Numerical control machine tool fault analysis method and terminal

Abstract

The invention discloses a numerical control machine tool fault analysis method and a terminal. Based on the machine tool fault data, performing statistics to obtain fault occurrence frequency and fault occurrence duration in a preset duration, and calculating average fault-free working time; constructing a multiple evaluation model according to the machine tool fault type, the machine tool fault grade and the average fault-free working time; a multi-evaluation model is trained by taking maximization of the fault severity rate and the optimal weight coefficient as targets; and performing fault analysis on the numerical control machine tool by using the trained multiple evaluation model. According to the method, the average fault-free working time is calculated based on the machine tool fault data, the multiple evaluation model is constructed based on the data as input, the maximum fault severity rate and the optimal weight coefficient are used as the target training model, and finally the trained model is used for fault analysis of the numerical control machine tool. The safety coefficient of the numerical control machine tool is generated, so that potential problems are found, and shutdown and productivity loss caused by faults are reduced.

Description

Technical Field

[0001] The invention relates to the field of fault analysis, and in particular to a fault analysis method and a terminal for a numerically controlled machine tool. Background Art

[0002] In modern manufacturing, CNC machine tools (CNC machine tools), as one of the core production equipment, are widely used in precision machining, automated production and other fields. With the increasing complexity of production processes and the continuous improvement of production efficiency requirements, the stability and fault warning of CNC machine tools are particularly important. Various faults may occur during the operation of the machine tool, including mechanical failure, electrical failure, control system failure, etc. These failures often have a direct impact on production progress, product quality and equipment life, and may even lead to production line shutdown and huge economic losses. Therefore, timely and accurate diagnosis of the fault type and severity of CNC machine tools, and taking appropriate maintenance or preventive measures are crucial to improving production efficiency, reducing maintenance costs and extending equipment life.

[0003] However, the existing fault analysis technology for CNC machine tools has the following problems: (1) Poor fault diagnosis accuracy: Traditional fault diagnosis methods mainly rely on manual experience and intuitive judgment, and are often difficult to adapt to the various, complex and changeable machine tool faults. Empirical diagnosis methods are not only easily affected by the personal ability of operators, but also often find it difficult to find the root cause of the problem in time when faced with sudden faults, resulting in low maintenance efficiency. (2) Fault analysis methods are highly limited: Many current fault analysis methods mainly focus on the processing of a single fault type, ignoring the complexity of different fault types, levels and their mutual influence. This makes it difficult for a single fault analysis method to meet the changing fault diagnosis needs in a complex industrial environment and cannot provide comprehensive and accurate fault prediction. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method and terminal for analyzing faults of numerically controlled machine tools, so as to solve the problem that fault analysis of numerically controlled machine tools is difficult.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for analyzing a fault of a numerically controlled machine tool comprises the following steps: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, obtain the number of faults occurring within a preset time and the duration of the faults, and calculate the average trouble-free working time; S3, constructing a multiple evaluation model according to the machine tool fault type, machine tool fault level and mean trouble-free working time; training the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; The multiple evaluation model is expressed as: ; In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; represents an output vector, which includes at least the total number of faults and the total cost of fault repair within a preset time; The constraints of the multiple evaluation model are as follows: ; ; ; ; In the formula, represents the first input vector, represents the first output vector; S4. Utilizing the trained multiple evaluation model to perform fault analysis on the CNC machine tool.

[0006] In order to solve the above technical problems, another technical solution adopted by the present invention is: A numerical control machine tool fault analysis terminal, characterized in that it comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor completes the following steps when executing the computer program: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, obtain the number of faults occurring within a preset time and the duration of the faults, and calculate the average trouble-free working time; S3, constructing a multiple evaluation model according to the machine tool fault type, machine tool fault level and mean trouble-free working time; training the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; The multiple evaluation model is expressed as: ; In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; represents an output vector, which includes at least the total number of faults and the total cost of fault repair within a preset time; The constraints of the multiple evaluation model are as follows: ; ; ; ; In the formula, represents the first input vector, represents the first output vector; S4. Utilizing the trained multiple evaluation model to perform fault analysis on the CNC machine tool.

