CN118114005A - Belt conveyor bearing self-adaptive phasing and digital-analog fusion life prediction method - Google Patents
Belt conveyor bearing self-adaptive phasing and digital-analog fusion life prediction method Download PDFInfo
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Abstract
The invention discloses a self-adaptive staged and digital-analog fusion life prediction method of a belt conveyor bearing, which comprises the steps of obtaining actual measurement data of the bearing, obtaining simulation data of the later stage of the actual measurement data, constructing health indexes, identifying the degradation stage, predicting the residual life and the like, wherein the accuracy of identifying the degradation stage is improved by combining the actual measurement data with the simulation data, and the life prediction is performed by identifying the degradation stage and selecting a corresponding model according to the identified stage.
Description
Technical Field
The invention belongs to the technical field of industrial equipment health management, and particularly relates to a self-adaptive staged and digital-analog fusion life prediction method for a belt conveyor bearing.
Background
The belt conveyor is main transportation equipment for mine production, and key components of the belt conveyor, namely a carrier roller, a steering roller and a power roller, comprise high-speed rotating bearings or bearing sets (bearings for short), and the belt conveyor runs continuously and high-strength under severe environments of coal mines, so that accidents such as longitudinal tearing, deviation, carrier roller damage, slipping and fire disaster are easy to occur. If the early warning can be carried out before the equipment failure occurs or early warning is carried out, the intelligent management of the belt conveyor bearing or the bearing group is realized, the safety of the coal flow transportation process is effectively improved, and the continuous, stable and efficient production of the coal mine is ensured.
The maintenance process of the traditional belt conveyor bearing usually depends on manual inspection, and the maintenance mode usually adopts a mode of stopping and replacing after damage. Firstly, traditional fault identification relies on manual inspection, and because the mine operation environment is complex, the inspection operation intensity is high, the time is long, and the inspection effect cannot be ensured. Secondly, the traditional maintenance management mode after the bearing group is damaged has the problems of long maintenance period and high shutdown cost. In addition, the lack of auxiliary health management does not maximize the normal operating time of the equipment and minimize maintenance and operating costs. In view of the above limitations, there is a need for an auxiliary maintenance decision and fault repair guidance that can more accurately detect fault conditions and predict the remaining useful life of equipment, providing a health management mode for equipment condition detection and preventative maintenance.
Regarding health management and life prediction of bearings or bearing groups, machine learning methods are commonly used in many current researches and applications, such as chinese patent publication No. CN117516933a entitled "LSTM-based bearing life prediction method and system" and publication No. CN116628482a entitled "a rotating disc bearing life prediction method based on neural network architecture search", where the prediction accuracy is mainly based on the sufficiency and diversity of training samples, however, in actual engineering, especially when the working environment is greatly different, it is difficult for users to obtain enough effective targeted sample data, so that the actual prediction result is not ideal. On the other hand, the reliability of pure simulation prediction (theoretical prediction) or single model actual measurement data life prediction is low, so that the method is rarely adopted in engineering implementation.
Disclosure of Invention
Aiming at the problems, the invention designs a self-adaptive staged and digital-analog fusion life prediction method for the belt conveyor bearing, which is used for adaptively distinguishing different degradation stages of bearing vibration signals through fuzzy clustering, corresponding to a proper degradation model and carrying out state estimation fitting through unscented Kalman filtering to obtain a more accurate life prediction result for the belt conveyor bearing.
The invention designs a self-adaptive staged and digital-analog fusion life prediction method of a belt conveyor bearing, which does not depend on machine learning and training, and by fusing the actual measurement data of the bearing with the theoretical simulation data of the bearing, the life prediction method is characterized in that firstly, the bearing degradation stage is identified or divided in a self-adaptive manner, then the life prediction based on the actual measurement data and a corresponding degradation stage model is carried out according to the stage identification result, and the data is the health monitoring data of the bearing, and specifically comprises the following steps:
S1, acquiring actual measurement data of the bearing, wherein the actual measurement data comprises at least one of vibration data, noise data and temperature data;
s2, acquiring bearing simulation data, wherein the simulation data at least comprises bearing operation simulation data of a rear section of actual measurement data, the bearing operation simulation data is combined with the existing actual measurement data to form a relatively complete health monitoring data set in the life cycle of the bearing, the rear section refers to means of actual measurement data time series, the sampling interval of the simulation data is consistent with that of the actual measurement data, and the simulation data comprises bearing theoretical model simulation data (namely, the result data of theoretical simulation by adopting factory theoretical parameters of the bearing) or rear section bearing theoretical simulation data driven by the actual measurement data;
s3, constructing a health index, namely constructing a health index EHI according to an effective data set (health monitoring data set) formed by actual measurement data and simulation data, and carrying out subsequent stage division and life prediction based on the health index, wherein the health index comprises a sectional mean value, a sectional root mean square value, a sectional standard deviation or a sectional standard deviation root mean square or other effective characteristic values, such as characteristic values extracted by methods of Fourier transformation, wavelet transformation, matrix transformation and the like;
S4, identifying a degradation stage, wherein the degradation stage at least comprises a running-in stage, a steady-state stage, a defect initiation stage, a defect expansion stage and the first three stages of a damage expansion stage, and the degradation stage identification is completed by adopting a clustering method by taking an evaluable health index on a vibration signal time domain in the step S3 as a clustering object;
S5, predicting the residual life, namely, filtering the actual measured value according to the degradation stage of the end point of the actual measured data by adopting a corresponding experience degradation model aiming at the complex working conditions of different belt conveyors, and predicting the life according to the latest filtering result after the filtering is finished, wherein the time from the end time of the actual measured value to the time when the predicted value reaches or exceeds a preset failure threshold value is the predicted residual life of the bearing.
