A method and apparatus for detecting belt breakage in a belt conveyor

Abstract

This application discloses a method and apparatus for detecting belt breakage in a belt conveyor. The belt breakage detection method includes: acquiring data on the increase or decrease of the conveyor belt speed; acquiring data on the increase or decrease of the drive motor torque; acquiring data on the increase or decrease of the belt tension; extracting features from the data on the increase or decrease of the conveyor belt speed as a first feature; extracting features from the data on the increase or decrease of the drive motor torque as a second feature; extracting features from the data on the increase or decrease of the belt tension as a third feature; acquiring a trained belt breakage model for the belt conveyor; and inputting the acquired features into the belt breakage model to obtain classification labels, including a belt breakage classification label.

Description

Technical Field

[0001] This application relates to the field of belt conveyor technology, specifically to a belt conveyor belt breakage detection method and belt conveyor belt breakage detection device. Background Technology

[0002] Belt conveyors play an increasingly important role in coal mining, and the tasks they undertake are becoming increasingly demanding. However, because belt conveyors in coal mines operate under high loads for relatively long periods, and are subject to many adverse factors during operation—such as impacts from materials like gangue striking the conveyor belt, causing significant damage, or obstructions from foreign objects—they are highly susceptible to belt breakage. If belt breaks occur during transmission and are not detected in time, it will seriously affect the safety of production equipment and personnel. Therefore, belt breakage detection devices are extremely important in belt conveyor systems.

[0003] During coal mining operations, many belt conveyors operate at a certain angle, and given the relatively long distances covered, a belt breakage can easily damage the conveyor frame, causing idlers to fly off and posing a safety threat to nearby workers. It can also potentially lead to fires. Furthermore, repairs and maintenance after a belt breakage require a considerable amount of time and involve strenuous manual labor, resulting in equipment damage, production delays, and significant economic losses for coal mining companies.

[0004] Therefore, timely detection of belt breakage and stopping the operation of the belt conveyor can not only prevent the waste of manpower and material resources caused by belt breakage in coal mines, but also eliminate the safety hazards caused by belt breakage and improve the inherent safety level of the mine. Summary of the Invention

[0005] The purpose of this invention is to provide a method for detecting belt breakage in belt conveyors, which at least solves one of the aforementioned technical problems.

[0006] One aspect of the present invention provides a method for detecting belt breakage in a belt conveyor, the method comprising:

[0007] Acquire data on the increase or decrease of conveyor belt speed;

[0008] Obtain data on the increase or decrease of the drive motor torque;

[0009] Obtain data on the increase or decrease of belt tension;

[0010] The features of the conveyor belt speed increase data or the conveyor belt speed decrease data are extracted as the first feature;

[0011] The features of the drive motor torque increase data or drive motor torque decrease data are extracted as the second feature;

[0012] The features of the belt tension increase data or belt tension decrease data are extracted as the third feature;

[0013] Obtain a trained belt conveyor breakage model;

[0014] The acquired features are input into the belt breakage model of the belt conveyor to obtain classification labels, including belt breakage classification labels.

[0015] Optionally, the process of acquiring data on the increase or decrease of conveyor belt speed includes:

[0016] Get the current speed of the conveyor belt during operation and the speed of the conveyor belt during operation at the previous time.

[0017] The data on the increase or decrease in conveyor belt speed is obtained by comparing the current conveyor belt speed with the speed at the previous moment.

[0018] When the difference between the current conveyor belt speed and the previous conveyor belt speed is positive, the data obtained is the increase in conveyor belt speed.

[0019] When the difference between the current conveyor belt speed and the previous conveyor belt speed is negative, the data obtained is the amount of decrease in conveyor belt speed.

[0020] Optionally, the data on the increase or decrease of the drive motor torque includes:

[0021] Obtain the current drive motor torque data and the drive motor torque data of the previous moment.

[0022] The difference between the current drive motor torque data and the drive motor torque data from the previous moment is used to obtain either the increase or decrease in drive motor torque data.

[0023] When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is a positive number, the obtained data is the drive motor torque increase data.

[0024] When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is negative, the obtained data is the amount of decrease in drive motor torque.