[0007] The beneficial effects of the present invention are: providing a CNC machine tool fault analysis method and terminal, calculating the mean time between failures (MTBF) based on machine tool fault data, and constructing a multiple evaluation model based on the above data as input, training the model with the goal of maximizing the fault severity rate and the optimal weight coefficient, and finally using the trained model to perform fault analysis on the CNC machine tool and generate the safety factor of the CNC machine tool, so as to discover potential problems and reduce downtime and productivity loss caused by faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart of a method for analyzing a fault of a numerically controlled machine tool in an embodiment of the present invention; Figure 2 A schematic diagram of a model construction in a method for analyzing a fault of a numerically controlled machine tool in an embodiment of the present invention; Figure 3 Schematic diagram of a CNC machine tool fault analysis terminal according to an embodiment of the present invention; Description of labels: 1. A CNC machine tool fault analysis terminal; 2. A memory; 3. A processor. DETAILED DESCRIPTION

[0009] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0010] Please refer to Figure 1 as well as Figure 2 , a method for analyzing a fault of a numerically controlled machine tool, comprising the steps of: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, obtain the number of faults occurring within a preset time and the duration of the faults, and calculate the average trouble-free working time; S3, constructing a multiple evaluation model according to the machine tool fault type, machine tool fault level and mean trouble-free working time; training the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; S4. Utilizing the trained multiple evaluation model to perform fault analysis on the CNC machine tool.

[0011] It should be noted that the mean time between failures (MTBF) is an indicator to measure the reliability of a product (especially electrical products). The unit is "hours". It reflects the time quality of the product and is an ability of the product to maintain its function within a specified time. Specifically, it refers to the average working time between two adjacent failures, also known as the mean failure interval.

[0012] From the above description, it can be seen that a method for fault analysis based on CNC machine tool fault data is provided, which can effectively improve the accuracy and efficiency of machine tool fault analysis. By obtaining fault data and counting the number of faults and fault duration, combined with the calculated mean time between failures (MTBF), it can lay the foundation for further fault prediction and analysis. This method can accurately reflect the operating status of the machine tool within the preset time, avoiding the problem of over-reliance on experience judgment in traditional methods, thereby improving the scientificity and reliability of data-driven decision-making. Especially in a complex production environment, the types and severity of faults of CNC machine tools vary. This method can comprehensively consider the fault type, fault level and working time of the machine tool to ensure that each decision unit can reflect the real machine tool status during evaluation. By accurately modeling the occurrence mode of machine tool faults, potential problems can be identified in advance, sudden faults can be reduced, and the operation stability and production efficiency of the production line can be improved.

[0013] In some embodiments, step S3 further comprises the steps of: The multiple evaluation model is expressed as: ; In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; represents an output vector, which includes at least the total number of faults and the total cost of fault repair within a preset time; Among them, there are differences in input variables, output variables, weight coefficients and slack variables between the evaluated decision units. Input variables may vary due to differences in fault type ratio, fault level ratio and mean fault-free working time; output variables are affected by the total number of faults and the total cost of fault repair within the preset time, and the values ​​of each decision unit may be different. In addition, the weight coefficient also varies due to the different input and output conditions of the unit, reflecting its relative importance in the overall evaluation. Slack variables further reflect the unit's performance in resource utilization and fault management, represents excess input, that is, whether there is a waste of resources, and Represents insufficient output, reflecting whether the ideal fault management effect has not been achieved. These factors jointly determine the performance of each unit in the optimization calculation and ultimately affect the maximum fault severity rate value. The calculation result of .

[0014] The constraints of the multiple evaluation model are as follows: ; ; ; ; In the formula, represents the first input vector, Represents the first output vector.

