Fault Warning Method, Device, System and Storage Medium of Belt Conveyor

Abstract

The present application provides a fault warning method and a fault warning device for a belt conveyor. The fault warning method includes: receiving impact signals, vibration signals, and temperature signals of a transmission device within a predetermined time period; respectively performing time-domain analysis on the impact signals and the vibration signals to obtain time-domain characteristic parameters, respectively performing frequency-domain analysis on the impact signals and the vibration signals to obtain frequency-domain characteristic parameters, and respectively performing shaft center trajectory analysis on the impact signals and the vibration signals to obtain shaft center characteristic parameters; forming a fault symptom domain from the time-domain characteristic parameters, the frequency-domain characteristic parameters, the shaft center characteristic parameters, and the temperature values of the temperature signals, and performing fault warning on the transmission device at least based on the fault symptom domain, ensuring relatively accurate fault warning for the transmission device, thereby solving the problems of low fault warning accuracy rate and low automation degree for the transmission device of the belt conveyor in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of belt conveyors, and more particularly, to a fault warning method for a belt conveyor, a fault warning device, a computer-readable storage medium, and a fault warning system for a belt conveyor. Background Art

[0002] The drive device of a belt conveyor is composed of components such as a motor, a speed reducer, bearings, and a drive roller. During the use of the belt conveyor, the drive device may malfunction, resulting in the shutdown of the belt conveyor and making it inoperable. Most of the traditional fault monitoring of the drive device of a belt conveyor is carried out by manual inspection or online vibration monitoring.

[0003] Among them, manual inspection is to randomly measure the vibration signal of the drive device with a hand-held instrument. When the vibration speed value of the vibration signal exceeds a predetermined threshold, it is determined that the drive device has a fault, but the location of the fault cannot be further determined. At the same time, due to the single acquisition parameter of the manual inspection method, the fault of the drive device cannot be detected in time.

[0004] The online vibration monitoring method is to perform frequency-domain analysis or time-domain analysis manually when the vibration signal of the drive device is detected, and judge whether the drive device has a fault based on one's own experience. However, during the operation of the belt conveyor, its own vibration is large, the external vibration interference is serious, and the load is constantly changing, all of which will affect the vibration signal of the drive device of the belt conveyor. In addition, since the analysis levels of different personnel are different, the above-mentioned various reasons make the online vibration monitoring method have problems of low fault warning accuracy and low automation.

[0005] Therefore, there is an urgent need for a method that can accurately and automatically warn of faults in the drive device of a belt conveyor. Summary of the Invention

[0006] The main object of the present application is to provide a fault warning method for a belt conveyor, a fault warning device, a computer-readable storage medium, and a fault warning system for a belt conveyor, so as to solve the problems of low fault warning accuracy and low automation in the prior art for the drive device of a belt conveyor.

[0007] According to one aspect of an embodiment of the present invention, a fault warning method for a belt conveyor is provided. The belt conveyor includes a transmission device, and the fault warning method includes: receiving an impact signal, a vibration signal, and a temperature signal of the transmission device within a predetermined time period; performing time-domain analysis on the impact signal and the vibration signal respectively to obtain time-domain characteristic parameters, performing frequency-domain analysis on the impact signal and the vibration signal respectively to obtain frequency-domain characteristic parameters, and performing axis orbit analysis on the impact signal and the vibration signal respectively to obtain axis characteristic parameters; constructing a fault symptom universe of discourse from the time-domain characteristic parameters, the frequency-domain characteristic parameters, the axis characteristic parameters, and the temperature value of the temperature signal, and performing fault warning on the transmission device at least according to the fault symptom universe of discourse.

[0008] Optionally, the transmission device is composed of a plurality of target components. Performing fault warning on the transmission device at least according to the fault symptom universe of discourse includes: performing fault symptom matching between the fault symptom universe of discourse and a standard parameter universe of discourse to determine whether the transmission device has a fault trend. The standard parameter universe of discourse includes standard time-domain characteristic parameters, standard frequency-domain characteristic parameters, standard axis characteristic parameters, or standard temperature values; in the case of determining that the transmission device has a fault trend, performing fault location on the transmission device at least according to the fault symptom universe of discourse and a fault cause universe of discourse. The fault cause universe of discourse includes target characteristic parameters for each of the target components to have a fault, and the target characteristic parameters are at least one of the time-domain characteristic parameters, the frequency-domain characteristic parameters, or the axis characteristic parameters.

