Intelligent analysis and early warning method and system for running state of bamboo press

Abstract

The invention discloses a bamboo press operation state intelligent analysis early warning method and system, and relates to the technical field of bamboo press early warning, and the method comprises the following steps: obtaining a bamboo press operation state and historical fault data, and carrying out the feature extraction of the historical fault data, so as to obtain a historical equipment maintenance influence coefficient and an equipment historical fault position; bamboo press performance data and bamboo press monitoring data within local time under the operation time node are obtained; according to the change condition of the bamboo press performance data and the bamboo press monitoring data, a bamboo press state abnormal coefficient is obtained; according to the bamboo press state abnormity coefficient and the historical equipment maintenance influence coefficient, a bamboo press fault evaluation model is constructed based on a bp neural network, and a fault evaluation coefficient is generated; and performing comparative analysis according to the fault evaluation coefficient and a preset standard evaluation value, and performing early warning in combination with the historical fault position of the equipment. Analysis is carried out by considering the equipment state of the bamboo press and historical fault data, and the problem that early warning of the running state of the bamboo press is not accurate enough is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent early warning for bamboo presses, and more specifically, to the technical field of an intelligent analysis and early warning method and system for the operation status of a bamboo press. Background Art

[0002] Bamboo press is a kind of mechanical equipment specially used for bamboo processing. It is mainly used to process bamboo into products of specific shapes or specifications through high pressure. Its basic principle is to compress, shape, solidify or dehydrate bamboo by mechanical pressure, so as to improve the physical properties of bamboo or make it convenient for subsequent processing. The main application areas of bamboo press include the production of bamboo flooring, bamboo furniture, bamboo board, bamboo products, etc. The working principle of bamboo press is to process bamboo by combining heat and pressure through heating and pressurization. Bamboo will undergo physical and chemical changes under high temperature and high pressure, thereby changing the structure and characteristics of bamboo.

[0003] At present, the intelligent analysis and early warning of the operation status of the bamboo press does not take into account the equipment status of the bamboo press itself and the historical fault data, resulting in insufficient and inaccurate early warning of the operation status of the bamboo press.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for intelligent analysis and early warning of the operating status of a bamboo press. By considering the equipment status of the bamboo press itself and historical fault data for analysis, the problem of insufficient and inaccurate early warning of the operating status of the bamboo press is solved.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An intelligent analysis and early warning method for the operation status of a bamboo press, characterized in that it includes the following steps: obtaining the operation status and historical fault data of the bamboo press, performing feature extraction on the historical fault data to obtain a historical equipment maintenance impact coefficient and a historical equipment fault location; dividing the operation status of the bamboo press into the same operation time nodes, and obtaining the bamboo press performance data and bamboo press monitoring data within the local time under the operation time nodes; obtaining a bamboo press state abnormality coefficient according to changes in the bamboo press performance data and bamboo press monitoring data under the operation time nodes; constructing a bamboo press fault assessment model based on a bp neural network according to the bamboo press state abnormality coefficient and the historical equipment maintenance impact coefficient, and generating a fault assessment coefficient; performing comparative analysis on the fault assessment coefficient and a preset standard assessment value, and issuing an early warning for the location where the fault occurs in combination with the historical fault location of the equipment.

[0008] In a preferred embodiment, the historical fault data is feature extracted to obtain a historical equipment maintenance impact coefficient and a historical equipment fault location. The specific steps are as follows: historical fault data, total equipment operation time and equipment initial value are obtained, and feature extraction is performed on the historical fault data to obtain the number of historical faults, the degree of repair and recovery, and the historical equipment fault location; the equipment failure rate is calculated based on the number of historical faults and the total equipment operation time; the time required for each equipment failure to be repaired is obtained, and compared with the last maintenance time to obtain the degree of attenuation of the equipment due to the failure, and the attenuation factor of the equipment is obtained based on regression analysis; the historical equipment maintenance impact coefficient is calculated based on the equipment initial value combined with the equipment failure rate and the equipment attenuation factor based on a preset equipment maintenance impact formula.