Further, step S1a and preprocessing of measured data are further included after step S1 and before step S3, and the preprocessing includes noise removal and/or outlier rejection.
Further, the simulation data includes bearing operation life-time simulation data.
Further, the method for obtaining the segment mean value comprises classifying data in the effective data set, and calculating a statistical average value (also called an effective value) in segments, wherein the segment root mean square value comprises root mean square values of different types of segment mean values.
Further, the degradation phase identification specifically includes the following steps:
S4a, setting the cluster number f to be more than or equal to 3, taking the constructed health index as a research object, and carrying out Gath-Geva fuzzy clustering; for the health index time sequence obtained in the step S3, dividing the health index time sequence into the phases with the highest possibility according to the corresponding maximum fuzzy membership matrix of each data point, and recording the effective data sets of the phases from the 1 st group/each to the p-th group/each of the belt conveyor bearings as Meanwhile, clustering is recorded to obtain a clustering center of different stages of degradation of each bearing as/>，/>For the stage identification, i is a group identification, namely, a plurality of bearings or bearing groups can be subjected to clustering identification at the same time;
further, the degradation phase identification further comprises the steps of:
S4b, for the abnormal clustering condition, adopting an abnormal value detection strategy (such as a threshold value detection strategy of adjacent point difference values, and considering abnormality when the adjacent point difference values are larger than a threshold value) to carry out state correction so as to solve the problem of boundary point state staggering in a degradation stage caused by random or false fluctuation of signals; if the clustering abnormality condition that the individual point outliers are divided into different stages is aimed at, the embodiment sets the threshold number of the individual points as n 1, if the individual points are less than n 1, the stage division of the current abnormal individual points is modified from the modified stage division, otherwise, the current stage division is maintained;
S4c, aiming at the clustering abnormal condition of the abnormal aggregation area, detecting the number of abnormal individual points by using a LOF local outlier factor method and setting a clustering center of each stage by taking the state division result after carrying out abnormal value detection strategy as a standard Values of (2)As a threshold boundary, if the number of outliers in the abnormal gathering area is less than/>I.e. the outlier number of the abnormal aggregation area is smaller than the total classification point number in the 1/2 boundary threshold range after the stage, the stage division slave/>, of the outlier of the current abnormal aggregation area is correctedChange to/>Otherwise, keeping the current stage division; wherein/>For the data mean value corresponding to the degradation phase,/>Is the variance of the data corresponding to the degradation phase.
Further, the cluster number f=4.
Further, the step S5 includes:
s5a, selecting a prediction model, selecting a corresponding degradation model aiming at different degradation stages of the vibration signal of the actual measurement data end point, and determining fitting models corresponding to a steady-state stage, a defect initiation stage, a defect expansion stage and a defect expansion stage:
the steady-state stage adopts a bearing theoretical life model;
The defect germination stage health index adopts a single-index model;
the defect expansion stage health index adopts a double-index model;
The health index in the injury expansion stage adopts a polynomial model;
S5b, model filtering, namely establishing a corresponding state space model according to a correspondingly selected health index model and a parameter change rule aiming at the difference of a defect initiation stage, a defect expansion stage and a defect expansion stage, and developing model Kalman filtering according to the actually measured health index acquired in the step S3 until actually measured data is finished, so as to obtain a latest state space evolution equation (simply referred to as a state equation, so that a latest parameter updating result in the model is obtained through actually measured data filtering evolution);
And S5c, model prediction, namely extrapolating a health index model (corresponding one of the formulas 2 to 4) after updating parameters according to the set health index failure threshold, wherein the time when the health index reaches the failure threshold is the failure time, and the time between the filtering end time and the failure time is the predicted residual service life of the bearing.
