A performance prediction method and device for industrial robot joints

Abstract

The present application provides a performance prediction method and device for an industrial robot joint, including S1, collecting original current signals and establishing an original data set; S2, performing noise reduction processing on the current signals and storing them in a preprocessed data set. A ; S3, calculate the RMS value of the current signal and use it as a degradation index to form a degradation index data set Z ; S4, establish a multi-task stage degradation impact model; S5, estimate the unknown parameters in the proposed model based on the data of each task stage; S6, divide the failure threshold according to the characteristics of each stage task, and form a failure threshold set oh ; S7. Derive the reliability function based on the model; S8. Form the reliability curve based on the reliability function; This method takes into account the influence of random loads generated by the switching of multi-task stages of industrial robots, and solves the problem that when evaluating the reliability of current signals, the signal amplitudes in different task stages vary greatly, and the evaluation results are inaccurate under the influence of random loads.

Description

Technical Field

[0001] The present application relates to the technical field of reliability assessment and health prediction, and in particular to a performance prediction method and device for industrial robot joints. Background Art

[0002] Industrial robots are automated equipment that integrate multiple advanced scientific technologies such as mechanics, electronics, control, computers, sensors, and artificial intelligence. They have the characteristics of multiple degrees of freedom, complex structure, and strong independence, and are widely used in automobile manufacturing, parts processing, electronic and electrical industries, and other fields.

[0003] The main limitations of the current degradation modeling method for precision reducers at the joints of industrial robots in multi-task stages are: the existing degradation impact model based on current signals does not consider the impact of load changes, emergency stop and commutation factors on the system when multiple different task stages are cyclically switched, and in the process of reliability assessment, the existing model based on current signals does not consider the problem of large differences in motor current amplitude due to load changes in different task stages, and only sets one failure threshold throughout the life cycle, which makes the model unable to predict system failure in time in task stages with small amplitudes, and predicts system failure too early in task stages with large amplitudes. The above two factors will affect the accuracy of the prediction results. Summary of the invention

[0004] The purpose of this application is to provide a performance prediction method and device for industrial robot joints in order to solve the above problems.

[0005] In a first aspect, the present application provides a performance prediction method for an industrial robot joint, comprising:

[0006] S1. Collect data on the current signal of the motor directly connected to the reducer at the joint of the industrial robot to establish an original data set;

[0007] S2. Filter and reduce noise on the current signal in the original data set and store it in a pre-processed data set. A ;

[0008] S3, calculating the preprocessed data set A The root mean square value of each data contained in is used as the degradation index to form a degradation index data set Z ;

[0009] S4. Establish a multi-task stage degradation impact model. Establish a multi-task stage degradation impact model. The multi-task stage degradation impact model can take into account the impact of random loads generated by load changes and emergency stop reversing on the system failure process when the industrial robot switches between different task stages;

[0010] S5. Based on the data set Z The unknown parameters in the multi-task stage degradation shock model are estimated using a maximum likelihood estimation method;

[0011] S6. For industrial robots in different task stages, the failure threshold is divided according to the characteristics of each task stage to form a failure threshold set oh ;

[0012] S7. According to the multi-task stage degradation impact model, a reliability function is derived, and the reliability function is used to perform reliability evaluation on the multi-task stage industrial robot system;

[0013] S8, comparing the estimated result of the unknown parameter with the failure threshold set oh Substitute it into the reliability function to form a reliability curve.

[0014] According to the technical solution provided in the embodiment of the present application, in step S2, the preprocessing data set A The representation is as follows:

[0015] Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n The current signals at the joints of the industrial robot in each task stage of each cycle are collected, and the collected current signals are subjected to noise reduction. The preprocessed data set obtained is A As shown below:

[0016]

[0017] Where: A ij The system is i The second cycle j Data of the mission phase;

[0018] i =1,2,3... m ;

[0019] j =1,2,3... n .

[0020] According to the technical solution provided in the embodiment of the present application, in step S3, when calculating the preprocessing data A After the RMS value of each data contained in the preprocessed data set A The processed data is sorted in a storage method to form the degradation index data set Z ;

[0021] Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n Second, the degradation index dataset Z Written in the following form:

[0022]

[0023] Where: Z ij The system is i The first task of j Data of the next cycle;

[0024] i =1,2,3... m ;

[0025] j =1,2,3... n .

