Incremental learning data stream analysis platform

Abstract

The invention relates to the technical field of data processing, and particularly discloses an incremental learning data flow analysis platform, which is used for solving the problem that in the prior art, the aging of a rolling bearing caused by different load fluctuations and the relation between the sensitivity of the aging to the load fluctuations are not mentioned. The working data flow of the rolling bearing is obtained; the working data flow comprises a first working data flow obtained when the rolling bearing operates for the first time and a second working data flow of each operation after the rolling bearing operates for the first time, analyzing a bearing standard coefficient according to the first working data flow, and analyzing a bearing aging coefficient based on the bearing standard coefficient and the second working data flow, calculating a fluctuation sensitive index according to the bearing aging coefficient and the second working data flow; according to the invention, the service life of the bearing is prolonged, and the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to an incremental learning data stream analysis platform. Background Art

[0002] Rolling bearings are important parts of mechanical equipment and are widely used in the industrial and transportation fields. However, rolling bearings often operate under extreme conditions such as high temperature and heavy load, and are prone to failure. In the field of rolling bearing fault diagnosis, traditional fault diagnosis models are usually based on large-scale offline data training. By extracting features such as bearing vibration signals and acoustic emission signals, they are combined with machine learning or deep learning models for classification and prediction. These methods have good performance in offline static environments, but face significant limitations in actual engineering applications, especially under long-term equipment operation conditions. Due to the complex and changeable environment in which rolling bearings operate, factors such as aging and load fluctuations can cause vibration signals and other features to evolve over time, resulting in drift or even pattern mutations in data distribution. Existing offline training models are usually difficult to adapt to such changes, which is manifested in a gradual decrease in model accuracy, and may even be unable to correctly diagnose new pattern faults.

[0003] Incremental learning technology can update the model without accessing a large amount of historical data, thereby effectively reducing storage requirements and computing costs. At the same time, incremental learning supports adding new features or new fault modes without forgetting old knowledge, thereby improving the adaptability and generalization ability of the model. Existing literature ([1] Zhao Shangjun. Research on rolling bearing fault diagnosis based on deep learning [D]. Shijiazhuang Tiedao University, 2023. DOI: 10.27334 / d.cnki.gstdy.2023.000619.) has studied the problems of low rolling bearing diagnosis accuracy under variable working conditions, the inability of traditional batch learning models to better identify newly generated data, and the inability of incremental learning models to better diagnose data under different test benches and different working conditions. However, it does not mention the relationship between the aging of rolling bearings caused by different load fluctuations and the sensitivity of aging to load fluctuations, which makes it difficult to adapt to the degradation of bearing performance under high load or frequent load changes, and the reliability of the diagnosis results is reduced.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides an incremental learning data flow analysis platform, which is used to solve the problem that the prior art does not mention the connection between the aging of rolling bearings due to the influence of different load fluctuations and the sensitivity of aging to load fluctuations, resulting in the difficulty in adapting to the degradation of bearing performance under high load or frequent load change environments, and the reliability of diagnostic results is reduced, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: An incremental learning data stream analysis platform includes a data stream import module, a first bearing analysis module, a second bearing analysis module and a data sensitivity monitoring module, wherein the data stream import module is used to obtain a working data stream of a rolling bearing; the working data stream includes a first working data stream obtained when the rolling bearing is first operated and a second working data stream obtained when the rolling bearing is first operated each time after the first operation; the first bearing analysis module is used to analyze a bearing standard coefficient according to the first working data stream; the second bearing analysis module is used to analyze a bearing aging coefficient according to the bearing standard coefficient and the second working data stream; the second bearing analysis module includes a data update unit; the data update unit is used to update data in real time according to the first working data stream and the second working data stream, and obtain The bearing standard coefficient and basic statistical data are the maximum, minimum and average values ​​of the first operating vibration frequency, the first operating vibration amplitude, the first operating sound frequency and the first operating sound amplitude respectively. When the second operating data stream reaches time s, the jth item of the second operating vibration frequency, the second operating vibration amplitude, the second operating sound frequency and the second operating sound amplitude at time s is calculated. Update, where the average update formula for each data item is: ; ; Where: is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1, is the number of data of the jth item up to time s.