Further, in the step S5c, the initial value of the filtering model parameter is obtained by means of super-parameter searching.
Further, the failure threshold value is set between 2 and 10 times the first predicted time health index value.
The invention has the advantages and beneficial effects that: the belt conveyor bearing self-adaptive phasing and digital-analog fusion life prediction method is mainly applied to the field of health management of industrial large-scale complex equipment, and by means of state monitoring and life prediction, the degradation stage of the belt conveyor bearing group is accurately monitored, maintenance replacement early warning is sent out before the life cycle of the bearing/bearing group is finished, the risk of large accidents and the benefit loss of manual inspection during shutdown maintenance are reduced, a more intelligent and efficient scheme is provided for health management of the industrial large-scale complex equipment, and the method has important significance in improving the safety level of coal mines and guaranteeing stable coal supply. For the current popular health management and life prediction system, the invention realizes the direct life prediction by utilizing the measured data and the simulation data under the condition of lacking sufficient and targeted learning training data, and compared with the theoretical simulation or the measured data prediction, the life prediction reliability is higher. The test shows that the root mean square error of the bearing health index prediction result reaches 6.523e-7.
Drawings
FIG. 1 is a block diagram of steps of a belt conveyor bearing adaptive phasing and digital-analog fusion life prediction method;
FIG. 2 is a graph of the results of comparing predicted and measured health metrics.
Detailed Description
The following describes the embodiments of the present invention further with reference to the drawings and examples. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
The invention relates to a belt conveyor bearing self-adaptive staged and digital-analog fusion life prediction method, which is independent of machine learning and training, fuses the bearing actual measurement data with the bearing theoretical simulation data, carries out bearing degradation stage identification or division in a self-adaptive manner, carries out life prediction based on actual measurement data and a corresponding degradation stage model according to a stage identification result, and the data is bearing health monitoring data, as shown in figure 1, and specifically comprises the following steps:
S1, acquiring actual measurement data of the bearing, wherein the actual measurement data comprises at least one of vibration data, noise data and temperature data, the vibration data comprises one or more of physical quantities such as amplitude (displacement), speed, acceleration or frequency, and the like, and the embodiment simultaneously acquires original vibration acceleration data in horizontal and vertical directions, namely a horizontal acceleration vibration signal and a vertical acceleration vibration signal;
S2, acquiring bearing simulation data, wherein the simulation data at least comprises bearing operation simulation data of a rear section of actual measurement data, the bearing operation simulation data are combined with the existing actual measurement data to form a relatively complete health monitoring data set in the life cycle of the bearing, the rear section refers to means of actual measurement data time series, the sampling interval of the simulation data is consistent with that of the actual measurement data, the simulation data comprise bearing theoretical model simulation data (namely, result data of theoretical simulation by adopting factory theoretical parameters of the bearing) or rear section bearing theoretical simulation data driven by the actual measurement data, the embodiment adopts rear section simulation data driven by the actual measurement data, the simulation data are continued with the actual measurement data from the time series, the simulation data can represent a possible evolution rule of the rear section of the bearing, and a new data set after general actual measurement data and rear section simulation data can cover five stages of bearing theory: the third stage defect initiation stage of the running-in stage, the steady-state stage, the defect initiation stage, the defect expansion stage and the defect expansion stage is only needed, namely if the actual measurement data enter the defect initiation stage from the running-in stage, the later-stage simulation data can be obtained without supplementing; of course, when designing the system, if the bearing theory policy model is very close to the actual situation, simulation data of all subsequent stages of the actual measurement data can be complemented, and in this embodiment, all stage data are complemented;
The effect of supplementing simulation data in the step is mainly to enable the classification recognition result to be more accurate through a clustering method in the degradation stage recognition of the follow-up step S4, because if the actual measurement data is too few in the early stage, the clustering accuracy is not high when only the first 1-2 stages are covered, and the deficiency of the method can be made up by supplementing part of simulation data.
The basic principle of the theoretical simulation model establishment is as follows: based on friction and impact generated among all parts of the bearing, the influence of the rolling bodies and the inner and outer ring raceways on the aspects of quality, shape and materials is analyzed, and the frequency caused by the inherent vibration of the inner and outer ring raceways of the bearing is obtained by analysis:
Wherein r is the radius from the rotation axis to the central axis, M is unit mass, E is elastic modulus, I is the moment of inertia of the section around the neutral axis, and M is the vibration order;
On the premise of not considering environmental influences such as wet corrosion and dust abrasion, the failure frequencies of the inner ring and the outer ring are obtained based on the damage of normal abrasion and part aging as follows:
wherein, The contact angle, D is the ball diameter, D is the bearing diameter, Z is the number of rolling element balls, and n is the rotational speed (in r/min).
