CN113221372B - BDD-based industrial robot PMS system reliability analysis method - Google Patents
BDD-based industrial robot PMS system reliability analysis method Download PDFInfo
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Abstract
The application discloses a reliability analysis method for a PMS system of an industrial robot based on BDD. The method comprises the following steps: dividing work tasks of the PMS of the industrial robot to obtain different task stages; dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage; establishing a reliability model of each task stage of the PMS, and calculating failure rates of each subsystem in different task stages under different working modes; calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; and calculating the sum of the failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot. Through before industrial robot produces, carry out the analysis to industrial robot's reliability, judge whether the robot can accomplish the production task smoothly.
Description
Technical Field
The disclosure relates to the technical field of industrial robot task reliability analysis, in particular to a BDD-based reliability analysis method for a PMS (permanent magnet system) of an industrial robot.
Background
Industrial robots are multi-joint manipulators or multi-degree-of-freedom machine devices widely used in the industrial field, and can automatically perform work and realize various industrial processing and manufacturing functions by means of the power energy and control capability of the industrial robots. Without the participation of people, various production operations such as carrying, welding, assembling, spraying, polishing, deburring and the like are realized. However, the long-time task execution and the continuous action of various loads can cause the performance of the robot to be reduced, even to have faults or failures, can cause the delay or failure of tasks, damage of products to different degrees, can become a potential danger area in the motion envelope range of the robot due to the multiple degrees of freedom of the robot, and serious faults can cause the injury of personnel and the damage of the environment. Therefore, various factors which can cause the robot to fail in the production process need to be considered, and the capability of the robot to complete the production of the product under specified conditions and within specified time needs to be estimated.
The traditional research on the reliability of industrial robots is centered on a robot system to establish a reliability model. And (3) disassembling the robot from the whole to the local by using a fault tree analysis method, determining the influence weight of faults of each part and each component unit on the reliability of the system, and evaluating the reliability of the system according to corresponding parameters such as service life distribution of each part of the component units. And (3) guidance for strengthening and improving parts which have great influence on the reliability of the system during the initial design of the robot. In the actual production, the bearing capacity and the performance of the part are dynamic, and the overall service life of the part is influenced by factors such as different production environments, loads and the like, such as the ambient temperature, the mutual interference among a plurality of robots in a production line, overload work and the like. These random factors are difficult to calculate quantitatively when building a reliability model centered on the system.
Therefore, a reliability analysis method for a BDD-based industrial robot PMS system is provided to solve the problem that the traditional reliability model is difficult to quantitatively calculate random influence factors.
Disclosure of Invention
In view of the above defects or shortcomings in the prior art, it is desirable to provide a reliability analysis method for a BDD-based industrial robot PMS system, which optimizes dynamic analysis of parts, accurately predicts the probability of successful completion of a production task before production, and ensures the operational reliability of an industrial robot.
In a first aspect, the application provides a reliability analysis method for a PMS system of a BDD-based industrial robot, comprising the following steps:
dividing work tasks of a PMS (permanent magnet synchronous machine) system of the industrial robot to obtain different task stages, and acquiring preset work parameters of each task stage;
dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage;
based on the BDD and the working topological graph of each task phase, establishing a reliability model of each task phase of the PMS of the industrial robot, and calculating the failure rate of each subsystem in different task phases under different working modes;
calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; and calculating the sum of the failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot.
According to the technical scheme provided by the embodiment of the application, the acquiring of the preset working parameters of each task stage comprises the following steps:
acquiring the initial working speed of the PMS system of the industrial robot, and calculating the initial working time of the PMS system of the industrial robot based on the negative correlation of the working speed and the working time;
determining the time point of the change of the working index of each subsystem in different task stages as a time division point;
and dividing the initial working time of the PMS of the industrial robot according to the time dividing points to obtain the initial working time corresponding to each task stage.