[0009] In a preferred embodiment, the bamboo press performance data is specifically acquired by the following method: acquiring the output data of the bamboo press within k time under the current node to obtain the working efficiency of the bamboo press; monitoring the vibration frequency of each area of ​​the bamboo press under the operating state in real time, and drawing a vibration frequency scatter plot according to the vibration frequency; obtaining the standard value of the bamboo press vibration frequency according to the vibration frequency under the normal operating state; placing the standard value of the bamboo press vibration frequency as a reference line into the vibration frequency scatter plot, and obtaining the abnormal vibration frequency value area based on the vibration frequency standard value; obtaining the equipment mechanical load and equipment aging rate based on the abnormal vibration frequency value area; and obtaining the bamboo press performance data based on the equipment mechanical load and equipment aging rate combined with the working efficiency of the bamboo press based on a preset bamboo press performance formula.

[0010] In a preferred embodiment, the bamboo press monitoring data includes an abnormality coefficient of the bamboo press feeding system, and the abnormality coefficient of the bamboo press feeding system is specifically obtained by the following method: obtaining the feeding speed of the feeding system and the gear speed in the conveyor belt; performing data analysis based on the gear speed in the conveyor belt to obtain the degree of close contact between the gears in the conveyor belt; obtaining the current waveform of the motor in the feeding system within a local time, and obtaining the current deviation value based on the difference between the current waveform and the preset stable current; calculating the degree of close contact between the gears and the current deviation value based on the preset bamboo press feeding system abnormality formula to obtain the bamboo press feeding system abnormality coefficient.

[0011] In a preferred embodiment, the bamboo press state abnormality coefficient is obtained according to the changes in the bamboo press performance data and the bamboo press monitoring data at the running time nodes, and the specific steps are as follows: obtain the bamboo press performance data and the bamboo press monitoring data at all running time nodes; analyze the bamboo press performance data at the running time nodes based on the bamboo press performance data at the previous running time nodes to obtain the difference value of the bamboo press performance data; analyze the bamboo press monitoring data at the running time nodes based on the bamboo press monitoring data at the previous running time nodes to obtain the difference value of the bamboo press monitoring data; calculate the bamboo press state abnormality coefficient based on the preset bamboo press state abnormality formula according to the difference value of the bamboo press performance data combined with the difference value of the bamboo press monitoring data.

[0012] In a preferred embodiment, the fault assessment coefficient is compared and analyzed with a preset standard assessment value, and combined with the historical fault location of the equipment, an early warning is issued for the location where the fault occurs. The specific steps are as follows: the fault assessment coefficient is compared and analyzed with the preset standard assessment value to make a judgment; if the fault assessment coefficient is greater than or equal to the preset standard assessment value, the bamboo press has a fault, and the early warning system prioritizes the historical fault location based on the historical fault location of the equipment for investigation and early warning. If there is no fault at the historical fault location of the equipment, early warning is issued for other equipment areas; if the fault assessment coefficient is less than the preset standard assessment value, there is no fault in the system, and the system continues to monitor.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0014] 1. Fault warning can be carried out through the equipment status of the bamboo press itself, which can monitor the equipment operation status in real time and identify potential faults in advance by analyzing data changes, thereby reducing downtime, reducing maintenance costs, extending equipment life, and improving production efficiency. This method can optimize the equipment maintenance process and achieve accurate preventive maintenance without relying on external equipment.

[0015] 2. Through the historical failure data of bamboo presses, fault warning can be carried out. By analyzing past failure modes and trends, it is possible to identify potential weak links in the equipment and predict the type and time of possible future failures, thereby achieving more accurate and targeted maintenance. This data-driven early warning method can not only improve the accuracy of fault diagnosis, but also help formulate scientific maintenance plans, reduce the occurrence of sudden failures, reduce the risk of production downtime, and improve the overall operating efficiency of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A structural schematic diagram of an intelligent analysis and early warning method for the operating status of a bamboo press provided in an embodiment of the present application.