Virtual simulation is carried out on the vibration signals according to the natural frequency and fault characteristics of the bearing, so that the full life cycle vibration signals of the bearing working under the non-environmental influence and subjected to component aging and vibration abrasion among components are obtained; the simulation result accords with the normal degradation rule of the bearing, and is combined with the actual measurement signal to carry out adjustment and correction, so that the method is particularly beneficial to the subsequent stage division of the full-cycle life signal.
S3, constructing a health index, namely constructing a health index EHI according to an effective data set (health monitoring data set) formed by actual measurement data and simulation data, and carrying out subsequent stage division and life prediction based on the health index, wherein the health index comprises a sectional mean value, a sectional root mean square value, a sectional standard deviation or a sectional standard deviation root mean square or other effective characteristic values, such as characteristic values extracted by methods of Fourier transformation, wavelet transformation, matrix transformation and the like, and the sectional root mean square value is adopted as the health index in the embodiment;
s4, identifying a degradation stage, wherein the degradation stage at least comprises a running-in stage, a steady-state stage, a defect initiation stage, a defect expansion stage and the first three stages of a damage expansion stage, and the degradation stage identification is completed by adopting a clustering method by taking an evaluable health index on a vibration signal time domain in the step S3 as a clustering object; the clustering method comprises a Gath-Geva fuzzy clustering method, specifically, setting related parameters according to an evaluable health index and Gath-Geva fuzzy clustering requirement, checking a maximum fuzzy membership matrix dividing state of each vibration signal point, solving the problem of boundary point state staggering in a degradation stage through a two-step abnormal value detection strategy, correcting the clustering abnormal state, realizing self-adaptive distinguishing degradation stage and early warning, and self-adaptively determining a first prediction time FPT according to the change of a single belt bearing system under different working conditions and a stage recognition result, wherein the first prediction time FPT is particularly the stage time for entering defect initiation from a steady state so as to be convenient for the later life prediction; the Gath-Geva fuzzy clustering is the prior art;
S5, predicting the residual life, namely, filtering the actual measured value according to the degradation stage of the end point of the actual measured data by adopting a corresponding experience degradation model aiming at the complex working conditions of different belt conveyors, and predicting the life according to the latest filtering result after the filtering is finished, wherein the time from the end time of the actual measured value to the time when the predicted value reaches or exceeds a preset failure threshold value is the predicted residual life of the bearing. Considering the nonlinear characteristics of the model, the UKF unscented Kalman filtering method is adopted to conduct filtering prediction, the damaged belt conveyor bearing is replaced before the service life period is finished, large losses such as shutdown maintenance are avoided, and intelligent health management of the belt conveyor bearing/bearing group is achieved.
Preferably, step S1a and pre-processing of measured data are further included after step S1 and before step S3, where the pre-processing includes a process of removing noise and/or eliminating outliers, in this embodiment, five-point three-time smoothing is adopted to remove white gaussian noise in the measured data and reduce outliers, and the step may also be completed after the effective data set is built, i.e. after step S2, where the difference is whether the pre-processed measured data is used for driving simulation or the original measured data is used for driving simulation, and when the outliers are not obvious, the difference is not large, and generally, the pre-processed measured data is used for driving simulation; conventional outlier judgment, elimination or replacement methods (such as front-to-back average replacement) can be adopted for outlier judgment elimination.
Preferably, the method for obtaining the segment mean value includes classifying data in the valid data set, and calculating a statistical mean value (also referred to as a valid value) for segments, such as classification of vibration data in a horizontal direction and vibration data in a vertical direction, wherein the segments include time segments; the length of the segments is recorded as N (i.e. the number of sampling points), the stability of statistical characteristics is considered, n=2560 is generally taken in this embodiment, namely, each 10s is a segment (the segment interval Δt=10s), for the vibration data in the horizontal and vertical directions in this embodiment, the effective values are recorded as H RMS (t) and V RMS (t) respectively, wherein t=1, 2,3. The segmented root mean square value comprises root mean square values of different types of segmented mean values of the same monitoring point. The different types of effective values (segment mean) in this embodiment are effective values in the horizontal and vertical directions, the constructed evaluable health indicator EHI is a segment root mean square value,
The health index EHI is essentially a time series and can represent the impact magnitude and damage degree of the vibration signals of the bearing set of the belt conveyor.
