CN117232843A - Rolling bearing fault characteristic diagnosis management system based on data analysis - Google Patents

Abstract

The invention relates to the technical field of rolling bearing fault management, in particular to a rolling bearing fault characteristic diagnosis management system based on data analysis, which comprises a management platform, a supervision analysis unit, an acquisition analysis unit, a filtering analysis unit, a diagnosis analysis unit, a transportation management analysis unit and an early warning display unit, wherein the monitoring analysis unit is used for monitoring the fault characteristic of the rolling bearing; according to the invention, the abnormal filter is subjected to secondary analysis, the result is transmitted in a data feedback mode, the fault characteristics of the rolling bearing are analyzed through converting the vibration signals into the time-frequency ridge line characteristic diagram, and then the rolling bearing is subjected to targeted fault management according to the fault characteristics, so that the diagnosis accuracy and the management rationality of the rolling bearing are improved, and the analysis is performed through a two-dimensional coordinate system and the operation risk assessment coefficient of the historical rolling bearing, so that management staff can reasonably and pertinently maintain and manage the rolling bearing according to the obtained different maintenance grades, and the fault rate of the rolling bearing is reduced.

Description

Rolling bearing fault characteristic diagnosis management system based on data analysis

Technical Field

The invention relates to the technical field of rolling bearing fault management, in particular to a rolling bearing fault characteristic diagnosis management system based on data analysis.

Background

The rolling bearing is a mechanical universal part with very wide application, the main function is to support a shaft, the rolling bearing keeps good working condition, and the rolling bearing is very important to the safe operation of mechanical equipment, so that the fault type and the fault severity of the rolling bearing are accurately diagnosed, and the rolling bearing is a basis for ensuring the normal operation of the machinery;

the rolling bearing is widely applied in an industrial system, is used as a key component of a rotary machine, the running state of the rolling bearing determines the performance of the whole system, and according to incomplete statistics, about 30% of reasons for the faults of the rotary machine are caused because the rolling bearing breaks down, but in the prior art, the rolling bearing has singleness in diagnosis data, the validity of the data is difficult to support, and further the fault diagnosis result is affected, and the corresponding normal rolling bearing cannot be reasonably maintained at a later stage according to the service condition of the rolling bearing, so that the subsequent fault rate is increased, and therefore, how to realize effective fault diagnosis and management of the rolling bearing so as to ensure that equipment can safely and efficiently run for a long time is a problem which needs to be solved by a pipe transporting person.

Disclosure of Invention

The invention aims to provide a rolling bearing fault characteristic diagnosis management system based on data analysis, which solves the technical defects, and is characterized in that an abnormal filter is subjected to secondary analysis, a result is transmitted in a data feedback mode, a vibration signal is converted into a time-frequency ridge line characteristic diagram to analyze the fault characteristic of a rolling bearing, the rolling bearing is subjected to targeted fault management according to the fault characteristic, so that the diagnosis accuracy and the management rationality of the rolling bearing are improved, the running risk trend rate is obtained by analyzing a two-dimensional coordinate system and a running risk evaluation coefficient combined with a historical rolling bearing, and the running risk trend rate is judged, so that a manager can reasonably and pertinently maintain and manage the rolling bearing according to different obtained pipe grades, and the fault rate of the rolling bearing is reduced.

The aim of the invention can be achieved by the following technical scheme: a rolling bearing fault characteristic diagnosis management system based on data analysis comprises a management platform, a supervision and analysis unit, an acquisition and analysis unit, a filtering and analysis unit, a diagnosis and analysis unit, a transportation and management analysis unit and an early warning display unit;

when the management platform generates a management instruction and sends the management instruction to the supervision and analysis unit, the supervision and analysis unit is used for collecting working data of the rolling bearing, the working data comprise the running temperature of the rolling bearing and a running track characteristic diagram of rolling bodies in the rolling bearing, fault risk assessment analysis is carried out on the working data, an obtained running signal is sent to the management and analysis unit, and an obtained risk signal is sent to the collection and analysis unit and the filtering analysis unit;

the operation analysis unit immediately retrieves the working data from the supervision analysis unit after receiving the operation signal, further trend development analysis is carried out on the working data, and the obtained primary-level vascular signal, secondary-level vascular signal and tertiary-level vascular signal are sent to the early warning display unit;