[0025] Optionally, acquiring the belt tension increase data or belt tension decrease data includes:

[0026] Obtain the belt tension data at the current moment and the belt tension data at the moment before the current moment;

[0027] The difference between the current belt tension data and the belt tension data from the previous moment is used to obtain the belt tension increase or decrease data.

[0028] When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is a positive number, the obtained data is the belt tension increase data.

[0029] When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is negative, the obtained data is the belt tension decrease data.

[0030] Optionally, the belt breakage model of the belt conveyor is trained in the following manner:

[0031] Obtain the training set;

[0032] Obtain the network structure of the BP neural network;

[0033] The training set is input into the network structure of the BP neural network;

[0034] Calculate the input and output values ​​of each neuron in the hidden layer;

[0035] Calculate the input and output values ​​of each neuron in the output layer;

[0036] The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. If the error meets the accuracy requirements, the training ends.

[0037] Optionally, the belt conveyor breakage model is trained using the following methods:

[0038] The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. It is then determined whether the error meets the accuracy requirements; if not, then...

[0039] Calculate the gradient of each weight and bias with respect to the loss function and backpropagate it back into the network; based on the gradients calculated by backpropagation, update the weights and biases of the neural network using gradient descent.

[0040] This application also provides a belt conveyor belt breakage detection device, the belt conveyor belt breakage detection device comprising:

[0041] The operating speed data acquisition module is used to acquire data on the increase or decrease of the conveyor belt operating speed.

[0042] A torque data acquisition module is used to acquire data on the increase or decrease of the drive motor torque.

[0043] Tension data acquisition module, which is used to acquire belt tension increase data or belt tension decrease data;

[0044] The feature extraction module is used to extract features of the conveyor belt speed increase data or conveyor belt speed decrease data as a first feature, extract features of the drive motor torque increase data or drive motor torque decrease data as a second feature, and extract features of the belt tension increase data or belt tension decrease data as a third feature.

[0045] A belt conveyor belt breakage model acquisition module is used to acquire a trained belt conveyor belt breakage model.

[0046] The classification module is used to input the acquired first feature, second feature, and third feature into the belt conveyor breakage model to obtain a classification label, which includes a belt breakage classification label.

[0047] This application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the belt breakage detection method for belt conveyors as described above.

[0048] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the belt breakage detection method for belt conveyors as described above.

[0049] Beneficial effects

[0050] The belt conveyor belt breakage detection method of this application inputs the belt running speed, drive motor torque, and belt tension rise or fall data into the belt conveyor belt breakage model to obtain a classification label. The classification label is then sent to the frequency converter, which controls the belt conveyor to stop its operation. Compared with the prior art, this invention is not only highly intelligent, but also realizes unmanned operation underground, saving labor costs and improving safety. Attached Figure Description

[0051] Figure 1 This is a schematic flowchart of a belt conveyor belt breakage detection method according to an embodiment of this application.

[0052] Figure 2 It is used to implement Figure 1 The diagram shows an electronic device for a liveness detection method.

[0053] Figure 3 This is a schematic diagram of the network structure of a BP neural network in one embodiment of this application.

[0054] Figure 4 This is a schematic diagram of the model training method in a real-time example of this application. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be described in more detail below with reference to the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, embodiments of this application. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0056] Figure 1 This is a schematic flowchart of a belt conveyor belt breakage detection method according to an embodiment of this application.

[0057] like Figure 1 The belt breakage detection method for the belt conveyor shown includes:

[0058] Acquire data on the increase or decrease of conveyor belt speed;

[0059] Obtain data on the increase or decrease of the drive motor torque;

[0060] Obtain data on the increase or decrease of belt tension;

[0061] The features of the conveyor belt speed increase data or the conveyor belt speed decrease data are extracted as the first feature;

[0062] The features of the drive motor torque increase data or drive motor torque decrease data are extracted as the second feature;

[0063] The features of the belt tension increase data or belt tension decrease data are extracted as the third feature;

[0064] Obtain a trained belt conveyor breakage model;

[0065] The acquired features are input into the belt breakage model of the belt conveyor to obtain classification labels, including belt breakage classification labels.

[0066] In this embodiment, the process of acquiring data on the increase or decrease of conveyor belt speed includes:

[0067] Get the current speed of the conveyor belt during operation and the speed of the conveyor belt during operation at the previous time.

