Fault analysis and prediction method and system for intelligent manufacturing equipment

Abstract

The invention discloses a fault analysis and prediction method for intelligent manufacturing equipment, and the method comprises the steps: obtaining the real-time order information of the intelligent manufacturing equipment based on an order platform; based on the database system, data reading is carried out on the database system, and historical data of the intelligent manufacturing equipment is obtained; performing data analysis processing according to the historical data of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment; based on the aging rate of the intelligent manufacturing equipment, obtaining predicted fault occurrence time of the intelligent manufacturing equipment; and performing judgment processing according to the real-time order information of the intelligent manufacturing equipment, and selecting whether to replace the intelligent manufacturing equipment for order processing or not. According to the method, the historical operation parameter data and the historical order information of the intelligent manufacturing equipment are analyzed and processed, the aging rate of the intelligent manufacturing equipment is determined, the time when the intelligent manufacturing equipment possibly breaks down is determined, and the situation that the production cycle is prolonged due to the fact that the intelligent manufacturing equipment breaks down when the order is processed is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical data processing, and in particular to a fault analysis and prediction method and system for intelligent manufacturing equipment. Background Art

[0002] Intelligent manufacturing equipment is an important part of modern manufacturing. It uses advanced information technology, automation equipment and data analysis methods to improve production efficiency, reduce production costs and improve product quality.

[0003] As the time of intelligent manufacturing equipment being put into use increases, the intelligent manufacturing equipment will age. When the intelligent manufacturing equipment ages to a certain extent, the intelligent manufacturing equipment will fail. If the intelligent manufacturing equipment is performing production tasks, it will cause production tasks to be interrupted, extending the production cycle. Therefore, it is necessary to perform fault analysis and prediction on the intelligent manufacturing equipment to avoid failures during the production process and affect production. Summary of the invention

[0004] In order to solve the above technical problems, a fault analysis and prediction method and system for intelligent manufacturing equipment are provided. This technical solution solves the problem raised in the above background technology that as the intelligent manufacturing equipment is put into use for a longer time, the intelligent manufacturing equipment will age. When the intelligent manufacturing equipment ages to a certain extent, the intelligent manufacturing equipment will fail. If the intelligent manufacturing equipment is performing a production task, it will cause the production task to be interrupted, thereby extending the production cycle.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0006] A fault analysis and prediction method for intelligent manufacturing equipment, comprising:

[0007] S100, based on the order platform, reads data from the order platform to obtain real-time order information of intelligent manufacturing equipment;

[0008] S200, based on the database system, reads data from the database system to obtain historical data of the intelligent manufacturing equipment;

[0009] S300, performing data analysis and processing according to historical data of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment, where the aging rate of the intelligent manufacturing equipment includes a natural aging rate and a usage aging rate;

[0010] S400, based on the aging rate of the intelligent manufacturing equipment, predicting the fault of the intelligent manufacturing equipment, and obtaining the predicted fault occurrence time of the intelligent manufacturing equipment; and

[0011] S500, judging and processing based on the real-time order information of the intelligent manufacturing equipment, and choosing whether to replace the intelligent manufacturing equipment for order processing.

[0012] Preferably, the step S300, performing data analysis and processing according to historical data of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps:

[0013] S301, performing data retrieval processing based on historical data of the intelligent manufacturing equipment to obtain historical order information of the intelligent manufacturing equipment;

[0014] S302, based on the historical order information of the intelligent manufacturing equipment, performing data retrieval processing on the historical data of the intelligent manufacturing equipment to obtain the historical operating parameter data of the intelligent manufacturing equipment; and

[0015] S303: Analyze and process historical operation parameter data of the intelligent manufacturing equipment and historical order information of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment.

[0016] Preferably, the step S303, analyzing and processing the historical operating parameter data of the intelligent manufacturing equipment and the historical order information of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps:

[0017] S3031, based on the database system, performing data reading and processing on the database system to obtain factory specification parameter data of the intelligent manufacturing equipment and the length of time the intelligent manufacturing equipment has been put into use;

[0018] S3032, based on the factory specification parameter data of the intelligent manufacturing equipment, classify and process the historical operation parameter data of the intelligent manufacturing equipment to obtain the normal operation historical parameter data and overload operation historical parameter data of the intelligent manufacturing equipment;

[0019] S3033, based on the normal operation historical parameter data and overload operation historical parameter data of the intelligent manufacturing equipment, analyzing and processing the historical order information of the intelligent manufacturing equipment to obtain the normal operation historical duration and overload operation historical duration of the intelligent manufacturing equipment; and

[0020] S3034, calculate and process the factory specification parameter data of the intelligent manufacturing equipment, the time the intelligent manufacturing equipment is put into use, the normal operation history of the intelligent manufacturing equipment and the overload operation history of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment.

