A time-series data-based device failure early warning system

Abstract

This invention relates to the field of industrial equipment monitoring technology, specifically disclosing an early warning system for equipment failure based on time-series data. The system includes modules for data acquisition and preprocessing, dynamic change analysis, abnormal signal identification, persistent anomaly determination, early warning grading and visualization, specialized assessment report generation, and dynamic feedback and monitoring optimization. It identifies potential anomalies by real-time acquisition of high-frequency time-series data, segmented processing, noise filtering, and vibration frequency scanning; it determines persistent abnormal signals by combining historical data, grades early warnings according to trends, and visualizes the results; it generates a specialized assessment report on bearing overheating risk, and optimizes monitoring parameters and response strategies in a closed-loop manner. This invention can accurately capture early, weak anomalies, reduce false alarms and missed alarms, improve equipment operational safety and reliability, and is suitable for early warning of failures in critical components such as rotating machinery bearings.

Description

Technical Field

[0001] This invention relates to the field of equipment condition monitoring and fault diagnosis technology, and in particular to an early warning system for equipment faults based on time-series data. Background Technology

[0002] In modern industrial intelligent manufacturing systems, early warning technology for equipment failure is a core supporting technology to ensure the continuity, safety and operational efficiency of industrial production, and it is also a key research direction in the fields of industrial internet and predictive maintenance of equipment.

[0003] As industrial equipment evolves towards larger scale, integration, higher speed, and greater intelligence, equipment operating conditions are becoming increasingly complex. The operating environment exhibits characteristics of multiple disturbances, strong coupling, and high dynamics, placing higher technical demands on the accurate identification, early prediction, and rapid response to potential equipment faults. Current industrial equipment fault early warning technologies and application systems generally suffer from insufficient technical adaptability and low data processing efficiency when facing scenarios involving high sampling rates, high throughput, and high real-time processing of equipment runtime sequence data streams.

[0004] Existing fault early warning methods mostly focus on static threshold determination and offline data analysis, which makes it difficult to efficiently analyze and mine features of continuous dynamic time-series data streams. They are unable to quickly capture weak dynamic changes that characterize the trend of equipment degradation, and are prone to having early potential abnormal signals of equipment drowned out by noise data. The timeliness, sensitivity and accuracy of fault early warning are difficult to guarantee.

[0005] The core issue with these technical deficiencies lies in the lack of a coordinated and hierarchical processing mechanism for key aspects such as high-frequency information extraction, key feature screening, abnormal signal identification, and persistent anomaly determination in continuous time-series data streams. On the one hand, the real-time segmentation, noise reduction, and feature purification technologies for high-frequency, massive time-series data are imperfect, making it impossible for the system to accurately separate core indicators strongly correlated with equipment health status (such as vibration frequency and temperature amplitude) within limited computing power and time windows. This makes the system susceptible to environmental interference, random fluctuations, and other redundant information. On the other hand, the lack of mechanisms for persistent determination of abnormal signals, fault mode matching, and graded early warning response makes it difficult to distinguish between fluctuations in normal operating conditions and early fault precursor signals. Especially for progressive and latent equipment faults such as bearing overheating, persistent tracking, trend analysis, and visualization of abnormal signals are impossible, resulting in delayed fault warnings and high false alarm and false negative rates.

[0006] The aforementioned technical bottlenecks directly hinder the implementation of predictive maintenance for industrial equipment, easily leading to unplanned equipment downtime and escalating faults, severely impacting the stable operation and economic benefits of industrial production systems. Therefore, developing an early warning system for equipment faults that adapts to high-frequency time-series data and possesses dynamic feature extraction, persistent anomaly detection, hierarchical early warning, and closed-loop optimization capabilities has become a pressing technical challenge in the field of intelligent monitoring and safe operation and maintenance of industrial equipment. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an early warning system for equipment faults based on time-series data. Through time-series data segmentation processing, high-frequency information extraction, noise filtering, vibration frequency scanning, persistent anomaly determination, graded early warning, special evaluation, and dynamic feedback optimization, it achieves early and accurate early warning of equipment faults such as bearing overheating.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: An early warning system for equipment failure based on time-series data, the system comprising: The data acquisition and preprocessing module is used to acquire continuous data streams from the device in real time, segment them according to preset sampling frequency and time window, extract high-frequency information distribution and filter noise to obtain a pre-processed data segment sequence. The dynamic change analysis module is used to extract features from the data segment sequence, obtain dynamic change information, calculate and correct the differences between adjacent data segments, determine the significance of dynamic changes, and obtain a set of significant change features. An abnormal signal identification module is used to detect signals within the set of significant change features by vibration frequency scanning, transmit signals exceeding the threshold to the dynamic change analysis module to mark them as potential anomalies, and obtain a list of marked abnormal signals. The persistent anomaly determination module is used to extract a subset of signals related to key indicators from the list of anomalies, combine historical data to determine the persistence of the anomalies and classify them according to the fault type, and determine the combination of persistent anomalies. The early warning classification and visualization module is used to identify the part of the persistent abnormal signal combination that is associated with high-frequency information. If the associated part shows an increasing trend, a classification response is triggered according to the early warning level classification standard and a visualization chart is generated to determine the early warning level. The special assessment report generation module is used to integrate the combination of early warning level and persistent anomaly to generate a special assessment report on bearing overheating risk and verify the accuracy of the report content to obtain the special assessment results of bearing overheating risk. The dynamic feedback and monitoring optimization module is used to continuously monitor based on the special assessment results, adjust the data stream storage format and analysis parameters for newly discovered dynamic changes, and determine the optimized monitoring response sequence.

[0009] Furthermore, the data acquisition and preprocessing module specifically performs the following: The data stream generated by the real-time acquisition device is segmented according to the preset sampling frequency and time window rules to obtain an initial set of segmented data. For each time window in the initial segmented data set, high-frequency information is extracted. Based on the distribution characteristics of the high-frequency information, a noise filtering mechanism is applied to identify and remove irrelevant interference in the data, resulting in a denoised data segment set. The abnormal fluctuations in the denoised data segment set are smoothed. The high-frequency information change trends in each time window are analyzed for the smoothed data segment sequence to determine the stability characteristics of the data segment sequence. Combined with the historical records of equipment operation status, the data segment sequence is classified and labeled to obtain the classified data stream feature set.