[0017] Figure 2 A schematic diagram of the structure of an intelligent analysis and early warning system for the operating status of a bamboo press provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Embodiment 1, Figure 1 A schematic diagram of a bamboo press operation status intelligent analysis and early warning method provided in an embodiment of the present application includes the following steps:

[0020] S1, obtaining the operation status and historical fault data of the bamboo press, performing feature extraction on the historical fault data to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location.

[0021] In this example, historical failure data refers to the historical data of equipment failures and repairs during the long-term operation of the bamboo press. Historical failure data usually includes various abnormalities and failure modes that occur during equipment operation. Through in-depth analysis of these data, it is possible to identify the failure trends that are prone to occur in equipment under specific working conditions and identify which components or systems are most prone to failure, and then take targeted improvement measures.

[0022] The historical fault data is subjected to feature extraction to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location. The specific steps are as follows:

[0023] Obtain historical fault data, total equipment operation time and equipment initial value, and perform feature extraction on the historical fault data to obtain the number of historical faults, the degree of repair and recovery, and the historical fault location of the equipment;

[0024] The failure rate of the equipment is calculated based on the number of historical failures and the total operating time of the equipment;

[0025] Obtain the time required for each equipment failure to be repaired, and compare it with the last maintenance time to obtain the degree of equipment attenuation caused by the failure, and obtain the equipment attenuation factor based on regression analysis;

[0026] The historical equipment maintenance impact coefficient is calculated based on the preset equipment maintenance impact formula according to the equipment initial value combined with the equipment failure rate and the equipment attenuation factor.

[0027] The specific calculation formula for the failure rate of the equipment is as follows:

[0028]

[0029] The specific calculation formula of the historical equipment maintenance impact coefficient is as follows:

[0030]

[0031] In the formula, is the historical equipment maintenance influence coefficient, E0 is the initial value of the equipment, η is the attenuation factor of the equipment, Y g is the failure rate of the equipment, w g is the degree of repair and restoration, C w is the number of maintenance times, C g is the number of historical failures, T S The total time the device is running.

[0032] It should be noted that the historical equipment maintenance impact coefficient refers to the situation in which the equipment has a fault in the previous working state. When a fault occurs, the staff will repair the fault location. Each fault will cause the performance or state of the equipment to decay, resulting in a decrease in the working capacity and production efficiency of the equipment, and it cannot reach the initial state. However, after the equipment is repaired, the fault will be eliminated, but due to the mismatch of the maintenance personnel's ability or the maintenance parts, the capacity of the equipment after the repair may be different from that of the previous equipment. In the long run, the faulty part will gradually have weak links, and the load-bearing capacity will become smaller and smaller, which will have a great impact on the load-bearing capacity of the bamboo press.

[0033] S2, dividing the operation status of the bamboo press into the same operation time nodes, and obtaining the bamboo press performance data and bamboo press monitoring data in the local time under the operation time nodes.

[0034] The performance data of the bamboo press refers to the working efficiency of the bamboo press at the current node and the vibration of the press, which can more accurately analyze the equipment status and detect abnormalities in advance. The acquisition of bamboo press performance data has the following advantages for analyzing the abnormal coefficient of the equipment status of the bamboo press:

[0035] Accurately diagnose the cause of failure: Obtaining real-time performance data of the bamboo press can help identify anomalies in the operation of the equipment. This data can provide information about whether the equipment is in normal working condition. For example, excessive power fluctuations or vibrations may be a sign of equipment failure or impending failure. By analyzing this data, potential problems can be discovered in advance, reducing blind repairs and downtime.