[0021] Preferably, the step S3034, performing calculations based on the factory specification parameter data of the intelligent manufacturing equipment, the length of time the intelligent manufacturing equipment has been put into use, the normal operation history of the intelligent manufacturing equipment, and the overload operation history of the intelligent manufacturing equipment, to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps:

[0022] S30341, calculating and processing according to the time the intelligent manufacturing equipment has been put into use, the historical time the intelligent manufacturing equipment has been in normal operation, and the historical time the intelligent manufacturing equipment has been in overload operation, to obtain the idle time of the intelligent manufacturing equipment;

[0023] S30342, using normal operation, overload operation and idleness of intelligent manufacturing equipment as characteristics, searching and processing the factory specification parameter data of intelligent manufacturing equipment, and obtaining the normal operation aging rate of intelligent manufacturing equipment, the overload operation aging rate of intelligent manufacturing equipment and the idle aging rate of intelligent manufacturing equipment;

[0024] S30343, calculating and processing according to the normal operation history duration of the intelligent manufacturing equipment, the overload operation history duration of the intelligent manufacturing equipment, the normal operation aging rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment to obtain the usage aging rate;

[0025] S30344, calculating and processing according to the idle aging rate of the intelligent manufacturing equipment and the idle time of the intelligent manufacturing equipment to obtain the natural aging rate; and

[0026] S30345, calculate and process according to the usage aging rate and the natural aging rate to obtain the aging rate of the intelligent manufacturing equipment.

[0027] Preferably, the specific calculation formula for obtaining the aging rate of the intelligent manufacturing equipment is:

[0028] A α =A β ×T1+A γ ×T2+A δ ×T3;

[0029] In the formula, A α is the aging rate of intelligent manufacturing equipment; A β is the normal operation aging rate of intelligent manufacturing equipment; T1 is the normal operation history of intelligent manufacturing equipment; A γ is the aging rate of overload operation of intelligent manufacturing equipment; T2 is the historical duration of overload operation of intelligent manufacturing equipment; A δ is the idle aging rate of intelligent manufacturing equipment; T3 is the idle time of intelligent manufacturing equipment.

[0030] Preferably, the step S400, based on the aging rate of the intelligent manufacturing equipment, performs fault prediction on the intelligent manufacturing equipment and obtains the predicted fault occurrence time of the intelligent manufacturing equipment, specifically comprises the following steps:

[0031] S401, if the intelligent manufacturing equipment is in a normal processing state, based on the aging rate of the intelligent manufacturing equipment, predicting the fault of the intelligent manufacturing equipment, and obtaining a first predicted fault occurrence time of the intelligent manufacturing equipment; and

[0032] S402: If the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, a fault prediction is performed on the intelligent manufacturing equipment based on an aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment.

[0033] Preferably, the step S401, if the intelligent manufacturing equipment is in a normal processing state, performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment, specifically comprises the following steps:

[0034] S4011, based on the aging rate of the intelligent manufacturing equipment, perform data retrieval and matching processing on the factory specification parameter data of the intelligent manufacturing equipment to obtain the normal operation time of the intelligent manufacturing equipment;

[0035] S4012, performing calculation and processing according to the factory specification parameter data of the intelligent manufacturing equipment and the aging rate of the intelligent manufacturing equipment to obtain a first normal utilization rate of the intelligent manufacturing equipment; and

[0036] S4013, performing calculation and processing according to the normal operation time of the intelligent manufacturing equipment and the first normal utilization rate of the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment;

[0037] The specific calculation formula for obtaining the first predicted fault occurrence time of the intelligent manufacturing equipment is:

[0038]

[0039] Where, T f is the first predicted fault occurrence time of intelligent manufacturing equipment; T ε It is the normal operation time of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment.

[0040] Preferably, the step S402, if the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment, specifically comprises the following steps:

[0041] S4021, calculating and processing according to the factory specification parameter data of the intelligent manufacturing equipment, the aging rate of the intelligent manufacturing equipment, the normal operation time of the intelligent manufacturing equipment and the normal operation aging rate of the intelligent manufacturing equipment, to obtain the second normal utilization rate of the intelligent manufacturing equipment, that is:

[0042] U s =1-A α -Tε ×A β ;

[0043] Where U s The second normal utilization rate of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment; β Normal operation aging rate of smart manufacturing equipment; and

[0044] S4022, calculating and processing according to the normal operation time of the intelligent manufacturing equipment, the second normal utilization rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment;

[0045] The specific calculation formula for obtaining the second predicted fault occurrence time of the intelligent manufacturing equipment is:

[0046]

[0047] Where, T s is the second predicted fault occurrence time of the intelligent manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment; γ U is the aging rate of overload operation of intelligent manufacturing equipment; s It is the second normal utilization rate of intelligent manufacturing equipment.

[0048] Preferably, the step S500, judging and processing according to the real-time order information of the intelligent manufacturing equipment, and selecting whether to replace the intelligent manufacturing equipment for order processing, specifically includes the following steps:

[0049] S501, analyzing and processing the real-time order information of the intelligent manufacturing equipment to obtain the real-time order duration;

[0050] S502, making a judgment based on the real-time order duration and the normal operation duration of the intelligent manufacturing equipment;

[0051] S503: If the real-time order duration is longer than the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment cannot complete the order processing, and the intelligent manufacturing equipment is replaced to process the order; and

[0052] S504: If the real-time order duration is less than or equal to the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment can complete the order processing.

