A fault diagnosis method for online sensor-based belt conveyors

Abstract

The application provides an online sensor-based belt conveyor fault diagnosis method, which is applied to a conveyor, the conveyor comprising a conveying belt and an online sensor, and the method comprising: determining target attribute information of a target conveying object of the conveyor; segmenting the conveying belt according to the target attribute information to obtain a plurality of segmented conveying belts; determining attribute information of each segmented conveying belt in the plurality of segmented conveying belts to obtain a plurality of attribute information; acquiring sensor data of each segmented conveying belt in the plurality of segmented conveying belts through the online sensor to obtain a plurality of sensor data; adjusting corresponding sensor data in the plurality of sensor data according to the plurality of attribute information to obtain a plurality of target sensor data; acquiring reference sensor data corresponding to each segmented conveying belt in the plurality of segmented conveying belts to obtain a plurality of reference sensor data; and performing fault diagnosis according to the plurality of target sensor data and the plurality of reference sensor data to obtain a fault diagnosis result.

Description

Technical Field

[0001] This application relates to the field of sensor technology, specifically to a fault diagnosis method for belt conveyors based on online sensors. Background Technology

[0002] In practical applications, conveyors play a vital role in mineral extraction. Typically, a conveyor consists of a conveyor belt, and the safety of the conveyor belt directly determines the safety of the operation. Therefore, monitoring the safety of the conveyor belt is of paramount importance in mining operations. Consequently, the problem of how to achieve accurate fault diagnosis of belt conveyors based on online sensors urgently needs to be solved. Summary of the Invention

[0003] This application provides a fault diagnosis method for belt conveyors based on online sensors, which can achieve accurate diagnosis of belt conveyor faults based on online sensors.

[0004] In a first aspect, embodiments of this application provide a fault diagnosis method for a belt conveyor based on online sensors, applied to a conveyor including a conveyor belt and online sensors, the method comprising:

[0005] Determine the target attribute information of the target object being transported by the conveyor;

[0006] The conveyor belt is segmented according to the target attribute information to obtain multiple segmented conveyor belts;

[0007] Determine the attribute information of each segment of the multiple segmented conveyor belts to obtain multiple attribute information;

[0008] The sensor data of each segment of the multiple segmented conveyor belts is obtained by acquiring sensor data of each segmented conveyor belt through the online sensor, thereby obtaining multiple sensor data;

[0009] Based on the multiple attribute information, the corresponding sensor data in the multiple sensor data are adjusted to obtain multiple target sensor data;

[0010] Acquire reference sensor data corresponding to each segment of the multiple segmented conveyor belts to obtain multiple reference sensor data;

[0011] Fault diagnosis is performed based on the data from the multiple target sensors and the multiple reference sensors to obtain the fault diagnosis result.

[0012] Implementing the embodiments of this application has the following beneficial effects:

[0013] As can be seen, the fault diagnosis method for a belt conveyor based on online sensors described in this application embodiment is applied to a conveyor, which includes a conveyor belt and online sensors. The method determines the target attribute information of the target conveyed object; segments the conveyor belt according to the target attribute information to obtain multiple segmented conveyor belts; determines the attribute information of each segmented conveyor belt to obtain multiple attribute information; acquires sensor data of each segmented conveyor belt through the online sensors to obtain multiple sensor data; adjusts the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data; and acquires the multiple segmented... Multiple reference sensor data points are obtained by analyzing the reference sensor data corresponding to each segment of the conveyor belt. Fault diagnosis is then performed based on these multiple target sensor data and the multiple reference sensor data to obtain fault diagnosis results. In this way, the conveyor belt can be monitored segment by segment, and segmented fault diagnosis can better and more accurately locate the fault location, improve fault diagnosis efficiency, and ensure the accuracy and timeliness of fault diagnosis. The attribute information reflects the performance of the conveyor belt to a certain extent. By adjusting the corresponding sensor data with each attribute information, the sensor data is more consistent with the actual situation. It can not only locate the fault location but also diagnose the severity of the fault. Thus, accurate fault diagnosis can be achieved based on online sensor belt conveyor fault diagnosis. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating a fault diagnosis method for a belt conveyor based on online sensors, provided in an embodiment of this application.

[0016] Figure 2 This is a schematic diagram of the structure of a conveyor belt provided in an embodiment of this application;

[0017] Figure 3 This is a schematic diagram of the structure of a conveyor provided in an embodiment of this application;

[0018] Figure 4 This is a functional unit block diagram of a fault diagnosis device for a belt conveyor based on online sensors provided in an embodiment of this application. Detailed Implementation

[0019] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that comprises a series of steps or units is not limited to the listed steps or units, but in one possible example includes steps or units not listed, or in one possible example includes other steps or units inherent to these processes, methods, products, or apparatuses.

[0020] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0022] In this application embodiment, the conveyor belt may include at least one of the following: ordinary conveyor belt, heat-resistant conveyor belt, flame-retardant conveyor belt, wear-resistant conveyor belt, tear-resistant conveyor belt, cold-resistant conveyor belt, etc., which are not limited here.