[0026] According to the technical solution provided by the embodiment of the present application, in the step S4, the process of establishing the multi-task stage degradation impact model specifically includes: based on the nonlinear Wiener process, the degradation process of the multi-task stage industrial robot is modeled to form a multi-task stage degradation model. In the degradation process of the multi-task stage, the degradation rate depends on the task stage, and the degradation rate of the multi-task stage industrial robot system is constant under the same task stage. Based on this, the system t The degradation at the moment can be expressed as:

[0027]

[0028] Where: X ( t )for t The amount of system degradation at time ;

[0029] β is the drift coefficient, which characterizes the degradation rate;

[0030] s is the diffusion coefficient, which is used to describe the impact of random factors such as product external noise on product performance. This model assumes that the product failure mechanism remains unchanged at different stress levels. s constant;

[0031] x (0) is t = the initial degradation amount at time 0;

[0032] B ( t ), t>0 conforms to the Wiener process, which is a standard Brownian motion;

[0033] σB ( t ) is subject to N (0, s 2 t )’s normal distribution;

[0034] Based on the multi-task stage degradation model, and taking into account the influence of random shocks caused by load transformation, emergency stop and reversing when the industrial robot switches different task stages, a multi-task stage degradation shock model is established. The multi-task stage degradation shock model uses a Poisson process to describe the occurrence of random shocks, and takes the influence of random shocks as the cumulative degradation increment of the system. The multi-task stage degradation shock model can be expressed as:

[0035]

[0036] Where: M ( t ) t The overall degradation of the system at that moment;

[0037] X ( t )for t The overall natural degradation of the system at that moment;

[0038] N c (t ) indicates the deadline t the number of non-lethal impacts;

[0039] Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is Normal distribution of

[0040] In the overall degradation i Mission stage n The degradation of the secondary circulation system is:

[0041]

[0042] Where: M in ( t ) is the iMission stage n The overall degradation of the subcirculatory system;

[0043] X in ( t ) is the i Mission stage n The amount of natural degradation of the subcirculatory system;

[0044] N c (t ) indicates the deadline t the number of non-lethal impacts;

[0045] Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is The normal distribution of .

[0046] According to the technical solution provided in the embodiment of the present application, in the step S5, the method used to estimate the unknown parameters is the maximum likelihood estimation method.

[0047] According to the technical solution provided in the embodiment of the present application, in step S6, the failure threshold set oh The expression is as follows:

[0048]

[0049] Where: oh m The system is m The failure threshold of the task stage, the system has m A task phase.

[0050] According to the technical solution provided in the embodiment of the present application, in step S7, the expression of the reliability function is as follows:

[0051]

[0052] Where: Indicated in i Mission Phase n After the cycle, the probability that the overall degradation of the system under the influence of natural degradation of the system and the impact of different task switching and external environmental impact does not exceed the failure threshold of the task stage;

[0053] Indicates that the deadline t The probability that the number of fatal impacts received by the system at any given moment is 0.

[0054] According to the technical solution provided in the embodiment of the present application, in step S8, the estimation result of the unknown parameter is compared with the failure threshold set oh Substituted into the reliability function, we can get the system n The second cycle, i The reliability curve of each task stage is n The second cycle, i The reliability curves obtained in the task stages are integrated to obtain a first reliability curve, where the first reliability curve is a reliability curve of the industrial robot system.

[0055] In a second aspect, the present application further provides a terminal device, wherein the performance prediction device for an industrial robot joint comprises:

[0056] A memory, a processor, and a computer program stored in the memory and executable on the processor;

[0057] When the computer program is executed by the processor, the steps of any one of the above-mentioned methods for predicting the performance of industrial robot joints are implemented.

[0058] In a third aspect, the present application also provides a computer-readable storage medium, on which a performance prediction program for industrial robot joints is stored. When the performance prediction program for industrial robot joints is executed by a processor, the steps of any of the above-mentioned performance prediction methods for industrial robot joints are implemented.

[0059] Compared with the prior art, the present invention has the following advantages: the present invention collects data on the current signal of the motor directly connected to the reducer at the joint of the industrial robot, and performs noise reduction on the collected signal to form a preprocessed data set. A , and calculate the preprocessed data set A The root mean square value of the data contained in and form the degradation index data set Z , a multi-task stage degradation impact model is established. This model can take into account the impact of random loads generated by load changes and emergency stop switching on the system failure process when the industrial robot switches between different task stages. Based on the data set Z The maximum likelihood estimation method is used to estimate the unknown parameters contained in the model. For industrial robots in different task stages, the failure threshold is divided according to the characteristics of each task stage, and a failure threshold set is formed. According to the model, the reliability can be derived, and the estimation results of the unknown parameters and the failure threshold set are brought into the reliability function to form a reliability curve.