[0053] Furthermore, a fault analysis and prediction system for intelligent manufacturing equipment is proposed, which is used to implement the fault analysis and prediction method for intelligent manufacturing equipment as described above, including:

[0054] An order platform, which is used to store real-time order information of intelligent manufacturing equipment;

[0055] A database system, wherein the database system is used to store historical data of intelligent manufacturing equipment, factory specification parameter data of intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been in use;

[0056] A data reading module, which is used to read the real-time order information of the intelligent manufacturing equipment, the historical data of the intelligent manufacturing equipment, the factory specification parameter data of the intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been put into use;

[0057] A data analysis module, wherein the data analysis module is used to analyze the historical operating parameter data of the intelligent manufacturing equipment, and obtain the normal operating historical parameter data and overload operating historical parameter data of the intelligent manufacturing equipment;

[0058] A data calculation module, wherein the data calculation module performs calculations based on the factory specification parameter data of the intelligent manufacturing equipment, the length of time the intelligent manufacturing equipment has been put into use, the historical length of time the normal operation of the intelligent manufacturing equipment has been running, and the historical length of time the overload operation of the intelligent manufacturing equipment has been running, to obtain the aging rate of the intelligent manufacturing equipment; the data calculation module performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment, to obtain the predicted fault occurrence time of the intelligent manufacturing equipment;

[0059] A comparison and judgment module, which performs judgment and processing based on the real-time order duration and the normal operation duration of the intelligent manufacturing equipment, and selects whether to replace the intelligent manufacturing equipment; and

[0060] A control module is used to realize data transmission and information interaction between modules.

[0061] Compared with the prior art, the present invention provides a fault analysis and prediction method and system for intelligent manufacturing equipment, which has the following beneficial effects:

[0062] The present invention determines the aging rate of the intelligent manufacturing equipment by analyzing and processing the historical operating parameter data of the intelligent manufacturing equipment and the historical order information of the intelligent manufacturing equipment, predicts the faults of the intelligent manufacturing equipment according to the aging rate of the intelligent manufacturing equipment, determines the time when the intelligent manufacturing equipment may fail, avoids the failure of the intelligent manufacturing equipment when processing the order, and prolongs the production cycle. Finally, the real-time order duration is compared with the normal operating duration of the intelligent manufacturing equipment, avoids the overload operation of the intelligent manufacturing equipment, and prolongs the service life of the intelligent manufacturing equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1A schematic flow chart of steps S100-S500 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0064] Figure 2 A schematic flow chart of steps S301-S303 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0065] Figure 3 A flowchart of steps S3031-S3034 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0066] Figure 4 This is a flow chart of steps S30341-S30345 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0067] Figure 5 A schematic flow chart of steps S401-S402 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0068] Figure 6 This is a flow chart of steps S4011-S4013 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0069] Figure 7 This is a flow chart of steps S4021-S4022 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0070] Figure 8 This is a flow chart of steps S501-S504 in a fault analysis and prediction method for intelligent manufacturing equipment proposed by the present invention;

[0071] Fig. 9 This is a structural block diagram of a fault analysis and prediction system for intelligent manufacturing equipment proposed by the present invention. DETAILED DESCRIPTION

[0072] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0073] Reference Figure 1 As shown, a fault analysis and prediction method for intelligent manufacturing equipment includes:

[0074] S100, based on the order platform, read the data of the order platform to obtain the real-time order information of the intelligent manufacturing equipment;

[0075] S200, based on the database system, reading data from the database system to obtain historical data of the intelligent manufacturing equipment;

[0076] S300, performing data analysis and processing according to historical data of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment, where the aging rate of the intelligent manufacturing equipment includes a natural aging rate and a usage aging rate;

[0077] S400, based on the aging rate of the intelligent manufacturing equipment, predicting the fault of the intelligent manufacturing equipment, and obtaining the predicted fault occurrence time of the intelligent manufacturing equipment; and

[0078] S500, judging and processing according to the real-time order information of the intelligent manufacturing equipment, and selecting whether to replace the intelligent manufacturing equipment for order processing;

[0079] The present invention analyzes and processes the historical operating parameter data and historical order information of the intelligent manufacturing equipment to determine the aging rate of the intelligent manufacturing equipment and the time when the intelligent manufacturing equipment may fail, thereby avoiding the extension of the production cycle due to failure of the intelligent manufacturing equipment when processing orders. Figure 2 As shown, step S300, performing data analysis and processing according to the historical data of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps:

[0080] S301, performing data retrieval processing based on historical data of intelligent manufacturing equipment to obtain historical order information of the intelligent manufacturing equipment;

[0081] S302, based on the historical order information of the intelligent manufacturing equipment, performing data retrieval processing on the historical data of the intelligent manufacturing equipment to obtain the historical operating parameter data of the intelligent manufacturing equipment; and

[0082] S303: Analyze and process historical operating parameter data of the intelligent manufacturing equipment and historical order information of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment.