[0007] As a further solution of the present invention, the data stream import module includes a first data stream aggregation unit and a second data stream aggregation unit; The first data stream aggregation unit is used to obtain the first working data stream obtained when the rolling bearing is first operated; the first working data stream includes a first operating vibration data stream and a first audio data stream; the first operating vibration data stream includes a first operating vibration frequency and a first operating vibration amplitude of the rolling bearing; the first audio data stream includes a first operating sound frequency and a first operating sound amplitude of the rolling bearing; The second data stream aggregation unit is used to obtain the second working data stream of each operation of the rolling bearing after the initial operation; the second working data stream includes a second operating vibration data stream, a second audio data stream and a first load data stream; the second operating vibration data stream includes the second operating vibration frequency and the second operating vibration amplitude of the rolling bearing; the second audio data stream includes the second operating sound frequency and the second operating sound amplitude of the rolling bearing; the first load data stream includes the load value of the rolling bearing.

[0008] As a further solution of the present invention, the first bearing analysis module includes a primary data stream extraction unit and a bearing standard coefficient calculation unit; the primary data stream extraction unit is connected to the bearing standard coefficient calculation unit; The primary data stream extraction unit is used to extract the first operating vibration data stream and the first audio data stream; The bearing standard coefficient calculation unit is used to substitute the first running vibration data stream and the first audio data stream into the bearing standard coefficient calculation formula to calculate the bearing standard coefficient. The bearing standard coefficient calculation formula is: ; Where: is the standard coefficient of the bearing, is the number of data items in the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency, and the first operation sound amplitude, is the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude at time t, The initial running time of the rolling bearing. is the maximum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, is the minimum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, It is the average value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude.

[0009] As a further solution of the present invention, the updating formula of the maximum value and the minimum value of each data item is: ; ; Where: is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value screening function, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value screening function, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1.

[0010] As a further solution of the present invention, the bearing aging coefficient calculation unit is used to obtain the bearing standard coefficient, substitute the bearing standard coefficient, the second operating vibration data stream and the second audio data stream into the bearing aging coefficient calculation formula to calculate the bearing aging coefficient. The bearing aging coefficient calculation formula is: ; Where: is the bearing aging coefficient during the hth rolling bearing operation, is the bearing standard coefficient, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the h-th running time of the rolling bearing.

[0011] As a further solution of the present invention, the data sensitivity monitoring module includes a three-level data stream extraction unit, a load fluctuation coefficient calculation unit and a fluctuation sensitivity index monitoring unit; the three-level data stream extraction unit is used to extract the first load data stream; the load fluctuation coefficient calculation unit is used to construct a load fluctuation model according to the first load data stream to obtain the load fluctuation coefficient of the bearing in real time, and the formula of the load fluctuation model is: ; Where: is the load fluctuation coefficient of the hth rolling bearing operation, is the load value of the rolling bearing at time s, is the load value of the rolling bearing at time s-1, is the maximum load value of the rolling bearing up to time s, is the minimum load value of the rolling bearing up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the h-th running time of the rolling bearing.

[0012] As a further solution of the present invention, the fluctuation sensitivity index monitoring unit is used to establish a fluctuation sensitivity analysis model according to the load fluctuation coefficient and the bearing aging coefficient to calculate the fluctuation sensitivity index. The formula of the fluctuation sensitivity analysis model is: ; Where: is the volatility sensitivity index, is the bearing aging coefficient during the hth rolling bearing operation, is the load fluctuation coefficient of the hth rolling bearing operation, is the bearing aging coefficient during the h-1th rolling bearing operation, is the load fluctuation coefficient of the rolling bearing during the h-1th operation, is the number of rolling bearing operations.