[0060] During use, it is necessary to first collect data and establish an original data set, perform noise reduction processing on the current signal in the original data set, calculate the root mean square value of the signal after noise reduction processing, and consider the influence of random loads generated by load changes and emergency stop reversing on the system failure process when the industrial robot switches between different task stages. A multi-task stage degradation impact model is established, and then the maximum likelihood estimation method is used to estimate the unknown parameters in the model. For industrial robots at different stages, the failure threshold set is divided according to the characteristics of each task stage. After the threshold set is formed, the reliability function is derived according to the model, and then the estimation results of the unknown parameters and the failure threshold set are brought into the reliability function to obtain the reliability curve.

[0061] The multi-task stage degradation impact model established in the present application takes into account the impact of random loads generated by load changes and emergency stop and reversing when the industrial robot switches between different task stages on the system failure process; and based on the multi-task stage degradation impact model, a complete set of reliability evaluation methods are proposed for the system in the multi-task stage; in the traditional reliability evaluation process, generally only one failure threshold is set for evaluation, but since the current amplitude difference of the industrial robot is large in different task stages, if only one failure threshold is set, it is easy to cause the reliability evaluation result to be too high in the task stage with a small current amplitude, and the reliability evaluation result to be too low when the current amplitude is large, resulting in inaccurate evaluation results. To solve this problem, different failure thresholds are set for different task stages to ensure the accuracy of the results. The multi-task stage degradation impact model established in the present application and the method for evaluating it solve the problem that the system reliability cannot be accurately evaluated in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a method for predicting the performance of an industrial robot joint provided in Example 1 of the present application;

[0063] Figure 2 A motion trajectory diagram of an industrial robot terminal according to a method for predicting the performance of an industrial robot joint provided in Example 1 of the present application;

[0064] Figure 3 The loading rules of the reducer of the industrial robot during the experiment provided in the first embodiment of the present application;

[0065] Figure 4 A first reliability curve provided in Example 1 of the present application;

[0066] Figure 5 A schematic diagram of the structure of the server provided in Example 4 of the present application.

[0067] The text annotations in the figure represent:

[0068] 400, server; 401, central processing unit (CPU); 402, read-only memory (ROM); 403, random access memory (RAM); 404, bus; 405, input / output (I / O) interface; 406, input part; 407, output part; 408, storage part; 409, communication part; 410, drive; 411, removable media. DETAILED DESCRIPTION

[0069] In order to enable those skilled in the art to better understand the technical solution of the present application, the present application is described in detail below in conjunction with the accompanying drawings. The description in this section is only exemplary and explanatory and should not have any limiting effect on the scope of protection of the present application.

[0070] Embodiment 1:

[0071] It should be noted that the system in this embodiment refers to an "industrial robot system".

[0072] The present invention proposes a performance prediction method for industrial robot joints based on current signals, and proposes a reliability evaluation method based on a multi-task stage degradation impact model. The evaluation process is as follows: Figure 1 shown.

[0073] The present invention takes the Xinbao WPS-42-50-SN-LE-8K harmonic reducer at the joint of the SCARA four-axis industrial robot as an example to illustrate this patent. In the assembly line application scenario, SCARA is mainly responsible for intelligent picking and sorting. During the working process, the operation curve of the four-axis industrial robot is two Figure 2 The superposition of the “door” shaped route.

[0074] The present invention uses a scientific test bench to restore the real working conditions of the joints of the industrial robot in the multi-tasking stage, and generates real harmonic reducer fault data through accelerated life testing.

[0075] The scientific test bench consists of a servo motor, a harmonic reducer, a sensor and a load motor; the harmonic reducer is powered by a 400W servo motor, and a 7.5kW servo spindle motor provides load for the load motor. The electric energy generated by the load motor is fed back through the control unit. Xinbao's WPS-42-50-SN-LE-8K harmonic reducer is selected as the test object; based on the working condition experiment of the harmonic reducer in the four-axis SCARA robot.