the filtering analysis unit immediately acquires operation data of the filter after receiving the risk signal, wherein the operation data comprises a line risk value and an abnormal characteristic value, performs filtering operation risk assessment analysis on the operation data, sends an obtained normal signal to the diagnosis analysis unit, and sends the obtained abnormal signal to the early warning display unit and the acquisition analysis unit;

the acquisition and analysis unit immediately acquires the operation data of the filter after the risk signal and the abnormal signal are received, performs secondary analysis on the operation data, sends an obtained feedback signal to the diagnosis and analysis unit, and sends an obtained display signal to the early warning display unit through the filtering and analysis unit;

the diagnosis analysis unit immediately collects vibration signals of the rolling bearing after receiving the feedback signals or the normal signals, converts the vibration signals into time-frequency ridge line characteristic diagrams according to the prior art, performs deep diagnosis, evaluation and analysis on the time-frequency ridge line characteristic diagrams, and sends the obtained fault results to the early warning display unit.

Preferably, the fault risk assessment analysis process of the supervision and analysis unit is as follows:

the method comprises the steps of collecting the duration from the starting operation time to the ending operation time of the rolling bearing, marking the duration as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, obtaining the operation temperature of the rolling bearing in each sub-time period, comparing the operation temperature with a preset operation temperature threshold, and if the operation temperature is larger than the preset operation temperature threshold, marking the part of the operation temperature larger than the preset operation temperature threshold as an overheat value, so as to obtain the sum of overheat values of the rolling bearing in the time threshold, and marking the sum as an overheat risk value;

acquiring a running track characteristic diagram of a rolling body in a rolling bearing in each sub-time period, performing coincidence ratio comparison analysis on the running track characteristic diagram and a preset running track characteristic diagram, acquiring a difference value between the running track characteristic diagram and the preset running track characteristic diagram, acquiring the number of sub-time periods corresponding to the difference value being larger than a preset standard value, and marking the ratio of the number of sub-time periods corresponding to the difference value being larger than the preset standard value to the total number of sub-time periods as an offset risk value;

comparing the over-temperature risk value and the offset risk value with a preset over-temperature risk value threshold value and a preset offset risk value threshold value which are recorded and stored in the over-temperature risk value and the offset risk value:

if the overtemperature risk value is smaller than or equal to a preset overtemperature risk value threshold value and the offset risk value is smaller than or equal to a preset offset risk value threshold value, generating an operation signal;

and if the overtemperature risk value is greater than a preset overtemperature risk value threshold or the offset risk value is greater than a preset offset risk value threshold, generating a risk signal.

Preferably, the further trend analysis process of the pipe analysis unit is as follows:

acquiring an overtemperature risk value and an offset risk value in a time threshold, and respectively marking the overtemperature risk value and the offset risk value as GW and PX;

according to the formulaObtaining an operation risk assessment coefficient, wherein b1 and b2 are preset weight coefficients of an over-temperature risk value and an offset risk value respectively, b3 is a preset fault tolerance coefficient, b1, b2 and b3 are positive numbers larger than zero, G is the operation risk assessment coefficient, the operation risk assessment coefficient of the rolling bearing which normally operates in a history k time thresholds is obtained at the same time, k is a natural number larger than zero, the time is taken as an X axis, a rectangular coordinate system is established by taking the operation risk assessment coefficient Gk as a Y axis, an operation risk assessment coefficient curve is drawn in a dot drawing mode, an ascending section and a descending section are obtained from the operation risk assessment coefficient curve, further, the difference value between the two endpoints of the ascending end is obtained, the sum value of the difference value between the two endpoints of the ascending end is obtained, the sum value is marked as a risk trend value FQ, the difference value between the two endpoints of the descending section is obtained, and the sum value of the difference value between the two endpoints of the descending section is marked as a safety value AD;

according to the formulaObtaining an operation risk trend rate, wherein F is the operation risk trend rate, and comparing and analyzing the operation risk trend rate F with a preset operation risk trend rate interval recorded and stored in the operation risk trend rate F:

if the running risk trend rate F is greater than the maximum value in the preset running risk trend rate interval, generating a primary pipe maintenance signal; if the running risk trend rate F is within a preset running risk trend rate interval, generating a secondary vascular signal; and if the running risk trend rate F is smaller than the minimum value in the preset running risk trend rate interval, generating a three-level vascular signal.

