Numerical control machine tool fault detection method and system based on big data

Abstract

The invention discloses a big-data-based numerical control machine tool fault detection method and system, and relates to the technical field of monitoring analysis, and the system comprises a data collection module which is used for collecting corresponding operation data during the operation of a target numerical control machine tool and corresponding workpiece data after the operation of the target numerical control machine tool is finished; the data processing module is used for analyzing and processing corresponding operation data and workpiece data when the target numerical control machine tool operates, determining an operation evaluation coefficient corresponding to the target numerical control machine tool and a corresponding workpiece evaluation coefficient after the operation of the target numerical control machine tool is finished, and calculating to obtain a comprehensive evaluation coefficient corresponding to the target numerical control machine tool; and the early warning module is used for monitoring the target numerical control machine tool based on the comprehensive evaluation coefficient corresponding to the target numerical control machine tool and performing early warning on the target numerical control machine tool based on a monitoring result. The method has the effect of improving the fault monitoring efficiency of the numerical control machine tool.

Description

Technical Field

[0001] The present application relates to the field of monitoring and analysis technology, and in particular to a method and system for CNC machine tool fault detection based on big data. Background Art

[0002] CNC machine tools are machine tools that are automatically controlled by computer programs and are widely used in modern manufacturing. Their core advantages lie in high precision, high efficiency and a high degree of automation. CNC machine tools can accurately control the movement and processing of machine tools through pre-programmed programs, thereby achieving high-precision processing of complex parts. Compared with traditional machine tools, CNC machine tools significantly reduce the need for human intervention, reduce the possibility of human errors, and improve production efficiency. CNC machine tool technology allows for rapid program adjustments and optimization during the processing process, allowing production lines to flexibly respond to different production needs. CNC machine tools have a wide range of applications, including automotive manufacturing, aerospace, electronic products, medical devices and other fields. Its processing capabilities are not limited to metal materials, but can also cover a variety of materials such as plastics and composite materials. With the advancement of science and technology, CNC machine tools continue to develop, integrating advanced sensing technology, intelligent control systems and human-computer interaction interfaces, further improving their intelligence level.

[0003] In the related technologies, traditional CNC machine fault detection technologies are all judged through the operating data of the CNC machine tools, which has certain limitations and does not analyze the operating trends of the operating data, thereby reducing the efficiency of CNC machine fault detection and there is room for improvement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present application provides a CNC machine tool fault detection method and system based on big data.

[0005] In a first aspect, the present application provides a CNC machine tool fault detection system based on big data, comprising: A data acquisition module is used to collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the end of the operation of the target CNC machine tool; A data processing module is used to analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then determine the operation evaluation coefficient corresponding to the target CNC machine tool, and to analyze and process the workpiece data corresponding to the target CNC machine tool after it is finished running, and then determine the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running, and calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running; The early warning module is used to monitor the target CNC machine tool based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and to issue an early warning to the target CNC machine tool based on the monitoring result.

[0006] Preferably, the operation data corresponding to the target numerical control machine tool during operation includes noise data and vibration data; The workpiece data corresponding to the target numerical control machine tool after operation includes workpiece quality data and workpiece quality data.

[0007] Preferably, the operation data corresponding to the target numerical control machine tool during operation is analyzed and processed to confirm the operation evaluation coefficient corresponding to the target numerical control machine tool, specifically including: During a preset time period, obtain the operation data corresponding to the target numerical control machine tool during operation, extract the noise data and vibration data corresponding to the target numerical control machine tool during operation from the operation data, and construct a noise change curve according to the time series and a vibration change curve ; Through the formula , confirm the operation evaluation coefficient corresponding to the target numerical control machine tool , where represents the preset time period, respectively represent the preset standard noise change curve and the preset standard vibration change curve.

[0008] Preferably, the workpiece data corresponding to the target numerical control machine tool after operation is analyzed and processed to confirm the workpiece evaluation coefficient corresponding to the target numerical control machine tool, specifically including: Obtain the workpiece data corresponding to the target numerical control machine tool after operation, and extract the workpiece quality data and workpiece quality data corresponding to the target numerical control machine tool after operation from the workpiece data. The workpiece quality data includes the number of defects and the defect area and defect position corresponding to each defect; Through the formula , confirm the workpiece evaluation coefficient corresponding to the target numerical control machine tool after operation , where respectively represent the workpiece quality corresponding to the target numerical control machine tool after operation and the preset standard finished workpiece quality, represents the number corresponding to each defect, , respectively represent the th defect corresponding to the defect area and defect position weight coefficient, represents the preset permitted defect area, respectively represent the preset weight coefficients.