[0023] Please see Figure 1 , Figure 1 This is a flowchart illustrating a fault diagnosis method for a belt conveyor based on online sensors, as provided in an embodiment of this application. The method is applied to a conveyor, which includes a conveyor belt and online sensors. The method includes:

[0024] 101. Determine the target attribute information of the target object to be conveyed by the conveyor.

[0025] In this embodiment of the application, the target attribute information may include at least one of the following: object type, size, shape, density, hardness, etc., which are not limited here.

[0026] The online sensors may include one or more sensors, which may include at least one of the following: camera, temperature sensor, humidity sensor, pressure sensor, tension sensor, infrared sensor, sound sensor, vibration sensor, sound-vibration-temperature sensor, weather sensor, etc., without limitation. The sensors may be placed on the conveyor belt or near the conveyor. The object type can be used to identify specific objects, and the object type may include coal mine, sand, copper mine, granite, rare earth, gold mine, silver mine, diamond, jade, etc., without limitation. The online sensors may be evenly distributed on the conveyor belt.

[0027] Different objects correspond to different attribute information.

[0028] The target transported object is the object that needs to be transported on the conveyor. In practice, the target attribute information of the target transported object can be obtained through online sensors.

[0029] 102. The conveyor belt is segmented according to the target attribute information to obtain multiple segmented conveyor belts.

[0030] Different attribute information has different effects on the stretching of the conveyor belt. Therefore, a preset mapping relationship between attribute information and segmentation parameters can be set in advance. Then, the target segmentation parameters corresponding to the target attribute information can be determined based on the mapping relationship. The conveyor belt can then be segmented based on the target segmentation parameters to obtain multiple segmented conveyor belts. The segmentation parameters can include at least one of the following: the area of ​​each segmented conveyor belt, the size of the segmented conveyor belt, the length and width of the segmented conveyor belt, the shape of the segmented conveyor belt, etc., which are not limited here. In this way, the conveyor belt can be monitored segment by segment, and segmented fault diagnosis can better and more accurately locate the fault location, improve fault efficiency, and ensure the accuracy and timeliness of fault diagnosis.

[0031] The segmented conveyor belt can be a virtual division, not a series of segments. Multiple monitoring points can be set in each segment, thus enabling partial monitoring of the conveyor belt, which helps to locate subsequent faults and ensure the efficiency of fault diagnosis.

[0032] 103. Determine the attribute information of each segment of the multiple segmented conveyor belts to obtain multiple attribute information.

[0033] In this embodiment of the application, the attribute information of the segmented conveyor belt may include at least one of the following: load-bearing parameters, tension-bearing parameters, tear-resistant parameters, conveyor belt material parameters, conveyor belt thickness parameters, corrosion-resistant parameters, etc., which are not limited here.

[0034] In practice, attribute information of each segment of the conveyor belt can be obtained through online sensors, thus obtaining multiple attribute information.

[0035] 104. Obtain sensor data for each segment of the multiple segmented conveyor belts using the online sensors to obtain multiple sensor data.

[0036] In this embodiment of the application, the sensor data may include at least one of the following: pressure data, sound data, vibration data, etc., which are not limited here.

[0037] In practice, sensor data from each segment of the conveyor belt can be obtained through online sensors, resulting in multiple sensor data. This enables segmented monitoring and improves fault diagnosis efficiency.

[0038] In practice, each segment of the conveyor belt corresponds to a slope, and different slopes can correspond to different preset time intervals. That is, the attribute information of each segment of the conveyor belt can be obtained based on different preset time intervals. The preset time interval can be preset or defaulted to by the system.

[0039] 105. Adjust the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data.

[0040] In practice, the corresponding sensor data in multiple sensor data can be adjusted based on multiple attribute information. That is, each attribute information is used to adjust the corresponding sensor data to obtain multiple target sensor data, so that the sensor data is more consistent with the actual situation.

[0041] Optionally, each of the multiple attribute information includes multiple historical attribute information and current attribute information, where each historical attribute information corresponds to a time point, and the current attribute information corresponds to a time point; step 105 above, adjusting the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data, may include the following steps:

[0042] 51. Obtain multiple historical attribute information and current attribute information of segmented conveyor belt i, wherein segmented conveyor belt i is any segmented conveyor belt among the multiple segmented conveyor belts;

[0043] 52. Generate multiple coordinate points in the target coordinate system by taking multiple historical attribute information and current attribute information of the segmented conveyor belt i and the corresponding time points, wherein the horizontal axis of the target coordinate system is time and the vertical axis is attribute information;

[0044] 53. Fit the lines based on the multiple coordinate points to obtain the fitted lines;

[0045] 54. Determine the target slope of the fitted line;

[0046] 55. Determine the target root mean square error of multiple historical attribute information and current attribute information of the segmented conveyor belt i;

[0047] 56. Adjust the sensor data of segmented conveyor belt i according to the target slope and the target root mean square error to obtain the target sensor data of segmented conveyor belt i.