Preferably, the filter operation risk assessment analysis process of the filter analysis unit is as follows:

obtaining a line risk value of a filter in each sub-time period, wherein the line risk value refers to a product value obtained by carrying out number normalization processing on an internal line reactive power value of the filter, an oxidation area of a line port and the number of line bulges, after taking time as X, establishing a rectangular coordinate system by taking the line risk value as a Y axis, drawing a line risk value curve in a dot drawing manner, obtaining a ratio of the number of sections corresponding to an ascending section to the number of sections corresponding to a descending section from the line risk value curve, and marking the ratio as a line risk trend value XQ;

obtaining an abnormal characteristic value of the filter in each sub-time period, wherein the abnormal characteristic value refers to a sum value obtained by dimensionalization processing of a part of a filter temperature difference and a noise decibel value exceeding a preset noise decibel value, wherein the temperature difference refers to a difference value between the internal temperature of the filter and the external shell temperature, the abnormal characteristic value is compared with a preset abnormal characteristic value threshold value for analysis, and if the abnormal characteristic value is larger than the preset abnormal characteristic value threshold value, the ratio of the part of the abnormal characteristic value larger than the preset abnormal characteristic value threshold value to the preset abnormal characteristic value threshold value is marked as an abnormal risk value YZ;

obtaining a filter risk assessment coefficient Q according to a formula, and comparing the filter risk assessment coefficient Q with a preset filter risk assessment coefficient threshold value recorded and stored in the filter risk assessment coefficient Q: if the ratio of the filtering risk assessment coefficient Q to the preset filtering risk assessment coefficient threshold is smaller than one, generating a normal signal; if the ratio of the filter risk assessment coefficient Q to the preset filter risk assessment coefficient threshold is greater than or equal to one, generating an abnormal signal.

Preferably, the secondary analysis process of the collection and analysis unit is as follows:

acquiring a secondary filtering risk assessment coefficient, and comparing the secondary filtering risk assessment coefficient with a preset filtering risk assessment coefficient threshold value recorded and stored in the secondary filtering risk assessment coefficient to analyze the secondary filtering risk assessment coefficient: if the secondary filtering risk assessment coefficient is smaller than a preset filtering risk assessment coefficient threshold value, generating a feedback signal; and if the secondary filtering risk assessment coefficient is greater than or equal to a preset filtering risk assessment coefficient threshold value, generating a display signal.

Preferably, the in-depth diagnosis evaluation analysis process of the diagnosis analysis unit is as follows:

and (3) acquiring a time-frequency ridge line characteristic diagram of the rolling bearing corresponding to each risk signal in the time threshold, comparing the time-frequency ridge line characteristic diagram with a preset fault sample recorded and stored in the time-frequency ridge line characteristic diagram to obtain a fault result, and displaying the fault result in a text and image mode after the early warning display unit receives the fault result.

The beneficial effects of the invention are as follows:

(1) According to the invention, by collecting working data of the rolling bearing, and carrying out fault risk assessment analysis and further trend development analysis on the working data, namely analyzing from two angles of the fault risk and the fault risk development trend of the rolling bearing, so that the rolling bearing is maintained and managed reasonably and pertinently, fault diagnosis is carried out on an abnormal rolling bearing, a filter which influences analysis and results of the collected data is analyzed before diagnosis, a line risk value and an abnormal characteristic value of the filter are collected, whether the filter operates normally or not is judged, so that interference of the environment on vibration signals of the rolling bearing is avoided, accuracy of fault diagnosis analysis results of the rolling bearing is influenced, and further the validity of subsequent analysis data is guaranteed;

(2) The invention also carries out secondary analysis on the abnormal filter, transmits the result in a data feedback mode, converts the vibration signal into a time-frequency ridge line characteristic diagram to analyze the fault characteristic of the rolling bearing, carries out targeted fault management on the rolling bearing according to the fault characteristic so as to improve the diagnosis accuracy and the management rationality of the rolling bearing, analyzes the running risk assessment coefficient of the rolling bearing through a two-dimensional coordinate system and a combination history rolling bearing so as to obtain the running risk trend rate, and judges the running risk trend rate, so that a transportation manager can reasonably and pertinently maintain and manage the rolling bearing according to the obtained different maintenance grades, and the fault rate of the rolling bearing is reduced.

