historical normal operation records of the electrical equipment to obtain a subset of historical classification data of the electrical equipment. By performing time-series aggregation statistics on the historical classification data subset, a baseline statistical distribution of the operating parameters in the electrical equipment is obtained; Based on the benchmark statistical distribution, the probability density of the parameter fluctuation boundary of the electrical equipment is evaluated to obtain the initial parameter safety boundary of the electrical equipment. Based on the real-time aging and degradation coefficient of the electrical equipment, the initial parameter safety boundary is adaptively shifted and contracted to obtain the warning threshold range of the electrical equipment.

[0033] The step of adaptively shifting and contracting the initial parameter safety boundary based on the real-time aging degradation coefficient of the electrical equipment to obtain the warning threshold range of the electrical equipment includes: Based on the historical cumulative equivalent operating time and wear index of key components of the electrical equipment, the performance degradation status of the electrical equipment is quantitatively evaluated to obtain the real-time aging degradation coefficient of the electrical equipment. The operating cycle normalization mapping is performed on the real-time aging degradation coefficient to obtain the standardized degradation impact factor of the electrical equipment. Based on the standardized decay impact factor, the upper and lower limits of the initial parameter safety boundary are linearly shifted and proportionally shrunk to obtain the adjusted safety boundary of the electrical equipment. The adjusted safety boundary is validated for reasonableness to obtain the warning threshold range of the electrical equipment.

[0034] The method of quantitatively assessing the performance degradation state of the electrical equipment based on its historical cumulative equivalent operating time and wear indicators of key components, and obtaining the real-time aging degradation coefficient of the electrical equipment, includes: The historical cumulative equivalent operating time of the electrical equipment is obtained, as well as the wear rate data of key components periodically collected by the embedded sensors of the electrical equipment; Based on preset failure weights, the wear rate data of the key components are weighted and fused to obtain the comprehensive wear index of the electrical equipment. Based on the historical load spectrum of the stainless steel product forming process, the average load strength coefficient corresponding to the historical cumulative equivalent running time is extracted. Based on the historical cumulative equivalent operating time, the comprehensive wear index, and the average load intensity coefficient, the real-time aging degradation coefficient of the electrical equipment is calculated, wherein the calculation formula for the real-time aging degradation coefficient is as follows: ; In the formula, This represents the real-time aging and degradation coefficient. This represents the historical cumulative equivalent runtime. This represents the basic value of the rated design life of the electrical equipment. This represents the natural exponential function. This represents the comprehensive wear index. This represents the baseline wear index of the electrical equipment in a brand-new condition. This indicates the preset load impact correction factor. This represents the average load intensity coefficient.

[0035] The real-time aging degradation coefficient is the target value of this formula. The historical cumulative equivalent operating time is obtained by summing up the actual operating time of the electrical equipment since it was put into use, converted into equivalent time according to the load level, and then summing them up. The rated design life base value is the basic value corresponding to the rated design life determined by the manufacturer during the design phase of the electrical equipment. The natural exponential function is a mathematical tool used to characterize the aging process over time. The comprehensive wear index is obtained by weighting and fusing the wear rate data of each key component with preset failure weights. The benchmark wear index in the brand-new state is the benchmark wear index value determined by testing when the electrical equipment is in a brand-new, unused state. The load influence correction factor is a fixed correction factor preset according to the load operating characteristics of the equipment. The average load intensity coefficient is obtained by extracting the average load intensity within the time period corresponding to the historical cumulative equivalent operating time from the historical load spectrum of the stainless steel product forming process.

[0036] This formula quantifies the real-time aging and degradation of electrical equipment by fusing factors across three dimensions. The first part uses a natural exponential function to characterize the impact of historical cumulative equivalent operating time on equipment aging, reflecting the gradual aging process of equipment as operating time increases. The second part uses the ratio of the comprehensive wear index to the new condition baseline wear index to reflect the impact of wear on key components on overall aging. The third part uses the combination of the load influence correction factor and the average load intensity coefficient to reflect the accelerating effect of equipment operating load on aging. The result obtained by multiplying the three factors can comprehensively integrate the aging effects of the three core factors of time, wear, and load, and accurately quantify the current real-time aging and degradation state of the equipment.

