Bean shelling equipment state monitoring and early warning method and system

Abstract

The application discloses a legume shelling equipment state monitoring and early warning method and system, and relates to the technical field of equipment state monitoring.The application establishes a legume shelling equipment state monitoring and early warning method and system, combines a blockage evaluation model and multi-dimensional dynamic monitoring of bearing operation states, realizes real-time evaluation and accurate early warning of the whole process of equipment operation, and effectively improves the accuracy of blockage prediction and fault detection through scientific model construction, dynamic adjustment mechanism and data acquisition mode, greatly reduces the risk of shutdown or damage caused by equipment abnormalities, thereby guaranteeing the continuity and efficiency of equipment operation.Meanwhile, intuitive control panel display and early warning information prompt are provided, so that operators can quickly understand the equipment state and take necessary measures, effectively simplifying the maintenance process.The overall method not only significantly improves the operation stability, safety and service life of the shelling equipment, but also improves the production management efficiency through intelligent means.

Description

Technical Field

[0001] This invention relates to the field of equipment condition monitoring technology, specifically to a method and system for condition monitoring and early warning of bean shelling equipment. Background Technology

[0002] As an important raw material in the food industry, the shelling process of beans plays a crucial role in the processing of beans. Existing bean shelling equipment is usually designed as a general-purpose machine, capable of shelling different types and sizes of beans. However, these machines mainly rely on monitoring a single parameter during operation, such as using vibration sensors to monitor equipment vibration or temperature sensors to monitor local temperature changes. While these methods can capture abnormal information about the equipment to some extent, they only provide limited reference to the equipment's operating status due to the limited monitoring range.

[0003] Current technologies lack effective real-time monitoring methods to address potential blockages in dehulling equipment, making timely warnings impossible. This is especially problematic when blockages are not identified in their early stages, easily leading to sudden shutdowns of the equipment. This not only increases operational risks but also directly impacts production line continuity and overall efficiency. Furthermore, some solutions attempt to optimize dehulling machine performance by setting mechanical structure parameters; however, these improvements often overlook the interplay of multi-dimensional data in a dynamic operating environment, resulting in inaccurate assessments of blockage issues.

[0004] In existing bean shelling equipment, the difficulty in accurately identifying blockages during the shelling process is a common problem. This phenomenon is usually accompanied by complex situations such as bean accumulation inside the shelling machine and a decrease in operating speed. However, due to the lack of a dynamic comprehensive evaluation model, equipment managers find it difficult to detect and address the problem in a timely manner, which may ultimately lead to production line shutdowns. Furthermore, the operation of the shelling machine relies on bearings as a core component, and the vibration, temperature, and speed parameters of the bearings have a significant impact on the overall performance of the equipment. However, existing technologies rarely link blockage problems with bearing conditions for overall assessment, lacking a scientific and comprehensive condition monitoring and early warning method, making it difficult to guarantee the safety and stability of equipment operation. Therefore, how to construct a technical solution that can comprehensively assess blockage conditions and bearing conditions, monitor the equipment's operating status in real time through multi-dimensional dynamic parameters, and effectively provide early warnings of potential risks and failures has become a key technical challenge that urgently needs to be solved in the field of bean shelling equipment. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for monitoring and early warning of the status of bean shelling equipment, thus solving the problems in the background technology.

[0006] To achieve the above objectives, the present invention provides a method for monitoring and early warning of the status of bean shelling equipment, comprising:

[0007] Step 1: Establish a blockage assessment model based on the target bean type;

[0008] Step 2: Determine the blockage failure risk coefficient during the current operation of the deshelling machine based on the blockage assessment model;

[0009] Step 3: During the operation of the shelling machine, determine the bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient based on the current bearing vibration amplitude, bearing temperature, and bearing rotation. Then, evaluate the overall operating status of the bearing based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient, and determine the current overall operating status evaluation coefficient of the bearing.

[0010] Step 4: Based on the blockage failure risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the shelling machine, monitor and issue early warnings for the current status of the shelling machine, and determine whether to generate early warning information.