Drawings

The invention is further described below with reference to the accompanying drawings;

FIG. 1 is a flow chart of the system of the present invention;

fig. 2 is a partial analysis reference diagram of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Example 1:

referring to fig. 1 to 2, the invention discloses a rolling bearing fault characteristic diagnosis management system based on data analysis, which comprises a management platform, a supervision and analysis unit, an acquisition and analysis unit, a filtering and analysis unit, a diagnosis and analysis unit, a pipe transportation and analysis unit and an early warning display unit, wherein the management platform is in one-way communication connection with the supervision and analysis unit, the filtering and analysis unit and the pipe transportation and analysis unit are in one-way communication connection, the acquisition and analysis unit is in two-way communication connection with the filtering and analysis unit, the acquisition and analysis unit and the filtering and analysis unit are in one-way communication connection with the diagnosis and analysis unit, and the pipe transportation and analysis unit, the filtering and the diagnosis and analysis unit are in one-way communication connection with the early warning display unit;

when the management platform generates a management command and sends the management command to the supervision and analysis unit, the supervision and analysis unit is used for collecting working data of the rolling bearing, the working data comprises the running temperature of the rolling bearing and a running track feature diagram of rolling bodies in the rolling bearing, fault risk assessment analysis is carried out on the working data, whether the rolling bearing has faults or not is judged, so that management can be carried out timely, and the specific fault risk assessment analysis process is as follows:

the method comprises the steps of collecting the duration from the starting operation time to the ending operation time of the rolling bearing, marking the duration as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, obtaining the operation temperature of the rolling bearing in each sub-time period, comparing the operation temperature with a preset operation temperature threshold, and analyzing the operation temperature, if the operation temperature is larger than the preset operation temperature threshold, marking the part of the operation temperature larger than the preset operation temperature threshold as an overheat value, obtaining the sum of overheat values of the rolling bearing in the time threshold, and marking the sum as an overheat risk value, wherein the larger the value of the overheat risk value is, the larger the abnormal risk of the rolling bearing is;

acquiring a running track feature map of a rolling body in a rolling bearing in each sub-time period, performing coincidence ratio comparison analysis on the running track feature map and a preset running track feature map, acquiring a difference value between the running track feature map and the preset running track feature map, acquiring the number of sub-time periods corresponding to the difference value being larger than a preset standard value, and marking the ratio of the number of sub-time periods corresponding to the difference value being larger than the preset standard value to the total sub-time period number as an offset risk value, wherein the larger the value of the offset risk value is, the larger the abnormal risk of the rolling bearing is;

comparing the over-temperature risk value and the offset risk value with a preset over-temperature risk value threshold value and a preset offset risk value threshold value which are recorded and stored in the over-temperature risk value and the offset risk value:

if the overtemperature risk value is smaller than or equal to a preset overtemperature risk value threshold value and the offset risk value is smaller than or equal to a preset offset risk value threshold value, generating an operation signal and sending the operation signal to a pipe transporting analysis unit;

if the over-temperature risk value is larger than a preset over-temperature risk value threshold or the offset risk value is larger than a preset offset risk value threshold, generating a risk signal, and sending the risk signal to a collection analysis unit and a filtering analysis unit to further analyze whether the rolling bearing fails or not so as to improve the accuracy of an analysis result;

the operation analysis unit immediately retrieves the working data from the supervision analysis unit after receiving the operation signal, and further trend development analysis is carried out on the working data so as to carry out reasonable management on the rolling bearing in the later period, thereby reducing the failure rate of the rolling bearing, and the specific further trend development analysis process is as follows:

acquiring an overtemperature risk value and an offset risk value in a time threshold, and respectively marking the overtemperature risk value and the offset risk value as GW and PX;

according to the formulaObtaining an operation risk assessment coefficient, wherein b1 and b2 are preset weight coefficients of an over-temperature risk value and an offset risk value respectively, b3 is a preset fault tolerance coefficient, b1, b2 and b3 are positive numbers larger than zero, G is the operation risk assessment coefficient, the operation risk assessment coefficient of the rolling bearing which normally operates in a history k time thresholds is obtained at the same time, k is a natural number larger than zero, the time is taken as an X axis, a rectangular coordinate system is established by taking the operation risk assessment coefficient Gk as a Y axis, an operation risk assessment coefficient curve is drawn in a dot drawing mode, an ascending section and a descending section are obtained from the operation risk assessment coefficient curve, further, the difference value between the two endpoints of the ascending end is obtained, the sum value of the difference value between the two endpoints of the ascending end is obtained, the sum value is marked as a risk trend value FQ, the difference value between the two endpoints of the descending section is obtained, and the sum value of the difference value between the two endpoints of the descending section is marked as a safety value AD;