[0037] As the historical cumulative equivalent operating time increases, the value of the first part will gradually approach one, causing the overall real-time aging degradation coefficient to show a continuous upward trend. As the comprehensive wear index increases, the value of the second part will increase synchronously, thereby driving the real-time aging degradation coefficient to rise. As the average load intensity coefficient increases, the value of the third part will increase accordingly, also causing the real-time aging degradation coefficient to rise. When the historical cumulative equivalent operating time approaches the base value of the rated design life, the value of the first part will approach one. At this time, the growth of the real-time aging degradation coefficient is mainly determined by the comprehensive wear index and the average load intensity coefficient. When the comprehensive wear index or the average load intensity coefficient remains stable, the real-time aging degradation coefficient will increase monotonically with the increase of the historical cumulative equivalent operating time.

[0038] The process parameters and load condition data corresponding to each process stage of stainless steel products are comprehensively sorted out, and all representational dimensions of the two types of data are clarified. Then, each historical normal operation record of electrical equipment is matched with the corresponding process parameters and load condition data of stainless steel products one by one according to the representational dimensions. This ensures that each historical normal operation record is associated with the corresponding process and load-related data. All the data after this association is used as the basis for similarity clustering analysis. By comparing the numerical similarity of each data in all representational dimensions, historical normal operation records with small numerical deviations in representational dimensions are grouped into the same category, and historical normal operation records with large numerical deviations in representational dimensions are divided into different categories. This completes the overall clustering and division of the historical normal operation records of electrical equipment. The data set of each independent category formed after the division is the historical classification data subset of electrical equipment.

[0039] For each subset of historical classification data for electrical equipment, all operating parameter data within the subset are time-series aggregated in chronological order at fixed time periods. Operating parameter data of the same type within the same time period are integrated into a data unit. A comprehensive statistical analysis is conducted on the operating parameter data within each data unit to identify the statistical characteristics of each operating parameter, such as the central tendency and dispersion, within that time period. Then, the statistical analysis results of the same type of operating parameters from different time periods are aggregated to form the complete statistical distribution characteristics of each operating parameter across the entire time series. The result set formed by integrating the statistical distribution characteristics of all operating parameters is the baseline statistical distribution of operating parameters in electrical equipment.

[0040] Based on the baseline statistical distribution of electrical equipment operating parameters, the numerical ranges of each operating parameter in the baseline statistical distribution are uniformly subdivided. The actual frequency of the operating parameter values ​​in each subdivided numerical range is statistically analyzed and converted into the corresponding probability density. The probability density change curves of each operating parameter are plotted, and the core numerical range in the curve where the probability density is high and changes steadily is determined. This core numerical range is the reasonable fluctuation boundary of each operating parameter. This probability density evaluation process is completed for the fluctuation boundaries of all operating parameters of electrical equipment in turn. The overall range formed by integrating the reasonable fluctuation boundaries of all operating parameters is the initial parameter safety boundary of the electrical equipment.

[0041] The actual operating time and corresponding load level of each operating period of the electrical equipment since its formal commissioning are analyzed. According to the preset load level and duration conversion rules, the actual operating time under different load levels is uniformly converted into the equivalent operating time under the standard load level. All converted equivalent operating times are successively accumulated to obtain the historical cumulative equivalent operating time of the electrical equipment. At the same time, the embedded wear monitoring sensor of the electrical equipment is activated, and the sensor continuously detects the wear amount, wear area and other core wear indicators of each key component of the equipment at fixed time intervals. After each detection, the wear rate within the cycle is calculated based on the time interval between two detections and the change in the wear indicators of the key components. The wear rate data of the key components of each detection cycle is continuously collected and stored. The wear rate data of the key components of the electrical equipment can be obtained by directly retrieving this type of data from the sensor's storage module.

[0042] Based on the functional positioning of each key component of the electrical equipment in the overall operation of the equipment and the impact of component wear on the safe operation of the equipment, a unique and fixed failure weight is assigned to each key component. This failure weight directly reflects the impact of the wear state of the corresponding key component on the overall failure of the equipment. The real-time wear rate data of each key component is matched with the corresponding failure weight one by one. The data of each key component after matching is calculated and transformed in a targeted manner. Then, the transformed data of all key components are summed and integrated. The integrated calculation result can comprehensively and holistically reflect the overall wear state of all key components of the electrical equipment. This calculation result is the comprehensive wear index of the electrical equipment.