[0076] Experimental parameter settings:

[0077] 1: The test bench servo motor runs at a speed of n = 2000r / min;

[0078] 2: The test load is loaded by step stress type, the load size is S1 (0.5 times rated load), S2 (rated load), S3 (1.5 times load), changing every four hours, and cyclically. (e.g. Figure 3 shown)

[0079] S1. Collect data on the current signal of the motor directly connected to the reducer at the joint of the industrial robot to establish an original data set;

[0080] In this experiment, the Fluke-80i-110s current probe is connected in series to the single-phase current output line of the motor driver to collect the dynamic current signal in real time, and is connected to the high-precision NIUSB-6281 data acquisition card to record the current signal. The recorded data is stored in a txt. type file through Labview software; the sampling frequency of the motor current data is set to 20kHz, the sampling time is about 9 seconds, and each signal records the effective data of 6 cycles of the harmonic reducer; the current signal is collected and the original data set is established.

[0081] S2. Filter and reduce noise on the current signal in the original data set and store it in a pre-processed data set. A ;

[0082] The collected current signals are mostly nonlinear and stable signals, which often have problems such as weak fault impact characteristics and serious background noise interference. Noise reduction processing is required to eliminate the high-frequency noise components in the current signal. This embodiment uses the median filtering method to perform noise reduction processing on the current signal, thereby eliminating the high-frequency noise components in the current signal, and retains the fault impact components in the signal through low-frequency noise reduction processing, and stores the signal after noise reduction filtering in the preprocessing data set A In order to provide high-quality source signals for subsequent analysis;

[0083] Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n The preprocessed dataset obtained A As shown below:

[0084] (1)

[0085] Where: A ij The system is i The first task of j Data of the next cycle;

[0086] i =1,2,3... m ;

[0087] j =1,2,3... n .

[0088] S3, calculating the preprocessed data set A The root mean square value of the data is used as the degradation index to form a degradation index data set Z ;

[0089] The root mean square (RMS) is calculated by squaring the data, averaging the data, and then performing square root processing on the data.

[0090] In this embodiment, the data set is preprocessed according to A The degradation index data is processed by the root mean square value according to the storage method, that is, the degradation index data is first squared, the degradation index data is averaged, and then the squared degradation index data and the averaged degradation index data are processed by square root. After the square root processing, the root mean square value processing is completed, and the processed degradation index data is processed according to the preprocessing data set. A The storage method is to organize the preprocessed data set A The storage method is the matrix form in formula 2, which is used as the degradation index to form the degradation index data set Z ,;

[0091] Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n The preprocessed dataset obtained A , the filtered data set Z Written in the following matrix form. (2)

[0092] Where: Z ij The system is i The first task of j Data of the next cycle;

[0093] i =1,2,3... m ;

[0094] j =1,2,3... n .

[0095] S4. Establish a multi-task stage degradation impact model, which can take into account the impact of random loads generated by load changes and emergency stop reversing on the system failure process when the industrial robot switches between different task stages;

[0096] Because the task stages of industrial robots are continuous and periodic, Brownian motion can well describe the continuous degradation process of industrial robots. In addition, the degradation rate in the degradation model established based on the Wiener process can be changed, which meets the needs of establishing a degradation model for precision reducers in multi-task stage industrial robots; at the same time, the degradation model based on the Wiener process has been widely used in systems with complex multi-source time-varying working conditions, individual differences, nonlinearity, multiple stages and random stress; therefore, this patent uses the Wiener process to describe the degradation process of the mechanical system.

[0097] Since the harmonic reducers at the joints of industrial robots in the multi-tasking stage are affected by various complex factors such as load mutation, steering change, start and stop, and external environment, which will cause independent degradation increments, it is necessary to consider the impact of the multi-tasking system due to task stage switching and external factors on the system, combine the impact of the impact with the degradation process, and establish a degradation impact model of the multi-tasking system as a prerequisite for system reliability evaluation.

[0098] 1. System Assumptions

[0099] This patent establishes a degradation impact model for the characteristics of industrial robots in the multi-task stage. The model is described and assumed as follows:

[0100] 1. The degradation process is irreversible, that is, the performance of the industrial robot system decreases monotonically over time;

[0101] 2. The failure mechanism under high stress level and normal stress level should be consistent;

[0102] 3. The task consists of a series of consecutive stages, that is, the task stages are continuous and the order is fixed;

[0103] 4. The duration of the different phases is known;

[0104] 5. Within the failure threshold, the degradation rate is different under different load levels;

[0105] 6. Within the failure threshold, the industrial robot system is in different mission stages, and the degradation rate of the industrial robot system will be different.