according to the formulaObtaining an operation risk trend rate, wherein F is the operation risk trend rate, and comparing and analyzing the operation risk trend rate F with a preset operation risk trend rate interval recorded and stored in the operation risk trend rate F: if the running risk trend rate F is greater than the maximum value in the preset running risk trend rate interval, generating a primary pipe maintenance signal; if the running risk trend rate F is within a preset running risk trend rate interval, generating a secondary vascular signal;

if the running risk trend rate F is smaller than the minimum value in the preset running risk trend rate interval, a three-level vascular signal is generated, wherein the overhaul degree corresponding to the one-level vascular signal, the two-level vascular signal and the three-level vascular signal is sequentially reduced, the one-level vascular signal, the two-level vascular signal and the three-level vascular signal are sent to an early warning display unit, and the early warning display unit immediately displays preset early warning characters corresponding to the one-level vascular signal, the two-level vascular signal and the three-level vascular signal after receiving the one-level vascular signal, the two-level vascular signal and the three-level vascular signal, so that operation staff can reasonably and pertinently maintain and manage the rolling bearing according to the early warning characters, and the failure rate of the rolling bearing is reduced.

Example 2:

the filter analysis unit immediately collects operation data of the filter after receiving the risk signal, wherein the operation data comprises a line risk value and an abnormal characteristic value, and carries out filter operation risk assessment analysis on the operation data to judge whether the filter normally operates or not so as to avoid the environment from interfering the vibration signal of the rolling bearing and influencing the accuracy of the fault diagnosis analysis result of the rolling bearing, and the specific filter operation risk assessment analysis process is as follows:

obtaining a line risk value of a filter in each sub-time period, wherein the line risk value refers to a product value obtained by carrying out number normalization processing on an internal line reactive power value of the filter, an oxidation area of a line port and the number of line bulges, after taking time as X, establishing a rectangular coordinate system by taking the line risk value as a Y axis, drawing a line risk value curve in a dot drawing manner, obtaining a ratio of the number of sections corresponding to an ascending section to the number of sections corresponding to a descending section from the line risk value curve, and marking the ratio as a line risk trend value XQ, wherein the larger the number of the line risk trend value XQ is, the larger the abnormal risk of the filter is filtered;

obtaining abnormal characteristic values of the filter in each sub-time period, wherein the abnormal characteristic values refer to sum values obtained by dimensionalization treatment of parts of the temperature difference of the filter and the noise decibel value exceeding a preset noise decibel value, wherein the temperature difference refers to difference values between the internal temperature of the filter and the external shell temperature, the abnormal characteristic values are compared with a preset abnormal characteristic value threshold value for analysis, if the abnormal characteristic values are larger than the preset abnormal characteristic value threshold value, the ratio of the parts of the abnormal characteristic values larger than the preset abnormal characteristic value threshold value to the preset abnormal characteristic value threshold value is marked as an abnormal risk value, the reference mark is YZ, and the abnormal risk value is an influence parameter reflecting the operation of the filter;

according to the formulaObtaining a filtering risk assessment coefficient, wherein a1 and a2 are preset scale factor coefficients of a line risk trend value and an abnormal risk magnitude respectively, the scale factor coefficients are used for correcting deviation of various parameters in a formula calculation process, so that calculation results are more accurate, a1 and a2 are positive numbers larger than zero, a3 is a preset correction factor coefficient, the value is 2.868, Q is the filtering risk assessment coefficient, the size of the coefficient is a specific numerical value obtained by quantizing the various parameters, the subsequent comparison is convenient, and the filtering risk assessment coefficient Q is compared with a preset filtering risk assessment coefficient threshold value recorded and stored in the filtering risk assessment coefficient Q as long as the proportional relation between the parameters and the quantized numerical value is not affected:

if the ratio of the filter risk assessment coefficient Q to the preset filter risk assessment coefficient threshold is smaller than one, generating a normal signal, and sending the normal signal to a diagnosis analysis unit;

