Hot rolling production line reliability evaluation method considering fuzzy maintenance quality

Abstract

A hot rolling production line reliability evaluation method considering fuzzy maintenance quality comprises the steps of determining task requirements of a hot rolling production line of a series structure and key machines in the series structure, identifying key processes of the hot rolling production line and collecting process data; estimating a performance state distribution function of each machine in the series structure and evaluating a fuzzy maintenance quality parameter of each machine so as to construct a random fuzzy flow manufacturing network model of the hot rolling production line; a subtask demand set in the random fuzzy flow network model is determined by analyzing a proportion set in the model, then a reliability model of each machine considering fuzzy maintenance quality is established, finally a reliability model of the hot rolling production line is established based on a structural relationship between machines in the hot rolling production line, and the reliability of the hot rolling production line is realized under the condition that the production demand is known. And analyzing the dynamic evolution trend of the performance in the production process of the hot rolling production line, and quantifying the production process of the hot rolling production line. According to the method, the production process of the hot rolling production line is simplified through the random fuzzy flow manufacturing network, the performance state change rule of the hot rolling production line is analyzed, the reliability is represented through opportunity measurement indexes based on the Markov model and fuzzy mathematics, and the result accuracy is remarkably improved.

Description

Technical Field

[0001] The present invention relates to a technology in the field of fuzzy theory, in particular to a hot rolling production line reliability assessment method considering fuzzy maintenance quality. Background Art

[0002] Existing hot rolling production line reliability assessment methods cannot characterize the degradation characteristics of machines under the combined effects of fuzziness and randomness, let alone describe the intrinsic relationship between production tasks, machines, products and maintenance. At the same time, due to the high degree of complexity of modern hot rolling production lines, it is more difficult to understand the operation mechanism and performance degradation law of hot rolling production lines. Summary of the invention

[0003] In view of the problem that the existing hot rolling production line reliability assessment method cannot accurately assess the operating status and cannot solve the reliability assessment under the fuzzy maintenance quality due to ignoring the fuzzy maintenance quality, the present invention proposes a hot rolling production line reliability assessment method considering fuzzy maintenance quality. Based on the polymorphic system reliability theory and the operating characteristics of the hot rolling production line, the system hot rolling production line reliability is investigated, the production process of the hot rolling production line is simplified through a random fuzzy flow manufacturing network, the performance state change law is analyzed, and the reliability is characterized by a chance measurement indicator based on a Markov model and fuzzy mathematics, which significantly improves the accuracy of the result.

[0004] The present invention is achieved through the following technical solutions:

[0005] The present invention relates to a hot rolling production line reliability assessment method considering fuzzy maintenance quality, which includes determining the task requirements of a hot rolling production line with a series structure and key machines in the series structure, identifying key processes of the hot rolling production line and collecting process data, estimating the performance state distribution function of each machine in the series structure and evaluating the fuzzy maintenance quality parameters of each machine, thereby constructing a random fuzzy flow manufacturing network model of the hot rolling production line; determining a subtask requirement set in the random fuzzy flow network model by analyzing a proportion set in the model, and then establishing a reliability model of each machine considering the fuzzy maintenance quality; finally, establishing a reliability model of the hot rolling production line based on the structural relationship between machines in the hot rolling production line, thereby realizing the analysis of the dynamic evolution trend of the performance during the production process of the hot rolling production line and quantifying the production process of the hot rolling production line when the production requirements are known.

[0006] The capacities of different arcs in the random fuzzy flow manufacturing network model are statistically independent and all nodes are reliable nodes, that is, only arcs are considered.

[0007] The performance status of the machine refers to the maximum workload it can withstand.

[0008] The degradation process of the hot rolling production line obeys a homogeneous Markov process, that is, the state at the current moment is only related to the state at the previous moment, but has nothing to do with the previous state; and the transfer intensity between states is known, specifically: the output of machine No. 1 has two quality states, qualified and unqualified, and only the output in the qualified state can be transmitted to the next machine; the relationship between the degradation process before and after the machine maintenance obeys a quasi-update process.

[0009] The membership functions of the fuzzy maintenance quality parameters are all triangular membership functions.

[0010] The determination of the task requirements and related key machines of the hot rolling production line refers to: determining the key machines required for reliability evaluation based on the task requirements (including product quantity and quality requirements).