[0009] Preferably, according to the operation evaluation coefficient corresponding to the target numerical control machine tool and the workpiece evaluation coefficient corresponding to the target numerical control machine tool after operation, calculate the comprehensive evaluation coefficient corresponding to the target numerical control machine tool, specifically including: The operation evaluation coefficient corresponding to the target CNC machine tool And the workpiece evaluation coefficient corresponding to the end of the target CNC machine tool operation Substitute into the formula In the calculation, the comprehensive evaluation coefficient corresponding to the target CNC machine tool is obtained ,in, They are respectively expressed as the weight coefficients corresponding to the operation evaluation coefficient and the workpiece evaluation coefficient.

[0010] Preferably, the target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result, specifically including: The comprehensive evaluation coefficient corresponding to the target CNC machine tool The preset comprehensive evaluation threshold interval For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is judged to be working normally, no early warning is needed; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool In When the target CNC machine tool is between , it is necessary to further monitor the target CNC machine tool; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is determined to be operating abnormally, a warning signal needs to be output based on the target CNC machine tool.

[0011] Preferably, further monitoring the target CNC machine tool specifically includes: Select a time window, collect the real-time comprehensive evaluation coefficients corresponding to the target CNC machine tool, form a time series, and use the function express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool ,in, Represented as a time window; The change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool When the target CNC machine tool is abnormal, it is determined that there is an abnormality, and a warning signal is output based on the target CNC machine tool.

[0012] In a second aspect, the present application provides a CNC machine tool fault detection method based on big data, comprising the following steps: Collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the target CNC machine tool after it is finished running; Analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then confirm the operation evaluation coefficient corresponding to the target CNC machine tool; analyze and process the workpiece data corresponding to the target CNC machine tool after the operation is completed, and then confirm the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed; according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed, calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool; The target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result.

[0013] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the above-mentioned CNC machine tool fault detection systems based on big data.

[0014] In summary, the present application includes at least one of the following beneficial technical effects: 1. The present application provides a CNC machine tool fault detection system based on big data, which collects and analyzes the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the target CNC machine tool after it is finished running, thereby confirming the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running, and then based on the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running, calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and monitor the target CNC machine tool based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and then effectively warn the target CNC machine tool based on the monitoring results, thereby effectively improving the efficiency of CNC machine tool fault detection; 2. By collecting the real-time comprehensive evaluation coefficient corresponding to the target CNC machine tool, the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool is confirmed, and the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool is compared with the preset change threshold, and then the target CNC machine tool is managed based on the comparison result, thereby effectively improving the efficiency of CNC machine tool fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 It is a system schematic diagram of CNC machine tool fault detection based on big data in an embodiment of the present application.

[0017] Figure 2 It is a flow chart of the method for CNC machine tool fault detection based on big data in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following is combined with Figure 1-2 This application is described in further detail.

[0019] Example 1 The embodiment of the present application discloses a CNC machine tool fault detection system based on big data.

[0020] Reference Figure 1 , a CNC machine tool fault detection system based on big data, comprising: A data acquisition module is used to collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the end of the operation of the target CNC machine tool; A data processing module is used to analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then determine the operation evaluation coefficient corresponding to the target CNC machine tool, and to analyze and process the workpiece data corresponding to the target CNC machine tool after it is finished running, and then determine the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running, and calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running; The early warning module is used to monitor the target CNC machine tool based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and to issue an early warning to the target CNC machine tool based on the monitoring result.

[0021] By adopting the above technical scheme, the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the target CNC machine tool after the operation are collected and analyzed, and then the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation are confirmed, and then based on the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed, the comprehensive evaluation coefficient corresponding to the target CNC machine tool is calculated, and the target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and then the target CNC machine tool is effectively warned based on the monitoring results, thereby effectively improving the efficiency of CNC machine tool fault detection.

[0022] Furthermore, the operation data corresponding to the target CNC machine tool when it is running includes noise data and vibration data; The workpiece data corresponding to the target numerical control machine tool after the operation is completed includes workpiece quality data and workpiece quality data.

[0023] It should be noted that the operation data corresponding to the target CNC machine tool is analyzed and processed during operation, and then the operation evaluation coefficient corresponding to the target CNC machine tool is determined, which specifically includes: Within a preset time period, the operating data corresponding to the target CNC machine tool is obtained, and the noise data and vibration data corresponding to the target CNC machine tool are extracted from the operating data, and a noise change curve is constructed according to the time series. And vibration change curve ; By formula , confirm the operation evaluation coefficient corresponding to the target CNC machine tool ,in, Indicates a preset time period. They are respectively represented as a preset standard noise change curve and a preset standard vibration change curve.