if the ratio of the filtering risk assessment coefficient Q to the preset filtering risk assessment coefficient threshold value is greater than or equal to one, generating an abnormal signal, sending the abnormal signal to an early warning display unit and an acquisition analysis unit, and immediately performing early warning display in a text filtering abnormality mode after the early warning display unit receives the abnormal signal, so as to timely overhaul an abnormal filter, ensure the validity of subsequent analysis data and ensure the accuracy of analysis results;

the method comprises the steps that an acquisition and analysis unit immediately acquires operation data of a filter after risk signals and abnormal signals are received, secondary analysis is carried out on the operation data to obtain secondary filtering risk assessment coefficients, and the secondary filtering risk assessment coefficients are compared with preset filtering risk assessment coefficient thresholds recorded and stored in the secondary filtering risk assessment coefficients:

if the secondary filtering risk assessment coefficient is smaller than a preset filtering risk assessment coefficient threshold value, generating a feedback signal, and sending the feedback signal to a diagnosis analysis unit;

if the secondary filtering risk assessment coefficient is greater than or equal to a preset filtering risk assessment coefficient threshold value, generating a display signal, sending the display signal to an early warning display unit through a filtering analysis unit, and immediately performing early warning display in a text 'filtering secondary abnormality' mode after the early warning display unit receives the display signal, so as to remind a pipe transporting personnel to ensure that the filter maintenance is normal;

the diagnosis analysis unit immediately collects vibration signals of the rolling bearing after receiving feedback signals or normal signals, converts the vibration signals into time-frequency ridge line characteristic diagrams according to the prior art, and carries out deep diagnosis evaluation analysis on the time-frequency ridge line characteristic diagrams, wherein the specific deep diagnosis evaluation analysis process is as follows:

the method comprises the steps of obtaining time-frequency ridge line feature diagrams of rolling bearings corresponding to risk signals in a time threshold, comparing the time-frequency ridge line feature diagrams with preset fault samples recorded and stored in the time-frequency ridge line feature diagrams to obtain fault results, sending the fault results to an early warning display unit, displaying the fault results in a text and image mode after the early warning display unit receives the fault results, and further conducting fault management on the rolling bearings.

In summary, the invention collects working data of the rolling bearing, and carries out fault risk assessment analysis and further trend development analysis on the working data, namely, analysis is carried out from two angles of fault risk and fault risk development trend of the rolling bearing, so that maintenance and management are carried out on the rolling bearing reasonably and pertinently, fault diagnosis is carried out on the abnormal rolling bearing, analysis is carried out on a filter which influences analysis and results of the collected data before diagnosis, line risk values and abnormal characteristic values of the filter are collected, whether the filter normally operates is judged, interference of the environment on vibration signals of the rolling bearing is avoided, accuracy of analysis results of fault diagnosis of the rolling bearing is influenced, further, follow-up analysis data are guaranteed, secondary analysis is carried out on the abnormal filter, the result is transmitted in a data feedback mode, and the fault characteristics of the rolling bearing are analyzed through conversion of the vibration signals into time-frequency ridge line characteristic diagrams, and further, pertinence fault management is carried out on the rolling bearing according to the fault characteristics, diagnosis accuracy and management of the rolling bearing are improved, and management rationality is carried out on the rolling bearing through a two-dimensional coordinate system and an operation risk coefficient which is combined with the historical rolling bearing, further, and the operation risk is reasonably analyzed according to the operation risk of the rolling bearing is further, and the operation risk is not managed, and the operation risk is reduced, and the operation risk is carried out on the rolling bearing is reasonably is carried out according to the operation risk management.

The size of the threshold is set for ease of comparison, and regarding the size of the threshold, the number of cardinalities is set for each set of sample data depending on how many sample data are and the person skilled in the art; as long as the proportional relation between the parameter and the quantized value is not affected. The above formulas are all formulas obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value, and coefficients in the formulas are set by a person skilled in the art according to practical situations, and the above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art is within the technical scope of the present invention, and the technical scheme and the inventive concept according to the present invention are equivalent to or changed and are all covered in the protection scope of the present invention.