[0010] Furthermore, the dynamic change analysis module specifically performs the following: For the data segment sequence after preliminary processing, analyze the variation difference between the sequence length and adjacent data segments, determine the distribution range of the variation difference, and, in conjunction with the equipment calibration parameters, correct the difference value to obtain the corrected difference data set; The difference values ​​exceeding the preset threshold in the corrected difference data set are corrected. Based on the corrected difference data set, the significance of dynamic changes is analyzed, the interval distribution of significant changes is determined, and the feature information in each interval is extracted to obtain the interval feature information set. Based on the set of interval feature information and combined with change analysis logic, the persistence characteristics of dynamic changes within each interval are determined, and the persistence feature information is correlated and matched with the equipment operating status to determine the final feature classification result and obtain the classification feature set.

[0011] Furthermore, the abnormal signal identification module specifically performs the following: The abnormal signals in the marked abnormal signal list are initially screened. If the fluctuation amplitude of the signal exceeds the preset threshold, it is classified as a high-priority signal. The duration and periodicity of the high-priority signal are detected. If the duration of the signal exceeds the preset duration, it is marked as a continuous abnormal signal. Continuous abnormal signals are classified according to the source device. If the number of signals corresponding to a certain device exceeds the preset ratio, it is marked as a key monitoring device and a list of key monitoring devices is obtained. Based on the list of key monitored equipment, the operating status of the equipment is monitored in real time through a remote data acquisition interface. If abnormal fluctuations occur in the monitoring data, an automatic recording mechanism is triggered to obtain an abnormal fluctuation log of the equipment. Pattern recognition is performed on the data of abnormal fluctuation logs of the equipment. If a specific abnormal pattern is identified and matches the historical records, it is classified as a known abnormal type. Based on the known abnormal type, it is matched and processed through a pre-established abnormal handling rule library. If the corresponding handling rule is matched, a handling instruction is automatically generated and a list of handling instructions is obtained. According to the list of processing instructions, the instructions are sent to the corresponding devices through the device control interface. If the instruction execution feedback data meets the preset standards, the execution status is recorded to obtain the instruction execution status record.

[0012] Furthermore, the persistent exception determination module specifically performs the following: Abnormal signals are extracted from the list of marked abnormal signals. Based on the screening rules of core indicators, a subset of signals that are highly correlated with the core indicators is separated. The signal subset is compared with the range of historical data archives. If the comparison results show that the continuous performance of the signal exceeds the preset threshold, it is marked as a signal to be analyzed. The signal to be analyzed is classified into faults. If the classification result meets the judgment criteria of persistent anomaly, it is classified into the persistent anomaly category. The persistent anomaly category is grouped according to the signal source. If the number of signals from a certain source exceeds the preset proportion, it is marked as a high-risk source and a list of high-risk sources is obtained. Based on the list of high-risk sources, the operating data of the source devices is collected through the remote monitoring interface. If the fluctuations in the collected data exceed the preset range, the automatic recording mechanism is triggered to obtain the device fluctuation record. The system performs pattern matching on equipment fluctuation records. If the matching result matches the historical fault pattern, it is classified as a confirmed fault. The system then performs strategy matching on the confirmed fault. If a corresponding processing strategy is matched, control commands are automatically generated.

[0013] Furthermore, the aforementioned early warning classification and visualization module specifically performs the following: Extract the signal portion that is directly related to high-frequency information from the combination of persistent abnormal signals. If the extracted signal portion shows an increasing trend, mark it as an object of key concern and obtain a set of signals of key concern. For the set of signals of key concern, the persistence of the increasing trend is evaluated by a pre-established trend analysis model. If the evaluation result exceeds the preset threshold, it is classified as a high-priority signal. According to the list of high-priority signals, the warning level is divided using a classification standard database. If the division result meets the triggering conditions of a specific level, the corresponding hierarchical response mechanism is activated and the response mechanism activation record is obtained. For the response mechanism activation record, the dynamic changes of high-priority signals are tracked in real time, and corresponding change charts are generated. Based on the change charts and the preset level matching rules, the warning level is finally confirmed. If the confirmation result is consistent with the initial classification, it is locked as the final warning level, and the final warning level list is obtained. For the final warning level list, tiered response instructions are pushed to relevant systems through a remote information transmission interface, and the instruction push status record is obtained to determine whether the push has been completed.

[0014] Furthermore, the specific evaluation report generation module is executed as follows: By integrating the warning levels and persistent abnormal signal data, and using pre-established classification mapping rules, persistent abnormal signals are correlated and matched with bearing overheating risks to obtain a preliminary risk classification list. Based on the preliminary risk classification list, historical abnormal signal records related to bearing overheating are obtained, the correlation strength between signals and risks is analyzed, and a set of risk signals with high correlation is determined. Based on a set of highly correlated risk signals and a preset time interval rule, periodic monitoring data for bearing overheating risk is generated. If the frequency of abnormal signals in the monitoring data exceeds a preset threshold, they are marked as high-risk signal groups, and detailed features of the high-risk signal groups are extracted to determine the high-risk signal feature set. Based on the high-risk signal feature set, the feature data and fault prediction content are structured and organized according to the output format specifications to generate a draft special report that conforms to the specifications. The abnormal signals and risk assessment content involved in the draft special report are compared item by item. If the comparison results show that the data deviation exceeds the preset range, the deviation part is automatically corrected to obtain the final special report content. Based on the final special report, the report content is pushed to the relevant equipment monitoring system through a remote data transmission interface. The system then determines whether the push is complete and obtains the push status record.

[0015] Furthermore, the dynamic feedback and monitoring optimization module specifically performs the following: Through the data acquisition phase of the special assessment results of bearing overheating risk, real-time information is obtained from the continuous data stream related to bearing overheating. The data stream is initially layered to obtain the layered data set. To meet the need for continuous monitoring, dynamic changes in the layered dataset are identified and labeled, historical data related to bearing overheating are obtained, and the difference between the current data set and the historical data is compared to determine whether there are abnormal fluctuations. If the difference exceeds the preset threshold, it is marked as an abnormal data set. The abnormal data group is format converted and the storage format is adjusted to adapt to the subsequent analysis process to obtain the data unit with adjusted format. The data unit is then calibrated according to the update rules of the analysis parameters to generate the calibrated monitoring data stream. Based on the calibrated monitoring data stream, a corresponding monitoring response sequence is generated. Combined with the dynamically changing identification results, the final optimized sequence is determined. For the continuous monitoring of bearing overheating risk, the optimized sequence is transmitted to the relevant data processing module to obtain the transmission status record.