[0083] In this embodiment, by analyzing the historical order information of the intelligent manufacturing equipment, the historical operating parameter data of the intelligent manufacturing equipment can be obtained, and then the historical operating parameter data of the intelligent manufacturing equipment is analyzed to determine the aging rate of the intelligent manufacturing equipment. The fault prediction of the intelligent manufacturing equipment is performed based on the aging rate to determine the time when the intelligent manufacturing equipment may fail.

[0084] Reference Figure 3 As shown, step S303, analyzing and processing the historical operating parameter data of the intelligent manufacturing equipment and the historical order information of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps:

[0085] S3031. Based on the database system, read and process the data of the database system to obtain the factory specification parameter data of the intelligent manufacturing equipment and the length of time the intelligent manufacturing equipment has been put into use;

[0086] The factory specification parameter data of the intelligent manufacturing equipment will record the normal operation time of the intelligent manufacturing equipment. When the operation time exceeds the normal operation time, it is overload operation. In addition, the factory specification parameter data of the intelligent manufacturing equipment will also record the loss of the intelligent manufacturing equipment during normal operation, overload operation and idle time, that is, the aging rate;

[0087] S3032. Based on the factory specification parameter data of the intelligent manufacturing equipment, classify and process the historical operation parameter data of the intelligent manufacturing equipment to obtain the normal operation historical parameter data and overload operation historical parameter data of the intelligent manufacturing equipment;

[0088] S3033. Analyze and process the historical order information of the intelligent manufacturing equipment based on the normal operation historical parameter data and the overload operation historical parameter data of the intelligent manufacturing equipment to obtain the normal operation historical duration and the overload operation historical duration of the intelligent manufacturing equipment; and

[0089] S3034. Calculate and process the factory specification parameter data of the intelligent manufacturing equipment, the length of time the intelligent manufacturing equipment has been put into use, the historical length of time the intelligent manufacturing equipment has been in normal operation, and the historical length of time the intelligent manufacturing equipment has been in overload operation to obtain the aging rate of the intelligent manufacturing equipment.

[0090] In this embodiment, each historical order information of the intelligent manufacturing equipment corresponds to each historical operating parameter data. Some historical order information can be processed during the normal operation stage of the intelligent manufacturing equipment, while some historical order information cannot be processed during the normal operation stage of the intelligent manufacturing equipment and requires overload operation to complete the processing. Therefore, the historical operating parameter data is analyzed according to the factory specification parameter data of the intelligent manufacturing equipment to determine the historical duration of normal operation of the intelligent manufacturing equipment and the historical duration of overload operation of the intelligent manufacturing equipment.

[0091] Reference Figure 4 As shown, step S3034, according to the factory specification parameter data of the intelligent manufacturing equipment, the time of commissioning of the intelligent manufacturing equipment, the normal operation history of the intelligent manufacturing equipment and the overload operation history of the intelligent manufacturing equipment, the aging rate of the intelligent manufacturing equipment is obtained by calculation and processing, which specifically includes the following steps:

[0092] S30341. Calculate and process the time the intelligent manufacturing equipment has been put into use, the normal operation history of the intelligent manufacturing equipment, and the overload operation history of the intelligent manufacturing equipment to obtain the idle time of the intelligent manufacturing equipment;

[0093] S30342. Retrieve and process the factory specification parameter data of the intelligent manufacturing equipment with the normal operation, overload operation and idleness of the intelligent manufacturing equipment as the characteristics, and obtain the normal operation aging rate of the intelligent manufacturing equipment, the overload operation aging rate of the intelligent manufacturing equipment and the idle aging rate of the intelligent manufacturing equipment;

[0094] S30343. Calculate and process the normal operation history of the intelligent manufacturing equipment, the overload operation history of the intelligent manufacturing equipment, the normal operation aging rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment to obtain the usage aging rate;

[0095] S30344. Calculate and process the idle aging rate of the intelligent manufacturing equipment and the idle time of the intelligent manufacturing equipment to obtain the natural aging rate; and

[0096] S30345. Calculate and process the aging rate of the intelligent manufacturing equipment based on the usage aging rate and the natural aging rate;

[0097] Among them, the specific calculation formula for obtaining the aging rate of intelligent manufacturing equipment is:

[0098] A α =A β ×T1+A γ ×T2+A δ ×T3;

[0099] In the formula, A α is the aging rate of intelligent manufacturing equipment; A β is the normal operation aging rate of intelligent manufacturing equipment; T1 is the normal operation history of intelligent manufacturing equipment; A γ is the aging rate of overload operation of intelligent manufacturing equipment; T2 is the historical duration of overload operation of intelligent manufacturing equipment; A δ is the idle aging rate of intelligent manufacturing equipment; T3 is the idle time of intelligent manufacturing equipment.