[0043] A comprehensive analysis of the historical load spectrum generated during the forming process of stainless steel products is conducted. This historical load spectrum is a collection of load intensity data segmented according to the operating time of electrical equipment. It includes core information such as real-time load intensity values ​​and load intensity change trends within each time segment. The historical cumulative equivalent operating time of the electrical equipment is accurately compared with the time segment nodes in the historical load spectrum to determine the specific time segment to which the historical cumulative equivalent operating time belongs. All load intensity values ​​within the specific time segment are statistically averaged, and the calculated average load intensity value is the average load intensity coefficient corresponding to the historical cumulative equivalent operating time.

[0044] Using three types of data—historical cumulative equivalent operating time, comprehensive wear index, and average load intensity coefficient—as the core calculation basis, the three types of data are first uniformly sorted out to ensure that the representation dimensions of the three types of data are suitable for subsequent fusion calculation. Then, based on the inherent laws of aging and degradation of electrical equipment, the effects of time aging reflected by historical cumulative equivalent operating time, the effects of component wear aging reflected by comprehensive wear index, and the effects of load aging reflected by average load intensity coefficient are comprehensively considered. The three types of data are fused and calculated according to their respective correlation with the aging and degradation of the equipment. The calculation result can accurately quantify the current actual aging and degradation degree of the electrical equipment. This calculation result is the real-time aging and degradation coefficient of the electrical equipment.

[0045] First, we analyze the complete operating cycle specified in the design of electrical equipment. Then, based on the equipment's design standards, we determine the reasonable range of variation in the degree of aging and degradation of the equipment within this complete operating cycle. We then accurately correlate the real-time aging and degradation coefficient of the electrical equipment with this reasonable range of variation. Following the rules of linear mapping, we convert the real-time aging and degradation coefficient into a standardized value within the reasonable range of aging and degradation during the equipment's operating cycle. This standardized value can intuitively and objectively reflect the actual impact of the equipment's aging and degradation state on the boundaries of the equipment's operating parameters. This standardized value is the standardized degradation impact factor of the electrical equipment.

[0046] Using the standardized degradation impact factor of electrical equipment as the core adjustment basis, the linear shift range of the upper and lower limits of the initial parameter safety boundary is determined according to the specific value of the factor. The upper limit of the initial parameter safety boundary is shifted downward and the lower limit is shifted upward according to the shift range, thus completing the linear shift adjustment of the initial parameter safety boundary. Then, the proportional contraction coefficient of the initial parameter safety boundary is determined according to the value of the standardized degradation impact factor. The upper and lower limits of the safety boundary after linear shift adjustment are contracted proportionally according to the proportional contraction coefficient, thereby adjusting the overall range of the initial parameter safety boundary. After completing the dual adjustment of linear shift and proportional contraction, the integrated new operating parameter safety boundary range is the adjusted safety boundary of the electrical equipment.

[0047] First, clarify the upper and lower limits of the rated operating parameters of the electrical equipment, the specific technical requirements of the equipment operating parameters at each process stage of stainless steel products, and the load constraints of the equipment in actual production. Then, compare the upper and lower limits of the adjusted safety boundary of the electrical equipment with the upper and lower limits of the rated operating parameters of the equipment one by one to verify whether the adjusted safety boundary is within the reasonable range of the rated operating parameters. Next, fully match the adjusted safety boundary with the parameter technical requirements of each process stage of stainless steel products and the load constraints of the equipment in actual production to verify whether the adjusted safety boundary meets the core requirements of actual production and operating conditions. For boundary values ​​that exceed the rated parameter range or do not meet the process technical requirements and operating condition constraints found during the verification process, make targeted corrections and adjustments. After correction, conduct a comprehensive rationality verification of the boundary range again until the boundary range fully meets all rationality requirements. The operating parameter boundary range that has passed the rationality verification and is finally determined is the warning threshold range of the electrical equipment.