Preferably, the step S4 degradation stage identification specifically includes the following steps:
S4a, setting the cluster number f to be more than or equal to 3, and generally more than or equal to the number of degradation stages possibly covered by an effective data set, wherein the five stages belong to the theory according to a general belt machine bearing: running-in, steady state, defect initiation, defect expansion and damage expansion, in this embodiment, f=5 is directly set, and the established health index is taken as a research object to perform Gath-Geva fuzzy clustering; for the health index time sequence obtained in the step S3, dividing the health index time sequence into the phases with the highest possibility according to the corresponding maximum fuzzy membership matrix of each data point, and recording the effective data sets of the phases from the 1 st group/each to the p-th group/each of the belt conveyor bearings as Meanwhile, clustering is carried out to obtain a clustering center of different stages of degradation of each bearing as，/>For the stage identification, i is a group identification, namely, a plurality of bearings or bearing groups can be subjected to clustering identification at the same time;
In this embodiment, a mature Gath-Geva fuzzy clustering method is adopted, and the ambiguity parameter is generally set to be between 1.1 and 2.0, and in order to obtain a clearer cluster, this embodiment sets the value to be 1.4.
Preferably, the step S5 includes:
s5a, selecting a prediction model, selecting a corresponding degradation model aiming at different degradation stages of the vibration signal of the actual measurement data end point, and determining fitting models corresponding to a steady-state stage, a defect initiation stage, a defect expansion stage and a defect expansion stage:
The steady-state stage adopts a bearing theoretical life model; the service life RUL in the steady state stage basically accords with a bearing service life calculation formula, and the service life of the RUL can be directly calculated:
(equation 1)
Wherein C is rated dynamic load, n is rotating speed,The ball bearing of this example has a life index of 3, a roller bearing of 0.3, P equivalent dynamic load, a and/>For the set variables, the randomness and noise influence of vibration impact change are represented, and the steady-state stage of the embodiment directly uses a theoretical model to calculate the service life without filtering prediction.
In the running-in stage, the service life of the bearing in the factory theory can be deducted from the running time to obtain the current life predicted value of the bearing.
The actual life prediction starts from the end of the steady-state phase of the bearing and the moment of entering the defect onset phase, so the period of time for steady-state to enter defect onset is called the first prediction time FPT.
The defect germination stage health index adopts a single-index model; the defect initiation stage has obvious trend, but the growth trend is more gentle, and the following single-index model is adopted in the embodiment:
(equation 2)
The defect expansion stage health index adopts a double-index model; since the defect expansion stage has a shock rising trend, the following double-index model is adopted in the embodiment:
(equation 3)
The health index in the injury expansion stage adopts a polynomial model; because the damage expansion stage is almost scrapped in practice, the change trend is severe, and the following polynomial model is adopted in the embodiment:
(equation 4)
Wherein the method comprises the steps ofAre model parameters,/>White noise compliant with Gaussian distribution is measured noise, and the covariance matrix of the measured noise in the filtering process is/>；
When the measured data is only in the running-in stage, the theoretical life of the bearing is generally taken as the predicted life, and the model is used for real-time prediction after entering a steady state;
S5b, model filtering, namely establishing a corresponding state space model according to a correspondingly selected health index model and a parameter change rule aiming at the difference of a defect initiation stage, a defect expansion stage and a defect expansion stage, and developing model Kalman filtering according to the actually measured health index acquired in the step S3 until actually measured data is finished, so as to obtain a latest state space evolution equation (simply referred to as a state equation, so that a latest parameter updating result in the model is obtained through actually measured data filtering evolution);
The state space model includes a state equation and a measurement equation, the measurement equation in this embodiment is one of formulas 2 to 4 corresponding to the defect initiation stage, the defect expansion stage and the defect expansion stage, and corresponds to the defect initiation stage formula 2, and its state variable is The corresponding state equation is/>
Corresponding to the defect expansion stage formula 3 or the damage expansion stage formula 4, the state variables are allThe corresponding state equation is/>
Wherein,Is process noise, is white noise subject to Gaussian distribution,/>A process noise covariance matrix;
the degradation health index of the embodiment adopts unscented Kalman filter UKF to continuously update the established model parameters to obtain the latest degradation model, wherein, the state variables are as follows The initial value of (2) is generally selected between 0.05 and 0.15, and the initial values are respectively set to be 0.05, 0.07, 0.10 and 0.15 in the embodiment; /(I)For the process noise covariance matrix, the initial value is set as a diagonal matrix, the diagonal eigenvalue of the corresponding state variable is selected between 0.02 and 0.03, for example, the eigenvalue of the 4-order diagonal matrix is sequentially set as 0.02, 0.025, 0.028 and 0.03 when in filtering according to formulas 3 and 4; for measuring the noise covariance matrix, it can be set between 0.3 and 0.5, the present embodiment is set to 0.4; generally, as the robustness and the convergence of the Kalman filtering are good, under the condition that the time sequence of the measured data is enough, the initial value of the filtering mainly influences the convergence speed, and the influence on the final result is small; of course, in order to obtain a better filtering result, the optimal initial parameter setting can be found by using a super-parameter searching mode, and the fitting performance of the initial parameter setting can be evaluated, in particular to the setting of two types of noise matrixes;
And S5c, model prediction, namely extrapolating a health index model (corresponding one of the formulas 2 to 4) after updating parameters according to the set health index failure threshold, wherein the time when the health index reaches the failure threshold is the failure time, and the time between the filtering end time and the failure time is the predicted residual service life of the bearing.