[0048] In the specific implementation, each of the multiple attribute information includes multiple historical attribute information and current attribute information. Each of the multiple historical attribute information corresponds to a point in time, and the current attribute information corresponds to a point in time.

[0049] Among them, the attribute information reflects the performance of the conveyor belt to a certain extent, and multiple historical attribute information and current attribute information reflect the performance changes of the conveyor belt to a certain extent.

[0050] In practice, when a heavy load is loaded on the conveyor belt, the conveyor belt will also be in a state of continuous vibration. Based on the performance changes, the impact of vibration can be fully estimated, so that the sensor data can be calibrated and the depth can match the actual situation.

[0051] In specific implementation, taking segmented conveyor belt i as an example, where i is any one of multiple segmented conveyor belts, multiple historical and current attribute information of segmented conveyor belt i can be obtained. These information, along with corresponding time points, are used to generate multiple coordinate points in a target coordinate system. The horizontal axis of the target coordinate system represents time, and the vertical axis represents attribute information; that is, the horizontal axis of each coordinate point is also time, and the vertical axis is also attribute information. Then, a fitting line is obtained based on these multiple coordinate points. This fitting line reflects, to a certain extent, the performance change trend of the conveyor belt. The target slope of the fitting line is determined, and the slope also has... Directionality and performance are not only affected by the conveyor belt itself, but also by changes in its environment. Therefore, the slope can be positive or negative, and consequently, the performance may improve or deteriorate. It is also possible to determine the target mean square error of multiple historical and current attribute information of segmented conveyor belt i. The mean square error reflects the impact of performance fluctuations to a certain extent. Therefore, the sensor data of segmented conveyor belt i can be adjusted according to the target slope and the target mean square error to obtain the target sensor data of segmented conveyor belt i. In this way, it is equivalent to fully considering the performance changes of the conveyor belt, which can fully estimate the impact of vibration, so that the sensor data is calibrated and the depth conforms to the actual situation.

[0052] Optionally, step 56 above, adjusting the sensor data of the segmented conveyor belt i according to the target slope and the target root mean square error to obtain the target sensor data of the segmented conveyor belt i, may include the following steps:

[0053] 561. Determine the target adjustment parameters corresponding to the target slope;

[0054] 562. Determine the target fine-tuning parameters corresponding to the target mean square error;

[0055] 563. Adjust the sensor data of the segmented conveyor belt i according to the target adjustment parameter and the target fine-tuning parameter to obtain the target sensor data of the segmented conveyor belt i.

[0056] In specific implementation, a preset mapping relationship between slope and adjustment parameter can be stored in advance. The value range of the adjustment parameter can be -0.1 to 0.1. Then, the target adjustment parameter corresponding to the target slope can be determined based on this mapping relationship. A preset mapping relationship between mean square error and fine-tuning parameter can also be stored in advance. The value range of the fine-tuning parameter is -0.02 to 0.02. Then, the target fine-tuning parameter corresponding to the target mean square error can be determined based on this mapping relationship. The sensor data of segmented conveyor belt i is adjusted according to the target adjustment parameter and the target fine-tuning parameter to obtain the target sensor data of segmented conveyor belt i, that is, the target sensor data of segmented conveyor belt i = (1 + target adjustment parameter) * (1 + target fine-tuning parameter) * sensor data of segmented conveyor belt i. That is, based on the performance changes of the conveyor belt, the impact of vibration can be fully estimated, so that the sensor data is calibrated and the depth conforms to the actual situation.

[0057] Optional steps may also include:

[0058] S1. When the target slope is greater than a preset threshold, it is determined that the segmented conveyor belt i has a serious fault;

[0059] S2. When the target slope is less than or equal to the preset threshold, perform the step of determining the target mean square error of multiple historical attribute information and current attribute information of the segmented conveyor belt i.

[0060] The preset threshold can be set in advance or set by system default.

[0061] In specific implementation, when the target slope is greater than the preset threshold, it can be determined that there is a serious fault in segmented conveyor belt i. Conversely, when the target slope is less than or equal to the preset threshold, the step of determining the target mean square error of multiple historical attribute information and current attribute information of segmented conveyor belt i is executed. In this way, a preliminary fault diagnosis can be made based on attribute information. If the preliminary fault diagnosis is not obvious, a refined fault diagnosis can be achieved.

[0062] 106. Obtain reference sensor data corresponding to each segment of the multiple segmented conveyor belts to obtain multiple reference sensor data.

[0063] In practice, reference sensor data corresponding to each segment of the conveyor belt can be obtained, resulting in multiple reference sensor data. The reference sensor data can be understood as a safety threshold, meaning that exceeding the reference sensor data may lead to a fault or a fault may have already occurred.