[0015] From the above description, it can be seen that by constructing a multiple evaluation model, the systematicness and flexibility of fault analysis are further enhanced. The core of the model is to obtain the optimal machine tool fault evaluation result by maximizing the fault severity rate and optimizing the weight coefficient. By linearly combining the input and output of each decision-making unit (i.e., machine tool status) in multiple dimensions such as fault type, fault level, and fault-free working time, the work efficiency and potential risks of each machine tool can be quantified more accurately. In addition, the introduction of constraints ensures that the complexity and diversity of faults are taken into account in the evaluation process, and the optimal weight coefficient and slack variable are obtained by optimization solution, so that the model is more in line with the fault occurrence law in the actual production environment. This method can not only provide accurate fault analysis reports for equipment maintenance personnel, but also help enterprises reduce downtime and productivity loss caused by equipment failures while predicting the occurrence of faults in advance, thereby realizing the refinement and intelligence of equipment management.

[0016] In some embodiments, step S3 further comprises the steps of: The weight coefficient is obtained by the constraint condition λ and slack variables , The optimal solution of , according to the optimal solution, if θ =1, and and , then the decision unit j is considered effective.

[0017] From the above description, it can be seen that in this example, the optimal solution is obtained by solving linear programming, which further improves the accuracy and operability of the fault analysis method. By solving the constraints, the optimal weight coefficient and slack variable can be obtained, thereby ensuring that the evaluation results of each decision unit are the most real and accurate. The introduction of this method avoids the uncertainty of manual adjustment of parameters in traditional methods and improves the degree of automation of fault analysis. In the solution process, the fault severity rate is maximized through multiple evaluation models, so that the fault analysis results are more consistent with the actual operation of the machine tool and ensure the effectiveness of the decision results. Especially in the production environment, if the decision unit is judged to be valid (θ=1, and the slack variable is 0), it means that the machine tool is running in the best state, which can greatly improve production efficiency and reduce fault downtime. Therefore, this method has significant advantages in production equipment management, fault warning and resource optimization, and provides a strong guarantee for the production safety and economic benefits of the enterprise. In addition, if θ is less than 1, the decision unit at this time is considered invalid.

[0018] In some implementations, step S3 further includes: The fault type ratio is the ratio of each machine tool fault type to the total number of faults; The fault level ratio is calculated by calculating the ratio of each machine tool fault level to the total number of faults.

[0019] In some embodiments, step S2 further comprises the steps of: According to the number of faults occurring within a preset time period and the duration of the faults occurring, the equipment operation time within the preset time period is obtained; based on the equipment operation time, the average trouble-free working time is calculated.

[0020] From the above description, it can be seen that a detailed calculation method for the proportion of fault types and fault levels is provided, making the fault analysis of CNC machine tools more detailed and accurate. By accurately calculating the proportion of each machine tool fault type and level, the specific circumstances of the fault can be deeply analyzed to help enterprise managers identify the most common or most dangerous fault modes. Specifically, through the proportional analysis of various types of faults, it is possible to better understand the impact of different types of faults on machine tool operation and guide the priority of equipment maintenance and repair work based on actual data. The proportional analysis of fault levels helps to quantify the severity of faults and provide enterprises with targeted preventive measures, thereby avoiding minor faults from turning into serious problems and reducing the risks and losses caused by equipment failures in the production process. Specifically, fault levels include general, severe, and particularly severe.

[0021] Please refer to Figure 3 A numerical control machine tool fault analysis terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor 3. When the processor 3 executes the computer program, the steps in the numerical control machine tool fault analysis method are completed. That is, an execution carrier in the numerical control machine tool fault analysis method is provided.

[0022] The present invention provides a CNC machine tool fault analysis method and terminal, which are mainly used to comprehensively analyze machine tool faults, thereby discovering potential problems and reducing downtime and productivity losses caused by faults. The following is a specific description in conjunction with an embodiment: Please refer to Figure 1 to Figure 2 , Embodiment 1 of the present invention is: A method for analyzing a fault of a numerically controlled machine tool comprises the following steps: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, the number of fault occurrences and the fault occurrence duration within a preset time are obtained by statistics, and the average trouble-free working time is calculated; specifically, the machine tool fault data statistics process is as follows: when a fault occurs, data is reported and the time when the fault is triggered is recorded; a complete alarm duration is obtained, that is, the difference between the alarm record trigger time and the alarm fault release time, and the complete number of fault occurrences within the preset time interval is calculated regularly; S3. Construct a multiple evaluation model based on the machine tool fault type, machine tool fault level and mean trouble-free working time; train the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; S4. Use the trained multiple evaluation model to perform fault analysis on CNC machine tools.