Specifically, as for formula 3, the UKF filtering algorithm updates the state equation and the measurement equation parameters to time according to the real-time monitoring data, and extrapolates according to the health index model to obtainTime-of-day bearing evaluable health indexThe method comprises the following steps:
Wherein the method comprises the steps of Record failure threshold as/>Then satisfy/>Or/>Nearest/>Least/>For/>Corresponding remaining service life of the moment bearing, said minimum/>Can be uniformly usedTo show, according to the setting of the measured data segmentation process in step S3, that is
Here, Δt is the measured data segment interval, and Δt=10s in this embodiment.
The failure threshold is typically empirically set, typically between 2 and 10 times the first predicted time health index value, i.e
Wherein,An evaluable health index value that is a first predicted time; in this embodiment, the failure threshold is set to/>, for the actual measurement data of the bearing set for the testFor example, the failure threshold for group 3-3 bearings is 2.57652.
In order to illustrate the effectiveness of the method of the present invention, the present embodiment adopts IEEE PHM2012 data challenge PHM2012 (FEMTO-ST bearing data set) organized by IEEE reliability association and FEMTO-ST institute to test, the data set is an actual measurement data set of a bearing acceleration test, the data uses a high-speed rolling bearing of a belt conveyor idler and a main driven roller as a monitoring object, a rolling bearing with a rated motor rotation speed of 2830rpm and a rated power of 250W, increases a radial load to a rated load extremum 4000N, and carries out an acceleration test, and an acceleration sensor with a sampling frequency of 256Hz is adopted, a sampling interval is about 3.9ms, and the experimental result shows that: taking the 3 rd group bearing to the 3 rd group bearing of the belt conveyor as an example, dividing the result according to the method, wherein the time 0 to the time 101 (which is equivalent to the filtering time) are running-in periods, the time 102 to the time 298 are steady-state periods, the time 299 to the time 332 are defect initiation phases, the time 333 to the time 419 are defect expansion phases, and the time 420 to the time 426 are damage expansion steps, so that the method meets the practical situation; the front-stage measured data with different lengths are intercepted, and accurate stage division results can be obtained, but if simulation data are not complemented during clustering, the clustering abnormal probability is obviously increased, and especially when the front-stage measured data or the two-stage measured data are only.
Regarding model fitting to verify predicted health indexes, the method adopts the first half part of an original data set as actual measurement data, the method is used for predicting the subsequent health indexes of the bearing, error comparison is carried out between the predicted result and the health indexes corresponding to actual measurement signals (signals in the original data set), the root mean square error statistical result is 6.523e-7, the predicted result is very close to the actual measurement result, and experiments show that the method has higher accuracy under different working conditions, namely, the method has stronger robustness. Fig. 2 shows a comparison chart of the predicted value and the measured value of the health index at the beginning of the third-stage defect initiation stage (the predicted value is shown by a cross "×" in the figure; the measured value is shown by a short line "—" in the figure).
It should be noted that the digital-analog fusion of the present invention includes two meanings: the method comprises the steps of integrating measured data and simulated data (degradation stage identification), and combining the measured data with a corresponding health index model (filtering prediction).
Preferably, in the step S5c, initial values of parameters of the filtering model are obtained by means of super-parameter searching, wherein the parameters include model parameters affecting degradation in the filtering model and a noise covariance matrix in the UKF state equation processAnd measurement noise covariance matrix/>, in observation equation。
Preferably, the failure threshold value is set between 2 and 10 times the first predicted time health index value.
Example 2
The difference from embodiment 1 is that the simulation data includes bearing operation life-time simulation data. The effective data set is composed of the life-cycle simulation data and the actual measurement data, and the difference from the embodiment 1 is that the simulation data is not needed to be selected, but the effective data is repeated in the actual measurement data time period, so that the cluster recognition is not greatly influenced; in principle, when the similarity between the measured data and the simulation data is high and the effective time sequence data amount in the individual stage is small, the adoption of the full-life simulation data to participate in the cluster recognition is beneficial to improving the classification accuracy, but the actual operation effect of the embodiment is not obviously different from that of the embodiment 1. It should be noted that the subsequent filtering is still only for the measured data.