[0024] Specifically, by analyzing the noise and vibration change curves of the target CNC machine tool during operation, the operating status and performance of the target CNC machine tool can be effectively and accurately evaluated. The above curves provide real-time feedback to help detect potential mechanical failures or abnormal conditions. The noise change curve can reveal the degree of wear, lubrication status, and tightness of assembly of machine tool components, while the vibration change curve can reflect the balance and stability of the machine tool. By comprehensively analyzing the above data, quantitative indicators are effectively provided for the health status of the target CNC machine tool, and based on the corresponding operation evaluation coefficient of the target CNC machine tool, operators and maintenance teams can perform preventive maintenance in a timely manner to avoid downtime caused by equipment failure, thereby improving production efficiency and product quality.

[0025] It should be noted that after the target CNC machine tool finishes running, the corresponding workpiece data is analyzed and processed, and then the workpiece evaluation coefficient corresponding to the target CNC machine tool is confirmed, which specifically includes: Obtain the workpiece data corresponding to the target CNC machine tool after it finishes running, and extract the workpiece quality data and workpiece quality data corresponding to the target CNC machine tool after it finishes running from the workpiece data. The workpiece quality data includes the number of defects, the defect area and defect location corresponding to each defect; Through the formula , confirm the workpiece evaluation coefficient corresponding to the target CNC machine tool after it finishes running , where respectively represent the workpiece quality corresponding to the target CNC machine tool after it finishes running and the quality of the preset standard finished workpiece, represents the number corresponding to each defect, , respectively represent the defect area and defect location weight coefficient corresponding to the th defect, represents the preset permitted defect area,

[0026] Specifically, by analyzing the workpiece quality data and workpiece quality data corresponding to the target CNC machine tool after it finishes running, the processing performance of the target CNC machine tool and the overall quality of the workpiece can be effectively evaluated. The workpiece quality directly reflects the accuracy and stability of the machine tool, while the defect area and location can reveal the possible error sources in the processing process, such as tool wear, inaccurate positioning or programming errors. Through the comprehensive analysis of the above data, a quantitative evaluation standard for the workpiece quality is provided. And using the workpiece evaluation coefficient corresponding to the target CNC machine tool after it finishes running can help production managers quickly identify and solve quality problems, optimize production parameters, thereby improving the product qualification rate and customer satisfaction.

[0027] Furthermore, according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it finishes running, calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool, which specifically includes: Substitute the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it finishes running into the formula to calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool , where respectively represent the weight coefficients corresponding to the operation evaluation coefficient and the workpiece evaluation coefficient.

[0028] It should be noted that the target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result, specifically including: The comprehensive evaluation coefficient corresponding to the target CNC machine tool The preset comprehensive evaluation threshold interval For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; Specifically, the comprehensive evaluation threshold interval can be obtained by fitting historical data; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is judged to be working normally, no early warning is needed; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool In When the target CNC machine tool is between , it is necessary to further monitor the target CNC machine tool; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is determined to be operating abnormally, a warning signal needs to be output based on the target CNC machine tool.

[0029] It should be noted that further monitoring of the target CNC machine tool specifically includes: Select a time window, collect the real-time comprehensive evaluation coefficients corresponding to the target CNC machine tool, form a time series, and use the function express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool ,in, Represented as a time window; The change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool When the target CNC machine tool is abnormal, it is determined that there is an abnormality, and a warning signal is output based on the target CNC machine tool.

[0030] Specifically, by collecting the real-time comprehensive evaluation coefficient of the target CNC machine tool and forming a time series, a function can be used to represent its changing trend over time, providing a dynamic view of the performance of the target CNC machine tool, helping to identify long-term trends and short-term fluctuations, and further reflecting the fluctuation amplitude and frequency of the performance of the target CNC machine tool. By comparing the change coefficient with the preset change threshold, abnormal changes can be quickly identified, indicating potential equipment problems or process deviations, thereby effectively achieving timely preventive maintenance and avoiding unexpected downtime and production losses caused by equipment failures. In addition, through continuous monitoring and comparison, managers can better understand the operating status of equipment, optimize maintenance plans and production processes, and improve equipment utilization and production efficiency.

[0031] Example 2 The embodiment of the present application also discloses a CNC machine tool fault detection method based on big data.

[0032] Reference Figure 2 , a CNC machine tool fault detection method based on big data, comprising the following steps: Collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the target CNC machine tool after it is finished running; Analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then confirm the operation evaluation coefficient corresponding to the target CNC machine tool; analyze and process the workpiece data corresponding to the target CNC machine tool after the operation is completed, and then confirm the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed; according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed, calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool; The target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result.

[0033] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention, they should all fall within the protection scope of the present invention.

[0034] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0035] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well.

Claims

1. A CNC machine tool fault detection system based on big data, characterized in that: include: A data acquisition module is used to collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the end of the operation of the target CNC machine tool; A data processing module is used to analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then determine the operation evaluation coefficient corresponding to the target CNC machine tool, and to analyze and process the workpiece data corresponding to the target CNC machine tool after it is finished running, and then determine the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running, and calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after it is finished running; The early warning module is used to monitor the target CNC machine tool based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and to issue an early warning to the target CNC machine tool based on the monitoring result.