[0036] Predictive maintenance: By collecting and analyzing the performance data of the bamboo press over a long period of time, the health trend of the equipment can be discovered, thereby achieving predictive maintenance. Unlike traditional scheduled maintenance, predictive maintenance can determine the maintenance time based on the actual use status of the equipment, avoiding unnecessary repairs, while also dealing with possible failures in advance, reducing equipment downtime and maintenance costs.

[0037] Optimizing equipment operating efficiency: Continuous monitoring of bamboo press performance data can help identify efficiency bottlenecks in equipment operation. For example, analyzing parameters such as power consumption, mechanical load, and operating speed under different working conditions can reveal whether the equipment has problems with unstable operation or low energy efficiency. By adjusting operating parameters or performing necessary equipment debugging, the overall operating efficiency of the bamboo press can be optimized, thereby improving productivity and reducing energy consumption.

[0038] Improve the service life of the equipment: By timely detecting abnormal conditions in the operation of the equipment and making adjustments, the frequency of equipment failures can be effectively reduced, and damage to the equipment caused by excessive wear or excessive load can be reduced, thereby extending the service life of the bamboo press. Performance data can also be used to analyze the wear of different components to facilitate targeted repairs and replacements to avoid major damage to the equipment.

[0039] Data-driven decision support: Performance data provides equipment managers with detailed digital evidence, making equipment status assessment more scientific. Through data analysis, management can more accurately formulate equipment maintenance strategies, make equipment investment decisions, or adjust production plans. Data-driven decisions are more reliable than experience-based decisions and can effectively reduce operating costs.

[0040] Improve equipment reliability: Regularly collecting and analyzing the performance data of the bamboo press can help accumulate historical data on equipment operation and understand the operating rules of the equipment through trend analysis. These data provide a basis for equipment reliability analysis, so that the overall reliability of the bamboo press can be further improved through measures such as improving design, optimizing operation, and improving maintenance level.

[0041] The specific method for obtaining the bamboo press performance data is as follows:

[0042] Get the output data of the bamboo press within k time under the current node and get the working efficiency of the bamboo press;

[0043] Real-time monitoring of the vibration frequency of each area of ​​the bamboo press under the operating state, and drawing a vibration frequency scatter plot according to the vibration frequency;

[0044] According to the vibration frequency of the bamboo press in normal operation state, the standard value of the vibration frequency of the bamboo press is obtained;

[0045] The standard value of the vibration frequency of the bamboo press is placed as a reference line in the vibration frequency scatter plot, and the abnormal vibration frequency value area is obtained based on the standard value of the vibration frequency;

[0046] Acquire the equipment mechanical load and equipment aging rate based on the abnormal vibration frequency value area;

[0047] The bamboo press performance data is calculated based on the preset bamboo press performance formula according to the equipment mechanical load and equipment aging rate combined with the working efficiency of the bamboo press.

[0048] The specific calculation formula of the bamboo press performance data is as follows:

[0049]

[0050] In the formula, l(E) is the performance data of the bamboo press, P max is the maximum performance of the bamboo press, μ is the working efficiency, β L is the aging rate of the equipment, L max is the maximum load that the equipment can bear, and L is the mechanical load of the equipment.

[0051] It should be noted that the impact on performance is adjusted by the size of the load. The greater the mechanical load, the lower the performance. And as the aging rate of the equipment increases, the performance of the equipment will decline. The aging rate of the equipment can be understood as that as the bamboo press is used for a longer time, the performance of the equipment may decrease due to component wear or technological decline, causing aging. The aging of the equipment will reduce its performance. The impact of the mechanical load of the equipment on performance is usually nonlinear. A larger load may lead to a decline in equipment performance.