Example 3
The difference from embodiment 1 is that the step S4 degradation phase identification further comprises the steps of:
S4b, for the abnormal clustering condition, adopting an abnormal value detection strategy (such as a threshold value detection strategy of adjacent point difference values, and considering abnormality when the adjacent point difference values are larger than a threshold value) to carry out state correction so as to solve the problem of boundary point state staggering in a degradation stage caused by random abnormality or false fluctuation of signals; for the clustering abnormality of the individual point outliers divided into different stages, the embodiment sets the threshold number of the individual points to n 1, and corrects the current abnormal individual point from the stage division if the individual points are less than n 1 Change to/>Otherwise, keeping the current stage division;
S4c, aiming at the clustering abnormal condition of the abnormal aggregation area, detecting the number of abnormal individual points by using a LOF local outlier factor method and setting a clustering center of each stage by taking the state division result after carrying out abnormal value detection strategy as a standard A kind of electronic deviceThe value is a threshold boundary provided that the outlier number of the outlier region is less than/>I.e. the outlier number of the abnormal aggregation area is smaller than the total classification point number in the 1/2 boundary threshold range after the stage, the stage division slave/>, of the outlier of the current abnormal aggregation area is correctedChange to/>Otherwise, keeping the current stage division; wherein/>For the data mean value corresponding to the degradation phase,/>Is the variance of the data corresponding to the degradation phase.
Example 4
The difference from example 1 is that the cluster number f=4. It is mainly considered that the effective data of the damage expansion phase may be lacking, or that the running-in phase is distinguished from the steady-state phase measured data and is sufficiently obvious.
The basic principle of the invention is as follows: firstly, the problems of less measured data and incomplete coverage area in the early stage can be solved by supplementing simulation data, so that the probability of occurrence of inaccurate classification events caused by insufficient data quantity in the follow-up process is reduced; secondly, carrying out accurate bearing degradation phase division, and then carrying out bearing life prediction, wherein the life prediction is carried out under the constraint of the phase division, namely, the life prediction with the constraint is carried out, and the prediction result is more reliable; thirdly, selecting a proper filtering prediction model according to a degradation stage where actual measurement data are ended in life prediction, so that stronger pertinence of prediction and more credible result can be ensured; finally, filtering is performed before prediction, so that the predicted model parameters reach the optimal state under the action of all existing measured data, and the reliability of a prediction result is further ensured. The bearing health monitoring or management or other life prediction system and the like realized by the invention can be synchronously put into use along with new equipment, and the model learning training is not needed to be carried out on a large amount of actual monitoring data of the same type and the same environmental equipment in the earlier stage, so that the practicability is stronger.
Although the invention focuses on the research of the belt conveyor bearing, the invention is also applicable to the on-line monitoring or health management of bearings in other industrial occasions, and the key is that the invention can adapt to different bearing application environments on the aspects of the division of degradation stages and the selection of corresponding degradation models or model parameters.
The foregoing is only an embodiment of a method for predicting the life of a belt conveyor bearing by adaptive phasing and digital-analog fusion, which is a part of the present invention, and in fact, the method for constructing health indexes, the filtering model, the nonlinear filtering method, the distribution characteristics of process noise and measurement noise, etc. may be selected and set according to the practical application environment, for example, the nonlinear filtering method may select extended kalman filter EKF or particle filter, etc., and these combinations or preferred schemes should also be regarded as the protection scope of the present invention, which is not listed here one by one.
Claims (10)
1. The self-adaptive staged and digital-analog fusion life prediction method for the belt conveyor bearing is characterized in that the bearing actual measurement data is fused with the bearing theoretical simulation data, bearing degradation stage identification is carried out in a self-adaptive mode, and life prediction based on the actual measurement data and a corresponding degradation stage model is carried out according to a stage identification result, and specifically comprises the following steps:
S1, acquiring actual measurement data of the bearing, wherein the actual measurement data comprises at least one of vibration data, noise data and temperature data;
s2, acquiring the bearing simulation data, wherein the simulation data at least comprises bearing operation simulation data of a rear section of measured data, the sampling interval of the simulation data is consistent with the measured data, and the simulation data comprises bearing theoretical model simulation data or rear section bearing theoretical simulation data driven by the measured data;
S3, constructing a health index, namely constructing a health index EHI according to an effective data set formed by actual measurement data and simulation data, wherein the health index comprises a sectional mean value, a sectional root mean square value, a sectional standard deviation or a sectional standard deviation root mean square;
S4, identifying a degradation stage, wherein the degradation stage at least comprises a running-in stage, a steady-state stage, a defect germination stage, a defect expansion stage and the first three stages of a damage expansion stage, and the degradation stage identification is completed by taking the health index in the step S3 as a clustering object by adopting a clustering method;
S5, predicting the residual life, namely, filtering the actual measured value according to the degradation stage of the end point of the actual measured data by adopting a corresponding experience degradation model aiming at the complex working conditions of different belt conveyors, and predicting the life according to the latest filtering result after the filtering is finished, wherein the time from the end time of the actual measured value to the time when the predicted value reaches or exceeds a preset failure threshold value is the predicted residual life of the bearing.