[0013] The technical effects and advantages of an incremental learning data stream analysis platform of the present invention are as follows: the present invention obtains the working data stream of the rolling bearing; the working data stream includes a first working data stream obtained when the rolling bearing is first run and a second working data stream of each operation of the rolling bearing after the first run, analyzes the bearing standard coefficient according to the first working data stream, analyzes the bearing aging coefficient based on the bearing standard coefficient and the second working data stream, and based on the incremental learning model, can analyze the working data stream of the rolling bearing in real time, and dynamically update the bearing aging coefficient and fluctuation sensitivity index; by comparing the initial operation data and the subsequent operation data, can accurately evaluate the standard state of the bearing and its aging trend; by calculating the aging coefficient and the fluctuation sensitivity index, can predict the possible failure trend of the bearing, avoid equipment interruption due to bearing failure in critical tasks, can comprehensively improve the monitoring and management efficiency of the rolling bearing, not only extend the service life of the bearing, but also reduce the operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of the structure of an incremental learning data stream analysis platform provided by the present invention; Figure 2 A schematic diagram of the working process of the second bearing analysis module provided by the present invention; Figure 3 An analysis trend diagram of the second operating vibration frequency provided by the present invention; Figure 4 An analysis trend diagram of the second operating sound frequency provided by the present invention; Figure 5 An analysis trend diagram of the bearing aging coefficient provided by the present invention; Figure 6 This is a fluctuation analysis trend diagram of the load fluctuation coefficient provided by the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the accompanying drawings in the present invention to clearly and completely describe the technical solution in the present invention. Obviously, the described technical solution is only a part of the present invention, not all of it. Based on the technical solution in the present invention, all other technical solutions obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0016] Figure 1 This is a schematic diagram of the structure of an incremental learning data flow analysis platform provided by the present invention. Figure 1 As shown, an incremental learning data flow analysis platform includes a data flow import module, a first bearing analysis module, a second bearing analysis module and a data sensitivity monitoring module; The data stream import module is used to obtain the working data stream of the rolling bearing; the working data stream includes a first working data stream obtained when the rolling bearing is first run and a second working data stream obtained when the rolling bearing is run for the first time and each time the rolling bearing runs after the first run; The first bearing analysis module is used to analyze the bearing standard coefficient according to the first working data stream; The second bearing analysis module is used to analyze the bearing aging coefficient according to the bearing standard coefficient and the second working data stream; The data sensitivity monitoring module is used to calculate the fluctuation sensitivity index according to the bearing aging coefficient and the second working data stream.

[0017] Specifically, the data stream import module includes a first data stream aggregation unit and a second data stream aggregation unit; The first data stream aggregation unit is used to obtain the first working data stream obtained when the rolling bearing is first operated; the first working data stream includes a first operating vibration data stream and a first audio data stream; the first operating vibration data stream includes a first operating vibration frequency and a first operating vibration amplitude of the rolling bearing; the first audio data stream includes a first operating sound frequency and a first operating sound amplitude of the rolling bearing; The second data stream aggregation unit is used to obtain the second working data stream of each operation of the rolling bearing after the initial operation; the second working data stream includes a second operation vibration data stream, a second audio data stream and a first load data stream; the second operation vibration data stream includes a second operation vibration frequency and a second operation vibration amplitude of the rolling bearing; the second audio data stream includes a second operation sound frequency and a second operation sound amplitude of the rolling bearing; the first load data stream includes a load value of the rolling bearing; like Figure 3 As shown in the analysis trend chart of the second operation vibration frequency, the value of the second operation vibration frequency is approximately between 140 and 160. The curve fluctuates slightly but is relatively stable. The values ​​from January to December generally fluctuate around 150. The difference between the highest and lowest points is not large, indicating that the vibration noise is relatively stable throughout the year. Figure 4 As shown in the analysis trend chart of the second operating sound frequency, the vertical axis value is approximately between 70 and 90, and the curve also fluctuates slightly throughout the year. It was around 80 in January, and then fluctuated between 80 and 85. It fell slightly or stabilized at the end of the year. Overall, the abnormal sound level is the same as the vibration noise, without any obvious sudden increase or decrease, and remains within a relatively controllable range.