[0048] S4. Compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment; In this embodiment of the invention, the step of comparing the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment includes: Based on the deep feature vector, the warning threshold range is mapped dimension by dimension, and the mapping results are analyzed for membership degree to obtain the mapping relationship table of the electrical equipment. The deviation metric is performed on the mapping table to obtain the real-time deviation sequence of the electrical equipment; Based on the real-time deviation sequence, multi-rule pattern identification is performed on the deviation pattern of the electrical equipment to obtain the preliminary abnormal pattern identification of the electrical equipment. The abnormal mode identifier is structured and encapsulated to obtain the initial warning signal of the electrical equipment.

[0049] The deep feature vector of electrical equipment is split into dimensions, resulting in single-dimensional features with the same number of dimensions as the deep feature vector. Each single-dimensional feature is matched and mapped one by one with the corresponding parameter boundary in the same dimension within the warning threshold range to clarify the specific position of the value of each single-dimensional feature within the corresponding warning threshold range. Then, a membership analysis is performed on the mapping results of each dimension to determine the degree to which the value of the feature in that dimension belongs to the warning threshold range. The mapping results and membership analysis results of all dimensions are systematically integrated and arranged in an orderly manner according to the dimension to form a standardized tabular data set. This data set is the mapping relationship table of electrical equipment.

[0050] The baseline state of the warning threshold range corresponding to each dimension in the mapping relationship table is sorted out. Using the baseline state as the core reference, the degree of deviation of the mapping results and membership analysis results of each dimension in the mapping relationship table is quantitatively determined. The actual deviation of the feature values ​​of each dimension relative to the baseline state is clarified and converted into the corresponding quantitative results. The deviation quantification results of all dimensions are arranged continuously and orderly according to the original dimension order of the deep feature vector. The resulting ordered set of quantified values ​​is the real-time deviation sequence of the electrical equipment.

[0051] Multiple judgment rules for identifying deviation patterns of electrical equipment are clearly defined. These rules include types such as deviation values ​​continuously exceeding the benchmark range, sudden changes in deviation values, and simultaneous deviation values ​​exceeding the benchmark range in multiple dimensions. The real-time deviation sequence of the electrical equipment is used as the identification basis. The changing characteristics of the quantified deviation values ​​of each dimension in the sequence, the correlation characteristics between the values ​​of each dimension, and the overall numerical change trend of the sequence are analyzed. All the analyzed characteristics are compared and matched one by one with the preset multiple judgment rules to identify the specific judgment rule type that the real-time deviation sequence conforms to. Based on the matching results, the actual deviation mode corresponding to the electrical equipment is determined, and a unique type identifier and feature identifier are added to the deviation mode. The resulting deviation mode with a unique identifier is the preliminary abnormal mode identifier of the electrical equipment.

[0052] All core information in the initial abnormal mode identification of electrical equipment is sorted out. The core information includes the specific type of abnormal mode, the corresponding characteristic dimension of deviation, the core manifestation characteristics of deviation, and the quantitative degree of deviation. According to the preset fixed structured framework, this core information is classified and collected, and similar information is integrated into a unified information module and hierarchically organized so that all core information forms a logically clear and structurally standardized information set. This information set is then standardized and encapsulated. The standardized information body formed after encapsulation that can intuitively and completely reflect the abnormal state of the equipment is the initial warning signal of the electrical equipment.

[0053] S5. Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is retrieved and matched, and the matched maintenance guidance scheme is encoded and encapsulated into a warning maintenance instruction for the electrical equipment. In this embodiment of the invention, the step of retrieving and matching the initial warning signal based on the maintenance knowledge base of the electrical equipment, and encoding and encapsulating the matched maintenance guidance scheme into a warning maintenance instruction for the electrical equipment, includes: Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is subjected to signal feature semantic analysis to obtain a standardized fault query description of the electrical equipment. The standardized fault query description is subjected to knowledge base similarity retrieval to obtain candidate maintenance guidance schemes related to historical cases in the maintenance knowledge base; Based on the current operating context information of the electrical equipment, the applicability scores of the candidate maintenance guidance schemes are evaluated, and the scheme with the highest applicability score is selected as the preferred maintenance guidance scheme for the electrical equipment. The preferred maintenance guidance scheme is deconstructed and encoded to obtain the early warning maintenance instructions for the electrical equipment.