2. The method for predicting the service life of the belt conveyor bearing by adopting the self-adaptive staged and digital-analog fusion method according to claim 1, wherein the method further comprises the step S1a of preprocessing measured data after the step S1 and before the step S3, and the preprocessing comprises the process of removing noise and/or eliminating outliers.
3. The method for predicting the life of a belt conveyor bearing in a self-adaptive phased-to-digital-to-analog fusion manner according to claim 1, wherein the simulation data comprises bearing operation life-time simulation data.
4. The method for predicting the life of a belt conveyor bearing in a self-adaptive staged and digital-analog fusion manner according to claim 1, wherein the method for acquiring the segment mean value comprises classifying data in the effective data set, calculating a statistical average value in segments, and the segment root mean square value comprises root mean square values of segment mean values of different types.
5. The method for predicting the life of a belt conveyor bearing by adopting adaptive phasing and digital-analog fusion according to claim 1, wherein the degradation phase identification specifically comprises the following steps:
S4a, setting the cluster number f to be more than or equal to 3, taking the constructed health index as a research object, and carrying out Gath-Geva fuzzy clustering; for the health index time sequence obtained in the step S3, dividing the health index time sequence into the phases with the highest possibility according to the corresponding maximum fuzzy membership matrix of each data point, and recording the effective data sets of the phases from the 1 st group/each to the p-th group/each of the belt conveyor bearings as Meanwhile, clustering is recorded to obtain a clustering center of different stages of degradation of each bearing as/>，/>For the stage identification, i is a group identification, namely, a plurality of bearings or bearing groups can be subjected to cluster identification at the same time.
6. The method for predicting the life of a belt conveyor bearing in a self-adaptive phased-to-digital-to-analog fusion manner according to claim 5, wherein the degradation phase identification further comprises the steps of:
S4b, carrying out state correction on the abnormal clustering condition by adopting an abnormal value detection strategy so as to solve the problem of boundary point state staggering in the degradation stage caused by random abnormality or false fluctuation of signals;
s4c, aiming at the clustering abnormal condition of the abnormal aggregation area, detecting the number of abnormal individual points by a local outlier factor method by taking the state division result after carrying out abnormal value detection strategy as a standard, and setting a clustering center of each stage />The value is a threshold boundary, and if the outlier number of the abnormal aggregation area is smaller than the total classification point in the threshold range of 1/2 of the post-stage boundary, the stage division of the outlier of the current abnormal aggregation area is corrected from/>Change to/>Otherwise, keeping the current stage division; wherein/>For the data mean value corresponding to the degradation phase,/>Is the variance of the data corresponding to the degradation phase.
7. The method for predicting the life of a belt conveyor bearing in a self-adaptive phased-to-digital-analog fusion manner according to any one of claims 5 and 6, wherein the cluster number f=4.
8. The method for predicting the life of a belt conveyor bearing by adaptive phasing and digital-analog fusion according to claim 1, wherein the step S5 comprises:
s5a, selecting a prediction model, selecting a corresponding degradation model aiming at different degradation stages of the vibration signal of the actual measurement data end point, and determining fitting models corresponding to a steady-state stage, a defect initiation stage, a defect expansion stage and a defect expansion stage:
the steady-state stage adopts a bearing theoretical life model;
The defect germination stage health index adopts a single-index model;
the defect expansion stage health index adopts a double-index model;
The health index in the injury expansion stage adopts a polynomial model;
S5b, model filtering is carried out, and according to the difference of the defect initiation stage, the defect expansion stage and the defect expansion stage, a corresponding state space model is established according to the corresponding selected health index model and the parameter change rule, and model filtering is carried out according to the actual measurement health index obtained in the step S3 until the actual measurement data is finished, so that the latest state space evolution equation is obtained;
And S5c, model prediction, namely extrapolating the health index model after updating parameters according to the set health index failure threshold, wherein the time when the health index reaches the failure threshold is the failure time, and the duration from the filtering end time to the failure time is the predicted residual service life of the bearing.
9. The method for predicting the life of the belt conveyor bearing by adaptive phasing and digital-analog fusion according to claim 8, wherein in the step S5c, the initial value of the filter model parameter is obtained by means of super-parameter searching.
10. The method of claim 8, wherein the failure threshold is set between 2 and 10 times the first predicted time health index value.
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