[0011] As a further aspect of the present invention: in step one, the specific method for establishing the blockage assessment model based on the target bean type is as follows:

[0012] AS1: For target beans of the same type, place the target beans to be shelled in the shelling funnel each time, and obtain the weight of the target beans to be shelled each time. Simultaneously, during the operation of the shelling machine, obtain the weight of the target beans in the shelling funnel in real time and record it as follows. ;

[0013] AS2: Next, obtain the shelling rate during each shelling process of the shelling machine and denote it as R(t);

[0014] AS3: During the operation of the shelling machine, the weight of the target shelled beans is acquired in real time and recorded as... ;

[0015] AS4: Based on the real-time target bean weight in the hulling funnel obtained each time. The shelling rate R(t) during each shelling process and the weight of the target shelled beans in each real-time step. A congestion assessment model is established, which is represented by the following formula:

[0016]

[0017] in, For the results of the congestion assessment model, It is expressed as a functional relationship between time, real-time target bean weight in the funnel, real-time target shelled bean weight, shelling rate, and the blockage assessment model.

[0018] As a further aspect of the present invention: in step two, the specific method for determining the blockage failure risk coefficient during the operation of the deshelling machine based on the blockage assessment model is as follows:

[0019] BS1: Obtain the current target bean weight for shelling and the shelling rate of the current shelling machine. Simultaneously, determine the duration of the current shelling machine's operation from startup to current running time. By using these three parameters as input, a congestion assessment model is used to predict and evaluate the current target bean weight for shelling, which is then recorded as... ;

[0020] BS2: At the same time, obtain the actual weight of the target shelled beans and record it as... ;

[0021] BS3: Next, the risk factor for blockage during the operation of the shelling machine is determined using the following formula:

[0022]

[0023] in, This is expressed as the congestion failure risk coefficient. This is a dynamic adjustment factor that reflects the trend of dynamic changes in the shelling machine, and , Represented as a change factor; Indicates permission The error range threshold.

[0024] As a further aspect of the present invention: In step three, the bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient are determined based on the current bearing vibration amplitude, bearing temperature, and bearing rotation, respectively. Then, the overall operating state of the bearing is evaluated based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient. The specific method for determining the overall operating state evaluation coefficient of the bearing is as follows:

[0025] CS1: Obtain the bearing vibration amplitude during the operation of the shelling machine and record it as... The bearing vibration coefficient is determined in real time using the following evaluation function:

[0026]

[0027] in, and These represent the lower and upper limits of the normal vibration range during bearing operation.

[0028] CS2: Obtain the bearing temperature during the operation of the shelling machine and record it as... The bearing temperature coefficient is determined by evaluating the bearing temperature in real time using the following evaluation function:

[0029]

[0030] in, and These represent the minimum and maximum values ​​of the normal operating temperature range for the bearing.

[0031] CS3: Next, obtain the setting parameters for the bearing rotation during the operation of the casing machine, and record them as... Simultaneously, it acquires and records the actual parameters of bearing rotation during operation in real time. ; combination and Determine the deviation value of bearing rotation during real-time operation. ,and Next, the bearing speed is evaluated in real time using the following evaluation function to determine the bearing speed coefficient:

[0032]

[0033] in, This represents the lower limit of the allowable bearing rotation deviation. This represents the upper limit of the allowable bearing rotation deviation.

[0034] As a further aspect of the present invention: after step CS3, the method further includes:

[0035] CS4: Based on bearing vibration coefficient Bearing temperature coefficient and bearing speed coefficient The overall operating condition of the bearing is evaluated, and the current overall operating condition evaluation coefficient of the bearing is determined:

[0036]

[0037] in, This is represented as the overall bearing operating condition evaluation coefficient. These are the weighting coefficients, and .

[0038] As a further aspect of the present invention: in step four, the specific method for monitoring and issuing early warnings about the current status of the deshelling machine based on the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient during the current deshelling machine operation is as follows:

[0039] DS1: Obtain the blockage failure risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the deshelling machine. The current operating indicators of the deshelling machine are determined through the following stability evaluation model, which is expressed by the following formula:

[0040]

[0041] in, This indicates the current operating status of the shelling machine. , and Represented as stability prediction coefficient;

[0042] DS2: Operating indicators based on the current status of the shelling machine. Monitor and issue early warnings regarding the current status of the shelling machine:

[0043] like This indicates that the current shelling machine is within the normal operating range;

[0044] like This indicates that the current shelling machine is out of normal operating range, and a warning message is generated.