[0023] That is, in this embodiment, by obtaining fault data and counting the number of faults and fault duration, combined with the calculated mean time between failures (MTBF), a foundation can be laid for further fault prediction and analysis. This method can accurately reflect the operating status of the machine tool within a preset time, avoiding the problem of over-reliance on experience judgment in traditional methods, thereby improving the scientificity and reliability of data-driven decision-making.

[0024] Embodiment 2 of the present invention is: Based on the first embodiment, the step S3 further includes the following steps: The multiple evaluation model is expressed as: ; In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; Represents the output vector, which at least includes the total number of failures and the total cost of failure repair within a preset time; the failure type ratio is the ratio of each machine tool failure type to the total number of failures; the failure level ratio is the ratio of each machine tool failure level to the total number of failures.

[0025] The constraints of the multiple evaluation model are as follows: ; In the formula, represents the first input vector; this constraint states that the input of the evaluated decision unit i can be represented by a linear combination of the inputs of all other decision units, and non-negative slack variables can be added This means finding an efficient frontier that allows the inputs of evaluation units to be replaced by the inputs of other units while allowing for the waste of inputs.

[0026] ; In the formula, represents the first output vector; this constraint represents the decision unit being evaluated The output of can also be represented by a linear combination of the outputs of all other decision units and can be reduced to a non-negative slack variable This indicates that on the efficient frontier, the output of the evaluation unit can be replaced by the output of other units while taking the output deficiency into account.

[0027] ; .

[0028] Obtaining weight coefficients through constraints and slack variables , The optimal solution of ,and and , then the decision unit j is considered effective.

[0029] The purpose of these constraints is to find an optimal set of weights and slack variables , , so that the efficiency index θ of the evaluated decision-making unit i reaches the maximum value. ,and and , then decision unit j is considered to be effective, that is, it achieves the maximum possible output at the current input level, or uses the minimum input to achieve the current output level. If θ<1, decision unit i is invalid, and higher efficiency can be achieved by adjusting the input and output. By solving the linear programming, adjustments will be made according to the objective function and constraints during the solution process until the optimal solution is found, which can make the efficiency score and optimal weight coefficient λ of the evaluated decision unit. These weight coefficients reflect the relative importance of each decision unit in evaluating the severity of CNC machine tool faults, so as to analyze the importance of different fault types, levels, MTBF and other factors to maintenance costs.

[0030] Please refer to Figure 3 , Embodiment 3 of the present invention is: A numerical control machine tool fault analysis terminal 1 includes a memory 2, a processor 3, and a computer program stored in the memory 2 and executable on the processor. When the processor 3 executes the computer program, the steps in the numerical control machine tool fault analysis method are completed, that is, an execution carrier in the numerical control machine tool fault analysis method is provided.

[0031] In summary, the present invention provides a method and terminal for analyzing faults in numerically controlled machine tools, and its comprehensive beneficial effects are mainly reflected in the following aspects: (1) Improve the accuracy of fault analysis: Through the comprehensive collection and analysis of machine tool fault data, especially the detailed statistics of fault occurrence times, duration, fault type and level, the present invention can more accurately reflect the fault occurrence mode of machine tools in the actual production process. This data-driven analysis method avoids the limitations of traditional reliance on experience and improves the accuracy and scientificity of fault diagnosis.

[0032] (2) Multi-dimensional fault assessment: By constructing multiple evaluation models, the present invention comprehensively considers multiple factors such as the machine tool's fault type, level, and trouble-free working time, ensuring the comprehensiveness and objectivity of the fault analysis results. The model can not only evaluate the operating efficiency of each decision-making unit, but also deeply analyze the impact of different fault modes on machine tool performance, helping enterprises to more accurately identify potential risks.