[0064] Optionally, step 106 above, obtaining reference sensor data corresponding to each segment of the multiple segmented conveyor belts to obtain multiple reference sensor data, may include the following steps:

[0065] 61. Determine the preset sensor data corresponding to each of the multiple attribute information to obtain multiple preset sensor data;

[0066] 62. Obtain the environmental parameters of each segment of the multiple segmented conveyor belts to obtain multiple environmental parameters;

[0067] 63. Determine the influence coefficients corresponding to the multiple environmental parameters to obtain multiple influence coefficients;

[0068] 64. The multiple preset sensor data are calibrated according to the multiple influence coefficients to obtain the multiple reference sensor data.

[0069] The environmental parameters may include at least one of the following: ambient temperature, ambient humidity, magnetic field interference intensity, atmospheric pressure, geographical location, etc., without limitation.

[0070] In practice, preset attribute information and mapping relationships between sensor data can be pre-set. Then, based on this mapping relationship, preset sensor data corresponding to each of the multiple attribute information can be determined, resulting in multiple preset sensor data sets. These preset sensor data sets can be pre-set or defaulted to by the system. The preset sensor data can be understood as sensor data used as a reference under ideal conditions.

[0071] Furthermore, environmental parameters of each segment of the conveyor belt can be obtained, resulting in multiple environmental parameters. A pre-stored mapping relationship between preset environmental parameters and influence coefficients can also be used. Based on this mapping relationship, influence coefficients corresponding to the multiple environmental parameters can be determined, resulting in multiple influence coefficients. The value range of the influence coefficients can be 0 to 0.1. Then, based on these multiple influence coefficients, the corresponding preset sensor data in the multiple preset sensor data can be calibrated to obtain multiple reference sensor data, i.e., reference sensor data = (1 - influence coefficient) * preset sensor data. In this way, the sensor data used for comparison can be dynamically optimized based on the actual environment, ensuring that the depth of the sensor data used for comparison matches the actual situation, thereby improving the efficiency and accuracy of fault diagnosis.

[0072] 107. Based on the data from the multiple target sensors and the multiple reference sensors, perform fault diagnosis to obtain fault diagnosis results.

[0073] In this embodiment, fault diagnosis can be performed based on multiple sensor data to obtain fault diagnosis results. That is, each sensor data is compared with the corresponding reference sensor data to diagnose the fault condition of each segment of the conveyor belt. In this way, the safety of the conveyor belt and the conveyor can be guaranteed.

[0074] Optionally, step 107 above, which involves performing fault diagnosis based on the data from the multiple target sensors and the multiple reference sensors to obtain a fault diagnosis result, may include the following steps:

[0075] 71. Acquire target sensor data and reference sensor data for segmented conveyor belt j; wherein segmented conveyor belt j is any one of the plurality of segmented conveyor belts;

[0076] 72. When the target sensor data of the segmented conveyor belt j is greater than the reference sensor data, determine the deviation between the target sensor data and the reference sensor data of the segmented conveyor belt j to obtain the target deviation.

[0077] 73. Obtain the segmented conveyor belt adjacent to the segmented conveyor belt j to obtain multiple segmented conveyor belts;

[0078] 74. Determine the deviation of each segment of the multiple segmented conveyor belts to obtain multiple deviations;

[0079] 75. Determine the mean among the multiple deviations to obtain the target mean;

[0080] 76. Obtain the reference threshold of the segmented conveyor belt j;

[0081] 77. Optimize the reference threshold based on the target mean to obtain the target threshold;

[0082] 78. Determine the fault diagnosis result of the segmented conveyor belt j based on the target deviation and the target threshold.

[0083] In specific implementation, taking segmented conveyor belt j as an example, segmented conveyor belt j can be any segmented conveyor belt among multiple segmented conveyor belts. Then, the target sensor data and reference sensor data of segmented conveyor belt j can be obtained. When the target sensor data of segmented conveyor belt j is greater than the reference sensor data, the deviation between the target sensor data and the reference sensor data of segmented conveyor belt j is determined, and the target deviation is obtained, that is, target deviation = (target sensor data of segmented conveyor belt j - reference sensor data of segmented conveyor belt j) / reference sensor data of segmented conveyor belt j.

[0084] Furthermore, it is also possible to obtain the segmented conveyor belt adjacent to segmented conveyor belt j, thus obtaining multiple segmented conveyor belts, such as... Figure 2 As shown, Figure 2 The diagram shows that a conveyor belt can include multiple segmented conveyor belts, and each conveyor belt has adjacent segmented conveyor belts. Based on the above method, the deviation of each segmented conveyor belt in the multiple segmented conveyor belts can be determined, resulting in multiple deviations. The mean value among the multiple deviations can also be determined to obtain the target mean value. In general, if a conveyor belt fails, some of the surrounding conveyor belts may also be affected to a certain extent. Therefore, the fault diagnosis threshold can be further optimized by combining the mutual influence between regions, making the fault diagnosis more accurate and also having a certain degree of predictive function.