[0016] The technical effects and advantages of this invention are as follows: 1. This application provides an early warning system for equipment failure based on time-series data. It sets a three-level gradient warning mechanism for high-priority signals. The first-level warning corresponds to severe anomalies, the second-level warning corresponds to moderate anomalies, and the third-level warning corresponds to mild anomalies. Each level has clearly defined quantitative triggering conditions for signal fluctuation amplitude and duration. This not only avoids the false alarm and missed alarm problems caused by a single warning threshold, but also activates a differentiated response mechanism according to the severity of the anomaly. For example, the first-level warning automatically activates emergency handling instructions, and the third-level warning tracks the signal trend in real time and reminds maintenance personnel to make predictions. This realizes full-process gradient prevention and control from mild hidden dangers to severe anomalies, significantly improving the accuracy and timeliness of equipment failure warnings and reducing the probability of serious equipment failures.

[0017] 2. This application provides an early warning system for equipment failure based on time-series data. Through a data acquisition and preprocessing module, it achieves high-frequency signal extraction, noise filtering, and data smoothing. Combined with difference correction and closed-loop verification by a dynamic change analysis module, and further through layer-by-layer screening and pattern matching by anomaly signal identification and persistent anomaly determination modules, a closed-loop processing logic is constructed from the raw data stream to persistent anomaly signals. Simultaneously, a special assessment and verification mechanism is designed for bearing overheating risks, ensuring accurate anomaly signal locking and reliable risk assessment, effectively eliminating irrelevant interference, and transforming abstract data into equipment status labels with clear physical meaning. This significantly improves the accuracy of early equipment failure identification and provides a scientific basis for predictive maintenance decisions.

[0018] 3. This application provides an early warning system for equipment failure based on time-series data. Through dynamic feedback and monitoring optimization modules, combined with the results of a bearing overheating assessment, the system adjusts the data stream storage format and analysis parameters in real time, optimizes the monitoring response sequence, and ensures that the system can adapt to the dynamic changes in equipment operating conditions. At the same time, the three-level early warning mechanism combined with dynamic self-healing links and remote monitoring push functions reduces the frequency of manual intervention, realizing the transformation of equipment failure from passive response to proactive prevention. This not only reduces the workload and maintenance costs of operation and maintenance personnel, but also ensures that the equipment is in the optimal operating range for a long time through continuous monitoring and optimization, significantly improving the stability and service life of equipment operation. Attached Figure Description

[0019] Figure 1 This is a timing diagram of the early warning system for equipment failure of the present invention; Figure 2 This is a schematic diagram of the data acquisition and preprocessing process of the present invention; Figure 3 This is a schematic diagram of the early warning classification and visualization processing flow of the present invention. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the embodiments given in the accompanying drawings.

[0021] See Figures 1-3 As shown in the figure, an early warning system for equipment failure based on time-series data in this embodiment specifically includes: a data acquisition and preprocessing module, a dynamic change analysis module, an abnormal signal identification module, a persistent anomaly determination module, an early warning classification and visualization module, a special assessment report generation module, and a dynamic feedback and monitoring optimization module. The modules work together to achieve early and accurate warnings for progressive failures such as bearing overheating.

[0022] The data acquisition and preprocessing module acquires continuous data streams from the operating device in real time. Based on preset data stream sampling frequencies and time window division rules, it segments the data stream. High-frequency information distribution within a specific time window is extracted using a high-frequency information extraction method, and irrelevant interference is removed using a noise filtering mechanism to obtain a pre-processed data segment sequence. The specific execution steps of this module are as follows: By acquiring data streams generated during real-time device operation, and segmenting the continuous data stream according to preset sampling frequency and time window rules, an initial segmented data set is obtained. Using a segmentation processing method, high-frequency information is extracted from each time window in the initial segmented data set to determine its distribution characteristics. Based on these high-frequency information distribution characteristics, a noise filtering mechanism is applied to identify and remove irrelevant interference from the data, resulting in a denoised data segment set. If abnormal fluctuations still exist in the denoised data segment set, they are smoothed using preset threshold judgment rules to obtain a smoothed data segment sequence. For the smoothed data segment sequence, the trend of high-frequency information changes within each time window is analyzed to determine the stability characteristics of the data segment sequence. Based on the stability characteristics and combined with historical data of device operation, the data segment sequences are classified and labeled to obtain a classified data stream feature set.

[0023] In one embodiment, for industrial centrifuge vibration monitoring, a high-precision sensor is used to acquire the raw current and vibration data stream generated during equipment operation in real time at a sampling frequency of 2000Hz; a sliding time window is set to 1 second, meaning that every 2000 sampling points constitute an initial segment of data. This combination of high-frequency sampling and short-window segmentation can capture instantaneous weak disturbances during the high-speed rotation of the equipment, providing high-resolution raw data for subsequent fault diagnosis.

[0024] For each initial data segment, a wavelet transform algorithm is used to extract high-frequency information within the 300Hz–800Hz range. The distribution characteristics of the high-frequency information are determined by calculating the energy distribution entropy within this frequency band. The formula for calculating the energy entropy is: ;in; For high-frequency information energy entropy; For the first The energy percentage of each frequency point; The total number of frequency points, It is the logarithm to the base 2. When the energy entropy value This indicates that the high-frequency signal distribution is relatively discrete, and the equipment faces the risk of complex mechanical friction. This feature extraction method can effectively separate the characteristic frequencies of equipment operation and improve the sensitivity to abnormal signals.

[0025] Based on the extracted high-frequency distribution characteristics, a medium-range filtering noise filtering mechanism is applied; if the amplitude of the high-frequency component is less than 0.2 times the standard amplitude unit, it is determined to be background white noise and removed, eliminating irrelevant interference such as environmental vibration, and ensuring that the subsequent analysis features all originate from the device itself.

[0026] If isolated abnormal pulse fluctuations with instantaneous amplitudes exceeding 1.5 times the mean appear in the denoised data segment, a 5-point, third-order smoothing algorithm is used. By weighted averaging of the outlier and its neighborhood data, sudden spikes are limited to a reasonable range, improving data continuity. The smoothing formula is: ;in: For the smoothed first Point value; These are the original data points.

[0027] For the smoothed data segment sequence, the standard deviation of the high-frequency components over 10 consecutive windows is calculated. If the standard deviation fluctuation range is ≤0.05, it is judged as a high stability feature, which quantifies the smoothness of equipment operation and provides a basis for progressive fault identification.