[0011] The identifying of the key processes of the hot rolling production line and collecting process data refers to: for the obtained key machines, identifying the key process flows that affect the reliability of the hot rolling production line, and then collecting relevant machine performance, process quality and maintenance record data.

[0012] The performance state distribution function of each machine is based on the fact that the performance state degradation of the machine in the series structure obeys the homogeneous Markov process, and is obtained by the Kolmogorov differential equation system dp l (t) / dt=p l (t)X i , find the performance state probability function of each machine at time t, where: S l is the state set of machine l, X l is the transfer intensity matrix of machine l, p l (t) is the state probability vector of machine l at time t.

[0013] The fuzzy maintenance quality parameters of each machine are based on the fact that the degradation process of the machines in the series structure before and after maintenance follows a quasi-update process, that is, Combine the collected performance degradation data with maintenance record data to evaluate the maintenance quality parameters of machine l in: and are the probability functions that machine l is in state j at time t before the first and kth maintenance, respectively.

[0014] The random fuzzy flow manufacturing network model refers to: according to the structural relationship between key machines and machines and the machine performance degradation function under the consideration of fuzzy maintenance quality, the hot rolling production line is converted into a random flow manufacturing network model to abstractly characterize the production process of the hot rolling production line for subsequent reliability evaluation.

[0015] The random fuzzy flow manufacturing network model includes: a directed arc set and a ratio set, wherein: the directed arc set is abstractly characterized according to the determined key processing machines to obtain the directed arc set, and the ratio set is calculated according to the product production qualification rate information and the process flow information between the key processing machines, and the qualification rate, the unqualified rate and the material transfer ratio are calculated to obtain the ratio set.

[0016] The construction is specifically as follows: the processing machine is abstractly represented as a directed arc, the input / output node of the machine is abstractly represented as a circle, the output material flow of the machine is abstractly represented as a double circle, and the machines are connected according to the production and processing relationship between the machines to construct a random fuzzy flow manufacturing network model. The directed dotted arcs used for connection represent the transfer of material flow between machines, and the direction of the dotted arcs represents the transfer direction of the material flow.

[0017] The ratio set is: the ratio set is P = {q l,1 ,q l,2 ,pl ,j}, where: q l,1 and q l,2 are the probabilities that the material flow output by machine l is in qualified state and unqualified state, q l,1 and q l,2 Determined based on process quality data; l,j The proportion of qualified material flow output by machine l that is passed to machine j.

[0018] The subtask requirement set refers to: when the production task requirement D is known, the input requirement of machine l is The output requirement is Where: L is the total number of machines in the hot rolling production line.

[0019] The reliability function Where: the input material flow f of machine l at time t after the kth maintenance l,1(k) (t), which is less than the input requirement of machine l The opportunity measure is Where: Cr(g) is the confidence function, Ψ is the possibility space, and the failure probability function with confidence level δ 1(g) is a judgment function, 1(true)=1, 1(false)=0.

[0020] The overall reliability function is Among them: the hot rolling production line is composed of L machines connected in series.

[0021] The present invention relates to a system for realizing the above method, comprising: a machine transfer intensity matrix estimation module, a fuzzy maintenance quality parameter estimation module, a random fuzzy flow manufacturing network model parameter evaluation module and a hot rolling production line reliability evaluation module, wherein: the machine state transfer intensity matrix estimation module uses the performance degradation data of the machine without maintenance to perform transfer intensity parameter estimation processing between any two states to obtain the machine's transfer intensity matrix; the fuzzy maintenance quality parameter estimation module uses the maintenance record information and the performance degradation data of the machine after maintenance to statistically compare the numerical changes of the transfer intensity matrix before and after maintenance, and performs fuzzification processing on the statistical results to obtain the fuzzy maintenance quality parameters; the random fuzzy flow manufacturing network model parameter evaluation module obtains the product qualification rate, product failure rate and material transfer ratio according to the process quality information and process flow information, and obtains the parameters in the random fuzzy flow manufacturing network model; the hot rolling production line reliability evaluation module uses the calculation results obtained by the machine transfer intensity matrix estimation module, the fuzzy maintenance quality parameter estimation module and the random fuzzy flow manufacturing network model parameter evaluation module to perform the above reliability evaluation technology processing to obtain the hot rolling production line reliability evaluation result. Technical Effects