[0045] in, These are preset values; the specific settings will be provided by professional staff.

[0046] DS3: Determines whether a warning message has been generated. If a warning message is detected, an alarm is issued to notify the user; otherwise, no action is taken.

[0047] As a further aspect of the present invention: after step four, the method further includes:

[0048] Step 5: When a warning message is generated, the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the shelling machine will be displayed on the control panel, prompting the operator to check the status of the shelling machine or adjust the process parameters to ensure the smooth operation of the shelling machine.

[0049] A status monitoring and early warning system for bean shelling equipment includes:

[0050] The blockage assessment model determination module is used to establish a blockage assessment model based on the target bean type.

[0051] The blockage failure risk determination module is used to determine the blockage failure risk coefficient during the current operation of the deshelling machine based on the blockage assessment model.

[0052] The bearing evaluation and determination module is used to determine the bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient based on the current bearing vibration amplitude, bearing temperature, and bearing rotation during the operation of the deshelling machine. Then, based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient, the overall operating status of the bearing is evaluated, and the current overall operating status evaluation coefficient of the bearing is determined.

[0053] The monitoring and early warning module is used to monitor and warn the current status of the deshelling machine based on the blockage fault risk coefficient and the current overall bearing operating status evaluation coefficient during the current operation of the deshelling machine, and to determine whether to generate an early warning message.

[0054] The monitoring and display terminal is used to display the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient on the control panel when an early warning information is generated. This prompts the operator to check the status of the deshelling machine or adjust the process parameters to ensure the smooth operation of the deshelling machine.

[0055] This invention provides a method and system for monitoring and early warning of the status of bean shelling equipment. Compared with the prior art, it has the following advantages:

[0056] This invention establishes a status monitoring and early warning method and system for bean shelling equipment. By combining a blockage assessment model with multi-dimensional dynamic monitoring of bearing operating status, it achieves real-time assessment and accurate early warning throughout the entire equipment operation process. Its scientific model construction, dynamic adjustment mechanism, and data acquisition method effectively improve the accuracy of blockage prediction and fault detection, significantly reducing the risk of downtime or damage caused by equipment malfunctions, thereby ensuring the continuity and efficiency of equipment operation.

[0057] Furthermore, the system provides an intuitive control panel display and early warning information prompts, enabling operators to quickly understand the equipment status and take necessary measures, effectively simplifying the maintenance process. This overall approach not only significantly improves the operational stability, safety, and service life of the shelling equipment, but also enhances production management efficiency through intelligent means, providing technical support and practical reference for the bean processing industry. Attached Figure Description

[0058] The invention will now be further described with reference to the accompanying drawings.

[0059] Figure 1 This is a flowchart illustrating the steps of the bean shelling equipment status monitoring and early warning method of the present invention;

[0060] Figure 2 This is a structural framework diagram of the status monitoring and early warning system for the bean shelling equipment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] Please see Figure 1 This invention provides a method for monitoring and early warning of the status of bean shelling equipment, including:

[0064] Step 1: Establish a blockage assessment model based on the target bean type;

[0065] It should be noted that the target bean refers to the bean that needs to be shelled by the shelling machine each time. The shelling machine can generally shell different types of beans. In this embodiment, different blockage assessment models need to be established for different types of beans.

[0066] The specific method for establishing a blockage assessment model based on the target bean type is as follows:

[0067] AS1: For target beans of the same type, place the target beans to be shelled in the shelling funnel each time, and obtain the weight of the target beans to be shelled each time. Simultaneously, during the operation of the shelling machine, obtain the weight of the target beans in the shelling funnel in real time and record it as follows. ;

[0068] Specifically, the shelling funnel of the shelling machine is equipped with a weight sensor. The target beans that need to be shelled are placed in the shelling funnel each time, so as to obtain the weight of the target beans to be shelled each time.