[0028] Based on stability characteristics and combined with the equipment's historical fault mode database, the data segment sequence is classified and labeled. When high-frequency information shows periodic enhancement and stability decreases, the bearing wear characteristic frequency distribution is matched, and the data stream feature set is labeled as the bearing early warning state, so as to realize an accurate profile of the equipment's operating status and support predictive maintenance decisions.

[0029] The dynamic change analysis module extracts features based on the length of the pre-processed data segment sequence to obtain the dynamic change information contained within. By calculating the differences between adjacent data segments and applying calibration parameters from the acquisition equipment, the module determines the significance of the dynamic changes and obtains a set of significant change features. The specific execution steps of this module are as follows: For the data segment sequence, a segmented comparison method is used to analyze the variation differences between the sequence length and adjacent data segments, determine the distribution range of the variation differences, and obtain a set of variation differences. Based on the set of variation differences and in conjunction with equipment calibration parameters, the variation values ​​are corrected using parameter adjustment tools to obtain a corrected set of variation data. For the corrected set of variation data, if the variation values ​​exceed a preset threshold range, a smoothing method is used to correct the excess portion, resulting in a corrected set of variation data. Based on the corrected set of variation data, the significance of dynamic changes is analyzed, and a statistical comparison method is used to determine the distribution of significant change intervals, obtaining a set of significant change intervals. For the set of significant change intervals, a time window segmentation method is used to extract feature information within each interval, obtaining a set of interval feature information. Based on the set of interval feature information and in conjunction with change analysis logic, the persistence characteristics of dynamic changes within each interval are determined, obtaining a set of persistent features. For the set of persistent features, a classification and labeling method is used to associate and match the feature information with the equipment operating status to determine the final feature classification result, obtaining a set of classified features.

[0030] In one embodiment, for the sequence of equipment vibration frequency data segments, the Euclidean distance algorithm is used for segmented comparison to calculate the Euclidean distance between data points within a 50ms time window, quantifying the differences in variation. The Euclidean distance formula is: In the formula: The difference distance between adjacent data segments; For two adjacent segments One data point; This refers to the number of data points in a single window. When the distance value... If the fluctuation is within a certain range, it is considered a normal fluctuation; otherwise, it is marked as an abnormal difference point, forming a set of changes and differences.

[0031] Based on the equipment's factory sensitivity calibration parameters, linear compensation is used to correct for differences; if the sensor has a fixed deviation... Then, the bias value is subtracted from all groups of differing data to ensure data objectivity. Correction formula: In the formula: The difference value after correction; These are the original difference values; To fix the sensor's bias, an abnormal jump exceeding 1.2 times the standard deviation after correction is corrected using a 5-point, 3-fold smoothing algorithm. This smooths isolated spike pulse signals to below a preset threshold, resulting in a more continuous set of corrected difference data.

[0032] The significance of dynamic changes is assessed using analysis of variance (ANOVA). The variance formula is: In the formula: Variance of the differential data; The mean of the differences; The number of data points. The variance value. The region is defined as the significant variation interval, and features such as peak value, mean value, and information entropy are extracted by sliding window. Logistic regression is used to judge the persistence of features. If the feature is stable for 10 consecutive sampling points and the amplitude fluctuation rate is < 0.08, it is judged as a high persistence feature.

[0033] Multidimensional feature mapping is used to compare persistent features with the equipment's historical operation records; when the center value of the feature vector frequency ∈ 500Hz–600Hz and the persistence score > 0.85, it is labeled as the load operation status, transforming abstract data into equipment status labels with clear physical meaning.

[0034] Cluster analysis is used to verify the accuracy of the annotation. If the dispersion of a certain feature is greater than 0.3, the interval partitioning logic is re-triggered to ensure that the feature classification results truly reflect the health status of the equipment and improve the system's adaptability to complex operating conditions.

[0035] The abnormal signal identification module analyzes each signal in a set of significantly changing features using vibration frequency scanning technology. If a vibration frequency exceeds a preset threshold, the abnormal signal is transmitted to the analysis module via a real-time transmission protocol, marked as a potential abnormal signal, and a list of marked abnormal signals is obtained. The specific execution steps of this module are as follows: For the marked list of abnormal signals, a signal filtering tool is used for initial screening. Signals with fluctuation amplitudes exceeding a threshold are classified as high-priority signals, resulting in high-priority signal groups. Based on these high-priority signal groups, time-series analysis is used to detect the duration and periodicity of the signals. Signals exceeding a preset duration are marked as continuous abnormal signals, thus determining the set of continuous abnormal signals. For this set of continuous abnormal signals, they are categorized hierarchically by source device. Devices with an abnormal signal ratio exceeding a preset proportion are marked as key monitoring devices, resulting in a list of key monitoring devices. Based on this list, the operating status of the devices is monitored in real time through a remote data acquisition interface. Abnormal fluctuations trigger automatic recording, resulting in a device abnormal fluctuation log. For this log, a Support Vector Machine (SVM) algorithm is used for pattern recognition. Abnormal patterns matching historical records are classified as known abnormal types, thus determining the set of known abnormal types. Based on this set, processing rules are matched against an anomaly handling rule base to automatically generate processing instructions, resulting in a list of processing instructions. For this list, instructions are issued through the device control interface. If the instruction execution feedback meets preset standards, the execution status is recorded, resulting in an instruction execution status record.

[0036] In one embodiment, for the marked list of abnormal signals, a signal fluctuation amplitude threshold of 0.75 is set using a signal filtering tool. If the instantaneous change of an abnormal signal exceeds the threshold, it is classified into a high-priority signal group. Based on the high-priority signal group, the persistence of the signal is quantitatively evaluated by a time-series analysis algorithm, and a duration threshold of 150s is set. If a high-priority signal does not return to normal within 150s, it is marked as a persistent abnormal signal.

[0037] In the data stratification process, the proportion of abnormal signals generated by each source device in the total abnormal signal sample is statistically analyzed. If the proportion of abnormal signals corresponding to a certain device is ≥20%, the device is listed as a key monitoring object. Data is collected in real time through remote interface to capture the operating status of the key monitoring device in real time. When the fluctuation difference of the monitoring data is >0.4, an abnormal fluctuation log is automatically generated.