[0022] The present invention establishes a random fuzzy flow manufacturing network model as a tool to abstractly represent the production process of the hot rolling production line, so as to accurately explain the operation mechanism of the hot rolling production line; based on the random fuzzy theory, the present invention evaluates the reliability of the hot rolling production line under the influence of fuzzy maintenance quality and inherent randomness. Compared with the prior art, the present invention significantly improves the accuracy of the hot rolling production line reliability evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of a random fuzzy flow manufacturing network model of a hot rolling production line of the present invention;

[0024] In the figure: (a) is a three-machine series-parallel system; (b) is a schematic diagram of a random fuzzy flow manufacturing network model of a three-machine series-parallel system;

[0025] Figure 2 It is a flow chart of the present invention;

[0026] Figure 3 This is a schematic diagram of the key machines of the 1580 hot rolling production line;

[0027] Figure 4 This is a schematic diagram of the random fuzzy flow manufacturing network model of the 1580 hot rolling production line;

[0028] Figure 5 Schematic diagram of machine reliability model considering fuzzy maintenance quality;

[0029] In the figure: (a) is the reliability model of machine 1 after the first, second and third maintenance; (b) is the reliability model of machine 2 after the first, second and third maintenance; (c) is the reliability model of machine 3 after the first, second and third maintenance;

[0030] Figure 6 Schematic diagram of the reliability model of the 1580 hot rolling manufacturing line considering fuzzy maintenance quality. DETAILED DESCRIPTION

[0031] like Figure 2 As shown, this embodiment relates to a hot rolling production line reliability assessment method considering fuzzy maintenance quality, including:

[0032] Step 1: Determine the mission requirements and related key machines of a company's 1580 hot rolling production line. After determining the mission requirements (including product quantity and quality requirements), determine the key machines required for reliability evaluation, including roughing mill (machine 1), finishing mill (machine 2) and coiling mill (machine 3). The key machines of the 1580 hot rolling production line are as follows: Figure 3 shown.

[0033] Step 2: Identify the key processes of a company's 1580 hot rolling production line and collect relevant data. There are three key processes in the 1580 hot rolling production line, namely, strip surface flatness, strip surface roughness, and strip surface defect count. Collect relevant data on this basis.

[0034] Step 3 estimates the performance state distribution function of each machine. The state set of each machine is shown in Table 1, and its corresponding transfer intensity is shown in Table 2. Based on the Kolmogorov differential equation group, the performance state distribution function of each machine can be obtained.

[0035] Table 1. Machine state collection.

[0036] Table 2. Transfer intensity of each machine.

[0037] Step 4 evaluates the fuzzy maintenance quality parameters of each machine. They are (0.82, 0.85, 0.95), (0.82, 0.85, 0.95), and (0.82, 0.85, 0.95) respectively.

[0038] Step 5 builds a random fuzzy flow manufacturing network model for hot rolling production line. Combined with steps 1 to 4, the following is constructed: Figure 4 The random fuzzy flow manufacturing network model shown in Figure a lFor the machine l in the hot rolling production line, Create an input node with directed edge arc l in the random fuzzy flow network model, For random fuzzy flow production network model, the output node of the directed edge arc l, p l,j is the proportion of material transferred from machine l to machine j, q l,1 is the probability that the quality of the output product of machine l is in a qualified state, q l,2 is the probability that the quality of the output product of machine l is unqualified, f l is the material flow rate flowing into machine l.

[0039] Step 6 analyzes the ratio set in the random fuzzy flow network model. The flow transfer ratio between machines is p 1,2 =p 2,3 =1. Because for the same machine, q l,1 and q l,2 The sum is 1. l,1 They are 0.98, 0.97 and 0.99 respectively.

[0040] Step 7 determines the subtask requirement set in the random fuzzy flow network model. Taking the task requirement D = 60 tons of thin and wide strip steel / day as an example, in order to complete the production task requirement, the input requirements of each machine are

[0041] Step 8: Establish the reliability model of each machine considering the fuzzy maintenance quality. The reliability model of each machine under different maintenance times k is as follows: Figure 5 shown.

[0042] Step 9 establishes a reliability model of the hot rolling production line based on the structural relationship between the machines in the hot rolling production line. Figure 6 As shown, a reliability model of the 1580 hot rolling production line is established.