[0100] Reference Figure 5 As shown, step S400, based on the aging rate of the intelligent manufacturing equipment, performs fault prediction on the intelligent manufacturing equipment and obtains the predicted fault occurrence time of the intelligent manufacturing equipment, which specifically includes the following steps:

[0101] S401. If the intelligent manufacturing equipment is in a normal processing state, based on the aging rate of the intelligent manufacturing equipment, predict the fault of the intelligent manufacturing equipment and obtain a first predicted fault occurrence time of the intelligent manufacturing equipment; and

[0102] S402: If the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, a fault prediction is performed on the intelligent manufacturing equipment based on an aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment;

[0103] In this embodiment, if the intelligent manufacturing equipment has been working in the normal operation stage, the time when the intelligent manufacturing equipment fails will be extended. If the intelligent manufacturing equipment is often overloaded, the time when the intelligent manufacturing equipment fails will be advanced. Therefore, it is necessary to analyze and predict the processing status of the intelligent manufacturing equipment. When the intelligent manufacturing equipment is processing, it is in the normal processing state at the beginning. When the processing time exceeds the normal processing time, the intelligent manufacturing equipment will be overloaded, that is, the intelligent manufacturing equipment is in a mixed state of normal processing state and overload processing state.

[0104] When the intelligent manufacturing equipment is in different processing states, the above method can make the prediction of the time when the intelligent manufacturing equipment fails more accurate, so that the staff can accurately control the status of the intelligent manufacturing equipment.

[0105] Reference Figure 6 As shown, step S401, if the intelligent manufacturing equipment is in a normal processing state, based on the aging rate of the intelligent manufacturing equipment, a fault prediction is performed on the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment, which specifically includes the following steps:

[0106] S4011. Based on the aging rate of the intelligent manufacturing equipment, perform data retrieval and matching processing on the factory specification parameter data of the intelligent manufacturing equipment to obtain the normal operation time of the intelligent manufacturing equipment;

[0107] The normal operation time of the intelligent manufacturing equipment is the time when the intelligent manufacturing equipment enters overload operation at the next moment. At this time, the intelligent manufacturing equipment is still in the normal operation stage;

[0108] S4012, performing calculation and processing according to the factory specification parameter data of the intelligent manufacturing equipment and the aging rate of the intelligent manufacturing equipment to obtain a first normal utilization rate of the intelligent manufacturing equipment; and

[0109] S4013. Calculate and process the first predicted fault occurrence time of the intelligent manufacturing equipment according to the normal operation time of the intelligent manufacturing equipment and the first normal utilization rate of the intelligent manufacturing equipment;

[0110] The specific calculation formula for obtaining the first predicted fault occurrence time of the intelligent manufacturing equipment is:

[0111]

[0112] Where, T fis the first predicted fault occurrence time of intelligent manufacturing equipment; T ε It is the normal operation time of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment.

[0113] The above method can accurately control the status of intelligent manufacturing equipment, avoiding the interruption of production tasks after the intelligent manufacturing equipment fails. At the same time, it can also monitor the intelligent manufacturing equipment according to the failure time of the intelligent manufacturing equipment, avoid producing a large number of defective products, and reduce the scrap rate.

[0114] Reference Figure 7 As shown, step S402, if the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, based on the aging rate of the intelligent manufacturing equipment, a fault prediction is performed on the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment, specifically including the following steps:

[0115] S4021. Calculate and process the factory specification parameter data of the intelligent manufacturing equipment, the aging rate of the intelligent manufacturing equipment, the normal operation time of the intelligent manufacturing equipment, and the normal operation aging rate of the intelligent manufacturing equipment to obtain the second normal utilization rate of the intelligent manufacturing equipment, that is:

[0116] U s =1-A α -T ε ×A β ;

[0117] Where U s The second normal utilization rate of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment; β Normal operation aging rate of smart manufacturing equipment; and

[0118] S4022. Calculate and process the second predicted fault occurrence time of the intelligent manufacturing equipment according to the normal operation time of the intelligent manufacturing equipment, the second normal utilization rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment;

[0119] The specific calculation formula for obtaining the second predicted fault occurrence time of the intelligent manufacturing equipment is:

[0120]

[0121] Where, T s is the second predicted fault occurrence time of the intelligent manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment;γ U is the aging rate of overload operation of intelligent manufacturing equipment; s The second normal utilization rate of intelligent manufacturing equipment;

[0122] In this embodiment, when the intelligent manufacturing equipment enters overload operation, there is a time period that is the normal operation stage. Therefore, it is necessary to include the duration of this stage in the second predicted fault occurrence time, and calculate the overload operation aging rate of the intelligent manufacturing equipment and the second normal usage rate of the intelligent manufacturing equipment to obtain the proportion of the overload operation aging rate of the intelligent manufacturing equipment in the second normal usage rate of the intelligent manufacturing equipment. Finally, multiply the proportion by 60 minutes to obtain the time that the intelligent manufacturing equipment can maintain when overloaded.