[0052] The monitoring data of the bamboo press includes the abnormal coefficient of the bamboo press feeding system. The abnormal coefficient of the bamboo press feeding system refers to the process when the bamboo press is working. The pre-processed bamboo is placed in the feeding area through the feeding system, and the bamboo is neatly placed under the press for pressing and processing. During this process, the abnormality of the feeding system may cause uneven placement of the bamboo or delays in feeding, resulting in errors in bamboo processing. Obtaining the abnormal coefficient of the bamboo press feeding system has the following advantages for analyzing the abnormal status of the bamboo press equipment:

[0053] Timely detection of equipment abnormalities: The abnormal coefficient can effectively reflect abnormal conditions in equipment operation. When a component of the feeding system (such as a motor, transmission system or sensor) fails, the system's abnormal coefficient will significantly deviate from the normal value. By monitoring and analyzing these coefficients, problems with the equipment can be detected early, thereby avoiding the deterioration of equipment failures.

[0054] Improve maintenance efficiency: By monitoring the abnormal coefficient of the feeding system, maintenance work can be arranged in a timely manner based on the data, rather than relying on regular inspections or occasional fault reports. This helps to optimize the equipment maintenance cycle, avoid equipment downtime due to sudden failures, and reduce unnecessary maintenance and inspection work, thereby improving resource utilization efficiency.

[0055] Accurately locate faults: The abnormal coefficient can assist in locating the specific fault location by analyzing multiple parameters of the equipment. For example, if the abnormal coefficient of a certain link in the feeding system changes abnormally, it may mean that there is a problem with the component. Through this, the specific location where the equipment fault occurs can be accurately locked, thereby reducing the inspection time of maintenance personnel and improving maintenance efficiency.

[0056] Improve equipment reliability: By analyzing the abnormal coefficient of the bamboo press feeding system, potential problems that occur during equipment operation can be identified and handled early, thereby reducing the number of unexpected shutdowns and improving equipment stability and reliability. This is crucial to ensure the smooth and continuous production process.

[0057] Improve production safety: As a heavy mechanical equipment, bamboo presses often have abnormalities in operation that involve dangerous factors such as high temperature, high pressure, and high speed. Timely detection of abnormal coefficients in the feeding system and taking corresponding measures can effectively avoid large-scale accidents in the equipment and reduce safety risks for operators.

[0058] Realize intelligent operation and maintenance: By introducing an intelligent monitoring system and combining it with real-time data of abnormal coefficients, automatic diagnosis and early warning of bamboo press equipment can be realized. This not only improves the automation level of the system, but also reduces the need for manual intervention, making the daily maintenance and management of the equipment more intelligent and efficient.

[0059] The specific method for obtaining the abnormal coefficient of the bamboo press feeding system is as follows:

[0060] Get the feeding speed of the feeding system and the gear speed in the conveyor belt;

[0061] Performing data analysis based on the rotation speed of the gears in the conveyor belt to obtain the degree of close contact between the gears in the conveyor belt;

[0062] Obtaining a current waveform of a motor in a feeding system in a local time, and obtaining a current deviation value based on the difference between the preset stable currents according to the current waveform;

[0063] The degree of close contact between gears and the deviation value of current are calculated based on the preset bamboo press feeding system abnormality formula to obtain the bamboo press feeding system abnormality coefficient.

[0064] The specific calculation formula of the abnormal coefficient of the bamboo press feeding system is as follows:

[0065]

[0066] In the formula, ω k is the abnormal coefficient of the bamboo press feeding system, v k is the feeding speed of the feeding system, v0 is the standard speed of the feeding system, μ k is the close contact between the gears in the conveyor belt, v μ is the speed of the conveyor belt gear, I t is the current deviation value at the tth moment, where t=1,2,3,...,R, R is an integer, and T is the local time.

[0067] It should be noted that the degree of close contact between the gears in the conveyor belt can be understood as the degree of welding between the gears. When the speed of the feeding system is too high or too low, it may be due to looseness, jamming or damage between the gears. The deviation value of the current can be understood as the current in the feeding system is too large or too small, causing problems in the feeding system. Obtaining the degree of welding between the gears and the deviation value of the current can better help us conduct better analysis.