[0106] 2. Establishment of industrial robot system degradation model in multi-task stage

[0107] Based on the nonlinear Wiener process, the degradation process of the multi-task stage industrial robot is modeled to form a multi-task stage degradation model. In the degradation process of the multi-task stage, the degradation rate depends on the task stage, and the degradation rate of the multi-task stage industrial robot system is constant under the same task stage. Based on this, the system t The degradation at the moment can be expressed as:

[0108] (3)

[0109] Where: X ( t )for t The amount of system degradation at time ;

[0110] β is the drift coefficient, which characterizes the degradation rate;

[0111] s is the diffusion coefficient, which is used to describe the impact of random factors such as product external noise on product performance. This model assumes that the product failure mechanism remains unchanged at different stress levels. s constant;

[0112] x (0) is t = the initial degradation amount at time 0;

[0113] B ( t ), t >0 conforms to the Wiener process, which is a standard Brownian motion;

[0114] σB ( t ) is subject to N (0, s 2 t ) has a normal distribution.

[0115] A system with m task stages runs for t time, and each task stage is cycled n times. Assuming that the duration of the i-th stage of the system is the same in each cycle, the overall degradation at time t is:

[0116]

[0117] Where: x(0) is the initial degradation amount at time t = 0;

[0118] v(t) represents the operating stage of the harmonic reducer at time t;

[0119] β i is the degradation rate of the i-th stage, i = 1, 2, 3, …, m;

[0120] B(t), t>0 conforms to the Wiener process, which is a standard Brownian motion;

[0121] σB(t) is subject to N(0, σ 2 t) is normally distributed.

[0122] Assuming that the degradation rate is constant at each stage in each cycle, the degradation amount generated by the system at the i-th task stage after n cycles can be expressed as follows:

[0123]

[0124] Then the overall degradation of the system is:

[0125]

[0126] Where: β i (i=1,2,…m) represents the degradation rate of the i-th task stage;

[0127] (t i-1 , t i ) represents the time interval,

[0128] β i =β(i),Δt i represents the duration of the i-th task phase,

[0129] (t (j-1)m+i-1 ,t (j-1)m+1 ) is the duration of the i-th task phase in the j-th cycle, and it is assumed that the duration of the i-th task phase is the same in each cycle.

[0130] 3. Introducing the impact of shocks to build a degradation shock model

[0131] For the precision reducers at the joints of industrial robots, in the transition between different task stages, the reducers not only experience natural degradation such as wear, corrosion, and fatigue, but are also subject to shocks caused by sudden load changes, steering changes, start-stop, and the influence of other axes and complex external environments. These shocks will accelerate the degradation of the reducers and bring a certain increment to the degradation process. In order to accurately describe the degradation failure process of the system, it is necessary to consider the impact of external shocks on the system.

[0132] External shocks can be divided into fatal shocks and non-fatal shocks that cause system degradation. An intensity of D The impact threshold is used to divide the two impacts. When the impact intensity is greater than D The shock is a fatal shock that causes system failure N f , when the impact strength is less than D When the system degradation increases, it can be regarded as a non-fatal shockN c .

[0133] Then the time-dependent system overall degradation and impact model of the linear degradation path with impact influence introduced based on equation (5) can be recorded as follows (that is, the multi-task stage degradation impact model is):

[0134] (7)

[0135] in: M ( t ) t The overall degradation of the system at that moment;

[0136] X ( t )for t The overall natural degradation of the system at that moment;

[0137] N c (t ) indicates the deadline t the number of non-lethal impacts;

[0138] Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is Normal distribution of

[0139] In the overall degradation i Mission stage n The degradation of the secondary circulation system is:

[0140] (8)

[0141] Where: M in ( t ) is the i Mission stage n The overall degradation of the subcirculatory system;

[0142] X in ( t ) is the i Mission stage n The amount of natural degradation of the subcirculatory system;

[0143] Nc (t ) indicates the deadline t the number of non-lethal impacts;

[0144] Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is The normal distribution of .