According to the technical scheme provided by the embodiment of the application, the calculating the failure rate of each subsystem in different task stages under different working modes comprises:
establishing failure functions of the subsystems in different task phases under different working modes, wherein the failure functions are expressed as Qi =1-Ri =1-Rs (t), (i =1,2,3,4, 5);
qi represents that the industrial robot PMS system fails in the i stage; ri indicates that the industrial robot PMS system works normally in the ith stage;
and calculating the failure rate of each subsystem in different task stages under different working modes.
According to the technical scheme provided by the embodiment of the application, the product of the failure rates of the subsystems at different task stages is calculated to obtain the failure rates at different task stages; calculating the sum of failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot, wherein the method comprises the following steps:
the failure rate function of different task stages is
When i =1, q 1 ＝1-p 1 ；
When i is greater than or equal to 2, q i ＝(1-∑q i-1 )(1-p i )；
Total failure rate function of PMS system of industrial robot
q i Represents R i Probability of failure at the ith task stage; p is a radical of formula i Represents R i Probability of working at the ith task stage;
calculating the total failure rate of the PMS system of the industrial robot;
the reliability function of the industrial robot PMS system is
r is the reliability of the industrial robot PMS system;
and calculating the reliability of the PMS system of the industrial robot.
In conclusion, the technical scheme specifically discloses a specific flow of the reliability analysis method for the industrial robot PMS system based on the BDD. The method comprises the steps of dividing a work task into different task stages, dividing a PMS (permanent magnet system) of the industrial robot into different subsystems, obtaining the subsystems working in different stages and establishing working topological diagrams in all stages; calculating failure rates of all stages by establishing BDD models of all stages, obtaining the failure rate of the PMS of the industrial robot by multiplying the failure rates of all stages, and finally calculating the reliability of the PMS of the industrial robot; through before industrial robot produces, carry out the analysis to industrial robot's reliability, judge whether the robot can accomplish the production task smoothly.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a schematic flow chart of a reliability analysis method for a BDD-based industrial robot PMS system.
Fig. 2 is a schematic diagram of the BDD model of the motion phase when the subsystems are connected in series mode.
FIG. 3 is a schematic diagram of a BDD model of the task execution phase when subsystems are connected in series mode.
FIG. 4 is a schematic diagram of the BDD model for the migration phase when the subsystems are connected in series-parallel-series mode when there is redundancy in the powered system.
FIG. 5 is a schematic diagram of a BDD model of the task execution phase when the subsystems are connected in series-parallel hybrid mode when redundancy exists in the power system.
Fig. 6 is a schematic diagram showing the relationship between the working time and the rotation angle when the six-axis robot performs the grinding task.
Fig. 7 is a schematic diagram of the relationship between the working time and the angular velocity when the six-axis robot performs the grinding task.
Fig. 8 is a schematic diagram of the operation of each axis of the six-axis robot at the initial position, the feed point.
Fig. 9 is a schematic diagram of the six axis robot working at the feed point-the start of the sharpening for each axis.
Fig. 10 is a schematic diagram of the operation of each axis of the six-axis robot from the start of grinding to the end of grinding.
FIG. 11 is a schematic diagram of the six-axis robot at the finish-retract point for each axis.
Fig. 12 is a schematic diagram of the six-axis robot in operation with each axis at the tool retracting point-initial position.
Fig. 13 is a schematic diagram of a BDD model of a six-axis robot at an initial position-point of approach phase.
Fig. 14 is a schematic diagram of a BDD model of a six-axis robot at the feed point-grind start phase.
Fig. 15 is a schematic diagram of a BDD model of a six-axis robot at the start-finish of a sanding phase.
Fig. 16 is a schematic diagram of a BDD model of a six-axis robot at the end-of-grind-retract-point phase.
Fig. 17 is a schematic diagram of a BDD model of a six-axis robot at the retract point-initial position stage.
Fig. 18 is a schematic view of the BDD model when the industrial robot PMS system is in a moving phase.