[0018] By dividing the first data stream summary unit and the second data stream summary unit, the data of the rolling bearing from the first operation to the long-term use is covered. The first working data stream provides the "benchmark data" of the rolling bearing in the initial stage, reflecting the standard state of the equipment, which is helpful for comparison with subsequent data. The second working data stream contains vibration, audio and load data, reflecting the dynamic behavior of the bearing during operation, covering a variety of environments and working conditions; the vibration frequency, vibration amplitude and audio data during the initial operation provide an accurate benchmark for subsequent aging evaluation, which helps to identify manufacturing defects or installation problems in the initial state; through real-time monitoring of the second operating vibration data stream and the second audio data stream, the state changes of the bearing during operation are accurately captured. The addition of load data allows the performance analysis of the bearing to be combined with the actual working conditions to evaluate the impact of load fluctuations on the bearing state. Changes in vibration frequency and vibration amplitude can reflect the aging of the internal components of the bearing, insufficient lubrication or external environmental influences.

[0019] Specifically, the first bearing analysis module includes a primary data stream extraction unit and a bearing standard coefficient calculation unit; the primary data stream extraction unit is connected to the bearing standard coefficient calculation unit; The primary data stream extraction unit is used to extract the first operating vibration data stream and the first audio data stream; The bearing standard coefficient calculation unit is used to substitute the first running vibration data stream and the first audio data stream into the bearing standard coefficient calculation formula to calculate the bearing standard coefficient. The bearing standard coefficient calculation formula is: ; Where: is the standard coefficient of the bearing, is the number of data items in the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency, and the first operation sound amplitude, is the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude at time t, The initial running time of the rolling bearing. is the maximum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, is the minimum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, It is the average value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude.

[0020] The primary data stream extraction unit extracts multi-dimensional data including vibration frequency, vibration amplitude, sound frequency and sound amplitude, which can fully capture the dynamic characteristics of the bearing in the initial operation, provide complete information on the working status of the bearing, and ensure the accuracy of the calculation; the bearing standard coefficient, as the benchmark value of the bearing, reflects the healthy status of the bearing in the initial operation stage, and can be used as a comparison benchmark in the subsequent operation process to help analyze the aging or failure trend of the bearing. By comparing with the subsequent operation data, it can accurately identify whether the bearing has abnormalities and perform targeted repairs; the calculated coefficient is a dynamic standard value, which is used to monitor the working status of the bearing in real time. When the standard coefficient of the bearing fluctuates abnormally, potential faults can be predicted in advance to avoid sudden failure of the bearing during operation; based on the analysis of the coefficient, the maintenance cycle can be scientifically planned to avoid premature or late maintenance decisions. Only when the standard coefficient of the bearing changes significantly will the maintenance warning be triggered, thereby optimizing the equipment's utilization efficiency and maintenance cost.

[0021] Specifically, the second bearing analysis module includes a secondary data stream extraction unit, a data update unit and a bearing aging coefficient calculation unit; the secondary data stream extraction unit is connected to the data update unit, and the data update unit is connected to the bearing aging coefficient calculation unit; The secondary data stream extraction unit is used to extract the second operating vibration data stream and the second audio data stream; The data updating unit is used to update data in real time according to the first working data stream and the second working data stream, and obtain The bearing standard coefficient and basic statistical data are the maximum, minimum and average values ​​of the first operating vibration frequency, the first operating vibration amplitude, the first operating sound frequency and the first operating sound amplitude respectively. When the second operating data stream reaches time s, the jth item of the second operating vibration frequency, the second operating vibration amplitude, the second operating sound frequency and the second operating sound amplitude at time s is calculated. Update, where the average update formula for each data item is: ; ; Where: is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1, is the number of data of the jth item up to time s; The update formula for the maximum and minimum values ​​of each data item is: ; ; Where: is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value screening function, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value screening function, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1; The bearing aging coefficient calculation unit is used to obtain the bearing standard coefficient, substitute the bearing standard coefficient, the second operating vibration data stream and the second audio data stream into the bearing aging coefficient calculation formula, and calculate the bearing aging coefficient. The bearing aging coefficient calculation formula is: ; Where: is the bearing aging coefficient during the hth rolling bearing operation, is the bearing standard coefficient, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the h-th running time of the rolling bearing.