[0038] Using the frequency distribution and peak characteristics in the logs as input vectors, an SVM algorithm is employed to match historical fault patterns. If the matching degree between the input vector and a typical fault pattern is > 0.88, it is determined to be a known anomaly type. Subsequently, a rule base is matched. When a frequency offset anomaly is detected, frequency compensation rules are automatically matched to generate processing instructions containing parameter adjustment values. These instructions are sent to the corresponding devices through the device control interface, and the feedback data after instruction execution is monitored in real time. If the parameter fluctuation range of the feedback data is ≤ 0.15, the execution is considered successful. If the instruction execution feedback data does not meet the preset standard, a secondary verification logic is triggered to reassess the device's operating status and generate correction instructions, forming a dynamic fault self-healing link. This hierarchical processing and automatic matching mechanism can significantly improve the stability of device operation, reduce the frequency of manual intervention, and ensure that the device can maintain its optimal operating range under complex operating conditions. Through precise location and key monitoring of continuous abnormal signals, potential equipment failure risks can be identified in advance, realizing the transformation of equipment failure from passive response to proactive prevention, and ensuring the continuity and reliability of the overall business process.

[0039] The persistent anomaly identification module extracts a subset of signals related to key indicators from a list of tagged anomaly signals; it analyzes the persistence of the anomaly signals by limiting the scope through historical data comparison, classifies the signals according to fault type labeling rules, and determines the combination of persistent anomaly signals. The specific execution steps of this module are as follows: Anomaly signals are extracted from the flagged list. Using core indicator filtering rules, signal subsets highly correlated with core indicators are separated to obtain signal subset groups. For each signal subset group, historical data archive range comparison is used; signals consistently exceeding thresholds are marked as signals to be analyzed, thus determining the set of signals to be analyzed. Based on the set of signals to be analyzed, fault classification is performed using a classification rule database. Signals meeting the persistent anomaly judgment criteria are categorized into persistent anomaly categories, resulting in a persistent anomaly category list. For the persistent anomaly category list, signals are grouped hierarchically by source; sources with a signal quantity exceeding a preset proportion are marked as high-risk sources, thus obtaining a high-risk source list. Based on the high-risk source list, equipment operation data is collected through a remote monitoring interface; fluctuations exceeding a preset range trigger automatic recording, resulting in equipment fluctuation records. For the equipment fluctuation records, SVM algorithm pattern matching is used; patterns consistent with historical fault patterns are classified as confirmed faults, thus determining the confirmed fault set. Based on the confirmed fault set, control commands are automatically generated using a fault handling rule base matching strategy, thus obtaining a control command list.

[0040] In one embodiment, during the abnormal signal extraction process, raw data such as vibration displacement and acceleration are used as input. A threshold of 12.5 mm / s is set for the root mean square value of vibration acceleration using core indicator screening rules. When the characteristic value of a signal exceeds this threshold, the signal is determined to be highly correlated with the core operating indicators of the equipment, thereby obtaining a subset of signals. This screening method effectively eliminates irrelevant random interference, ensuring that subsequent analysis focuses on the key physical quantities of equipment operation. The historical operating records of the equipment over the past 30 days are retrieved for range comparison of the signal subset groups. If the amplitude fluctuation of the current signal is more than 2.5 times the historical average for a continuous 180 seconds, the signal is confirmed as the signal to be analyzed, thus determining the set of signals to be analyzed. Through the above long-term historical data comparison, non-instantaneous potential fault hazards can be effectively identified.

[0041] When the frequency offset of the signal to be analyzed remains stable within 5 consecutive sampling periods and meets the judgment logic for persistent anomalies, the signal to be analyzed is classified into the persistent anomaly category list. This process realizes the transformation from raw fluctuating signals to fault logic classification, providing a basis for accurately locating the nature of the fault.

[0042] For example, the data layering tool statistically analyzes persistent abnormal signals based on the device's unique identification code. If the number of abnormal signals generated by a specific bearing unit accounts for more than 35% of the total number of persistent abnormal signals, then that bearing unit is marked as a high-risk source. Subsequently, current and speed data of the high-risk source device are collected at a frequency of 50 times per second through a remote monitoring interface. If the fluctuation rate of the collected data exceeds the rated reference value of 15%, an automatic recording mechanism is triggered, encapsulating the waveform data of the device within 10 seconds before and after the fault as a device fluctuation record. This real-time triggering mechanism ensures the integrity of the fault site data.

[0043] Peak-to-peak value, skewness, and kurtosis from equipment fluctuation records are used as feature vectors and input into a support vector machine algorithm for pattern matching. If the projection of this feature vector into the hyperplane space matches an imbalanced fault pattern in the historical fault pattern library with a degree of 0.92 or higher, it is classified as a confirmed fault. Through high-dimensional linear classification, normal load fluctuations and substantial mechanical damage can be accurately distinguished. Based on the confirmed fault set, corresponding fault response strategies are retrieved from the fault handling rule library. If a lubrication failure fault handling rule is matched, a control command to reduce the equipment speed to 80% of the rated value and activate the backup oil pump is automatically generated, forming a control command list. This closed-loop processing flow can shorten the response time from anomaly detection to intervention execution, effectively preventing further equipment damage.

[0044] The early warning grading and visualization module identifies the portion of persistent abnormal signals directly related to high-frequency information distribution. If this portion exhibits an increasing trend, a tiered response mechanism is triggered based on the early warning level classification criteria. Simultaneously, a dynamic change chart is generated using abnormal trend visualization to determine the corresponding early warning level. The specific execution steps of this module are as follows: From the abnormal signal combinations, signals directly related to high-frequency information are extracted according to signal filtering rules. Signals exhibiting an increasing trend are marked as key targets of attention, resulting in a set of key focus signals. For this set, the persistence of the increasing trend is assessed using a trend analysis model. Signals exceeding a threshold are classified as high-priority signals, establishing a list of high-priority signals. Based on this list, warning levels are assigned according to a classification standard database. If trigger conditions are met, the corresponding tiered response mechanism is activated, and activation records are obtained. For these activation records, the dynamic changes of high-priority signals are tracked in real-time using visualization tools, generating change charts for visualization. Based on the visualization results and level matching rules, the warning levels are finally confirmed. If they match the initial classification, they are locked as the final warning level, resulting in a final warning level list. For the final warning level list, tiered response commands are pushed through a remote information transmission interface, and the command push status is recorded to determine completion.

[0045] In one embodiment, for screening persistent anomalous signal combinations, a sliding window frequency statistics method is used to extract high-frequency correlated signals. The sliding window length is set to 10 seconds. If a signal appears more than 5 times / second within the sliding window, it is determined to be a high-frequency correlated signal. For the extracted high-frequency correlated signals, a linear regression slope is used to evaluate the increasing trend. When the slope value is greater than 0.8 and the duration is greater than 30 seconds, it is marked as a key focus. Slope formula: In the formula: The trend slope; It is a time series; The value is the signal value. This represents the number of sampling points.