Claims (6)

1. The rolling bearing fault characteristic diagnosis management system based on data analysis is characterized by comprising a management platform, a supervision and analysis unit, an acquisition and analysis unit, a filtering and analysis unit, a diagnosis and analysis unit, a transportation and management analysis unit and an early warning display unit;

when the management platform generates a management instruction and sends the management instruction to the supervision and analysis unit, the supervision and analysis unit is used for collecting working data of the rolling bearing, the working data comprise the running temperature of the rolling bearing and a running track characteristic diagram of rolling bodies in the rolling bearing, fault risk assessment analysis is carried out on the working data, an obtained running signal is sent to the management and analysis unit, and an obtained risk signal is sent to the collection and analysis unit and the filtering analysis unit;

the operation analysis unit immediately retrieves the working data from the supervision analysis unit after receiving the operation signal, further trend development analysis is carried out on the working data, and the obtained primary-level vascular signal, secondary-level vascular signal and tertiary-level vascular signal are sent to the early warning display unit;

the filtering analysis unit immediately acquires operation data of the filter after receiving the risk signal, wherein the operation data comprises a line risk value and an abnormal characteristic value, performs filtering operation risk assessment analysis on the operation data, sends an obtained normal signal to the diagnosis analysis unit, and sends the obtained abnormal signal to the early warning display unit and the acquisition analysis unit;

the acquisition and analysis unit immediately acquires the operation data of the filter after the risk signal and the abnormal signal are received, performs secondary analysis on the operation data, sends an obtained feedback signal to the diagnosis and analysis unit, and sends an obtained display signal to the early warning display unit through the filtering and analysis unit;

the diagnosis analysis unit immediately collects vibration signals of the rolling bearing after receiving the feedback signals or the normal signals, converts the vibration signals into time-frequency ridge line characteristic diagrams according to the prior art, performs deep diagnosis, evaluation and analysis on the time-frequency ridge line characteristic diagrams, and sends the obtained fault results to the early warning display unit.

2. The rolling bearing fault signature diagnosis management system based on data analysis according to claim 1, wherein the fault risk assessment analysis process of the supervision analysis unit is as follows:

the method comprises the steps of collecting the duration from the starting operation time to the ending operation time of the rolling bearing, marking the duration as a time threshold, dividing the time threshold into i sub-time periods, wherein i is a natural number larger than zero, obtaining the operation temperature of the rolling bearing in each sub-time period, comparing the operation temperature with a preset operation temperature threshold, and if the operation temperature is larger than the preset operation temperature threshold, marking the part of the operation temperature larger than the preset operation temperature threshold as an overheat value, so as to obtain the sum of overheat values of the rolling bearing in the time threshold, and marking the sum as an overheat risk value;

acquiring a running track characteristic diagram of a rolling body in a rolling bearing in each sub-time period, performing coincidence ratio comparison analysis on the running track characteristic diagram and a preset running track characteristic diagram, acquiring a difference value between the running track characteristic diagram and the preset running track characteristic diagram, acquiring the number of sub-time periods corresponding to the difference value being larger than a preset standard value, and marking the ratio of the number of sub-time periods corresponding to the difference value being larger than the preset standard value to the total number of sub-time periods as an offset risk value;

comparing the over-temperature risk value and the offset risk value with a preset over-temperature risk value threshold value and a preset offset risk value threshold value which are recorded and stored in the over-temperature risk value and the offset risk value:

if the overtemperature risk value is smaller than or equal to a preset overtemperature risk value threshold value and the offset risk value is smaller than or equal to a preset offset risk value threshold value, generating an operation signal;

and if the overtemperature risk value is greater than a preset overtemperature risk value threshold or the offset risk value is greater than a preset offset risk value threshold, generating a risk signal.

3. The rolling bearing fault signature diagnosis and management system based on data analysis according to claim 1, wherein the further trend development analysis process of the transportation analysis unit is as follows:

acquiring an overtemperature risk value and an offset risk value in a time threshold, and respectively marking the overtemperature risk value and the offset risk value as GW and PX;