[0022] like Figure 5 The analysis trend chart of the bearing aging coefficient is shown in Figure 2. The aging coefficient shows a slow upward trend, indicating that the aging degree of the equipment or material gradually deepens over time. The aging coefficient was around 0.09 in January, and then slowly increased to reach 0.12 around October, and fluctuated slightly in November and December. Overall, the aging trend is in line with expectations, that is, as time goes by, the material or equipment gradually ages, but there is no sudden accelerated aging phenomenon.

[0023] The data stream extraction module integrates the second operating vibration data stream and the second audio data stream, covering multi-dimensional data such as vibration frequency, vibration amplitude, sound frequency, and sound amplitude. This data integration can comprehensively reflect the operating status of the bearing, and can capture both physical vibration characteristics and identify acoustic anomalies. Using the bearing standard coefficient in the first working data stream and the second working data stream, a comparison system from the initial standard to dynamic operation is established, providing a strong benchmark for aging assessment. By comparing the aging coefficients of multiple runs, the long-term aging trend of the bearing can be captured to help predict its service life. Real-time updated statistical data and aging coefficients can quickly capture anomalies in operation (such as abnormal increase in vibration amplitude or violent fluctuations in audio signals), provide early warnings, and avoid the expansion of faults. According to the changes in the aging coefficient, the maintenance cycle can be scientifically planned to prevent premature or late maintenance and optimize maintenance resources. Combined with statistical analysis of different data dimensions (vibration and audio), it can quantitatively evaluate the specific impact of different environmental factors (such as load, lubrication conditions, and operating temperature) on bearing aging.

[0024] like Figure 2 The workflow diagram of the second bearing analysis module shown in FIG. 1 includes data flow input, data update, and coefficient calculation, as shown in FIG. Figure 2 The data stream 1 shown is the first working data stream and the second working data stream. Data stream 1 is extracted, updated and calculated respectively. Data stream 1 is extracted to obtain data stream 2, that is, the required data stream. Data stream 2 and data stream 1 are merged for update operation to obtain data stream 3. Data stream 3 and data stream 1 are merged again to calculate the bearing aging coefficient when the rolling bearing is running.

[0025] Specifically, the data sensitivity monitoring module includes a three-level data flow extraction unit, a load fluctuation coefficient calculation unit, and a fluctuation sensitivity index monitoring unit; The third-level data stream extraction unit is used to extract the first load data stream; The load fluctuation coefficient calculation unit is used to construct a load fluctuation model according to the first load data stream to obtain the load fluctuation coefficient of the bearing in real time. The formula of the load fluctuation model is: ; Where: is the load fluctuation coefficient of the hth rolling bearing operation, is the load value of the rolling bearing at time s, is the load value of the rolling bearing at time s-1, is the maximum load value of the rolling bearing up to time s, is the minimum load value of the rolling bearing up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the h-th running time of the rolling bearing; like Figure 6 As shown in the fluctuation analysis trend chart of the load fluctuation coefficient, the load shows certain fluctuations between January and December, and the overall value varies between 2.0KN and 2.7KN. The load has obvious peaks in March, June and September, indicating that there may be higher load pressure in these months, while there is a slight decrease in April and August, which may be the result of load reduction or equipment adjustment.