[0046] For the set of key signals, an exponential smoothing model is used to assess trend persistence, with a smoothing coefficient of 0.3. If the deviation between the model's predicted value and the actual observed value is greater than 1.5 units, the persistence of the increasing trend is determined to exceed a preset threshold. For example, when the predicted growth curve of a signal deviates from the actual collected fluctuation curve by 20%, the signal is classified as a high-priority signal. This process ensures accurate capture of abnormal evolution trends by quantifying the deviation value. For the warning level classification of the high-priority signals, a rule-based logical judgment method is used, setting three warning levels: Level 1 warning trigger condition: signal fluctuation amplitude > 5.0 units and duration > 60 seconds; Level 2 warning trigger condition: 3.0 units < signal fluctuation amplitude ≤ 5.0 units and duration > 40 seconds; Level 3 warning trigger condition: 1.0 unit < signal fluctuation amplitude ≤ 3.0 units and duration > 20 seconds. For example, when the system detects that the temperature signal of a critical component rises from 40°C to 65°C within 60 seconds and triggers a preset logic threshold, it automatically activates a tiered response mechanism. This mechanism records the response status as activated through a preset instruction set and synchronizes it to the visualization module. As another example, if the detected equipment vibration signal fluctuation amplitude is 4.2 units and the duration reaches 45 seconds, meeting the trigger conditions for a level two warning, the system will activate the level two response mechanism, simultaneously pushing warning information to the equipment maintenance terminal and recording the response status. If the detected lubricating oil pressure signal fluctuation amplitude is 2.5 units and the duration reaches 25 seconds, meeting the trigger conditions for a level three warning, the system will activate the level three response mechanism, tracking the signal change trend in real time and synchronizing it to the visualization module, reminding maintenance personnel to pay attention to the equipment's operating status and prepare for predictions.

[0047] For visualizing signal changes, a dynamic line chart rendering technique is used, mapping real-time signal values ​​to coordinate points and generating a dynamic chart that refreshes every second. For example, in the visualization interface, the overlap between the current signal curve and the historical baseline curve is compared; if the overlap is less than 70%, the corresponding warning level is locked. Finally, a data packet containing the device identifier, warning level, and response command is sent to the execution terminal via a remote information transmission interface; the command push status is determined based on the handshake signal returned by the receiving end; if no confirmation is received within 5 seconds, a retransmission mechanism is triggered to ensure the reliability of command delivery.

[0048] The special assessment report generation module integrates early warning levels and persistent abnormal signals, and generates a comprehensive fault prediction report for equipment bearing overheating risk based on the report generation time interval and output format definition. The report content is then verified using anomaly signal locking accuracy checks to obtain the special assessment results for bearing overheating risk. The specific execution steps of this module are as follows: By integrating early warning levels and abnormal signal data, abnormal signals are correlated and matched with bearing overheating risks according to classification mapping rules to obtain a preliminary risk classification list. For this preliminary risk classification list, historical abnormal signal records are retrieved, and the SVM algorithm is used to analyze the correlation strength between signals and risks, identifying a set of highly correlated risk signals. Based on this set of highly correlated risk signals, periodic monitoring data for bearing overheating risks is generated at preset time intervals, resulting in a periodic monitoring data table. For this periodic monitoring data table, abnormal signals exceeding a threshold frequency are marked as high-risk signal groups, and detailed features are extracted to determine a high-risk signal feature set. Based on this high-risk signal feature set, the data is structured and organized according to the output format specifications to generate a draft special report. For this draft special report, an accuracy verification tool is used to compare each abnormal signal with the risk assessment content; deviations exceeding a preset range are automatically corrected, resulting in the final special report content. Based on the final special report content, it is pushed to the equipment monitoring system via a remote data transmission interface, and the push status is recorded to determine whether the push is complete.

[0049] In one embodiment, abnormal signals such as vibration frequency and lubricating oil pressure collected are correlated and matched with the bearing overheating risk level using a preset classification mapping rule. For example, when the vibration frequency deviation reaches 15%, it is associated with a level-two overheating risk using the classification mapping rule, forming a preliminary risk classification list containing signal number and risk category. This association method can quickly narrow down the scope of fault investigation and improve the targeting of risk identification. For the data in the preliminary risk classification list, historical abnormal signal records of the equipment over the past 72 hours are retrieved as input samples. Using a support vector machine algorithm, the signal amplitude and duration are used as feature vectors to calculate the correlation strength between the feature vectors and the bearing overheating fault. If the calculated correlation degree is greater than 0.80, the signal is included in the high-correlation risk signal set. This machine learning-based analysis process can effectively eliminate random interference and ensure the accuracy of subsequent monitoring.

[0050] Based on the aforementioned set of highly correlated risk signals, a monitoring cycle of 300 seconds is set to automatically collect bearing housing temperature and speed data. The collected data is integrated into a periodic monitoring data table, recording the numerical fluctuations at each time point. If the bearing housing temperature > 85℃ within one hour, and the frequency is ≥ 5 times, it is marked as a high-risk signal group. Using data filtering tools, 12 key features, such as peak factor and root mean square value, are extracted from the high-risk signal group. Based on the high-risk signal feature set and combined with the preset output format specifications, the extracted 12 key feature parameters are structurally concatenated with the bearing overheating fault prediction content. The high-risk signal feature set and prediction template are input, and a draft special report containing risk level and abnormal parameter list is output. This structured processing method can unify data presentation standards and facilitate subsequent automated review.

[0051] For the initial draft of the special report, a lock-on accuracy verification tool is used to compare the amplitude of abnormal signals in the report with the baseline data in the original acquisition library item by item. If a vibration acceleration record value is found to deviate from the baseline value by more than 5%, linear interpolation correction is triggered to smooth the deviation data and overwrite the original record, resulting in the final special report content. Correction formula: In the formula: These are the corrected values; The x-coordinates of adjacent reference points; The ordinates are the ordinates of adjacent reference points. This verification process ensures the accuracy of the reported data and avoids misjudgments due to data distortion.

[0052] After obtaining the final special report content, the report data packet is pushed to the central equipment monitoring platform via a remote data transmission interface. During the push process, the system listens for confirmation messages returned by the interface in real time. If a success response code is received within 200ms, the push is considered complete, and a push status record containing a timestamp and data packet size is generated. This push mechanism ensures the timely delivery of early warning information and realizes closed-loop management of equipment monitoring operations.