according to the formulaObtaining an operation risk assessment coefficient, wherein b1 and b2 are preset weight coefficients of an over-temperature risk value and an offset risk value respectively, b3 is a preset fault tolerance coefficient, b1, b2 and b3 are positive numbers larger than zero, G is the operation risk assessment coefficient, the operation risk assessment coefficient of the rolling bearing which normally operates in k time thresholds of history is obtained at the same time, k is a natural number larger than zero, the time is taken as an X axis, a rectangular coordinate system is established by taking the operation risk assessment coefficient Gk as a Y axis, an operation risk assessment coefficient curve is drawn in a dot drawing mode, and an ascending section and a descending section are obtained from the operation risk assessment coefficient curveThe method comprises the steps of descending a segment, further obtaining a difference value between two endpoints of an ascending end, obtaining a sum value of the difference value between the two endpoints of the ascending end, marking the sum value as a risk trend value FQ, obtaining a difference value between the two endpoints of the descending segment, obtaining a sum value of the difference value between the two endpoints of the descending segment, and marking the sum value as a safety trend value AD;

according to the formulaObtaining an operation risk trend rate, wherein F is the operation risk trend rate, and comparing and analyzing the operation risk trend rate F with a preset operation risk trend rate interval recorded and stored in the operation risk trend rate F:

if the running risk trend rate F is greater than the maximum value in the preset running risk trend rate interval, generating a primary pipe maintenance signal; if the running risk trend rate F is within a preset running risk trend rate interval, generating a secondary vascular signal; and if the running risk trend rate F is smaller than the minimum value in the preset running risk trend rate interval, generating a three-level vascular signal.

4. The rolling bearing fault characteristic diagnosis management system based on data analysis according to claim 1, wherein the filter operation risk assessment analysis process of the filter analysis unit is as follows:

obtaining a line risk value of a filter in each sub-time period, wherein the line risk value refers to a product value obtained by carrying out number normalization processing on an internal line reactive power value of the filter, an oxidation area of a line port and the number of line bulges, after taking time as X, establishing a rectangular coordinate system by taking the line risk value as a Y axis, drawing a line risk value curve in a dot drawing manner, obtaining a ratio of the number of sections corresponding to an ascending section to the number of sections corresponding to a descending section from the line risk value curve, and marking the ratio as a line risk trend value XQ;

obtaining an abnormal characteristic value of the filter in each sub-time period, wherein the abnormal characteristic value refers to a sum value obtained by dimensionalization processing of a part of a filter temperature difference and a noise decibel value exceeding a preset noise decibel value, wherein the temperature difference refers to a difference value between the internal temperature of the filter and the external shell temperature, the abnormal characteristic value is compared with a preset abnormal characteristic value threshold value for analysis, and if the abnormal characteristic value is larger than the preset abnormal characteristic value threshold value, the ratio of the part of the abnormal characteristic value larger than the preset abnormal characteristic value threshold value to the preset abnormal characteristic value threshold value is marked as an abnormal risk value YZ;

obtaining a filter risk assessment coefficient Q according to a formula, and comparing the filter risk assessment coefficient Q with a preset filter risk assessment coefficient threshold value recorded and stored in the filter risk assessment coefficient Q: if the ratio of the filtering risk assessment coefficient Q to the preset filtering risk assessment coefficient threshold is smaller than one, generating a normal signal; if the ratio of the filter risk assessment coefficient Q to the preset filter risk assessment coefficient threshold is greater than or equal to one, generating an abnormal signal.

5. The rolling bearing fault signature diagnosis and management system based on data analysis according to claim 1, wherein the secondary analysis process of the acquisition and analysis unit is as follows:

acquiring a secondary filtering risk assessment coefficient, and comparing the secondary filtering risk assessment coefficient with a preset filtering risk assessment coefficient threshold value recorded and stored in the secondary filtering risk assessment coefficient to analyze the secondary filtering risk assessment coefficient: if the secondary filtering risk assessment coefficient is smaller than a preset filtering risk assessment coefficient threshold value, generating a feedback signal; and if the secondary filtering risk assessment coefficient is greater than or equal to a preset filtering risk assessment coefficient threshold value, generating a display signal.

6. The rolling bearing fault signature diagnosis management system based on data analysis according to claim 1, wherein the in-depth diagnosis evaluation analysis process of the diagnosis analysis unit is as follows:

and (3) acquiring a time-frequency ridge line characteristic diagram of the rolling bearing corresponding to each risk signal in the time threshold, comparing the time-frequency ridge line characteristic diagram with a preset fault sample recorded and stored in the time-frequency ridge line characteristic diagram to obtain a fault result, and displaying the fault result in a text and image mode after the early warning display unit receives the fault result.