[0123] When the operating state of the intelligent manufacturing equipment is a mixed state, the analysis method of the intelligent manufacturing equipment in the normal processing state is no longer applicable to the mixed state. Therefore, the above method is specially designed for the mixed state of the intelligent manufacturing equipment in order to improve the accuracy of predicting the failure time of the intelligent manufacturing equipment.

[0124] Reference Figure 8 As shown, step S500, judging and processing based on the real-time order information of the intelligent manufacturing equipment, and selecting whether to replace the intelligent manufacturing equipment for order processing, specifically includes the following steps:

[0125] S501. Analyze and process the real-time order information of the intelligent manufacturing equipment to obtain the real-time order duration;

[0126] S502, judging and processing based on the real-time order duration and the normal operation duration of the intelligent manufacturing equipment;

[0127] S503: If the real-time order duration is longer than the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment cannot complete the order processing, and the intelligent manufacturing equipment is replaced to process the order; and

[0128] S504: If the real-time order duration is less than or equal to the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment can complete the order processing;

[0129] In this embodiment, in order to extend the service life of the intelligent manufacturing equipment and prevent the intelligent manufacturing equipment from processing orders during the overload operation stage, the real-time order duration and the normal operation duration of the intelligent manufacturing equipment are compared to determine whether the intelligent manufacturing equipment can complete the order processing during the normal operation stage. If it cannot be completed, another intelligent manufacturing equipment is replaced.

[0130] When the operating status of the intelligent manufacturing equipment has reached the limit of the normal state, in order to avoid overload operation of the intelligent manufacturing equipment, the remaining orders will be replaced by another intelligent manufacturing equipment for processing, so that the processing of the intelligent manufacturing equipment is carried out in a normal state, thereby extending the service life of the intelligent manufacturing equipment.

[0131] Reference Fig. 9 As shown, the present application also discloses a fault analysis and prediction system for intelligent manufacturing equipment, which is used to implement the fault analysis and prediction method for intelligent manufacturing equipment as described above, including:

[0132] An order platform 101, wherein the order platform 101 is used to store real-time order information of intelligent manufacturing equipment;

[0133] A database system 103, wherein the database system 103 is used to store historical data of intelligent manufacturing equipment, factory specification parameter data of intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been put into use;

[0134] A data reading module 105, which is used to read the real-time order information of the intelligent manufacturing equipment, the historical data of the intelligent manufacturing equipment, the factory specification parameter data of the intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been put into use;

[0135] A data analysis module 107, wherein the data analysis module 107 is used to analyze the historical operating parameter data of the intelligent manufacturing equipment, and obtain the normal operating historical parameter data and overload operating historical parameter data of the intelligent manufacturing equipment;

[0136] The data calculation module 109 calculates and processes the factory specification parameter data of the intelligent manufacturing equipment, the time the intelligent manufacturing equipment is put into use, the normal operation history of the intelligent manufacturing equipment, and the overload operation history of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment; the data calculation module predicts the fault of the intelligent manufacturing equipment according to the aging rate of the intelligent manufacturing equipment to obtain the predicted fault occurrence time of the intelligent manufacturing equipment;

[0137] A comparison and judgment module 111, wherein the comparison and judgment module 111 performs judgment processing according to the real-time order duration and the normal operation duration of the intelligent manufacturing equipment, and selects whether to replace the intelligent manufacturing equipment; and

[0138] The control module 113 is used to realize data transmission and information interaction between modules.

[0139] After the intelligent manufacturing equipment is put into use, it will experience wear and tear. When the intelligent manufacturing equipment is in normal operation, it will experience wear and tear. When the intelligent manufacturing equipment is in overload operation, it will experience excessive wear and tear. The intelligent manufacturing equipment will also experience wear and tear when it is idle. Therefore, by analyzing the historical data of the intelligent manufacturing equipment, the normal operation history of the intelligent manufacturing equipment and the overload operation history of the intelligent manufacturing equipment are determined. Then, the factory specification parameter data of the intelligent manufacturing equipment is retrieved to determine the normal operation aging rate of the intelligent manufacturing equipment corresponding to the normal operation of the intelligent manufacturing equipment and the overload operation aging rate of the intelligent manufacturing equipment corresponding to the overload operation history of the intelligent manufacturing equipment. The wear and tear generated by the intelligent manufacturing equipment during use, that is, the aging rate, is determined. Finally, the intelligent manufacturing equipment is analyzed and predicted through the aging rate of the intelligent manufacturing equipment to determine the predicted failure time of the intelligent manufacturing equipment. In addition, the real-time order information is analyzed through the predicted failure time of the intelligent manufacturing equipment to determine whether the intelligent manufacturing equipment can complete the order processing, thereby avoiding failures of the intelligent manufacturing equipment during the processing and extending the processing cycle.