[0085] The reference threshold of segmented conveyor belt j can be obtained. This threshold can be understood as the deviation threshold. A preset mapping relationship between the mean and the tuning parameter can be stored in advance. The value range of the tuning parameter can be -0.1 to 0.1. Then, the target tuning parameter corresponding to the target mean can be determined according to the mapping relationship. The reference threshold is tuned based on the target tuning parameter to obtain the target threshold. The target threshold = (1 + target tuning parameter) * reference threshold. Then, the fault diagnosis result of segmented conveyor belt j can be determined according to the target deviation and the target threshold. That is, the difference between the target deviation and the target threshold can be determined to obtain the target difference. Then, according to the preset mapping relationship between the difference and the fault degree, the target fault degree corresponding to the target difference can be determined. In this way, not only can the fault location be located, but the severity of the fault can also be diagnosed. Thus, accurate diagnosis can be achieved based on online sensor belt conveyor fault diagnosis.

[0086] As can be seen, the fault diagnosis method for a belt conveyor based on online sensors described in this application embodiment is applied to a conveyor, which includes a conveyor belt and online sensors. The method determines the target attribute information of the target conveyed object; segments the conveyor belt according to the target attribute information to obtain multiple segmented conveyor belts; determines the attribute information of each segmented conveyor belt to obtain multiple attribute information; acquires sensor data of each segmented conveyor belt through the online sensors to obtain multiple sensor data; adjusts the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data; and acquires the multiple segmented... Multiple reference sensor data points are obtained by analyzing the reference sensor data corresponding to each segment of the conveyor belt. Fault diagnosis is then performed based on these multiple target sensor data and the multiple reference sensor data to obtain fault diagnosis results. In this way, the conveyor belt can be monitored segment by segment, and segmented fault diagnosis can better and more accurately locate the fault location, improve fault diagnosis efficiency, and ensure the accuracy and timeliness of fault diagnosis. The attribute information reflects the performance of the conveyor belt to a certain extent. By adjusting the corresponding sensor data with each attribute information, the sensor data is more consistent with the actual situation. It can not only locate the fault location but also diagnose the severity of the fault. Thus, accurate fault diagnosis can be achieved based on online sensor belt conveyor fault diagnosis.

[0087] Consistent with the above embodiments, please refer to Figure 3 , Figure 3 This is a schematic diagram of a conveyor provided in an embodiment of this application. As shown in the figure, the conveyor includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. In this embodiment, the programs include instructions for performing the following steps:

[0088] Determine the target attribute information of the target object being transported by the conveyor;

[0089] The conveyor belt is segmented according to the target attribute information to obtain multiple segmented conveyor belts;

[0090] Determine the attribute information of each segment of the multiple segmented conveyor belts to obtain multiple attribute information;

[0091] The sensor data of each segment of the multiple segmented conveyor belts is obtained by acquiring sensor data of each segmented conveyor belt through the online sensor, thereby obtaining multiple sensor data;

[0092] Based on the multiple attribute information, the corresponding sensor data in the multiple sensor data are adjusted to obtain multiple target sensor data;

[0093] Acquire reference sensor data corresponding to each segment of the multiple segmented conveyor belts to obtain multiple reference sensor data;

[0094] Fault diagnosis is performed based on the data from the multiple target sensors and the multiple reference sensors to obtain the fault diagnosis result.

[0095] Optionally, each of the plurality of attribute information includes a plurality of historical attribute information and current attribute information, wherein each of the plurality of historical attribute information corresponds to a time point, and the current attribute information corresponds to a time point; in terms of adjusting the corresponding sensor data in the plurality of sensor data according to the plurality of attribute information to obtain a plurality of target sensor data, the above program includes instructions for performing the following steps:

[0096] Obtain multiple historical attribute information and current attribute information of segmented conveyor belt i, wherein segmented conveyor belt i is any segmented conveyor belt among the multiple segmented conveyor belts;

[0097] The historical and current attribute information of the segmented conveyor belt i, along with the corresponding time points, are used to generate multiple coordinate points in the target coordinate system, where the horizontal axis of the target coordinate system represents time and the vertical axis represents attribute information.

[0098] A fitted straight line is obtained by fitting the multiple coordinate points.

[0099] Determine the target slope of the fitted line;

[0100] Determine the target root mean square error of multiple historical and current attribute information of the segmented conveyor belt i;

[0101] The sensor data of segmented conveyor belt i is adjusted according to the target slope and the target root mean square error to obtain the target sensor data of segmented conveyor belt i.

[0102] Optionally, in adjusting the sensor data of the segmented conveyor belt i according to the target slope and the target root mean square error to obtain the target sensor data of the segmented conveyor belt i, the above procedure includes instructions for performing the following steps:

[0103] Determine the target adjustment parameters corresponding to the target slope;

[0104] Determine the target fine-tuning parameters corresponding to the target mean square error;

[0105] The sensor data of the segmented conveyor belt i are adjusted according to the target adjustment parameters and the target fine-tuning parameters to obtain the target sensor data of the segmented conveyor belt i.