[0068] The data on the increase or decrease in conveyor belt speed is obtained by comparing the current conveyor belt speed with the speed at the previous moment.

[0069] When the difference between the current conveyor belt speed and the previous conveyor belt speed is positive, the data obtained is the increase in conveyor belt speed.

[0070] When the difference between the current conveyor belt speed and the previous conveyor belt speed is negative, the data obtained is the amount of decrease in conveyor belt speed.

[0071] In this embodiment, the data on the increase or decrease of the drive motor torque includes:

[0072] Obtain the current drive motor torque data and the drive motor torque data of the previous moment.

[0073] The difference between the current drive motor torque data and the drive motor torque data from the previous moment is used to obtain either the increase or decrease in drive motor torque data.

[0074] When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is a positive number, the obtained data is the drive motor torque increase data.

[0075] When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is negative, the obtained data is the amount of decrease in drive motor torque.

[0076] In this embodiment, obtaining the belt tension increase data or belt tension decrease data includes:

[0077] Obtain the belt tension data at the current moment and the belt tension data at the moment before the current moment;

[0078] The difference between the current belt tension data and the belt tension data from the previous moment is used to obtain the belt tension increase or decrease data.

[0079] When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is a positive number, the obtained data is the belt tension increase data.

[0080] When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is negative, the obtained data is the belt tension decrease data.

[0081] In this embodiment, the belt conveyor breakage model is trained in the following manner:

[0082] Obtain the training set;

[0083] Obtain the network structure of the BP neural network;

[0084] The training set is input into the network structure of the BP neural network;

[0085] Calculate the input and output values ​​of each neuron in the hidden layer;

[0086] Calculate the input and output values ​​of each neuron in the output layer;

[0087] The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. If the error meets the accuracy requirements, the training ends.

[0088] In this embodiment, the belt conveyor breakage model is trained using the following method:

[0089] The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. It is then determined whether the error meets the accuracy requirements; if not, then...

[0090] Calculate the gradient of each weight and bias with respect to the loss function and backpropagate it back into the network; based on the gradients calculated by backpropagation, update the weights and biases of the neural network using gradient descent.

[0091] The following examples further illustrate this application in detail. It is understood that these examples do not constitute any limitation on this application.

[0092] See Figure 4 Data on the operation of the belt conveyor is collected, input data samples are selected, and the collected data is preprocessed to create a training set.

[0093] The BP neural network algorithm is used to train the preprocessed training set to establish a BP neural network model for testing whether the belt conveyor breaks. The validation set is then input into the trained BP neural network model for calculation to determine whether the belt conveyor has broken.

[0094] In this embodiment, an important step in detecting belt breakage in a belt conveyor is to select appropriate input variables. Data with high correlation to belt breakage is selected as input data for the prediction model. By analyzing data on belt breakage and consulting relevant articles, the belt speed, drive motor torque, and belt tension during belt operation are selected as key parameters for belt breakage.

[0095] When the belt breaks, the three key parameters of the conveyor belt running speed, the drive motor torque, and the belt tension will change rapidly. Therefore, this invention transforms these three parameter variables into the amount of increase in conveyor belt running speed, decrease in conveyor belt running speed, increase in drive motor torque, decrease in drive motor torque, increase in belt tension, and decrease in belt tension within a certain time t.

[0096] Set up a test fixture and record 100 data points, of which approximately 70% are normal operation data and approximately 30% are data during tape interruption; combine the normal operation data and the tape interruption data as the training set;

[0097] Transform the data into the following quantities within a certain time t: increase in conveyor belt speed, decrease in conveyor belt speed, increase in drive motor torque, decrease in drive motor torque, increase in belt tension, and decrease in belt tension. Divide time t into n equal parts, calculate the difference between the current value and the previous value, and sum the differences that are greater than 0 to obtain the increase; sum the absolute values ​​of the differences that are less than 0 to obtain the decrease.

[0098]

[0099]

[0100] In the formula, up represents the increase, down represents the decrease, and x i Indicates the current value of the variable, x i-1 This is the value of the variable from the previous time.