[0069] AS2: Next, obtain the shelling rate during each shelling process of the shelling machine and denote it as R(t);

[0070] AS3: During the operation of the shelling machine, the weight of the target shelled beans is acquired in real time and recorded as... ;

[0071] It should be noted that: target shelled beans refer to beans that have been completely shelled by the shelling machine and are collected by a collection box equipped with a weight sensor, while the weight of the target shelled beans is acquired in real time.

[0072] AS4: Based on the real-time acquisition of the target bean weight in the hulling funnel each time. The shelling rate R(t) during each shelling process and the weight of the target shelled beans in each real-time step. A congestion assessment model is established, which is represented by the following formula:

[0073]

[0074] in, For the results of the congestion assessment model, It is expressed as a functional relationship between time, real-time target bean weight in the funnel, real-time target shelled bean weight, shelling rate, and the blockage assessment model;

[0075] It should be noted that in step AS4, by acquiring a large number of recorded data from AS1-AS3 and conducting experiments, the relationship between the shelling rate of the shelling machine and the target bean weight over time can be obtained; through multiple recordings, a blockage assessment model is established, which can be obtained through existing techniques such as regression analysis and machine learning, which will not be elaborated here.

[0076] By establishing a blockage assessment model for different types of beans, taking into account the target bean weight, shelling rate, and real-time data changes, the blockage situation in the shelling process can be accurately assessed, and the operating status of the shelling machine can be dynamically reflected. In addition, the use of weight sensors and other real-time data recording devices makes the model construction process more scientific and rigorous. Through the accumulation and analysis of multiple experimental data, advanced technologies such as machine learning are used to optimize the model, ultimately achieving high-precision prediction of blockage and providing stability assurance for the shelling process.

[0077] Step 2: Determine the blockage failure risk coefficient during the current operation of the deshelling machine based on the blockage assessment model;

[0078] The specific method for determining the blockage failure risk coefficient during the operation of the deshelling machine based on the blockage assessment model is as follows:

[0079] BS1: Obtain the current target bean weight for shelling and the shelling rate of the current shelling machine. Simultaneously, determine the duration of the current shelling machine's operation from startup to current running time. By using these three parameters as input, a congestion assessment model is used to predict and evaluate the current target bean weight for shelling, which is then recorded as... ;

[0080] BS2: At the same time, obtain the actual weight of the target shelled beans and record it as... ;

[0081] BS3: Next, the risk factor for blockage during the operation of the shelling machine is determined using the following formula:

[0082]

[0083] in, This is expressed as the congestion failure risk coefficient. This is a dynamic adjustment factor that reflects the trend of dynamic changes in the shelling machine, and , This is represented as a variation factor, specifically set by professional staff, and the running time. The longer the duration, the greater the dynamic adjustment factor for congestion. The closer to 1; Indicates permission The error range threshold;

[0084] This step uses a blockage assessment model combined with key parameters such as real-time target bean weight and shelling rate to dynamically calculate the blockage failure risk coefficient. The calculation of the blockage failure risk coefficient incorporates dynamic adjustment factors and error thresholds, making the assessment process more adaptable and flexible, and continuously optimizing as the shelling machine runs longer. This mechanism enables early warning of blockage risk, helping operators to take intervention measures before blockage occurs, thereby effectively reducing the impact of blockage on equipment operation and extending equipment life.

[0085] Step 3: During the operation of the shelling machine, determine the bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient based on the current bearing vibration amplitude, bearing temperature, and bearing rotation. Then, evaluate the overall operating status of the bearing based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient, and determine the current overall operating status evaluation coefficient of the bearing.

[0086] The bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient are determined based on the current bearing vibration amplitude, bearing temperature, and bearing rotation, respectively. Then, the overall operating state of the bearing is evaluated based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient. The specific method for determining the overall bearing operating state evaluation coefficient is as follows:

[0087] CS1: Obtain the bearing vibration amplitude during the operation of the shelling machine and record it as... The bearing vibration coefficient is determined in real time using the following evaluation function:

[0088]

[0089] in, and These represent the lower and upper limits of the normal vibration range during bearing operation.