[0140] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A fault analysis and prediction method for intelligent manufacturing equipment, characterized in that: Includes steps: S100, based on the order platform, reads data from the order platform to obtain real-time order information of intelligent manufacturing equipment; S200, based on the database system, reads data from the database system to obtain historical data of the intelligent manufacturing equipment; S300, performing data analysis and processing according to historical data of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment, where the aging rate of the intelligent manufacturing equipment includes a natural aging rate and a usage aging rate; S400, based on the aging rate of the intelligent manufacturing equipment, predicting the fault of the intelligent manufacturing equipment, and obtaining the predicted fault occurrence time of the intelligent manufacturing equipment; as well as S500, judging and processing based on the real-time order information of the intelligent manufacturing equipment, and choosing whether to replace the intelligent manufacturing equipment for order processing.

2. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 1 is characterized in that: The step S300, performing data analysis and processing based on the historical data of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps: S301, performing data retrieval processing based on historical data of the intelligent manufacturing equipment to obtain historical order information of the intelligent manufacturing equipment; S302, based on the historical order information of the intelligent manufacturing equipment, performing data retrieval processing on the historical data of the intelligent manufacturing equipment to obtain the historical operating parameter data of the intelligent manufacturing equipment; as well as S303: Analyze and process historical operation parameter data of the intelligent manufacturing equipment and historical order information of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment.

3. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 2 is characterized in that: The step S303, analyzing and processing the historical operating parameter data of the intelligent manufacturing equipment and the historical order information of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, specifically includes the following steps: S3031, based on the database system, performing data reading and processing on the database system to obtain factory specification parameter data of the intelligent manufacturing equipment and the length of time the intelligent manufacturing equipment has been put into use; S3032, based on the factory specification parameter data of the intelligent manufacturing equipment, classify and process the historical operation parameter data of the intelligent manufacturing equipment to obtain the normal operation historical parameter data and overload operation historical parameter data of the intelligent manufacturing equipment; S3033, based on the normal operation historical parameter data and overload operation historical parameter data of the intelligent manufacturing equipment, analyzing and processing the historical order information of the intelligent manufacturing equipment, and obtaining the normal operation historical duration and overload operation historical duration of the intelligent manufacturing equipment; as well as S3034, calculate and process the factory specification parameter data of the intelligent manufacturing equipment, the time the intelligent manufacturing equipment is put into use, the normal operation history of the intelligent manufacturing equipment and the overload operation history of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment.

4. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 3 is characterized in that: The step S3034 is to calculate and process the factory specification parameter data of the intelligent manufacturing equipment, the time the intelligent manufacturing equipment is put into use, the normal operation history of the intelligent manufacturing equipment, and the overload operation history of the intelligent manufacturing equipment to obtain the aging rate of the intelligent manufacturing equipment, and specifically includes the following steps: S30341, calculating and processing according to the time the intelligent manufacturing equipment has been put into use, the historical time the intelligent manufacturing equipment has been in normal operation, and the historical time the intelligent manufacturing equipment has been in overload operation, to obtain the idle time of the intelligent manufacturing equipment; S30342, using normal operation, overload operation and idleness of intelligent manufacturing equipment as characteristics, searching and processing the factory specification parameter data of intelligent manufacturing equipment, and obtaining the normal operation aging rate of intelligent manufacturing equipment, the overload operation aging rate of intelligent manufacturing equipment and the idle aging rate of intelligent manufacturing equipment; S30343, calculate and process the normal operation history duration of the intelligent manufacturing equipment, the overload operation history duration of the intelligent manufacturing equipment, the normal operation aging rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment to obtain the usage aging rate; S30344, calculating and processing according to the idle aging rate of the intelligent manufacturing equipment and the idle time of the intelligent manufacturing equipment to obtain the natural aging rate; as well as S30345, use the aging rate and natural aging rate for calculation and processing to obtain the aging rate of the intelligent manufacturing equipment.

5. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 4 is characterized in that: The specific calculation formula for obtaining the aging rate of intelligent manufacturing equipment is: A α =A β ×T1+A γ ×T2+A δ ×T3; In the formula, A α is the aging rate of intelligent manufacturing equipment; A β is the normal operation aging rate of intelligent manufacturing equipment; T1 is the normal operation history of intelligent manufacturing equipment; A γ is the aging rate of overload operation of intelligent manufacturing equipment; T2 is the historical duration of overload operation of intelligent manufacturing equipment; A δ is the idle aging rate of intelligent manufacturing equipment; T3 is the idle time of intelligent manufacturing equipment.

6. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 1 is characterized in that: The step S400, based on the aging rate of the intelligent manufacturing equipment, performs fault prediction on the intelligent manufacturing equipment and obtains the predicted fault occurrence time of the intelligent manufacturing equipment, specifically includes the following steps: S401, if the intelligent manufacturing equipment is in a normal processing state, based on the aging rate of the intelligent manufacturing equipment, a fault prediction is performed on the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment; as well as S402: If the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, a fault prediction is performed on the intelligent manufacturing equipment based on an aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment.