[0054] The maintenance knowledge base for electrical equipment is retrieved. This knowledge base contains mapping rules between early warning signal features and semantic descriptions, as well as standardized specifications for fault query descriptions. First, the initial early warning signal is decomposed in all dimensions, extracting all core signal features such as abnormal mode type, deviation feature dimension, deviation quantification degree, and equipment aging and degradation status. Then, according to the mapping rules in the maintenance knowledge base, each core signal feature is converted into a standardized natural language semantic description. Subsequently, in accordance with the standardized fault query description format requirements of the knowledge base, all converted semantic descriptions are systematically integrated and standardized to form a text description that is structurally unified, expresses standardizedly, and accurately reflects the fault characteristics of the equipment. This text description is the standardized fault query description for electrical equipment.

[0055] Based on standardized fault query descriptions, all historical fault cases stored in the maintenance knowledge base are sorted out. Each historical fault case is associated with a corresponding fault feature description and a corresponding maintenance guidance plan. All fault features in the standardized fault query description are compared with the fault feature descriptions of each historical fault case in a dimension-by-dimensional manner. The similarity between the two in terms of feature dimension, feature performance, and degree of impact is determined one by one. All historical fault cases that meet the preset judgment criteria are selected. The maintenance guidance plans corresponding to these selected historical fault cases are extracted and integrated into a plan set. This plan set is the candidate maintenance guidance plan related to the historical cases in the maintenance knowledge base.

[0056] Comprehensive data is collected on the current operating context of the electrical equipment. This data includes the equipment's current stainless steel manufacturing process stage, real-time load conditions, historical cumulative equivalent operating time, current comprehensive wear index of key components, and surrounding environmental conditions. Based on this data, applicability evaluation dimensions for candidate maintenance guidance schemes are established. For each candidate scheme, its matching degree with the equipment's current operating context is verified across all evaluation dimensions. A score is assigned to each dimension based on the matching results. The scores from each dimension are integrated to calculate the comprehensive applicability score for each candidate maintenance guidance scheme. All candidate schemes are ranked according to their comprehensive applicability scores, and the scheme with the highest comprehensive applicability score is selected as the preferred maintenance guidance scheme for the electrical equipment.

[0057] The optimal maintenance guidance scheme for electrical equipment is comprehensively deconstructed. According to the execution logic of maintenance operations, the scheme is broken down into core execution elements such as the target maintenance component, specific maintenance operation steps, operation execution standards for each step, time requirements for maintenance operations, and precautions during maintenance. Combined with the instruction recognition specifications of the electrical equipment control system, a unified deconstruction coding rule is set. This rule clarifies the standardized coding conversion method corresponding to each type of core execution element. Each core execution element after deconstruction is converted into standardized coding content that meets the recognition requirements of the equipment control system according to the rule. Then, according to the logical sequence of maintenance operations, all standardized coding content is orderly combined and integrated to form a set of standardized instructions that can be directly recognized and executed by the electrical equipment control system. This set of standardized instructions is the early warning maintenance instruction for the electrical equipment.

[0058] S6. Monitor the response to the early warning maintenance command, and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

[0059] In this embodiment of the invention, the step of monitoring the response to the early warning maintenance command and updating the maintenance knowledge base with the monitored response feedback data of the early warning maintenance command and the updated equipment status data includes: The execution process of the early warning maintenance command is tracked in real time to obtain the command execution log of the electrical equipment; The key operation nodes and device response parameters of the instruction execution log are analyzed to obtain the response feedback data of the electrical equipment. Based on the response feedback data and the real-time equipment operation status data of the electrical equipment, the effectiveness of the maintenance operation of the electrical equipment is evaluated, and a maintenance effect evaluation report of the electrical equipment is obtained. The maintenance effect evaluation report is structured and integrated to obtain maintenance knowledge entries for the electrical equipment, and these maintenance knowledge entries are then updated to the maintenance knowledge base.

[0060] Continuous real-time status monitoring is performed on the entire execution process of early warning maintenance instructions. Each maintenance operation in the instruction is tracked step by step, including the initiation, execution, and completion stages. The actual initiation time, actual execution duration, operation completion status, and operation execution details of each stage are recorded. At the same time, the real-time status changes of electrical equipment in each operation stage are recorded. All the monitoring information and records obtained are systematically sorted and integrated with the operation execution stages in chronological order to form a complete record document containing relevant information on the entire execution process of early warning maintenance instructions. This record document is the instruction execution log of the electrical equipment.