[0090] CS2: Obtain the bearing temperature during the operation of the shelling machine and record it as... The bearing temperature coefficient is determined by evaluating the bearing temperature in real time using the following evaluation function:

[0091]

[0092] in, and These represent the minimum and maximum values ​​of the normal operating temperature range for the bearing.

[0093] CS3: Next, obtain the setting parameters for the bearing rotation during the operation of the casing machine, and record them as... Simultaneously, it acquires and records the actual parameters of bearing rotation during operation in real time. ; combination and Determine the deviation value of bearing rotation during real-time operation. ,and Next, the bearing speed is evaluated in real time using the following evaluation function to determine the bearing speed coefficient:

[0094]

[0095] in, This represents the lower limit of the allowable bearing rotation deviation. This is expressed as the upper limit of the allowable bearing rotation deviation;

[0096] CS4: Based on bearing vibration coefficient Bearing temperature coefficient and bearing speed coefficient The overall operating condition of the bearing is evaluated, and the current overall operating condition evaluation coefficient of the bearing is determined:

[0097]

[0098] in, This is represented as the overall bearing operating condition evaluation coefficient. These are the weighting coefficients, and ;

[0099] It should be noted that the above bearing vibration amplitude is obtained by a vibration sensor fixed on the bearing housing or support structure near the core area of ​​the bearing. When obtaining the actual parameters of bearing temperature and bearing rotation during the operation of the deshelling machine, temperature sensors and speed sensors are installed.

[0100] This step comprehensively assesses the bearing's operating status. Through dynamic monitoring of key indicators such as vibration amplitude, temperature, and speed, the bearing's vibration coefficient, temperature coefficient, and speed coefficient are accurately calculated. Combined with a weighted model, the overall operating status assessment coefficient is derived. By collecting data from sensors in real time and comparing it with standard value ranges, abnormalities in bearing operation can be quickly located, avoiding equipment downtime or damage caused by bearing failure.

[0101] Step 4: Based on the blockage failure risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the shelling machine, monitor and issue early warnings for the current status of the shelling machine, and determine whether to generate early warning information;

[0102] The specific method for monitoring and issuing early warnings about the current status of the deshelling machine based on the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the deshelling machine is as follows:

[0103] DS1: Obtain the blockage failure risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the deshelling machine. The current operating indicators of the deshelling machine are determined through the following stability evaluation model, which is expressed by the following formula:

[0104]

[0105] in, This indicates the current operating status of the shelling machine. , and Represented as stability prediction coefficient;

[0106] DS2: Operating indicators based on the current status of the shelling machine. Monitor and issue early warnings regarding the current status of the shelling machine:

[0107] like This indicates that the current shelling machine is within the normal operating range;

[0108] like This indicates that the current shelling machine is out of normal operating range, and a warning message is generated.

[0109] in, These are preset values; the specific settings will be provided by professional staff.

[0110] DS3: Determines whether a warning message has been generated. If a warning message is detected, an alarm is issued to notify the user; otherwise, no action is taken.

[0111] This step combines the blockage failure risk coefficient and the overall bearing operating status evaluation coefficient, and uses a stability evaluation model to dynamically calculate the operating status indicators of the shelling machine, thereby comprehensively monitoring the overall status of the equipment. By defining the normal and abnormal operating states through clear indicator ranges, early warning information is generated in real time and alarms are triggered, effectively reducing the risk of sudden failures during equipment operation. This step makes the early warning mechanism more intelligent and real-time, ensuring that the shelling machine can respond in a timely manner when abnormal situations occur, and ensuring the continuity and stability of the production process.

[0112] Step 5: When a warning message is generated, the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient during the current operation of the shelling machine will be displayed on the control panel, prompting the operator to check the status of the shelling machine or adjust the process parameters to ensure the smooth operation of the shelling machine.

[0113] Example 2

[0114] Please see Figure 2In this embodiment, based on Embodiment 1, but differing from Embodiment 1 in that this embodiment also provides a status monitoring and early warning system for bean shelling equipment, including:

[0115] The blockage assessment model determination module is used to establish a blockage assessment model based on the target bean type.