7. A fault analysis and prediction method for intelligent manufacturing equipment according to claim 6, characterized in that: The step S401, if the intelligent manufacturing equipment is in a normal processing state, performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment, specifically includes the following steps: S4011, based on the aging rate of the intelligent manufacturing equipment, perform data retrieval and matching processing on the factory specification parameter data of the intelligent manufacturing equipment to obtain the normal operation time of the intelligent manufacturing equipment; S4012, performing calculation and processing according to the factory specification parameter data of the intelligent manufacturing equipment and the aging rate of the intelligent manufacturing equipment to obtain a first normal utilization rate of the intelligent manufacturing equipment; as well as S4013, performing calculation and processing according to the normal operation time of the intelligent manufacturing equipment and the first normal utilization rate of the intelligent manufacturing equipment to obtain a first predicted fault occurrence time of the intelligent manufacturing equipment; The specific calculation formula for obtaining the first predicted fault occurrence time of the intelligent manufacturing equipment is: Where, T f First predict failure occurrence time for smart manufacturing equipment; T ε It is the normal operation time of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment.

8. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 6 is characterized in that: The step S402, if the intelligent manufacturing equipment is in a mixed state of a normal processing state and an overload processing state, performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment, specifically includes the following steps: S4021, calculating and processing according to the factory specification parameter data of the intelligent manufacturing equipment, the aging rate of the intelligent manufacturing equipment, the normal operation time of the intelligent manufacturing equipment and the normal operation aging rate of the intelligent manufacturing equipment, to obtain the second normal utilization rate of the intelligent manufacturing equipment, that is: U s =1-A α -T ε ×A β ; Where U s The second normal utilization rate of intelligent manufacturing equipment; (1-A α ) is the first normal utilization rate of intelligent manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment; β The normal operation aging rate of smart manufacturing equipment; as well as S4022, calculating and processing according to the normal operation time of the intelligent manufacturing equipment, the second normal utilization rate of the intelligent manufacturing equipment, and the overload operation aging rate of the intelligent manufacturing equipment, to obtain a second predicted fault occurrence time of the intelligent manufacturing equipment; The specific calculation formula for obtaining the second predicted fault occurrence time of the intelligent manufacturing equipment is: Where, T s Second prediction of failure occurrence time for smart manufacturing equipment; T ε A is the normal operation time of intelligent manufacturing equipment; γ U is the aging rate of overload operation of intelligent manufacturing equipment; s It is the second normal utilization rate of intelligent manufacturing equipment.

9. The fault analysis and prediction method of intelligent manufacturing equipment according to claim 1, characterized in that: The step S500 is to judge and process the real-time order information of the intelligent manufacturing equipment and select whether to replace the intelligent manufacturing equipment for order processing, which specifically includes the following steps: S501, analyzing and processing the real-time order information of the intelligent manufacturing equipment to obtain the real-time order duration; S502, making a judgment based on the real-time order duration and the normal operation duration of the intelligent manufacturing equipment; S503: If the real-time order duration is longer than the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment cannot complete the order processing, and the intelligent manufacturing equipment is replaced to process the order; as well as S504: If the real-time order duration is less than or equal to the normal operating duration of the intelligent manufacturing equipment, the intelligent manufacturing equipment can complete the order processing.

10. A fault analysis and prediction system for intelligent manufacturing equipment, used to implement a fault analysis and prediction method for intelligent manufacturing equipment as claimed in any one of claims 1 to 9, characterized in that: include: An order platform (101), the order platform (101) is used to store real-time order information of intelligent manufacturing equipment; A database system (103), wherein the database system (103) is used to store historical data of intelligent manufacturing equipment, factory specification parameter data of intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been put into use; A data reading module (105), wherein the data reading module (105) is used to read real-time order information of intelligent manufacturing equipment, historical data of intelligent manufacturing equipment, factory specification parameter data of intelligent manufacturing equipment, and the length of time the intelligent manufacturing equipment has been put into use; A data analysis module (107), wherein the data analysis module (107) is used to analyze the historical operating parameter data of the intelligent manufacturing equipment to obtain the normal operating historical parameter data and the overload operating historical parameter data of the intelligent manufacturing equipment; A data calculation module (109), wherein the data calculation module (109) performs calculations based on factory specification parameter data of the intelligent manufacturing equipment, the length of time the intelligent manufacturing equipment has been put into use, the normal operation history of the intelligent manufacturing equipment, and the overload operation history of the intelligent manufacturing equipment to obtain an aging rate of the intelligent manufacturing equipment; the data calculation module (109) performs fault prediction on the intelligent manufacturing equipment based on the aging rate of the intelligent manufacturing equipment to obtain a predicted fault occurrence time of the intelligent manufacturing equipment; A comparison and judgment module (111), wherein the comparison and judgment module (111) performs judgment processing based on the real-time order duration and the normal operation duration of the intelligent manufacturing equipment, and selects whether to replace the intelligent manufacturing equipment; as well as A control module (113), wherein the control module (113) is used to realize data transmission and information interaction between various modules.

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