[0043] Compared with the existing technology, this method takes into account the influence of fuzzy maintenance quality and improves the accuracy of hot rolling production line reliability assessment results.

[0044] The above-mentioned specific implementation can be partially adjusted in different ways by those skilled in the art without departing from the principle and purpose of the present invention. The protection scope of the present invention shall be based on the claims and shall not be limited by the above-mentioned specific implementation. Each implementation scheme within its scope shall be subject to the constraints of the present invention.

Claims

1. A hot rolling production line reliability assessment method considering fuzzy maintenance quality, characterized in that: By determining the task requirements of the hot rolling production line with a series structure and the key machines in the series structure, identifying the key processes of the hot rolling production line and collecting process data, estimating the performance state distribution function of each machine in the series structure and evaluating the fuzzy maintenance quality parameters of each machine, a random fuzzy flow manufacturing network model of the hot rolling production line is constructed; by analyzing the proportion set in the model, the sub-task requirement set in the random fuzzy flow network model is determined, and then a reliability model considering the fuzzy maintenance quality of each machine is established. Finally, a reliability model of the hot rolling production line is established based on the structural relationship between the machines in the hot rolling production line. When the production demand is known, the dynamic evolution trend of the performance of the hot rolling production line in the production process is analyzed, and the production process of the hot rolling production line is quantified; The subtask requirement set refers to: when the production task requirement D is known, the input requirement of machine l is The output requirement is Where: L is the total number of machines in the hot rolling production line; The reliability function Where: the input material flow f of machine l at time t after the kth maintenance l,1(k) (t), which is less than the input requirement of machine l The opportunity measure is Where: Cr(g) is the confidence function, Ψ is the possibility space, and the failure probability function with confidence level δ 1(g) is the judgment function, 1(true) = 1, 1(false) = 0; The overall reliability function is Among them: the hot rolling production line is composed of L machines connected in series.

2. The hot rolling production line reliability assessment method considering fuzzy maintenance quality according to claim 1 is characterized in that: The capacities of different arcs in the random fuzzy flow manufacturing network model are statistically independent and all nodes are reliable nodes, that is, only arcs are considered; The performance status of the machine mentioned refers to the maximum workload it can withstand; The degradation process of the hot rolling production line obeys the homogeneous Markov process, that is, the state at the current moment is only related to the state at the previous moment, but not to the previous state; and the transfer strength between states is known, specifically: the output of the No. 1 machine has two quality states, qualified and unqualified, and only the output in the qualified state can be transmitted to the next machine; the relationship between the degradation process before and after the machine maintenance obeys the quasi-update process; The membership functions of the fuzzy maintenance quality parameters are all triangular membership functions; The determination of the mission requirements and related key machines of the hot rolling production line refers to: determining the key machines required for reliability evaluation based on the mission requirements.

3. The hot rolling production line reliability assessment method according to claim 1 or 2, characterized in that: The identifying of the key processes of the hot rolling production line and collecting process data refers to: for the obtained key machines, identifying the key process flows that affect the reliability of the hot rolling production line, and then collecting relevant machine performance, process quality and maintenance record data.

4. The hot rolling production line reliability assessment method according to claim 1 or 2, characterized in that: The performance state distribution function of each machine is based on the fact that the performance state degradation of the machine in the series structure obeys the homogeneous Markov process, and is obtained by the Kolmogorov differential equation system dp l (t)dt=p l (t)X i , find the performance state probability function of each machine at time t, where: S l is the state set of machine l, X l is the transfer intensity matrix of machine l, p l (t) is the state probability vector of machine l at time t.

5. The hot rolling production line reliability assessment method according to claim 1 or 2, characterized in that: The fuzzy maintenance quality parameters of each machine are based on the fact that the degradation process of the machines in the series structure before and after maintenance follows a quasi-update process, that is, Combine the collected performance degradation data with maintenance record data to evaluate the maintenance quality parameters of machine l in: and are the probability functions that machine l is in state j at time t before the first and kth maintenance, respectively.