[0101] Normalize all input samples:

[0102]

[0103] In the formula, x is any element in the input sample;

[0104] See Figure 3 The process of establishing a BP neural network model, inputting the validation set into the trained BP neural network model for calculation, and determining whether the belt conveyor has broken includes the following steps:

[0105] Determine the network structure;

[0106] The number of hidden layer nodes is calculated using a formula: Where y is the number of hidden layer nodes, m is the number of input layer nodes, n is the number of output layer nodes, and a is a constant, typically ranging from 1 to 10. The final data of the hidden layer is determined through specific experiments during implementation. The output layer has one node, used to indicate whether the belt conveyor has broken, where 0 represents normal operation and 1 represents belt breakage.

[0107] Calculate the input and output values ​​of each neuron in the hidden layer;

[0108] Calculate the weighted sum of the output values ​​of the elements in the previous layer for the j-th cell in the current layer, and use it as the input value for that cell;

[0109]

[0110] a j =α(Z) j )

[0111] Where w ij x represents the weight connecting the i-th neuron in the input layer and the j-th neuron in the hidden layer. i θ represents the input of the i-th node in the input layer. j z represents the bias of the j-th neuron in the hidden layer, and α represents the activation function of the hidden layer. j Let a be the input value of the j-th node in the hidden layer. j This is represented as the output value of the j-th node in the hidden layer;

[0112] Calculate the input and output values ​​of each neuron in the output layer;

[0113]

[0114] O = z

[0115] In the formula, w j θ represents the weight connecting the j-th neuron in the hidden layer, θ represents the output layer bias, z represents the input value of the output layer, and O represents the output value of the output layer.

[0116] The mean squared error is used to calculate the error between the neural network's predicted value and the actual value, and to determine whether the error meets the accuracy requirements.

[0117] The output layer data needs to be within the error range. Therefore, the network model determines whether to proceed after judging the error.

[0118] End the calculation and output the final data. If the error meets the requirements, output the data and end the model calculation. If the error does not meet the requirements, it is necessary to reverse the calculation to adjust the weights of each layer. The mean squared error is used to calculate the error (loss function) between the neural network prediction and the actual values.

[0119]

[0120] In the formula, Err represents the error value, and y k This represents the actual value of the k-th sample in the output layer, O k This represents the predicted value corresponding to the kth sample in the output layer. In this invention, the number of nodes in the output layer is 1, which is used to indicate whether the belt conveyor has broken.

[0121] If the error does not meet the requirements, calculate the gradient of each weight and bias with respect to the loss function and backpropagate it back into the network; based on the gradient calculated by backpropagation, update the weights and biases of the neural network using gradient descent.

[0122] Calculate the gradient of each weight and bias with respect to the loss function. The formula for calculating the gradient is:

[0123] in,

[0124] y represents the gradient of the weights from the j-th node in the hidden layer to the output layer with respect to the loss function. k Represents the actual value of the k-th sample in the output layer, O k a represents the predicted value corresponding to the k-th sample in the output layer. j This represents the output value of the j-th node in the hidden layer;

[0125] in,

[0126] y represents the gradient of the output layer bias with respect to the loss function. kRepresents the actual value of the k-th sample in the output layer, O k This represents the predicted value corresponding to the k-th sample in the output layer;

[0127] in,

[0128] y represents the gradient of the weights from the i-th node in the input layer to the j-th node in the hidden layer with respect to the loss function. k Represents the actual value of the k-th sample in the output layer, O k α represents the predicted value corresponding to the k-th sample in the output layer. ′ x represents the derivative of the activation function. i This represents the input of the i-th node in the input layer;

[0129] in,

[0130] y represents the gradient of the bias of the j-th neuron in the hidden layer with respect to the loss function. k Represents the actual value of the k-th sample in the output layer, O k α represents the predicted value corresponding to the k-th sample in the output layer. ′ This represents the derivative of the activation function;

[0131] When updating the weights and biases of a neural network using gradient descent, the formula for calculating the weight adjustment is as follows:

[0132] in,

[0133] Δw j γ represents the weight adjustment amount of the j-th node in the hidden layer, and γ represents the learning rate;

[0134] in,

[0135] Δw ij γ represents the weight adjustment amount of the i-th neuron in the input layer and the j-th neuron in the hidden layer, and γ represents the learning rate;

[0136] When updating the weights and biases of a neural network using gradient descent, the formula for calculating the bias correction is as follows:

[0137] in,

[0138] Δθ represents the output layer bias correction amount, and γ represents the learning rate;

[0139] in,

[0140] Δθ jThe bias correction of the j-th node in the hidden layer, where γ represents the learning rate;

[0141] Return to step 2 and repeat the above steps until the error meets the set precision.