[0106] Optionally, in obtaining reference sensor data corresponding to each segment of the plurality of segmented conveyor belts to obtain multiple reference sensor data, the above program includes instructions for performing the following steps:

[0107] Determine the preset sensor data corresponding to each of the multiple attribute information to obtain multiple preset sensor data;

[0108] The environmental parameters of each segment of the multiple segmented conveyor belts are obtained to obtain multiple environmental parameters.

[0109] Determine the influence coefficients corresponding to the multiple environmental parameters to obtain multiple influence coefficients;

[0110] The multiple preset sensor data are calibrated based on the multiple influence coefficients to obtain the multiple reference sensor data.

[0111] Optionally, in terms of performing fault diagnosis based on the data from the plurality of target sensors and the plurality of reference sensors to obtain a fault diagnosis result, the above procedure includes instructions for performing the following steps:

[0112] Acquire target sensor data and reference sensor data for segmented conveyor belt j; wherein segmented conveyor belt j is any one of the plurality of segmented conveyor belts.

[0113] When the target sensor data of the segmented conveyor belt j is greater than the reference sensor data, the deviation between the target sensor data and the reference sensor data of the segmented conveyor belt j is determined to obtain the target deviation.

[0114] Obtain the segmented conveyor belt adjacent to the segmented conveyor belt j to obtain multiple segmented conveyor belts;

[0115] The deviation of each segment of the multiple segmented conveyor belts is determined to obtain multiple deviations;

[0116] Determine the mean among the multiple deviations to obtain the target mean;

[0117] Obtain the reference threshold of the segmented conveyor belt j;

[0118] The reference threshold is optimized based on the target mean to obtain the target threshold;

[0119] The fault diagnosis result of the segmented conveyor belt j is determined based on the target deviation and the target threshold.

[0120] Optionally, the above procedure may also include instructions for performing the following steps:

[0121] When the target slope is greater than a preset threshold, it is determined that the segmented conveyor belt i has a serious fault;

[0122] When the target slope is less than or equal to the preset threshold, the step of determining the target mean square error of multiple historical attribute information and current attribute information of the segmented conveyor belt i is performed.

[0123] As can be seen, the conveyor described in this application embodiment includes a conveyor belt and online sensors. The conveyor belt is segmented according to the target attribute information to be conveyed, resulting in multiple segmented conveyor belts. The attribute information of each segmented conveyor belt is determined, resulting in multiple attribute information. Sensor data of each segmented conveyor belt is acquired through the online sensors, resulting in multiple sensor data. The corresponding sensor data in the multiple sensor data is adjusted according to the multiple attribute information to obtain multiple target sensor data. The conveyor belt data of each segmented conveyor belt is then acquired. Multiple reference sensor data are obtained by taking corresponding reference sensor data; fault diagnosis is performed based on the multiple target sensor data and the multiple reference sensor data to obtain fault diagnosis results. In this way, the conveyor belt can be monitored in segments, and segmented fault diagnosis can better and more accurately locate the fault location, improve fault diagnosis efficiency, and ensure the accuracy and timeliness of fault diagnosis. The attribute information reflects the performance of the conveyor belt to a certain extent. By adjusting the corresponding sensor data with each attribute information, the sensor data is more consistent with the actual situation. It can not only locate the fault location, but also diagnose the severity of the fault. Thus, accurate diagnosis can be achieved based on online sensor belt conveyor fault diagnosis.

[0124] Figure 4 This is a functional unit block diagram of a fault diagnosis device 400 for a belt conveyor based on online sensors, as described in this application embodiment. The device 400 is applied to a conveyor, which includes a conveyor belt and online sensors. The device 400 includes: a determination unit 401, a segmentation unit 402, an acquisition unit 403, an adjustment unit 404, and a diagnosis unit 405.

[0125] The determining unit 401 is used to determine the target attribute information of the target conveyed object of the conveyor.

[0126] The segmentation unit 402 is used to segment the conveyor belt according to the target attribute information to obtain multiple segmented conveyor belts;

[0127] The determining unit 401 is further configured to determine the attribute information of each segment of the multiple segmented conveyor belts, thereby obtaining multiple attribute information.

[0128] The acquisition unit 403 is used to acquire sensor data of each segment of the multiple segmented conveyor belts through the online sensor, thereby obtaining multiple sensor data.

[0129] The adjustment unit 404 is used to adjust the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data.

[0130] The acquisition unit 403 is also used to acquire reference sensor data corresponding to each segment of the multiple segmented conveyor belts, thereby obtaining multiple reference sensor data.

[0131] The diagnostic unit 405 is used to perform fault diagnosis based on the data from the plurality of target sensors and the data from the plurality of reference sensors, and to obtain fault diagnosis results.