Fig. 19 is a schematic diagram of the BDD model when the industrial robot PMS system is in the task execution phase.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Please refer to fig. 1, which is a schematic flow chart of a reliability analysis method for a PMS system of a BDD-based industrial robot, comprising the following steps:
dividing work tasks of a PMS (permanent magnet synchronous machine) system of the industrial robot to obtain different task stages, and acquiring preset work parameters of each task stage;
dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage;
based on the BDD and the working topological graph of each task phase, establishing a reliability model of each task phase of the PMS of the industrial robot, and calculating the failure rate of each subsystem in different task phases under different working modes;
calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; and calculating the sum of the failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot.
In the embodiment, the work tasks of the industrial robot PMS are divided to obtain different task stages, and preset work parameters of each task stage are obtained;
specifically, the method comprises the following steps:
in actual use, the industrial robot PMS system is susceptible to the influences of factors such as external environment, production use, human misoperation, overload work and the like, so that the data of the reliability of the traditional computing system cannot accurately reflect the service life of the industrial robot PMS system, and equipment with faults in the production process cannot be replaced or maintained in time, and the production efficiency is influenced;
through before industrial robot produces, carry out the analysis to industrial robot's reliability, judge whether the robot can accomplish the production task smoothly.
Dividing the work tasks of the industrial robot PMS according to the work process of the industrial robot PMS to obtain different task stages, and acquiring preset work parameters of each task stage;
wherein, the task stage has 5 stages, includes in proper order: a starting stage, a preparation stage, a task execution stage, a reset stage and an ending stage;
in the starting stage, the preparation stage, the resetting stage and the ending stage, subsystems of the industrial robot PMS system in working states are consistent, and the four stages can be classified into a moving stage;
the preset working parameters comprise: the working speed and the corresponding working time are preset.
Dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage;
specifically, the method comprises the following steps:
dividing a PMS system of the industrial robot according to the failure characteristics and task execution conditions of the industrial robot to obtain four subsystems, wherein the subsystems are respectively a control system, a servo motor, a speed reducer and a tail end execution system; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage;
wherein, the subsystems corresponding to the moving stage are a control system, a servo motor and a reducer;
the subsystems of the corresponding work in the task execution stage are a control system, a servo motor, a speed reducer and a tail end execution system.
Based on the BDD and the working topological graph of each task phase, establishing a reliability model of each task phase of the PMS of the industrial robot, and calculating the failure rate of each subsystem in different task phases under different working modes;
specifically, the method comprises the following steps:
the BDD model is a directed acyclic graph which comprises terminal nodes and non-terminal nodes, and the nodes are connected by means of 1 or 0 branches; the non-terminal node represents a basic event, the terminal node represents the state of the basic event, and the state has two states, wherein 0 represents that the basic event is normal, and 1 represents that the basic event is invalid;
when the industrial robot PMS system is in a moving stage, the subsystems in working states in the moving stage are a control system, a servo motor and a speed reducer, the three subsystems are in working states and used as basic events of the system, a BDD model is built, as shown in FIG. 18,
only when all the three subsystems are in a normal working state, the system is in the normal working state in the moving stage;
when the PMS system of the industrial robot is in a task execution stage, the subsystems in working states in the stage are a control system, a servo motor, a reducer and an end execution system, the four subsystems are in working states to serve as basic events of the system, a BDD model is built, as shown in figure 19,
and only when all the four subsystems are in a normal working state, the system is in the normal working state in the task execution stage.
Dividing the system into four working modes according to different connection relations of the four subsystems; the four operating modes include: a series mode, a parallel mode, a voting mode, and a series-parallel module;
establishing a failure function of each subsystem in different task phases under different working modes, wherein the failure function is expressed as Qi =1-Ri =1-Rs (t), (i =1,2,3,4, 5);
qi represents that the industrial robot PMS system fails in the i stage; ri represents that the industrial robot PMS works normally in the ith stage;
calculating the failure rate of each subsystem in different task stages under different working modes;
when the working mode of the PMS system of the industrial robot is a serial mode, the function of the fault occurring to the task is
When the working mode of the PMS system of the industrial robot is in a parallel mode, the function of the fault of the task is
When the working mode of the PMS system of the industrial robot is a voting mode, the function of the fault of the task is
When the subsystems are connected in a series mode, the subsystem is a BDD model in a moving phase as shown in fig. 2, and is a BDD model in a task execution phase as shown in fig. 3, and K, S, J and M respectively represent a control system, a servo motor, a reducer and an end effector; 0 represents system operation, 1 represents system failure;
when the power system has redundancy and the subsystems are connected in a series-parallel mode, the BDD model in the moving stage is shown in fig. 4, the BDD model in the task execution stage is shown in fig. 5, and K, S, J and M respectively represent a control system, a servo motor, a reducer and an end effector; 0 indicates system operation and 1 indicates system failure.
Calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; calculating the sum of failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot;
specifically, the method comprises the following steps: the failure rate function of different task stages is
When i =1, q 1 ＝1-p 1 ；
When i is greater than or equal to 2, q i ＝(1-∑q i-1 )(1-p i )；
Total failure rate function of industrial robot PMS system
q i Represents R i Probability of failure at the ith task stage; p is a radical of i Represents R i Probability of working at the ith task stage;
the reliability function of the industrial robot PMS system is
r is the reliability of the industrial robot PMS system;
and calculating the reliability of the PMS system of the industrial robot.
Example one
Based on the content, dividing the work tasks of the PMS of the industrial robot to obtain different task stages;
acquiring the initial working speed of the PMS system of the industrial robot, and calculating the initial working time of the PMS system of the industrial robot based on the negative correlation of the working speed and the working time;
determining the time point of the change of the working index of each subsystem in different task stages as a time division point;
dividing the initial working time of the PMS of the industrial robot according to the time dividing points to obtain the initial working time corresponding to each task stage;
dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems working correspondingly at different task stages, and establishing a working topological graph of each task stage;
based on the BDD and the working topological graph of each task phase, establishing a reliability model of each task phase of the PMS of the industrial robot, and calculating the failure rate of each subsystem in different task phases under different working modes;
calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; and calculating the sum of the failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot.
The specific analysis process is as follows:
taking a six-axis robot as an example,
the system comprises four subsystems, namely a control system, a servo motor, a speed reducer and a tail end execution system which are sequentially represented as K, S, J and M; each subsystem is connected in series;
five task stages of the grinding process of the six-axis robot are respectively as follows: initial position-feed point phase; feed point-grinding start stage; starting polishing and finishing polishing; finishing polishing and withdrawing a tool point; retracting point-initial position stage;
the initial position-feed point stage, the feed point-grinding start stage, the grinding end-retraction point stage and the retraction point-initial position stage are classified into a moving stage;
a polishing starting stage and a polishing finishing stage are task execution stages;
the six-axis robot was selected for 75% sanding speed and the working time for each task stage was obtained, as shown in the table below,
the reliability model of the moving stage is as follows:
R＝R K R S R J
the reliability model of the task execution stage is as follows:
R＝R K R S R J R M
as shown in fig. 6 and 7, the relationship between the working time and the rotation angle and the relationship between the working time and the angular velocity when the robot performs the grinding task are shown;
setting parameters of a PMS reliability model according to the working conditions of six axes of the robot at each stage, wherein the parameters are respectively shown in FIG. 8, FIG. 9, FIG. 10, FIG. 11 and FIG. 12;
at the stage of feed point-grinding start stage and grinding end-tool withdrawal point, the shaft J 1 Not working; the middle shaft J at the grinding beginning-grinding ending stage 5 Not working;
the failure rates of each subsystem at different mission stages, as shown in the table below,
wherein, the stage 1, the stage 2, the stage 3, the stage 4 and the stage 5 respectively represent an initial position-feed point stage, a feed point-grinding starting stage, a grinding starting-grinding finishing stage, a grinding finishing-retracting point stage and a retracting point-initial position stage;
λ S ＝nλ Sj wherein λ is Sj The failure rate of a j axis in a working state at the current stage is shown, n is the number of working axes at the current stage, and n =6 in the initial position-feed point stage and the tool withdrawal point-initial position stage; in the feed point-grinding start stage, grinding start-grinding end stage and grinding end-retracting point stage, n =5.