[0142] This application also provides a belt conveyor belt breakage detection device, which includes a speed data acquisition module, a torque data acquisition module, a tension data acquisition module, a feature extraction module, a belt conveyor belt breakage model acquisition module, and a classification module.

[0143] The operating speed data acquisition module is used to acquire data on the increase or decrease of the conveyor belt operating speed;

[0144] The torque data acquisition module is used to acquire data on the increase or decrease of the drive motor torque.

[0145] The tension data acquisition module is used to acquire data on the increase or decrease of belt tension.

[0146] The feature extraction module is used to extract the features of the conveyor belt speed increase data or the conveyor belt speed decrease data as the first feature, extract the features of the drive motor torque increase data or the drive motor torque decrease data as the second feature, and extract the features of the belt tension increase data or the belt tension decrease data as the third feature.

[0147] The belt conveyor breakage model acquisition module is used to acquire a trained belt conveyor breakage model;

[0148] The classification module is used to input the acquired features into the belt conveyor breakage model to obtain classification labels, including belt breakage classification labels.

[0149] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and will not be repeated here.

[0150] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to implement the above-described belt conveyor breakage detection method.

[0151] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, enables the implementation of the belt breakage detection method for belt conveyors described above.

[0152] Figure 2This is an exemplary structural diagram of an electronic device capable of implementing the belt breakage detection method for a belt conveyor according to an embodiment of this application.

[0153] like Figure 2 As shown, the electronic device includes an input device 501, an input interface 502, a central processing unit 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, central processing unit 503, memory 504, and output interface 505 are interconnected via a bus 507. The input device 501 and output device 506 are connected to the bus 507 via the input interface 502 and output interface 505, respectively, and thus connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits it to the central processing unit 503 via the input interface 502. The central processing unit 503 processes the input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently storing the output information in the memory 504, and then transmitting the output information to the output device 506 via the output interface 505. The output device 506 outputs the output information to the outside of the electronic device for user use.

[0154] In other words, Figure 2 The illustrated electronic device may also be implemented as including: a memory storing computer-executable instructions; and one or more processors, which can be coupled when executing the computer-executable instructions. Figure 1 The described method for detecting belt breakage in belt conveyors.

[0155] In one embodiment, Figure 2 The electronic device shown can be implemented as including: a memory 504 configured to store executable program code; and one or more processors 503 configured to run the executable program code stored in the memory 504 to perform the belt conveyor breakage detection method in the above embodiments.

[0156] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0157] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0158] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, DVD or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0161] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutively marked blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or the overall flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0162] In this embodiment, the processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0163] Memory can be used to store computer programs and / or modules. The processor implements various functions of the device / terminal equipment by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can mainly include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area can store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). In addition, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0164] In this embodiment, if the modules / units integrated into the device / terminal equipment are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any person skilled in the art can make possible changes and modifications without departing from the spirit and scope of this application. Therefore, the scope of protection of this application should be determined by the scope defined in the claims of this application.

[0165] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] Furthermore, it is clear that the word "comprising" does not exclude other units or steps. Multiple units, modules, or devices recited in the apparatus claims may also be implemented by a single unit or overall apparatus via software or hardware.