[0145] The increments generated by random shocks follow a normal distribution ,in, , They represent the mean and variance of the normal distribution of the degradation increment caused by random shocks; the random shock intensity follows the normal distribution ,in, , They represent the mean and variance of the normal distribution of the random shock intensity. These two pairs of parameters are assumed based on the actual situation of the system. The random shock follows a constant arrival rate. l Homogeneous Poisson process with arrival rate l Based on the actual situation of the system, as of t Happens all the time n The probability of a random shock is:

[0146] (9)

[0147] Where: Indicates that an impact occurs n The probability of n =0,1,2....;

[0148] is the number of impacts that occurred.

[0149] In addition, the probability that a random shock is a non-fatal shock can be determined based on the distribution of shock intensity and is recorded as p 1. Since there are only two types of random shocks, fatal shocks and non-fatal shocks, the probability of a non-fatal shock is 1- p 1, then the non-fatal impact follows the arrival rate p 1× l The homogeneous Poisson process of fatal shock follows the arrival rate (1- p 1)× l Homogeneous Poisson process; Substituting into equation (9) we get the expression of the probability of non-fatal impact:

[0150] (10)

[0151] Where: Indicates that an impact occurs n The probability of times;

[0152] is the number of impacts;

[0153] P 1 is the probability that when a shock occurs, it is a non-fatal shock (a shock that causes an increase in system degradation).

[0154] The expression of fatal impact:

[0155] (11)

[0156] Where: Indicates that an impact occurs n The probability of is the number of impacts;

[0157] (1- p 1) is the probability that when an impact occurs, it is a fatal impact (an impact that causes direct damage to the system).

[0158] In summary, based on formula (6), a degradation impact model of the reducer based on multi-task stages is proposed, which consists of two parts: natural degradation process and random impact process. i Degradation rate during mission phase β i , and taking the shock into account in the model increases the practicality of the model;

[0159] S5. Estimating unknown parameters in the multi-task stage degradation shock model using a maximum likelihood estimation method based on the data set Z;

[0160] Assume that the system has m The tasks of the stage were run t moment, all stages passed n cycles to obtain the preprocessed data set A , as shown in the above formula (1), the system i Stages at time t i1 , t i2 , …, t in ;

[0161] Assume that the system has m The task of the first stage is iTask phases, running n The data after filtering and RMS processing in the second cycle is recorded as z i1 , z i2 , z i3 , …, z in , the preprocessed dataset for this task stage is { z i1 , z i2 , z i3 , …, z in}, on this basis, the incremental data set can be written ΔZ , as shown in formula (12):

[0162] (12)

[0163] (13)

[0164] Where: i =1,2,3,…, m ;

[0165] j =1,2,3,…, n ;

[0166] z i0 =0, represents the i The initial degradation amount of the task is 0;

[0167] t ij - t i(j-1) Executes once for all phases for a duration.

[0168] ΔZ It obeys the multivariate normal distribution and assumes that the failure mechanism of the product remains unchanged at different stress levels. s 2 constant. ΔZ Obey N( β i ( t ij - t i(j-1) ),σ 2 ( t ij - t i(j-1)), the maximum likelihood estimation method is used to estimate the unknown parameters of each task in the multi-stage task degradation model β i , s 2 .

[0169] So the likelihood function is:

[0170] (14)

[0171] Where: m represents m task stages;

[0172] n represents n cycles.

[0173] Formula (14) is about β i , s 2 The first-order partial derivative of , we get:

[0174] (15)

[0175] (16)

[0176] Let equation (15) and equation (16) equal to zero, and we get:

[0177] (17)

[0178] (18)

[0179] According to the real-time data changes, the maximum likelihood estimation method is used to solve the unknown parameters in the degradation model through equations (17) and (18): β i , s 2 , the results are shown in Table 1.

[0180] Table 1 Degradation model parameters

[0181]

[0182] S6. For industrial robots in different task stages, the failure threshold is divided according to the characteristics of each task stage to form a failure threshold set oh ;

[0183] Since the current signal at the joint of the industrial robot in the multi-task stage has different loads in different task stages, which will cause its amplitude to change greatly. When the reliability of the system is evaluated by a single failure threshold, the evaluation result will be inaccurate. Based on this problem, different failure thresholds under different signal amplitude conditions in different task stages are established to form a failure threshold set. oh . (19)

[0184] Where: oh i The system is i The failure threshold of the task stage, the system has m A task phase.