[0061] A comprehensive analysis and information extraction process is performed on the instruction execution logs of electrical equipment. All key operation nodes in the execution process of early warning maintenance instructions are selected from the logs, clarifying the specific operation content, trigger time, and execution status of each key operation node. At the same time, the changes in various operating parameters of the electrical equipment, the response status of the core components of the equipment, and the overall operating feedback status of the equipment are extracted from the logs at each key operation node. The relevant information of each key operation node is matched with its corresponding equipment response parameters one by one. All the information after the matching is completed is systematically integrated and standardized, and the complete information set formed is the response feedback data of the electrical equipment.

[0062] The response feedback data of electrical equipment is comprehensively integrated with the current real-time operating status data of the equipment. The abnormal state and deviation of operating parameters of the equipment before the maintenance operation are sorted out and compared with the actual state and recovery of operating parameters of the equipment after the maintenance operation. The degree of improvement of equipment abnormality and the trend of change of operating parameters are clarified. Based on the preset maintenance operation effectiveness evaluation criteria, the maintenance operation is analyzed and comprehensively evaluated from the dimensions of the completion of early warning maintenance instructions, the elimination of equipment abnormal mode, the degree of return of equipment operating parameters to the normal range, and the recovery effect of the overall operating status of the equipment. The evaluation results of each dimension, the core comparative information of equipment status, and the overall effectiveness judgment of the maintenance operation are systematically integrated and sorted into a complete text report according to a fixed standard format. This text report is the maintenance effect evaluation report of electrical equipment.

[0063] The maintenance effectiveness evaluation report of electrical equipment is structurally decomposed, and core information is precisely extracted from the report. This includes the abnormal fault characteristics of the equipment, the initial warning signal triggered, the corresponding warning maintenance instructions, the specific execution process of the maintenance operation, the equipment response status of each key operation node, and the effectiveness evaluation results and validity judgment conclusions of this maintenance operation. According to the established knowledge item structure specifications of the maintenance knowledge base, all extracted core information is classified, collected, and systematically integrated, so that various types of information form independent information units that are logically clear, complete in content, and conform to the knowledge base storage standards. This information unit is the maintenance knowledge item of the electrical equipment. Through the established update entry of the maintenance knowledge base, the maintenance knowledge item is entered into the corresponding storage area according to the classification rules of the knowledge base, thus completing the content update operation of the maintenance knowledge base.

[0064] like Figure 2 The diagram shown is a functional block diagram of an artificial intelligence-based fault early warning and analysis system for electrical equipment in stainless steel product forming, provided in an embodiment of the present invention.

[0065] The artificial intelligence-based fault early warning and analysis system 100 for electrical equipment in stainless steel product forming, as described in this invention, can be installed in electronic devices. Depending on the functions implemented, the artificial intelligence-based fault early warning and analysis system 100 for electrical equipment in stainless steel product forming may include a data preprocessing module 101, a feature extraction module 102, a threshold definition module 103, a deviation comparison module 104, an instruction generation module 105, and a response monitoring module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0066] In this embodiment, the functions of each module / unit are as follows: The data preprocessing module 101 is used to perform normalization and noise reduction processing on the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment. The feature extraction module 102 is used to perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment. The threshold definition module 103 is used to dynamically define the warning threshold based on the process stage parameters and load condition data of the stainless steel products, and obtain the warning threshold range of the electrical equipment. The deviation comparison module 104 is used to compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment. The instruction generation module 105 is used to retrieve and match the initial warning signal based on the maintenance knowledge base of the electrical equipment, and encapsulate the matched maintenance guidance scheme into a warning maintenance instruction for the electrical equipment. The response monitoring module 106 is used to monitor the response to the early warning maintenance command and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