6. The hot rolling production line reliability assessment method according to claim 1 or 2, characterized in that: The random fuzzy flow manufacturing network model refers to: according to the structural relationship between key machines and machines and the machine performance degradation function under the consideration of fuzzy maintenance quality, the hot rolling production line is converted into a random flow manufacturing network model, so as to abstractly represent the production process of the hot rolling production line for subsequent reliability evaluation; The random fuzzy flow manufacturing network model includes: a directed arc set and a ratio set, wherein: the directed arc set is abstractly characterized according to the determined key processing machines to obtain the directed arc set, and the ratio set is calculated according to the product production qualification rate information and the process flow information between the key processing machines, and the qualification rate, the unqualified rate and the material transfer ratio are calculated to obtain the ratio set.

7. The hot rolling production line reliability assessment method according to claim 6 and claim 1, wherein the method is characterized in that: The construction is specifically as follows: the processing machine is abstractly represented as a directed arc, the input / output node of the machine is abstractly represented as a circle, the output material flow of the machine is abstractly represented as a double circle, and the machines are connected according to the production and processing relationship between the machines to construct a random fuzzy flow manufacturing network model. The directed dotted arc used for connection represents the transfer of material flow between machines, and the direction of the dotted arc represents the transfer direction of the material flow; The ratio set is: the ratio set is P = {q l,1 ,q l,2 ,p l,j }, where: q l,1 and q l,2 are the probabilities that the material flow output by machine l is in qualified state and unqualified state, q l,1 and q l,2 Determined based on process quality data; l,j The proportion of qualified material flow output by machine l that is passed to machine j.

8. The hot rolling production line reliability assessment method considering fuzzy maintenance quality according to any one of claims 1 to 7, characterized in that: include: Step 1: Determine the mission requirements and related key machines of the hot rolling production line: After determining the mission requirements, determine the key machines required for reliability assessment, including roughing mill, finishing mill and coiling mill; Step 2: Identify the key processes of the hot rolling production line and collect relevant data: There are three key processes of the hot rolling production line, namely, strip surface flatness, strip surface roughness, and strip surface defect count. Collect relevant data on this basis; Step 3: Estimate the performance state distribution function of each machine. Based on the Kolmogorov differential equation group, the performance state distribution function of each machine can be obtained; Step 4: Evaluate the fuzzy maintenance quality parameters of each machine; Step 5, constructing a random fuzzy flow manufacturing network model of the hot rolling production line: combining steps 1 to 4, constructing a random fuzzy flow manufacturing network model; Step 6: Analyze the ratio set in the random fuzzy flow network model: the flow transfer ratio between machines is p 1,2 =p 2,3 =1, because for the same machine, q l,1 and q l,2 The sum of the sum is 1, and the q of each machine l,1 They are 0.98, 0.97, and 0.99 respectively; Step 7: Determine the subtask requirement set in the random fuzzy flow network model: Taking the task requirement D = 60 tons of thin and wide strip steel / day as an example, in order to complete the production task requirement, the input requirements of each machine are Step 8: Establish a reliability model of each machine considering the fuzzy maintenance quality; Step 9: Establish a reliability model of the hot rolling production line based on the structural relationship between machines in the hot rolling production line.

9. A system for implementing the hot rolling production line reliability assessment method considering fuzzy maintenance quality as described in any one of claims 1 to 8, characterized in that: include: The machine transfer intensity matrix estimation module, the fuzzy maintenance quality parameter estimation module, the random fuzzy flow manufacturing network model parameter evaluation module and the hot rolling production line reliability evaluation module include: the machine state transfer intensity matrix estimation module uses the performance degradation data of the machine without maintenance to perform transfer intensity parameter estimation processing between any two states to obtain the machine transfer intensity matrix; the fuzzy maintenance quality parameter estimation module uses the maintenance record information and the performance degradation data of the machine after maintenance to statistically compare the numerical changes of the transfer intensity matrix before and after maintenance, fuzzifies the statistical results, and obtains the fuzzy maintenance quality parameters; the random fuzzy flow manufacturing network model parameter evaluation module obtains the product qualification rate, product failure rate and material transfer ratio according to the process quality information and process flow information, and obtains the parameters in the random fuzzy flow manufacturing network model; the hot rolling production line reliability evaluation module uses the calculation results obtained by the machine transfer intensity matrix estimation module, the fuzzy maintenance quality parameter estimation module and the random fuzzy flow manufacturing network model parameter evaluation module to perform the above reliability evaluation technology processing to obtain the hot rolling production line reliability evaluation result.

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