[0026] The fluctuation sensitivity index monitoring unit is used to establish a fluctuation sensitivity analysis model according to the load fluctuation coefficient and the bearing aging coefficient to calculate the fluctuation sensitivity index. The formula of the fluctuation sensitivity analysis model is: ; Where: is the volatility sensitivity index, is the bearing aging coefficient during the hth rolling bearing operation, is the load fluctuation coefficient of the hth rolling bearing operation, is the bearing aging coefficient during the h-1th rolling bearing operation, is the load fluctuation coefficient of the rolling bearing during the h-1th operation, is the number of rolling bearing operations.

[0027] The real-time load changes are standardized and the load fluctuations during each bearing operation are dynamically quantified. By considering the load value changes at each time point and the current maximum and minimum values, the degree and trend of load fluctuations can be sensitively captured. The changes in the load fluctuation coefficient and the bearing aging coefficient are comprehensively considered to quantitatively describe the sensitivity of the bearing to load fluctuations. By comparing the aging coefficient and the load fluctuation coefficient in different operating cycles, the changing trend of the bearing performance can be accurately predicted. If the load fluctuation coefficient changes too much, it may indicate unstable load conditions, thereby identifying potential mechanical problems in advance. Based on the results of the fluctuation sensitivity index, the load fluctuation mode that has a greater impact on the bearing can be identified, and the load parameters can be adjusted to reduce the impact on the bearing.

[0028] The embodiment of the present invention obtains the working data stream of the rolling bearing; the working data stream includes a first working data stream obtained when the rolling bearing is initially operated and a second working data stream of each operation of the rolling bearing after the initial operation, analyzes the bearing standard coefficient according to the first working data stream, analyzes the bearing aging coefficient based on the bearing standard coefficient and the second working data stream, and based on the incremental learning model, can analyze the working data stream of the rolling bearing in real time, and dynamically update the bearing aging coefficient and the fluctuation sensitivity index; by comparing the initial operation data and the subsequent operation data, can accurately evaluate the standard state of the bearing and its aging trend; by calculating the aging coefficient and the fluctuation sensitivity index, can predict the possible failure trend of the bearing, avoid equipment interruption due to bearing failure in critical tasks, can comprehensively improve the monitoring and management efficiency of the rolling bearing, not only extend the service life of the bearing, but also reduce the operation and maintenance costs.

[0029] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0030] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An incremental learning data flow analysis platform, comprising a data flow import module, a first bearing analysis module, a second bearing analysis module and a data sensitivity monitoring module, characterized in that: The data stream import module is used to obtain the working data stream of the rolling bearing; the working data stream includes a first working data stream obtained when the rolling bearing is first run and a second working data stream obtained when the rolling bearing is run for the first time and each time the rolling bearing runs after the first run; The first bearing analysis module is used to analyze the bearing standard coefficient according to the first working data stream; the second bearing analysis module is used to analyze the bearing aging coefficient according to the bearing standard coefficient and the second working data stream; the second bearing analysis module includes a data update unit; the data update unit is used to update data in real time according to the first working data stream and the second working data stream to obtain the bearing standard coefficient and basic statistical data, the basic statistical data are the maximum value, minimum value and average value corresponding to the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude respectively. When the second operation data stream reaches time s, the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s is to update.

2. The incremental learning data flow analysis platform according to claim 1, characterized in that: For the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s The formula for updating is: ； ； Where: is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1, is the number of data of the jth item up to time s.