[0132] Optionally, each of the plurality of attribute information includes a plurality of historical attribute information and current attribute information, wherein each of the plurality of historical attribute information corresponds to a time point, and the current attribute information corresponds to a time point; in terms of adjusting the corresponding sensor data in the plurality of sensor data according to the plurality of attribute information to obtain a plurality of target sensor data, the adjustment unit 404 is specifically used for:

[0133] Obtain multiple historical attribute information and current attribute information of segmented conveyor belt i, wherein segmented conveyor belt i is any segmented conveyor belt among the multiple segmented conveyor belts;

[0134] The historical and current attribute information of the segmented conveyor belt i, along with the corresponding time points, are used to generate multiple coordinate points in the target coordinate system, where the horizontal axis of the target coordinate system represents time and the vertical axis represents attribute information.

[0135] A fitted straight line is obtained by fitting the multiple coordinate points.

[0136] Determine the target slope of the fitted line;

[0137] Determine the target root mean square error of multiple historical and current attribute information of the segmented conveyor belt i;

[0138] The sensor data of segmented conveyor belt i is adjusted according to the target slope and the target root mean square error to obtain the target sensor data of segmented conveyor belt i.

[0139] Optionally, in adjusting the sensor data of the segmented conveyor belt i according to the target slope and the target root mean square error to obtain the target sensor data of the segmented conveyor belt i, the adjustment unit 404 is specifically used for:

[0140] Determine the target adjustment parameters corresponding to the target slope;

[0141] Determine the target fine-tuning parameters corresponding to the target mean square error;

[0142] The sensor data of the segmented conveyor belt i are adjusted according to the target adjustment parameters and the target fine-tuning parameters to obtain the target sensor data of the segmented conveyor belt i.

[0143] Optionally, in acquiring reference sensor data corresponding to each segment of the plurality of segmented conveyor belts to obtain multiple reference sensor data, the acquisition unit 403 is specifically used for:

[0144] Determine the preset sensor data corresponding to each of the multiple attribute information to obtain multiple preset sensor data;

[0145] The environmental parameters of each segment of the multiple segmented conveyor belts are obtained to obtain multiple environmental parameters.

[0146] Determine the influence coefficients corresponding to the multiple environmental parameters to obtain multiple influence coefficients;

[0147] The multiple preset sensor data are calibrated based on the multiple influence coefficients to obtain the multiple reference sensor data.

[0148] Optionally, in the step of performing fault diagnosis based on the data from the plurality of target sensors and the plurality of reference sensors to obtain a fault diagnosis result, the diagnostic unit 405 is specifically used for:

[0149] Acquire target sensor data and reference sensor data for segmented conveyor belt j; wherein segmented conveyor belt j is any one of the plurality of segmented conveyor belts.

[0150] When the target sensor data of the segmented conveyor belt j is greater than the reference sensor data, the deviation between the target sensor data and the reference sensor data of the segmented conveyor belt j is determined to obtain the target deviation.

[0151] Obtain the segmented conveyor belt adjacent to the segmented conveyor belt j to obtain multiple segmented conveyor belts;

[0152] The deviation of each segment of the multiple segmented conveyor belts is determined to obtain multiple deviations;

[0153] Determine the mean among the multiple deviations to obtain the target mean;

[0154] Obtain the reference threshold of the segmented conveyor belt j;

[0155] The reference threshold is optimized based on the target mean to obtain the target threshold;

[0156] The fault diagnosis result of the segmented conveyor belt j is determined based on the target deviation and the target threshold.

[0157] Optionally, the online sensor-based belt conveyor fault diagnosis device 400 is also specifically used for:

[0158] When the target slope is greater than a preset threshold, it is determined that the segmented conveyor belt i has a serious fault;

[0159] When the target slope is less than or equal to the preset threshold, the step of determining the target mean square error of multiple historical attribute information and current attribute information of the segmented conveyor belt i is performed.

[0160] As can be seen, the fault diagnosis device for a belt conveyor based on online sensors described in this application embodiment is applied to a conveyor, which includes a conveyor belt and online sensors. The device determines the target attribute information of the target conveyed object; segments the conveyor belt according to the target attribute information to obtain multiple segmented conveyor belts; determines the attribute information of each segmented conveyor belt to obtain multiple attribute information; acquires sensor data of each segmented conveyor belt through the online sensors to obtain multiple sensor data; adjusts the corresponding sensor data in the multiple sensor data according to the multiple attribute information to obtain multiple target sensor data; and acquires the multiple segmented... Multiple reference sensor data points are obtained by analyzing the reference sensor data corresponding to each segment of the conveyor belt. Fault diagnosis is then performed based on these multiple target sensor data and the multiple reference sensor data to obtain fault diagnosis results. In this way, the conveyor belt can be monitored segment by segment, and segmented fault diagnosis can better and more accurately locate the fault location, improve fault diagnosis efficiency, and ensure the accuracy and timeliness of fault diagnosis. The attribute information reflects the performance of the conveyor belt to a certain extent. By adjusting the corresponding sensor data with each attribute information, the sensor data is more consistent with the actual situation. It can not only locate the fault location but also diagnose the severity of the fault. Thus, accurate fault diagnosis can be achieved based on online sensor belt conveyor fault diagnosis.