[0116] The blockage failure risk determination module is used to determine the blockage failure risk coefficient during the current operation of the deshelling machine based on the blockage assessment model.

[0117] The bearing evaluation and determination module is used to determine the bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient based on the current bearing vibration amplitude, bearing temperature, and bearing rotation during the operation of the deshelling machine. Then, based on the determined bearing vibration coefficient, bearing temperature coefficient, and bearing speed coefficient, the overall operating status of the bearing is evaluated, and the current overall operating status evaluation coefficient of the bearing is determined.

[0118] The monitoring and early warning module is used to monitor and warn the current status of the deshelling machine based on the blockage fault risk coefficient and the current overall bearing operating status evaluation coefficient during the current operation of the deshelling machine, and to determine whether to generate an early warning message.

[0119] The monitoring and display terminal is used to display the blockage fault risk coefficient and the overall bearing operating status evaluation coefficient on the control panel when an early warning information is generated. This prompts the operator to check the status of the deshelling machine or adjust the process parameters to ensure the smooth operation of the deshelling machine.

[0120] Example 3

[0121] In its specific implementation, this embodiment includes all the implementation processes of the two sets of embodiments described above.

[0122] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0123] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for monitoring and early warning of the state of a bean hulling plant, characterized by, Comprising: Step one: according to the target bean species to establish the jam evaluation model; Step two: according to the jam evaluation model to determine the current hulling machine running process in the jam fault risk coefficient; Step three: in the process of running the hulling machine, according to the current bearing vibration amplitude, bearing temperature and bearing rotation respectively determine the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient, then according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient to the bearing overall operation state evaluation, determine the current bearing overall operation state evaluation coefficient; Step four: according to the current hulling machine running process in the jam fault risk coefficient and the current bearing overall operation state evaluation coefficient, the current hulling machine state monitoring and early warning, judge whether to produce early warning information; In the step one, the specific way of establishing jam evaluation model according to target bean species is: AS1: for the target beans belonging to the same kind, the target beans of each time of shelling are placed in the shelling funnel, the weight of the target beans of each time of shelling is obtained, and the weight of the target beans in the shelling funnel is obtained in real time during the operation of the shelling machine and recorded as ; AS2: then get the hulling rate in the process of each hulling machine, and recorded as R(t); AS3: During the operation of the shelling machine, the weight of the target shelled beans is obtained in real time, and is recorded as ; AS4: Real-time target bean weight in the hulling funnel is acquired in real time according to each time , the hulling rate R(t) in the hulling process of each time and the real-time target hulling bean weight of each time A clogging evaluation model is established, and the clogging evaluation model is embodied by the following formula: ； wherein, for the clogging assessment model result, is a function of time, target bean weight in the dehulling funnel, target dehulled bean weight, dehulling rate, and the clogging assessment model; In the step two, the specific way of determining the jam fault risk coefficient in the process of running the hulling machine according to the jam evaluation model is: BS1: obtain the target bean weight in the current hulling funnel and the current hulling rate in the current hulling process of the current huller, determine the time length from the start of the current huller to the current operation, and input the three parameters into the blockage evaluation model to evaluate the target hulling bean weight in the current operation, and record the target hulling bean weight in the current operation as ; BS2: At the same time, the weight of the actual target shelled beans is obtained and recorded as ; BS3: then determine the jam fault risk coefficient in the process of running the hulling machine through the following formula: ； wherein, is expressed as a plugging failure risk coefficient, is a dynamic adjustment factor, reflecting the trend of the dynamic change of the sheller, and , is expressed as a change factor; is expressed as an error range threshold value. is expressed as an error range threshold value.