[0067] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0068] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0069] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0070] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0071] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A fault early warning analysis method for electrical equipment in stainless steel product forming based on artificial intelligence, characterized in that, The method includes: S1. Normalize and denoise the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment; S2. Perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment; S3. Based on the process stage parameters and load condition data of stainless steel products, a dynamic early warning threshold is defined for the historical normal operation records of the electrical equipment to obtain the early warning threshold range of the electrical equipment. S4. Compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment; S5. Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is retrieved and matched, and the matched maintenance guidance scheme is encoded and encapsulated into a warning maintenance instruction for the electrical equipment. S6. Monitor the response to the early warning maintenance command, and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

2. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The normalization and noise reduction processing of the multi-source operating data of the electrical equipment yields the operating data sequence of the electrical equipment, including: The monitoring sensors integrated into the electrical equipment are used to synchronously collect multi-source operating data of the electrical equipment during its operation. The multi-source operating data are timestamped according to a unified time base to obtain the aligned operating data of the electrical equipment. Fill in the missing data points in the alignment operation data and remove the abnormal data points in the alignment operation data to obtain the complete operation data of the electrical equipment; Frequency domain noise is filtered out from the complete operating data to obtain the noise-reduced operating data of the electrical equipment; Based on the historical benchmark data of the electrical equipment, the noise-reduced operating data is linearly normalized and mapped to obtain the operating data sequence of the electrical equipment.

3. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The step of extracting high-dimensional features from the operational data sequence to obtain the depth feature vector of the electrical equipment includes: The time-series structure of the running data sequence is divided by a sliding window to obtain data segments of equal length from the running data sequence; The operating mode is identified by the equal-length data segments to obtain the steady-state operating segments and transient process segments of the electrical equipment. The steady-state characteristics of the electrical equipment are obtained by extracting features from the statistical distribution characteristics of the steady-state operation segment. The transient characteristics of the electrical equipment are obtained by performing a multi-scale description of the change trajectory morphology of the transient process segment. The steady-state features and the transient features are fused by feature dimensionality reduction to obtain the deep feature vector of the electrical equipment.

4. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The process stage parameters and load condition data based on stainless steel products are used to dynamically define early warning thresholds based on the historical normal operation records of the electrical equipment, resulting in an early warning threshold range for the electrical equipment, including: Based on the process stage parameters and load condition data of stainless steel products, a similarity clustering analysis is performed on the historical normal operation records of the electrical equipment to obtain a subset of historical classification data of the electrical equipment. By performing time-series aggregation statistics on the historical classification data subset, a baseline statistical distribution of the operating parameters in the electrical equipment is obtained; Based on the benchmark statistical distribution, the probability density of the parameter fluctuation boundary of the electrical equipment is evaluated to obtain the initial parameter safety boundary of the electrical equipment. Based on the real-time aging and degradation coefficient of the electrical equipment, the initial parameter safety boundary is adaptively shifted and contracted to obtain the warning threshold range of the electrical equipment.

5. The method for fault early warning analysis of electrical equipment in stainless steel product forming based on artificial intelligence as described in claim 4, characterized in that, The step of adaptively shifting and contracting the initial parameter safety boundary based on the real-time aging degradation coefficient of the electrical equipment to obtain the warning threshold range of the electrical equipment includes: Based on the historical cumulative equivalent operating time and wear index of key components of the electrical equipment, the performance degradation status of the electrical equipment is quantitatively evaluated to obtain the real-time aging degradation coefficient of the electrical equipment. The operating cycle normalization mapping is performed on the real-time aging degradation coefficient to obtain the standardized degradation impact factor of the electrical equipment. Based on the standardized decay impact factor, the upper and lower limits of the initial parameter safety boundary are linearly shifted and proportionally shrunk to obtain the adjusted safety boundary of the electrical equipment. The adjusted safety boundary is validated for reasonableness to obtain the warning threshold range of the electrical equipment.

6. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 5, characterized in that, The method of quantitatively assessing the performance degradation state of the electrical equipment based on its historical cumulative equivalent operating time and wear indicators of key components, and obtaining the real-time aging degradation coefficient of the electrical equipment, includes: The historical cumulative equivalent operating time of the electrical equipment is obtained, as well as the wear rate data of key components periodically collected by the embedded sensors of the electrical equipment; Based on preset failure weights, the wear rate data of the key components are weighted and fused to obtain the comprehensive wear index of the electrical equipment. Based on the historical load spectrum of the stainless steel product forming process, the average load strength coefficient corresponding to the historical cumulative equivalent running time is extracted. Based on the historical cumulative equivalent operating time, the comprehensive wear index, and the average load intensity coefficient, the real-time aging degradation coefficient of the electrical equipment is calculated, wherein the calculation formula for the real-time aging degradation coefficient is as follows: ； In the formula, This represents the real-time aging and degradation coefficient. This represents the historical cumulative equivalent runtime. This represents the basic value of the rated design life of the electrical equipment. This represents the natural exponential function. This represents the comprehensive wear index. This represents the baseline wear index of the electrical equipment in a brand-new condition. This indicates the preset load impact correction factor. This represents the average load intensity coefficient.

7. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The step of comparing the depth feature vector with the warning threshold range to obtain the initial warning signal for the electrical equipment includes: Based on the deep feature vector, the warning threshold range is mapped dimension by dimension, and the mapping results are analyzed for membership degree to obtain the mapping relationship table of the electrical equipment. The deviation metric is performed on the mapping table to obtain the real-time deviation sequence of the electrical equipment; Based on the real-time deviation sequence, multi-rule pattern identification is performed on the deviation pattern of the electrical equipment to obtain the preliminary abnormal pattern identification of the electrical equipment. The abnormal mode identifier is structured and encapsulated to obtain the initial warning signal of the electrical equipment.

8. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The maintenance knowledge base of the electrical equipment is used to retrieve and match the initial warning signal, and the matched maintenance guidance scheme is encoded and encapsulated into a warning maintenance instruction for the electrical equipment, including: Based on the maintenance knowledge base of the electrical equipment, the initial warning signal is subjected to signal feature semantic analysis to obtain a standardized fault query description of the electrical equipment. The standardized fault query description is subjected to knowledge base similarity retrieval to obtain candidate maintenance guidance schemes related to historical cases in the maintenance knowledge base; Based on the current operating context information of the electrical equipment, the applicability scores of the candidate maintenance guidance schemes are evaluated, and the scheme with the highest applicability score is selected as the preferred maintenance guidance scheme for the electrical equipment. The preferred maintenance guidance scheme is deconstructed and encoded to obtain the early warning maintenance instructions for the electrical equipment.

9. The method for fault early warning analysis of electrical equipment for stainless steel product forming based on artificial intelligence as described in claim 1, characterized in that, The step of monitoring the response to the early warning maintenance command and updating the maintenance knowledge base with the monitored response feedback data and updated equipment status data includes: The execution process of the early warning maintenance command is tracked in real time to obtain the command execution log of the electrical equipment; The key operation nodes and device response parameters of the instruction execution log are analyzed to obtain the response feedback data of the electrical equipment. Based on the response feedback data and the real-time equipment operation status data of the electrical equipment, the effectiveness of the maintenance operation of the electrical equipment is evaluated, and a maintenance effect evaluation report of the electrical equipment is obtained. The maintenance effect evaluation report is structured and integrated to obtain maintenance knowledge entries for the electrical equipment, and these maintenance knowledge entries are then updated to the maintenance knowledge base.

10. A fault early warning and analysis system for electrical equipment in stainless steel product forming based on artificial intelligence, characterized in that, The system is used to implement the artificial intelligence-based fault early warning analysis method for stainless steel product forming electrical equipment as described in claims 1-9, the system comprising: The data preprocessing module is used to normalize and denoise the multi-source operating data of the electrical equipment to obtain the operating data sequence of the electrical equipment; The feature extraction module is used to perform high-dimensional feature extraction on the running data sequence to obtain the depth feature vector of the electrical equipment. The threshold definition module is used to dynamically define the warning threshold based on the process stage parameters and load condition data of the stainless steel products, and obtain the warning threshold range of the electrical equipment. The deviation comparison module is used to compare the depth feature vector with the warning threshold range to obtain the initial warning signal of the electrical equipment. The instruction generation module is used to retrieve and match the initial warning signal based on the maintenance knowledge base of the electrical equipment, and encapsulate the matched maintenance guidance scheme into a warning maintenance instruction for the electrical equipment. The response monitoring module is used to monitor the response to the early warning maintenance command and update the monitored response feedback data of the early warning maintenance command and the updated equipment status data to the maintenance knowledge base.

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