[0161] It is understood that the functions of each program module of the online sensor belt conveyor fault diagnosis device in this embodiment can be specifically implemented according to the methods in the above method embodiments. The specific implementation process can be referred to the relevant descriptions in the above method embodiments, and will not be repeated here.

[0162] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes a conveyor.

[0163] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer includes a conveyor.

[0164] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0165] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0167] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0168] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0169] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0170] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0171] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A fault diagnosis method for belt conveyors based on online sensors, characterized in that, Applied to a conveyor, the conveyor including a conveyor belt and an online sensor, the method includes: Determine the target attribute information of the target object being transported by the conveyor; The conveyor belt is segmented according to the target attribute information to obtain multiple segmented conveyor belts; Determine the attribute information of each segment of the multiple segmented conveyor belts to obtain multiple attribute information; The sensor data of each segment of the multiple segmented conveyor belts is obtained by acquiring sensor data of each segmented conveyor belt through the online sensor, thereby obtaining multiple sensor data; Based on the multiple attribute information, the corresponding sensor data in the multiple sensor data are adjusted to obtain multiple target sensor data; Acquire reference sensor data corresponding to each segment of the multiple segmented conveyor belts to obtain multiple reference sensor data; Fault diagnosis is performed based on the data from the multiple target sensors and the multiple reference sensors to obtain fault diagnosis results; Wherein, each of the plurality of attribute information includes a plurality of historical attribute information and current attribute information, each of the plurality of historical attribute information corresponds to a time point, and the current attribute information corresponds to a time point; the step of adjusting the corresponding sensor data in the plurality of sensor data according to the plurality of attribute information to obtain a plurality of target sensor data includes: Obtain multiple historical attribute information and current attribute information of segmented conveyor belt i, wherein segmented conveyor belt i is any segmented conveyor belt among the multiple segmented conveyor belts; The historical and current attribute information of the segmented conveyor belt i, along with the corresponding time points, are used to generate multiple coordinate points in the target coordinate system, where the horizontal axis of the target coordinate system represents time and the vertical axis represents attribute information. A fitted straight line is obtained by fitting the multiple coordinate points. Determine the target slope of the fitted line; Determine the target root mean square error of multiple historical and current attribute information of the segmented conveyor belt i; The sensor data of segmented conveyor belt i is adjusted according to the target slope and the target root mean square error to obtain the target sensor data of segmented conveyor belt i.

2. The method according to claim 1, characterized in that, The step of adjusting the sensor data of segmented conveyor belt i according to the target slope and the target root mean square error to obtain the target sensor data of segmented conveyor belt i includes: Determine the target adjustment parameters corresponding to the target slope; Determine the target fine-tuning parameters corresponding to the target mean square error; The sensor data of the segmented conveyor belt i are adjusted according to the target adjustment parameters and the target fine-tuning parameters to obtain the target sensor data of the segmented conveyor belt i.

3. The method according to claim 1 or 2, characterized in that, The step involves acquiring reference sensor data corresponding to each segment of the multiple segmented conveyor belts, resulting in multiple reference sensor data sets, including: Determine the preset sensor data corresponding to each of the multiple attribute information to obtain multiple preset sensor data; The environmental parameters of each segment of the multiple segmented conveyor belts are obtained to obtain multiple environmental parameters. Determine the influence coefficients corresponding to the multiple environmental parameters to obtain multiple influence coefficients; The multiple preset sensor data are calibrated based on the multiple influence coefficients to obtain the multiple reference sensor data.

4. The method according to claim 1 or 2, characterized in that, The step of performing fault diagnosis based on the data from the multiple target sensors and the multiple reference sensors to obtain fault diagnosis results includes: Acquire target sensor data and reference sensor data for segmented conveyor belt j; wherein segmented conveyor belt j is any one of the plurality of segmented conveyor belts. When the target sensor data of the segmented conveyor belt j is greater than the reference sensor data, the deviation between the target sensor data and the reference sensor data of the segmented conveyor belt j is determined to obtain the target deviation. Obtain the segmented conveyor belt adjacent to the segmented conveyor belt j to obtain multiple segmented conveyor belts; The deviation of each segment of the multiple segmented conveyor belts is determined to obtain multiple deviations; Determine the mean among the multiple deviations to obtain the target mean; Obtain the reference threshold of the segmented conveyor belt j; The reference threshold is optimized based on the target mean to obtain the target threshold; The fault diagnosis result of the segmented conveyor belt j is determined based on the target deviation and the target threshold.

5. The method according to claim 1, characterized in that, The method further includes: When the target slope is greater than a preset threshold, it is determined that the segmented conveyor belt i has a serious fault; When the target slope is less than or equal to the preset threshold, the step of determining the target mean square error of multiple historical attribute information and current attribute information of the segmented conveyor belt i is performed.

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