[0068] S3, according to the change of the bamboo press performance data and the bamboo press monitoring data at the operation time node, the bamboo press state abnormality coefficient is obtained, and the specific steps are as follows:

[0069] Obtain bamboo press performance data and bamboo press monitoring data at all operating time nodes;

[0070] Analyze the performance data of the bamboo press at the running time node based on the performance data of the bamboo press at the last running time node to obtain a difference value of the performance data of the bamboo press;

[0071] Analyze the monitoring data of the bamboo press at the operation time node based on the monitoring data of the bamboo press at the previous operation time node to obtain the difference value of the monitoring data of the bamboo press;

[0072] The bamboo press state abnormality coefficient is calculated based on the preset bamboo press state abnormality formula according to the difference value of the bamboo press performance data combined with the difference value of the bamboo press monitoring data.

[0073] The specific calculation method of the abnormal coefficient of the bamboo press state is as follows:

[0074]

[0075] In the formula, λ z is the abnormal coefficient of bamboo press state, For the tth z The performance data of the bamboo press at the running time nodes, where t z =1,2,3,...,R, R is an integer, TZ is the total operation time of the bamboo press, For the tth z Bamboo press monitoring data at each operating time node.

[0076] It should be noted that by refining the operating time of the bamboo press and comparing and analyzing the different operating time nodes according to the time gradient sequence of the bamboo press performance data and the bamboo press monitoring data at local time, the abnormal situation of the bamboo press equipment status can be better reflected.

[0077] S4, build a bamboo press fault assessment model based on the BP neural network according to the bamboo press state abnormality coefficient and the historical equipment maintenance impact coefficient, and generate a fault assessment coefficient.

[0078] The bamboo press state abnormality coefficient and historical equipment maintenance influence coefficient are based on the bp neural network algorithm to construct a bamboo press fault assessment model, specifically:

[0079] The acquired bamboo press state abnormality coefficient and historical equipment maintenance influence coefficient are used to construct a bamboo press fault output evaluation model to generate a fault evaluation coefficient;

[0080] The specific calculation formula of the fault assessment coefficient is as follows:

[0081]

[0082] In the formula, θ is the fault assessment coefficient, λ z is the abnormal coefficient of bamboo press state, is the historical equipment maintenance impact coefficient, is the weight coefficient of the bamboo press state abnormality coefficient, is the weight coefficient of the historical equipment maintenance impact coefficient.

[0083] S5: Compare and analyze the fault assessment coefficient with the preset standard assessment value, and give an early warning of the fault location based on the historical fault location of the equipment. The specific steps are as follows:

[0084] Compare and analyze the fault assessment coefficient with the preset standard assessment value to make a judgment;

[0085] If the fault assessment coefficient is greater than or equal to the preset standard assessment value, the bamboo press has a fault. The early warning system will prioritize the historical fault location based on the historical fault location of the equipment and issue an early warning. If there is no fault at the historical fault location of the equipment, then an early warning will be issued for other equipment areas.

[0086] If the fault assessment coefficient is less than the preset standard assessment value, the system has no faults and the system continues to monitor.

[0087] Embodiment 2, Figure 2A schematic diagram of the structure of an intelligent analysis and early warning system for the operation status of a bamboo press provided in an embodiment of the present application, including a historical data analysis module, a bamboo press status data acquisition module, a bamboo press status data analysis module, a model building module and an early warning module:

[0088] A historical data analysis module is used to obtain the operation status and historical fault data of the bamboo press, and to extract features from the historical fault data to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location;

[0089] A bamboo press state data acquisition module, used to divide the bamboo press operation state into the same operation time nodes, and acquire the bamboo press performance data and bamboo press monitoring data in the local time under the operation time node;

[0090] The bamboo press state data analysis module is used to obtain the bamboo press state abnormality coefficient according to the change of the bamboo press performance data and the bamboo press monitoring data at the operation time node;

[0091] A model building module is used to build a bamboo press fault assessment model based on a BP neural network according to the bamboo press state abnormality coefficient and the historical equipment maintenance influence coefficient, and generate a fault assessment coefficient;

[0092] The early warning module is used to compare and analyze the fault assessment coefficient with the preset standard assessment value, and to issue an early warning for the location where the fault occurs in combination with the historical fault location of the equipment.