[0185] S7. According to the multi-task stage degradation impact model, a reliability function is derived, and the reliability function is used to perform reliability evaluation on the multi-task stage industrial robot system.

[0186] Based on the proposed system i Task, n The degradation shock model of the sub-cycle (8) and the probabilities of the two types of shocks (10) and (11) are used to derive the analytical expression of the reliability function. The failure threshold of the system at different stages is oh , end t All task stages of the moment system have passed n The second cycle, i Reliability function of each task stage:

[0187] (20)

[0188] Where: M in ( t ) is the equation (8) after substituting into equation (5) and equation (10), and the corresponding impact probability is the equation (10) and equation (11) after substituting into the corresponding values.

[0189] S8, comparing the estimated result of the unknown parameter with the failure threshold set oh Substitute it into the reliability function to form a reliability curve.

[0190] The degradation index dataset of the degradation current signal of the industrial robot in the multi-task stage is β i and s 2 Substituting the parameter estimation result and the failure threshold set (19) into the reliability function, we can get the system n The second cycle, i The reliability curve of each task stage is n The second cycle, i The reliability curves obtained in the task stages are integrated to obtain a first reliability curve, where the first reliability curve is a reliability curve of the industrial robot system. Figure 4As shown in the figure, the reliability estimation results of the system at each moment in this stage can be determined.

[0191] Embodiment 2:

[0192] This embodiment provides a terminal device, wherein the performance prediction device for an industrial robot joint comprises:

[0193] A memory, a processor, and a computer program stored in the memory and executable on the processor;

[0194] When the computer program is executed by the processor, the following is achieved: Figure 1 The steps of any performance prediction method of an industrial robot joint.

[0195] Embodiment three:

[0196] This embodiment provides a computer-readable storage medium on which a performance prediction program for an industrial robot joint is stored. When the performance prediction program for an industrial robot joint is executed by a processor, the following is achieved: Figure 1 The steps of any performance prediction method of an industrial robot joint.

[0197] Embodiment 4:

[0198] This embodiment provides a server 400, such as Figure 5 As shown, the server 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part to the random access memory (RAM) 403. Various programs and data required for system operation are also stored in the RAM 403. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0199] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0200] In particular, according to an embodiment of the present invention, the above reference Figure 1 The described processes may be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a computer readable medium, the computer program including a computer program for executing Figure 1 In such an embodiment, the computer program may be downloaded and installed from a network via the communication section 409 and / or installed from the removable medium 411 .

[0201] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0202] The flow chart and block diagram in the accompanying drawings illustrate the possible implementation architecture, function and operation of the method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0203] The units involved in the embodiments of the present invention may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves. The units or modules described may also be arranged in a processor, for example, they may be described as: a processor includes a first generation module, an acquisition module, a search module, a second generation module, and a merging module. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves, for example, the acquisition module may also be described as "an acquisition module for acquiring multiple instances to be detected in the basic table."

[0204] As another aspect, the present application also provides a computer-readable medium, which may be included in the server described in the above embodiment; or may exist independently without being installed in the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the smart device rental method described in the above embodiment. For example, the electronic device can implement Figure 1 The steps shown in .

[0205] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0206] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and its core ideas of this application. The above is only the preferred implementation method of this application. It should be pointed out that due to the limitations of textual expression and the objective existence of infinite specific structures, ordinary technicians in this technical field can make several improvements, modifications or changes without departing from the principles of the present invention, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of this application.

Claims

1. A method for predicting the performance of an industrial robot joint, characterized in that: include: S1. Collect data on the current signal of the motor directly connected to the reducer at the joint of the industrial robot to establish an original data set; S2. Filter and reduce noise on the current signal in the original data set and store it in a pre-processed data set. A ; S3, calculating the preprocessed data set A The root mean square value of each data contained in is used as the degradation index to form a degradation index data set Z ; S4. Establish a multi-task stage degradation impact model, which can take into account the impact of random loads generated by load changes and emergency stop reversing on the system failure process when the industrial robot switches between different task stages; S5. Based on the data set Z The unknown parameters in the multi-task stage degradation shock model are estimated using a maximum likelihood estimation method; S6. For industrial robots in different task stages, the failure threshold is divided according to the characteristics of each task stage to form a failure threshold set ω ; S7. According to the multi-task stage degradation impact model, a reliability function is derived, and the reliability function is used to perform reliability evaluation on the multi-task stage industrial robot system; S8, comparing the estimated result of the unknown parameter with the failure threshold set ω Substitute it into the reliability function to form a reliability curve.

2. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In step S2, the preprocessing data set A The representation is as follows: Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n The current signals at the joints of the industrial robot in each task stage of each cycle are collected, and the collected current signals are subjected to noise reduction. The preprocessed data set obtained is A As shown below: Where: A ij The system is i The second cycle j Data of the mission phase; i =1，2，3... m ； j =1，2，3... n 。 3. The performance prediction method of an industrial robot joint according to claim 2, characterized in that: In step S3, the preprocessing data is calculated. A After the RMS value of each data contained in the preprocessed data set A The processed data is sorted in a storage method to form the degradation index data set Z ; Assume that the industrial robot under test has m mission phases, running t Time, mission phase cycle n Second, the degradation index dataset Z Written in the following form: Where: Z ij The system is i The first task of j Data of the next cycle; i =1，2，3... m ； j =1，2，3... n 。 4. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In the step S4, the process of establishing the multi-task stage degradation impact model specifically includes: based on the nonlinear Wiener process, the degradation process of the multi-task stage industrial robot is modeled to form a multi-task stage degradation model. In the degradation process of the multi-task stage, the degradation rate depends on the task stage, and the degradation rate of the multi-task stage industrial robot system is constant under the same task stage. Based on this, the system t The degradation at the moment can be expressed as: Where: X ( t )for t The amount of system degradation at time ; β is the drift coefficient, which characterizes the degradation rate; σ is the diffusion coefficient, which is used to describe the impact of random factors such as external noise on product performance. This model assumes that the product failure mechanism remains unchanged under different stress levels. σ constant; x (0) is t = the initial degradation amount at time 0; B ( t ), t >0 conforms to the Wiener process, which is a standard Brownian motion; σB ( t ) is subject to N (0, σ 2 t )’s normal distribution; Based on the multi-task stage degradation model, and taking into account the influence of random shocks caused by load transformation, emergency stop and reversing when the industrial robot switches different task stages, a multi-task stage degradation shock model is established. The multi-task stage degradation shock model uses a Poisson process to describe the occurrence of random shocks, and takes the influence of random shocks as the cumulative degradation increment of the system. The multi-task stage degradation shock model can be expressed as: Where: M ( t )for t The overall degradation of the system at that moment; X ( t )for t The overall natural degradation of the system at that moment; N c ( t ) indicates the deadline t the number of non-lethal impacts; Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is Normal distribution of In the overall degradation i Mission stage n The degradation of the secondary circulation system is: Where: M in ( t ) is the i Mission stage n The overall degradation of the subcirculatory system; X in ( t ) is the i Mission stage n The amount of natural degradation of the subcirculatory system; N c ( t ) indicates the deadline t the number of non-lethal impacts; Y i ( i =1,2,3,…) means the i The incremental degradation caused by a shock to the degradation process, Y i ( i =1,2,3,…) is subject to the mean , the variance is The normal distribution of .

5. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In step S5, unknown parameters in the model are estimated using a maximum likelihood estimation method.

6. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In step S6, the failure threshold set ω The expression is as follows: Where: ω m The system is m The failure threshold of the task stage, the system has m A task phase.

7. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In step S7, the reliability function is expressed as follows: Where: Indicated in i Mission Phase n After the cycle, the probability that the overall degradation of the system under the influence of natural degradation of the system and the impact of different task switching and external environmental impact does not exceed the failure threshold of the task stage; Indicates that the deadline t The probability that the number of fatal impacts received by the system at any given moment is 0.

8. The performance prediction method of an industrial robot joint according to claim 1, characterized in that: In step S8, the estimation result of the unknown parameter is compared with the failure threshold set ω Substituted into the reliability function, we can get the system n The second cycle, i The reliability curve of each task stage is n The second cycle, i The reliability curves obtained in the task stages are integrated to obtain a first reliability curve, where the first reliability curve is a reliability curve of the industrial robot system.

9. A terminal device, characterized in that: The performance prediction device of the industrial robot joint comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor; When the computer program is executed by the processor, the steps of the method for predicting the performance of an industrial robot joint according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a performance prediction program for an industrial robot joint, and when the performance prediction program for an industrial robot joint is executed by a processor, the steps of the performance prediction method for an industrial robot joint as described in any one of claims 1 to 8 are implemented.

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