2. The soybean shelling plant condition monitoring and early warning method according to claim 1, characterized in that, In the step three, the specific way of determining the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient according to the current bearing vibration amplitude, bearing temperature and bearing rotation, and then evaluating the bearing overall operation state according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient, and determining the bearing overall operation state evaluation coefficient is: CS1: Obtain the bearing vibration amplitude during the operation of the sheller, and record it as The bearing vibration coefficient is determined by real-time bearing vibration evaluation through the following evaluation function: ； wherein with denotes the lower and upper limit value of the range of normal vibration values for the bearing vibration in operation. CS2: Obtain the bearing temperature during the operation of the decanning machine and denote it as The bearing temperature coefficient is determined by real-time bearing temperature evaluation through the following evaluation function: ； wherein with denotes the minimum and maximum of the range of normal temperature values when the bearing is operated at a temperature; CS3: Then get the set parameters of the bearing rotation in the process of the shell machine running, and mark them as At the same time, the actual parameters of the bearing rotation in the process of running are obtained in real time, and are marked as ; combined with and , the deviation value of the bearing rotation in the process of real-time running is determined , and ; then the bearing speed evaluation is carried out in real time through the following evaluation function to determine the bearing speed coefficient: ； wherein, represents a lower limit value of the allowable bearing rotation deviation, represents an upper limit value of the allowable bearing rotation deviation.

3. The soybean shelling plant condition monitoring and early warning method according to claim 2, characterized in that, The step CS3 still includes: CS4: according to the bearing vibration coefficient , the bearing temperature coefficient and the bearing rotation speed coefficient evaluate the overall operation state of the bearing, and determine the current overall operation state evaluation coefficient of the bearing: ； wherein, represents the bearing overall operation state evaluation coefficient, is a weight coefficient, and .

4. The soybean shelling plant condition monitoring and early warning method according to claim 3, characterized in that, In the step four, the specific way of monitoring and early warning of the current hulling machine state according to the jam fault risk coefficient in the current hulling machine running process and the current bearing overall operation state evaluation coefficient is: DS1: get the jam fault risk coefficient in the current hulling machine running process and the current bearing overall operation state evaluation coefficient through the following stability evaluation model to determine the current hulling machine state running index, and the stability evaluation model is embodied by the following formula: ； wherein, denotes the current peeler state operating indicator, , and denotes the stability prediction coefficient; DS2: running index according to current shell breaking machine state monitoring and early warning of current shell breaking machine state: If , indicates that the current state of the decanning machine is within the normal operating range; If , it indicates that the current state of the shell separator deviates from the normal operation range, and a pre-warning information is generated; wherein, is a preset value, which is specifically set by a professional staff; DS3: judge whether to produce early warning information, if it is judged that there is early warning information, issue a warning to inform, otherwise do not do anything.

5. The soybean shelling plant condition monitoring and early warning method according to claim 4, characterized in that, The step four still includes: Step five: when it is judged that there is early warning information, display the jam fault risk coefficient in the current hulling machine running process and the current bearing overall operation state evaluation coefficient in the control panel, prompt the operator to check the hulling machine state or adjust the process parameters, to ensure the smooth running of the hulling machine.

6. The system for monitoring and early warning of the state of the bean shelling equipment, applied to the method for monitoring and early warning of the state of the bean shelling equipment according to any one of claims 1-5, characterized in that, Comprising: Jam evaluation model determination module, for establishing jam evaluation model according to target bean species; Jam fault risk determination module, for determining the jam fault risk coefficient in the current hulling machine running process according to the jam evaluation model; Bearing evaluation determination module, for determining the bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient according to the current bearing vibration amplitude, bearing temperature and bearing rotation in the process of running the hulling machine, and then evaluating the bearing overall operation state according to the determined bearing vibration coefficient, bearing temperature coefficient and bearing speed coefficient, and determining the current bearing overall operation state evaluation coefficient; The monitoring and early warning module is used for monitoring and early warning of the current shelling machine state according to the jam fault risk coefficient in the current shelling machine operation process and the current bearing overall operation state evaluation coefficient, and judging whether the early warning information is generated or not. The monitoring display terminal is used for displaying the jam fault risk coefficient in the current shelling machine operation process and the current bearing overall operation state evaluation coefficient in the control panel when it is judged that the early warning information is generated, prompting the operator to check the shelling machine state or adjust the process parameters, and ensuring the smooth operation of the shelling machine.

Images