[0093] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0094] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0095] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0096] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0097] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0098] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A bamboo press operation status intelligent analysis and early warning method, characterized in that: The steps include: Obtaining the operation status and historical fault data of the bamboo press, and extracting features from the historical fault data to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location; Divide the operation state of the bamboo press into the same operation time nodes, and obtain the bamboo press performance data and bamboo press monitoring data in the local time under the operation time nodes; The abnormal coefficient of bamboo press state is obtained according to the changes of bamboo press performance data and bamboo press monitoring data at the operation time node; According to the abnormal coefficient of bamboo press state and the influence coefficient of historical equipment maintenance, a bamboo press fault assessment model is constructed based on BP neural network to generate fault assessment coefficient; A comparative analysis is performed based on the fault assessment coefficient and the preset standard assessment value, and combined with the historical fault locations of the equipment, an early warning is issued for the location where the fault occurs.

2. According to claim 1, a bamboo press operation status intelligent analysis and early warning method is characterized in that: The historical fault data is subjected to feature extraction to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location. The specific steps are as follows: Obtain historical fault data, total equipment operation time and equipment initial value, and perform feature extraction on the historical fault data to obtain the number of historical faults, the degree of repair and recovery, and the historical fault location of the equipment; The failure rate of the equipment is calculated based on the number of historical failures and the total operating time of the equipment; Obtain the time required for each equipment failure to be repaired, and compare it with the last maintenance time to obtain the degree of equipment attenuation caused by the failure, and obtain the equipment attenuation factor based on regression analysis; The historical equipment maintenance impact coefficient is calculated based on the preset equipment maintenance impact formula according to the equipment initial value combined with the equipment failure rate and the equipment attenuation factor.

3. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 1 is characterized in that: The specific method for obtaining the bamboo press performance data is as follows: Get the output data of the bamboo press within k time under the current node and get the working efficiency of the bamboo press; Real-time monitoring of the vibration frequency of each area of ​​the bamboo press under the operating state, and drawing a vibration frequency scatter plot according to the vibration frequency; According to the vibration frequency of the bamboo press in normal operation state, the standard value of the vibration frequency of the bamboo press is obtained; The standard value of the vibration frequency of the bamboo press is placed as a reference line in the vibration frequency scatter plot, and the abnormal vibration frequency value area is obtained based on the standard value of the vibration frequency; Acquire the equipment mechanical load and equipment aging rate based on the abnormal vibration frequency value area; The bamboo press performance data is calculated based on the preset bamboo press performance formula according to the equipment mechanical load and equipment aging rate combined with the working efficiency of the bamboo press.

4. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 1 is characterized in that: The monitoring data of the bamboo press includes the abnormal coefficient of the bamboo press feeding system. The specific acquisition method of the abnormal coefficient of the bamboo press feeding system is as follows: Get the feeding speed of the feeding system and the gear speed in the conveyor belt; Performing data analysis based on the rotation speed of the gears in the conveyor belt to obtain the degree of close contact between the gears in the conveyor belt; Obtaining a current waveform of a motor in a feeding system within a local time, and obtaining a current deviation value based on the difference between the current waveform and a preset stable current; The degree of close contact between gears and the deviation value of current are calculated based on the preset bamboo press feeding system abnormality formula to obtain the bamboo press feeding system abnormality coefficient.

5. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 1 is characterized in that: The specific steps of obtaining the abnormal coefficient of the bamboo press state according to the changes in the bamboo press performance data and the bamboo press monitoring data at the operating time node are as follows: Obtain bamboo press performance data and bamboo press monitoring data at all operating time nodes; Analyze the performance data of the bamboo press at the running time node based on the performance data of the bamboo press at the last running time node to obtain a difference value of the performance data of the bamboo press; Analyze the monitoring data of the bamboo press at the operation time node based on the monitoring data of the bamboo press at the previous operation time node to obtain the difference value of the monitoring data of the bamboo press; The bamboo press state abnormality coefficient is calculated based on the preset bamboo press state abnormality formula according to the difference value of the bamboo press performance data combined with the difference value of the bamboo press monitoring data.

6. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 1 is characterized in that: The specific steps of comparing and analyzing the fault assessment coefficient with the preset standard assessment value and warning the location of the fault in combination with the historical fault location of the equipment are as follows: Compare and analyze the fault assessment coefficient with the preset standard assessment value to make a judgment; If the fault assessment coefficient is greater than or equal to the preset standard assessment value, the bamboo press has a fault. The early warning system will prioritize the historical fault location based on the historical fault location of the equipment and issue an early warning. If there is no fault at the historical fault location of the equipment, it will issue an early warning to other equipment areas. If the fault assessment coefficient is less than the preset standard assessment value, the system has no faults and the system continues to monitor.

7. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 2 is characterized in that: The specific calculation formula for the failure rate of the equipment is as follows: The specific calculation formula of the historical equipment maintenance impact coefficient is as follows: In the formula, is the historical equipment maintenance influence coefficient, E0 is the initial value of the equipment, η is the attenuation factor of the equipment, Y g is the failure rate of the equipment, w g is the degree of repair and restoration, C w is the number of maintenance times, C g is the number of historical failures, T S The total time the device is running.

8. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 5 is characterized in that: The specific calculation formula of the bamboo press performance data is as follows: In the formula, l(E) is the performance data of the bamboo press, P max is the maximum performance of the bamboo press, μ is the working efficiency, β L is the aging rate of the equipment, L max is the maximum load that the equipment can bear, and L is the mechanical load of the equipment.

9. The method for intelligent analysis and early warning of the operation status of a bamboo press according to claim 4 is characterized in that: The specific calculation formula of the abnormal coefficient of the bamboo press feeding system is as follows: In the formula, ω k is the abnormal coefficient of the bamboo press feeding system, v k is the feeding speed of the feeding system, v0 is the standard speed of the feeding system, μ k is the close contact between the gears in the conveyor belt, v μ is the speed of the conveyor belt gear, I t is the current deviation value at the tth moment, where t=1,2,3,...,R, R is an integer, and T is the local time.

10. A system using the method for intelligent analysis and early warning of bamboo press operation status as claimed in any one of claims 1 to 9, characterized in that: It includes historical data analysis module, bamboo press status data acquisition module, bamboo press status data analysis module, model building module and early warning module: A historical data analysis module is used to obtain the operation status and historical fault data of the bamboo press, and to extract features from the historical fault data to obtain the historical equipment maintenance impact coefficient and the historical equipment fault location; A bamboo press state data acquisition module, used for dividing the bamboo press operation state into the same operation time nodes, and acquiring the bamboo press performance data and bamboo press monitoring data in the local time under the operation time nodes; The bamboo press state data analysis module is used to obtain the bamboo press state abnormality coefficient according to the change of the bamboo press performance data and the bamboo press monitoring data at the operation time node; A model building module is used to build a bamboo press fault assessment model based on a BP neural network according to the bamboo press state abnormality coefficient and the historical equipment maintenance influence coefficient, and generate a fault assessment coefficient; The early warning module is used to compare and analyze the fault assessment coefficient with the preset standard assessment value, and to issue an early warning for the location where the fault occurs in combination with the historical fault location of the equipment.

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