As shown in fig. 13, 14, 15, 16, and 17, which are BDD models of six-axis robots at each stage, where 0 indicates success and 1 indicates failure;
failure rate of each stage is
The six-axis robot has the reliability of
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by a person skilled in the art that the scope of the invention according to the present application is not limited to the specific combination of the above-mentioned features, but also covers other embodiments where any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.
Claims (4)
1. A reliability analysis method for a PMS system of an industrial robot based on BDD is characterized by comprising the following steps:
dividing work tasks of a PMS (permanent magnet synchronous machine) system of the industrial robot to obtain different task stages, and acquiring preset work parameters of each task stage; the task stage comprises five stages, including a start stage, a preparation stage, a task execution stage, a reset stage and an end stage in sequence;
wherein, the starting phase, the preparation phase, the resetting phase and the ending phase are classified into a moving phase;
dividing a PMS system of the industrial robot to obtain four subsystems; acquiring subsystems in working states at different task stages, and establishing a working topological graph of each task stage; the subsystems are respectively a control system, a servo motor, a speed reducer and a tail end execution system;
the subsystems corresponding to the moving stage are a control system, a servo motor and a speed reducer; the subsystems of the work corresponding to the task execution stage are a control system, a servo motor, a speed reducer and a tail end execution system;
based on the BDD and the working topological graph of each task phase, establishing a reliability model of each task phase of the PMS of the industrial robot, and calculating failure rates of each subsystem in different task phases under different working modes;
when the industrial robot PMS system is in a moving stage and all three subsystems in the moving stage are in a normal working state, the industrial robot PMS system is in the normal working state in the moving stage;
when the industrial robot PMS system is in a task execution stage and all four subsystems in the task execution stage are in a normal working state, the industrial robot PMS system is in the normal working state in the task execution stage;
calculating the product of the failure rates of the subsystems at different task stages to obtain the failure rates at different task stages; calculating the sum of failure rates of different task stages to obtain the total failure rate of the PMS of the industrial robot, and calculating the reliability of the PMS of the industrial robot; through before industrial robot produces, obtain industrial robot PMS system's reliability, judge whether industrial robot can accomplish the production task.
2. The method for analyzing the reliability of the BDD-based PMS system of the industrial robot is characterized in that the step of obtaining the preset working parameters of each task phase comprises the following steps:
acquiring the initial working speed of the PMS system of the industrial robot, and calculating the initial working time of the PMS system of the industrial robot based on the negative correlation of the working speed and the working time;
determining the time point of the change of the working index of each subsystem in different task stages as a time division point;
and dividing the initial working time of the PMS of the industrial robot according to the time division points to obtain the initial working time corresponding to each task stage.
3. The reliability analysis method for the BDD-based industrial robot PMS system according to claim 1, wherein said calculating failure rates of subsystems in different task phases under different working modes comprises:
establishing failure functions of subsystems at different task stages under different working modes, wherein the failure functions are expressed as Q i ＝1-R i ＝1-R s (t)，(i＝1，2，3，4，5)；
Q i Indicating that the industrial robot PMS system failed in phase i; r is i Indicating that the industrial robot PMS system is working normally in phase i;
and calculating the failure rate of each subsystem in different task stages under different working modes.
4. The reliability analysis method for the BDD-based PMS system of the industrial robot is characterized in that the product of the failure rates of the subsystems in different task stages is calculated to obtain the failure rates of the different task stages; calculating the sum of the failure rates of different task stages to obtain the total failure rate of the PMS system of the industrial robot, and calculating the reliability of the PMS system of the industrial robot, wherein the calculation comprises the following steps:
the failure rate function of different task stages is
When i =1, q 1 ＝1-p 1 ；
When i is greater than or equal to 2, q i ＝(1-∑q i-1 )(1-p i )；
Total failure rate function of PMS system of industrial robot
q i Represents R i Probability of failure at the ith task stage; p is a radical of i Represents R i Probability of working at the ith task stage;
calculating the total failure rate of the PMS system of the industrial robot;
the reliability function of the industrial robot PMS system is
r is the reliability of the industrial robot PMS system;
and calculating the reliability of the PMS system of the industrial robot.
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