3. The incremental learning data flow analysis platform according to claim 1, characterized in that: The data stream import module includes a first data stream aggregation unit and a second data stream aggregation unit; The first data stream aggregation unit is used to obtain a first working data stream obtained when the rolling bearing is first operated; the first working data stream includes a first operating vibration data stream and a first audio data stream; the first operating vibration data stream includes a first operating vibration frequency and a first operating vibration amplitude of the rolling bearing; The first audio data stream includes a first operating sound frequency and a first operating sound amplitude of the rolling bearing; The second data stream aggregation unit is used to obtain the second working data stream of each operation of the rolling bearing after the initial operation; The second working data stream includes a second operating vibration data stream, a second audio data stream and a first load data stream; The second operation vibration data stream includes the second operation vibration frequency and the second operation vibration amplitude of the rolling bearing; the second audio data stream includes the second operation sound frequency and the second operation sound amplitude of the rolling bearing; and the first load data stream includes the load value of the rolling bearing.

4. The incremental learning data flow analysis platform according to claim 1, characterized in that: The update formula for the maximum and minimum values ​​of each data item is: ； ； Where: is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value screening function, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value screening function, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s-1, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the number of data of the jth item up to time s-1.

5. The incremental learning data flow analysis platform according to claim 1, characterized in that: The first bearing analysis module includes a primary data stream extraction unit and a bearing standard coefficient calculation unit; the primary data stream extraction unit is connected to the bearing standard coefficient calculation unit; The primary data stream extraction unit is used to extract the first operating vibration data stream and the first audio data stream; The bearing standard coefficient calculation unit is used to substitute the first running vibration data stream and the first audio data stream into the bearing standard coefficient calculation formula to calculate the bearing standard coefficient. The bearing standard coefficient calculation formula is: ； Where: is the bearing standard coefficient, is the number of data items in the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency, and the first operation sound amplitude, is the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude at time t, The initial running time of the rolling bearing. is the maximum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, is the minimum value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude, It is the average value corresponding to the i-th item among the first operation vibration frequency, the first operation vibration amplitude, the first operation sound frequency and the first operation sound amplitude.

6. The incremental learning data flow analysis platform according to claim 5, characterized in that: The bearing aging coefficient calculation unit is used to obtain the bearing standard coefficient, substitute the bearing standard coefficient, the second operating vibration data stream and the second audio data stream into the bearing aging coefficient calculation formula, and calculate the bearing aging coefficient. The bearing aging coefficient calculation formula is: ； Where: is the bearing aging coefficient during the hth rolling bearing operation, is the bearing standard coefficient, is the average value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the maximum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the minimum value corresponding to the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the jth item among the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency and the second operation sound amplitude at time s, is the h-th running time of the rolling bearing.

7. The incremental learning data flow analysis platform according to claim 1, characterized in that: The data sensitivity monitoring module includes a three-level data flow extraction unit, a load fluctuation coefficient calculation unit and a fluctuation sensitivity index monitoring unit; the three-level data flow extraction unit is used to extract the first load data flow; The load fluctuation coefficient calculation unit is used to construct a load fluctuation model according to the first load data stream to obtain the load fluctuation coefficient of the bearing in real time. The formula of the load fluctuation model is: ； Where: is the load fluctuation coefficient of the hth rolling bearing operation, is the load value of the rolling bearing at time s, is the load value of the rolling bearing at time s-1, is the maximum load value of the rolling bearing up to time s, is the minimum load value of the rolling bearing up to time s, is the number of data items in the second operation vibration frequency, the second operation vibration amplitude, the second operation sound frequency, and the second operation sound amplitude, is the h-th running time of the rolling bearing.

8. The incremental learning data flow analysis platform according to claim 7, characterized in that: The fluctuation sensitivity index monitoring unit is used to establish a fluctuation sensitivity analysis model according to the load fluctuation coefficient and the bearing aging coefficient to calculate the fluctuation sensitivity index. The formula of the fluctuation sensitivity analysis model is: ； Where: is the volatility sensitivity index, is the bearing aging coefficient during the hth rolling bearing operation, is the load fluctuation coefficient of the hth rolling bearing operation, is the bearing aging coefficient during the h-1th rolling bearing operation, is the load fluctuation coefficient of the rolling bearing during the h-1th operation, is the number of rolling bearing operations.

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