[0033] (3) Optimizing equipment management decisions: The present invention obtains the optimal fault assessment result by solving the linear programming optimization weight coefficient and slack variable. This enables equipment managers to carry out resource allocation, equipment maintenance and fault warning in a more scientific way. By identifying faults in advance and optimizing equipment operation configuration, the downtime and productivity loss caused by sudden faults can be significantly reduced, thereby improving production efficiency and reducing operating costs.

[0034] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for analyzing a fault of a numerically controlled machine tool, characterized in that: Includes steps: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, obtain the number of faults occurring within a preset time and the duration of the faults, and calculate the average trouble-free working time; S3, constructing a multiple evaluation model according to the machine tool fault type, machine tool fault level and mean trouble-free working time; training the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; The multiple evaluation model is expressed as: ； In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; represents an output vector, which includes at least the total number of faults and the total cost of fault repair within a preset time; The constraints of the multiple evaluation model are as follows: ； ； ； ； In the formula, represents the first input vector, represents the first output vector; S4. Utilizing the trained multiple evaluation model to perform fault analysis on the CNC machine tool.

2. A method for analyzing a fault of a numerically controlled machine tool according to claim 1, characterized in that: The step S3 further comprises the steps of: The weight coefficient is obtained by the constraint condition and slack variables , The optimal solution of ,and and , then the decision unit j is considered effective.

3. A method for analyzing a fault of a numerically controlled machine tool according to claim 1, characterized in that: The step S3 further comprises: The fault type ratio is the ratio of each machine tool fault type to the total number of faults; The fault level ratio is calculated by calculating the ratio of each machine tool fault level to the total number of faults.

4. A method for analyzing a fault of a numerically controlled machine tool according to claim 1, characterized in that: The step S2 further comprises the steps of: According to the number of faults occurring within a preset time period and the duration of the faults occurring, the equipment operation time within the preset time period is obtained; based on the equipment operation time, the average trouble-free working time is calculated.

5. A CNC machine tool fault analysis terminal, characterized in that: The invention comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are performed: S1. Obtain machine tool fault data; S2. Based on the machine tool fault data, obtain the number of faults occurring within a preset time and the duration of the faults, and calculate the average trouble-free working time; S3, constructing a multiple evaluation model according to the machine tool fault type, machine tool fault level and mean trouble-free working time; training the multiple evaluation model with the goal of maximizing the fault severity rate and the optimal weight coefficient; The multiple evaluation model is expressed as: ； In the formula, is the maximum fault severity value; j is the index of the decision-making unit being evaluated; is the weight coefficient; is the weight coefficient of the jth decision-making unit being evaluated; , is the slack variable, where For excessive input variables, For insufficient output variables; represents an input vector, including at least the fault type ratio, fault level ratio and mean time between failures; represents an output vector, which includes at least the total number of faults and the total cost of fault repair within a preset time; The constraints of the multiple evaluation model are as follows: ； ； ； ； In the formula, represents the first input vector, represents the first output vector; S4. Utilizing the trained multiple evaluation model to perform fault analysis on the CNC machine tool.

6. A CNC machine tool fault analysis terminal according to claim 5, characterized in that: The step S3 further comprises the steps of: The weight coefficient is obtained by the constraint condition and slack variables , The optimal solution of , according to the optimal solution, if ,and and , then the decision unit j is considered effective.

7. The CNC machine tool fault analysis terminal according to claim 5, characterized in that: The step S3 further comprises: The fault type ratio is the ratio of each machine tool fault type to the total number of faults; The fault level ratio is calculated by calculating the ratio of each machine tool fault level to the total number of faults.

8. The CNC machine tool fault analysis terminal according to claim 5, characterized in that: The step S2 further comprises the steps of: According to the number of faults occurring within a preset time period and the duration of the faults occurring, the equipment operation time within the preset time period is obtained; based on the equipment operation time, the average trouble-free working time is calculated.

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