2. A CNC machine tool fault detection system based on big data according to claim 1, characterized in that: The operation data corresponding to the operation of the target numerical control machine tool includes noise data and vibration data; The workpiece data corresponding to the target numerical control machine tool after the operation is completed includes workpiece quality data and workpiece quality data.

3. A CNC machine tool fault detection system based on big data according to claim 2, characterized in that: The operation data corresponding to the target CNC machine tool is analyzed and processed, and then the operation evaluation coefficient corresponding to the target CNC machine tool is confirmed, including: Within a preset time period, the operating data corresponding to the target CNC machine tool is obtained, and the noise data and vibration data corresponding to the target CNC machine tool are extracted from the operating data, and a noise change curve is constructed according to the time series. And vibration change curve ; By formula , confirm the operation evaluation coefficient corresponding to the target CNC machine tool ,in, Indicates a preset time period. They are respectively represented as a preset standard noise change curve and a preset standard vibration change curve.

4. The CNC machine tool fault detection system based on big data according to claim 2 is characterized in that: After the target CNC machine tool is finished running, the corresponding workpiece data is analyzed and processed, and then the workpiece evaluation coefficient corresponding to the target CNC machine tool is confirmed, including: Acquire workpiece data corresponding to the end of the operation of the target CNC machine tool, and extract workpiece quality data and workpiece quality data corresponding to the end of the operation of the target CNC machine tool from the workpiece data, wherein the workpiece quality data includes the number of defects and the defect area and defect position corresponding to each defect; By formula , confirm the corresponding workpiece evaluation coefficient after the target CNC machine tool ends operation ,in, They are respectively the corresponding workpiece quality after the target CNC machine tool is finished running and the preset standard finished workpiece quality. It is represented by the number corresponding to each defect. , Respectively expressed as The defect area and defect position weight coefficient corresponding to each defect, Expressed as the preset allowable defect area, They are respectively represented as preset weight coefficients.

5. The CNC machine tool fault detection system based on big data according to claim 4 is characterized in that: According to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed, the comprehensive evaluation coefficient corresponding to the target CNC machine tool is calculated, including: The operation evaluation coefficient corresponding to the target CNC machine tool And the workpiece evaluation coefficient corresponding to the end of the target CNC machine tool operation Substitute into the formula In the calculation, the comprehensive evaluation coefficient corresponding to the target CNC machine tool is obtained ,in, They are respectively expressed as the weight coefficients corresponding to the operation evaluation coefficient and the workpiece evaluation coefficient.

6. A CNC machine tool fault detection system based on big data according to claim 5, characterized in that: The target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result, specifically including: The comprehensive evaluation coefficient corresponding to the target CNC machine tool The preset comprehensive evaluation threshold interval For comparison, Represented as the preset first comprehensive evaluation threshold, It is represented as a preset second comprehensive evaluation threshold; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is judged to be working normally, no early warning is needed; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool In When the target CNC machine tool is between , it is necessary to further monitor the target CNC machine tool; If the comprehensive evaluation coefficient corresponding to the target CNC machine tool When the target CNC machine tool is determined to be operating abnormally, a warning signal needs to be output based on the target CNC machine tool.

7. A CNC machine tool fault detection system based on big data according to claim 6, characterized in that: Further monitoring the target CNC machine tool includes: Select a time window, collect the real-time comprehensive evaluation coefficients corresponding to the target CNC machine tool, form a time series, and use the function express; By formula , confirm the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool ,in, Represented as a time window; The change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool The preset change threshold Make a comparison; If the change coefficient corresponding to the comprehensive evaluation coefficient of the target CNC machine tool When the target CNC machine tool is abnormal, it is determined that there is an abnormality, and a warning signal is output based on the target CNC machine tool.

8. A method for fault detection of CNC machine tools based on big data, applied to a fault detection system for CNC machine tools based on big data as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Collect the operation data corresponding to the target CNC machine tool when it is running and the workpiece data corresponding to the target CNC machine tool after it is finished running; Analyze and process the operation data corresponding to the target CNC machine tool when it is running, and then confirm the operation evaluation coefficient corresponding to the target CNC machine tool; analyze and process the workpiece data corresponding to the target CNC machine tool after the operation is completed, and then confirm the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed; according to the operation evaluation coefficient corresponding to the target CNC machine tool and the workpiece evaluation coefficient corresponding to the target CNC machine tool after the operation is completed, calculate the comprehensive evaluation coefficient corresponding to the target CNC machine tool; The target CNC machine tool is monitored based on the comprehensive evaluation coefficient corresponding to the target CNC machine tool, and an early warning is issued to the target CNC machine tool based on the monitoring result.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute a CNC machine tool fault detection system based on big data as described in any one of claims 1 to 7.

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