[0053] The dynamic feedback and monitoring optimization module continuously monitors subsequent data streams based on the bearing overheating risk assessment results and feedback data integration logic. It adjusts the data stream storage format and analysis parameters for newly discovered dynamic changes to determine the optimized monitoring response sequence. The specific execution steps of this module are as follows: Through a dedicated data acquisition phase, real-time information is obtained from bearing overheating-related data streams. This information is initially stratified according to classification rules to obtain a stratified dataset. Based on this stratified dataset, an SVM algorithm is used to identify and label dynamic changes, resulting in labeled change data groups. For each labeled change data group, historical data differences are compared; groups with differences exceeding a threshold are marked as anomalous. Based on these anomalous data groups, the data format is converted according to data integration logic, and the storage format is adjusted to adapt to subsequent analysis processes, resulting in format-adjusted data units. For these format-adjusted data units, parameters are calibrated according to analysis parameter update rules, generating a calibrated monitoring data stream. Based on the calibrated monitoring data stream, a monitoring response sequence is generated, and the final optimized sequence is determined by combining the dynamic change identification results. Using the final optimized sequence, the sequence is transmitted to the data processing module, the transmission status is recorded, and continuous monitoring of bearing overheating risk is completed.

[0054] In the data acquisition phase, real-time information (e.g., temperature data collected per second by a temperature sensor) is obtained from a continuous data stream related to bearing overheating. The continuous data stream is initially segmented using a threshold-based segmentation rule to obtain a segmented dataset. Normal layer data corresponds to temperatures below 50℃, while abnormal layer data corresponds to temperatures above 60℃. This segmentation method facilitates subsequent targeted processing. For the segmented dataset, a support vector machine algorithm is applied to identify dynamic changes. For example, the segmented temperature sequence is used as input, and the algorithm's hyperplane separates normal and abnormal temperature patterns, annotating dynamic temperature changes (e.g., a sudden increase in temperature from 55℃ to 65℃), and outputting annotated change data set to improve monitoring accuracy.

[0055] For the labeled change data group, historical temperature data from the past week is retrieved (e.g., the historical average temperature is 55℃). By comparing the current change data group with the historical data, if the current average temperature reaches 70℃, exceeding a preset threshold of 10℃, the change data group is marked as an abnormal data group. This comparison method ensures that abnormal fluctuations are captured in a timely manner. Based on the abnormal data group, a feedback data integration logic is used to perform format conversion (e.g., converting JSON format to CSV format), adjusting the data stream storage format to adapt to subsequent analysis processes, resulting in format-adjusted data units. Each data unit contains a timestamp and a corresponding temperature value for compatibility with downstream analysis tools. For the format-adjusted data units, parameter calibration is performed according to preset analysis parameter update rules; for example, adjusting the temperature offset value from 0.5℃ to 0.2℃ to generate a calibrated monitoring data stream, ensuring the accuracy of data collection. Based on the calibrated monitoring data stream, a corresponding monitoring response sequence (e.g., an alarm trigger sequence) is generated. Combining the identification results of dynamic changes, an optimized monitoring response sequence is determined. The optimized monitoring response sequence prioritizes high-risk monitoring points to improve anomaly response efficiency. Specifically, the optimized monitoring response sequence is transmitted to the relevant data processing module through the API interface to obtain the transmission status record (such as "transmission successful"), thereby completing the monitoring optimization for bearing overheating risk and ensuring the continuous and stable operation of the system.

[0056] This invention discloses an early warning system for equipment failure based on time-series data. Addressing the unique business scenario of bearing overheating risk during equipment operation, it integrates real-time data stream acquisition, segmented processing, abnormal signal detection, and early warning response, solving the challenges of early identification and accurate early warning of equipment failures. By extracting high-frequency information and filtering noise from continuous data streams, combined with vibration frequency scanning technology, it identifies potential abnormal signals and determines persistent abnormal signal combinations through historical data comparison and trend analysis. Ultimately, it triggers a tiered early warning mechanism and generates a comprehensive fault prediction report. Through multi-level data processing and abnormal trend visualization, it ensures accurate assessment and timely response to bearing overheating risks, significantly improving the safety and reliability of equipment operation.

[0057] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the inventive concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A time-series data based early warning system for equipment failure, characterized in that, The system includes: The data acquisition and preprocessing module is used to acquire continuous data streams from the device in real time, segment them according to preset sampling frequency and time window, extract high-frequency information distribution and filter noise to obtain a pre-processed data segment sequence. The dynamic change analysis module is used to extract features from the data segment sequence, obtain dynamic change information, calculate and correct the differences between adjacent data segments, determine the significance of dynamic changes, and obtain a set of significant change features. An abnormal signal identification module is used to detect signals within the set of significant change features by vibration frequency scanning, transmit signals exceeding the threshold to the dynamic change analysis module to mark them as potential anomalies, and obtain a list of marked abnormal signals. The persistent anomaly determination module is used to extract a subset of signals related to key indicators from the list of anomalies, combine historical data to determine the persistence of the anomalies and classify them according to the fault type, and determine the combination of persistent anomalies. The early warning classification and visualization module is used to identify the part of the persistent abnormal signal combination that is associated with high-frequency information. If the associated part shows an increasing trend, a classification response is triggered according to the early warning level classification standard and a visualization chart is generated to determine the early warning level. The special assessment report generation module is used to integrate the combination of early warning level and persistent anomaly to generate a special assessment report on bearing overheating risk and verify the accuracy of the report content to obtain the special assessment results of bearing overheating risk. The dynamic feedback and monitoring optimization module is used to continuously monitor based on the special assessment results, adjust the data stream storage format and analysis parameters for newly discovered dynamic changes, and determine the optimized monitoring response sequence.

2. The system according to claim 1, wherein, The data acquisition and preprocessing module specifically performs the following: The data stream generated by the real-time acquisition device is segmented according to the preset sampling frequency and time window rules to obtain an initial set of segmented data. For each time window in the initial segmented data set, high-frequency information is extracted. Based on the distribution characteristics of the high-frequency information, a noise filtering mechanism is applied to identify and remove irrelevant interference in the data, resulting in a denoised data segment set. The abnormal fluctuations in the denoised data segment set are smoothed. The high-frequency information change trends in each time window are analyzed for the smoothed data segment sequence to determine the stability characteristics of the data segment sequence. Combined with the historical records of equipment operation status, the data segment sequence is classified and labeled to obtain the classified data stream feature set.

3. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The dynamic change analysis module specifically performs the following: For the data segment sequence after preliminary processing, analyze the variation difference between the sequence length and adjacent data segments, determine the distribution range of the variation difference, and, in conjunction with the equipment calibration parameters, correct the difference value to obtain the corrected difference data set; The difference values ​​exceeding the preset threshold in the corrected difference data set are corrected. Based on the corrected difference data set, the significance of dynamic changes is analyzed, the interval distribution of significant changes is determined, and the feature information in each interval is extracted to obtain the interval feature information set. Based on the set of interval feature information and combined with change analysis logic, the persistence characteristics of dynamic changes within each interval are determined, and the persistence feature information is correlated and matched with the equipment operating status to determine the final feature classification result and obtain the classification feature set.

4. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The abnormal signal identification module specifically performs the following: The abnormal signals in the marked abnormal signal list are initially screened. If the fluctuation amplitude of the signal exceeds the preset threshold, it is classified as a high-priority signal. The duration and periodicity of the high-priority signal are detected. If the duration of the signal exceeds the preset duration, it is marked as a continuous abnormal signal. Continuous abnormal signals are classified according to the source device. If the number of signals corresponding to a certain device exceeds the preset ratio, it is marked as a key monitoring device and a list of key monitoring devices is obtained. Based on the list of key monitored equipment, the operating status of the equipment is monitored in real time through a remote data acquisition interface. If abnormal fluctuations occur in the monitoring data, an automatic recording mechanism is triggered to obtain an abnormal fluctuation log of the equipment. Pattern recognition is performed on the data of abnormal fluctuation logs of the equipment. If a specific abnormal pattern is identified and matches the historical records, it is classified as a known abnormal type. Based on the known abnormal type, it is matched and processed through a pre-established abnormal handling rule library. If the corresponding handling rule is matched, a handling instruction is automatically generated and a list of handling instructions is obtained. According to the list of processing instructions, the instructions are sent to the corresponding devices through the device control interface. If the instruction execution feedback data meets the preset standards, the execution status is recorded to obtain the instruction execution status record.

5. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The persistent exception determination module specifically executes as follows: Abnormal signals are extracted from the list of marked abnormal signals. Based on the screening rules of core indicators, a subset of signals that are highly correlated with the core indicators is separated. The signal subset is compared with the range of historical data archives. If the comparison results show that the continuous performance of the signal exceeds the preset threshold, it is marked as a signal to be analyzed. The signal to be analyzed is classified into faults. If the classification result meets the judgment criteria of persistent anomaly, it is classified into the persistent anomaly category. The persistent anomaly category is grouped according to the signal source. If the number of signals from a certain source exceeds the preset proportion, it is marked as a high-risk source and a list of high-risk sources is obtained. Based on the list of high-risk sources, the operating data of the source devices is collected through the remote monitoring interface. If the fluctuations in the collected data exceed the preset range, the automatic recording mechanism is triggered to obtain the device fluctuation record. The system performs pattern matching on equipment fluctuation records. If the matching result matches the historical fault pattern, it is classified as a confirmed fault. The system then performs strategy matching on the confirmed fault. If a corresponding processing strategy is matched, control commands are automatically generated.

6. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The specific execution of the early warning classification and visualization module is as follows: Extract the signal portion that is directly related to high-frequency information from the combination of persistent abnormal signals. If the extracted signal portion shows an increasing trend, mark it as an object of key concern and obtain a set of signals of key concern. For the set of signals of key concern, the persistence of the increasing trend is evaluated by a pre-established trend analysis model. If the evaluation result exceeds the preset threshold, it is classified as a high-priority signal. According to the list of high-priority signals, the warning level is divided using a classification standard database. If the division result meets the triggering conditions of a specific level, the corresponding hierarchical response mechanism is activated and the response mechanism activation record is obtained. For the response mechanism activation record, the dynamic changes of high-priority signals are tracked in real time, and corresponding change charts are generated. Based on the change charts and the preset level matching rules, the warning level is finally confirmed. If the confirmation result is consistent with the initial classification, it is locked as the final warning level, and the final warning level list is obtained. For the final warning level list, tiered response instructions are pushed to relevant systems through a remote information transmission interface, and the instruction push status record is obtained to determine whether the push has been completed.

7. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The specific execution of the special assessment report generation module is as follows: By integrating the warning levels and persistent abnormal signal data, and using pre-established classification mapping rules, persistent abnormal signals are correlated and matched with bearing overheating risks to obtain a preliminary risk classification list. Based on the preliminary risk classification list, historical abnormal signal records related to bearing overheating are obtained, the correlation strength between signals and risks is analyzed, and a set of risk signals with high correlation is determined. Based on a set of highly correlated risk signals and a preset time interval rule, periodic monitoring data for bearing overheating risk is generated. If the frequency of abnormal signals in the monitoring data exceeds a preset threshold, they are marked as high-risk signal groups, and detailed features of the high-risk signal groups are extracted to determine the high-risk signal feature set. Based on the high-risk signal feature set, the feature data and fault prediction content are structured and organized according to the output format specifications to generate a draft special report that conforms to the specifications. The abnormal signals and risk assessment content involved in the draft special report are compared item by item. If the comparison results show that the data deviation exceeds the preset range, the deviation part is automatically corrected to obtain the final special report content. Based on the final special report, the report content is pushed to the relevant equipment monitoring system through a remote data transmission interface. The system then determines whether the push is complete and obtains the push status record.

8. The equipment fault early warning system based on time-series data according to claim 1, characterized in that, The dynamic feedback and monitoring optimization module specifically performs the following: Through the data acquisition phase of the special assessment results of bearing overheating risk, real-time information is obtained from the continuous data stream related to bearing overheating. The data stream is initially layered to obtain the layered data set. To meet the need for continuous monitoring, dynamic changes in the layered dataset are identified and labeled, historical data related to bearing overheating are obtained, and the difference between the current data set and the historical data is compared to determine whether there are abnormal fluctuations. If the difference exceeds the preset threshold, it is marked as an abnormal data set. The abnormal data group is format converted and the storage format is adjusted to adapt to the subsequent analysis process to obtain the data unit with adjusted format. The data unit is then calibrated according to the update rules of the analysis parameters to generate the calibrated monitoring data stream. Based on the calibrated monitoring data stream, a corresponding monitoring response sequence is generated. Combined with the dynamically changing identification results, the final optimized sequence is determined. For the continuous monitoring of bearing overheating risk, the optimized sequence is transmitted to the relevant data processing module to obtain the transmission status record.

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