[0167] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

[0168] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for detecting belt breakage in a belt conveyor, characterized in that, The belt conveyor breakage detection method includes: Acquire data on the increase or decrease of conveyor belt speed; Obtain data on the increase or decrease of the drive motor torque; Obtain data on the increase or decrease of belt tension; The features of the conveyor belt speed increase data or the conveyor belt speed decrease data are extracted as the first feature; The features of the drive motor torque increase data or drive motor torque decrease data are extracted as the second feature; The features of the belt tension increase data or belt tension decrease data are extracted as the third feature; Obtain a trained belt conveyor belt breakage model; the belt conveyor belt breakage model is trained in the following manner: Obtain the training set; Obtain the network structure of the BP neural network; The training set is input into the network structure of the BP neural network; The input and output values ​​of each neuron in the hidden layer are calculated using the training set. Calculate the input and output values ​​of each neuron in the output layer; The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. It is then determined whether the error meets the accuracy requirements. If yes, the training ends. If not, the gradient of each weight and bias with respect to the loss function is calculated and backpropagated back into the network. Based on the gradients calculated by backpropagation, the weights and biases of the neural network are updated using gradient descent. The acquired first feature, second feature, and third feature are input into the belt breakage model of the belt conveyor to obtain a classification label, which includes a belt breakage classification label.

2. The belt breakage detection method for a belt conveyor as described in claim 1, characterized in that, The acquisition of data on the increase or decrease of conveyor belt speed includes: Get the current speed of the conveyor belt during operation and the speed of the conveyor belt during operation at the previous time. The data on the increase or decrease in conveyor belt speed is obtained by comparing the current conveyor belt speed with the speed at the previous moment. When the difference between the current conveyor belt speed and the previous conveyor belt speed is positive, the data obtained is the increase in conveyor belt speed. When the difference between the current conveyor belt speed and the previous conveyor belt speed is negative, the data obtained is the amount of decrease in conveyor belt speed.

3. The belt breakage detection method for a belt conveyor as described in claim 2, characterized in that, The data on the increase or decrease of the drive motor torque includes: Obtain the current drive motor torque data and the drive motor torque data of the previous moment. The difference between the current drive motor torque data and the drive motor torque data from the previous moment is used to obtain either the increase or decrease in drive motor torque data. When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is a positive number, the obtained data is the drive motor torque increase data. When the difference between the current drive motor torque data and the drive motor torque data of the previous moment is negative, the obtained data is the amount of decrease in drive motor torque.

4. The belt breakage detection method for a belt conveyor as described in claim 3, characterized in that, The acquisition of belt tension increase data or belt tension decrease data includes: Obtain the belt tension data at the current moment and the belt tension data at the moment before the current moment; The difference between the current belt tension data and the belt tension data from the previous moment is used to obtain the belt tension increase or decrease data. When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is a positive number, the obtained data is the belt tension increase data. When the difference between the belt tension data at the current moment and the belt tension data at the previous moment is negative, the obtained data is the belt tension decrease data.

5. A belt breakage detection device for a belt conveyor, characterized in that, The belt conveyor belt breakage detection device includes: The operating speed data acquisition module is used to acquire data on the increase or decrease of the conveyor belt operating speed. A torque data acquisition module is used to acquire data on the increase or decrease of the drive motor torque. Tension data acquisition module, which is used to acquire belt tension increase data or belt tension decrease data; The feature extraction module is used to extract features of the conveyor belt speed increase data or conveyor belt speed decrease data as a first feature, extract features of the drive motor torque increase data or drive motor torque decrease data as a second feature, and extract features of the belt tension increase data or belt tension decrease data as a third feature. A belt conveyor belt breakage model acquisition module is used to acquire a trained belt conveyor belt breakage model; the belt conveyor belt breakage model is trained in the following manner: Obtain the training set; Obtain the network structure of the BP neural network; The training set is input into the network structure of the BP neural network; The input and output values ​​of each neuron in the hidden layer are calculated using the training set. Calculate the input and output values ​​of each neuron in the output layer; The mean squared error is used to calculate the error between the neural network's predicted value and the actual value. It is then determined whether the error meets the accuracy requirements. If yes, the training ends. If not, the gradient of each weight and bias with respect to the loss function is calculated and backpropagated back into the network. Based on the gradients calculated by backpropagation, the weights and biases of the neural network are updated using gradient descent. The classification module is used to input the acquired first feature, second feature, and third feature into the belt conveyor breakage model to obtain a classification label, which includes a belt breakage classification label.

6. An electronic device, characterized in that, The electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. When the processor executes the computer program, it implements the belt breakage detection method for belt conveyors as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the belt breakage detection method for a belt conveyor as described in any one of claims 1 to 4.

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