[0009] Optionally, performing fault symptom matching between the fault symptom universe of discourse and a standard parameter universe of discourse to determine whether the transmission device has a fault trend includes: in the case of the fault symptom universe of discourse not matching the standard parameter universe of discourse, determining that the transmission device has a fault trend; in the case of the fault symptom universe of discourse matching the standard parameter universe of discourse, determining that the transmission device does not have a fault trend.

[0010] Optionally, in the case of determining that the transmission device has a fault trend, performing fault location on the transmission device at least according to the fault symptom universe of discourse and a fault cause universe of discourse includes: determining the membership degrees of each target element in the fault symptom universe of discourse, and constructing a fault symptom matrix from the membership degrees of each target element. The target elements are the time-domain characteristic parameters, the frequency-domain characteristic parameters, the axis characteristic parameters, and the temperature value; determining the membership degrees of each of the target characteristic parameters in the fault cause universe of discourse, and constructing a fault cause matrix from the membership degrees of each target characteristic parameter; performing fault location on the transmission device according to the fault symptom matrix, the fault cause matrix, and a fuzzy relation matrix to obtain a target fault component, and the target fault component is one or more of the plurality of target components.

[0011] Optionally, after performing fault location on the transmission device according to the fault symptom matrix, the fault cause matrix, and the fuzzy relation matrix to obtain the target fault component, the fault warning method further includes: performing a fast Fourier transform on the impact signal and the vibration signal to obtain an FFT spectrum; determining a fuzzy fault matrix at least based on the FFT spectrum, and determining a fuzzy cause matrix according to the fuzzy fault matrix and the fuzzy relation matrix; determining the fault probability of the target fault component according to the fuzzy cause matrix, and sending the target fault component and the fault probability of the target fault component to a terminal device, so that the terminal device performs a visual display in a target model according to the fault probability, where the target model is a 3D model of the belt conveyor.

[0012] Optionally, determining a fuzzy fault matrix at least based on the FFT spectrum includes: determining the amplitude of the characteristic frequency in the FFT spectrum, and forming a preset fuzzy fault matrix from the amplitudes of the characteristic frequencies, where the characteristic frequency is the frequency at an interval of a target time in the FFT spectrum; using to perform a fuzzification process on the preset fuzzy fault matrix to obtain the fuzzy fault matrix, where x is the amplitude of the characteristic frequency, a is 0, and k is a coefficient.

[0013] Optionally, the time-domain characteristic parameters include probability density, autocorrelation function, cross-correlation function, and bar graph, the bar graph is composed of mean value, peak value, peak-to-peak value, effective value, and mean square deviation, the frequency-domain characteristic parameters include FFT spectrum, auto-power spectrum, cross-power spectrum, coherence spectrum, envelope spectrum, waterfall plot, and order spectrum, and the shaft center characteristic parameter is the shaft center locus.

[0014] According to another aspect of the embodiments of the present invention, there is also provided a fault warning device for a belt conveyor, the belt conveyor includes a transmission device, and the fault warning device includes: a receiving unit, configured to receive an impact signal, a vibration signal, and a temperature signal of the transmission device within a predetermined time period; an analysis unit, configured to perform time-domain analysis on the impact signal and the vibration signal respectively to obtain time-domain characteristic parameters, perform frequency-domain analysis on the impact signal and the vibration signal respectively to obtain frequency-domain characteristic parameters, and perform shaft center locus analysis on the impact signal and the vibration signal respectively to obtain shaft center characteristic parameters; a warning unit, configured to form a fault symptom universe of discourse from the time-domain characteristic parameters, the frequency-domain characteristic parameters, the shaft center characteristic parameters, and the temperature value of the temperature signal, and perform fault warning on the transmission device at least based on the fault symptom universe of discourse.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, which includes a stored program, wherein the program executes any one of the fault warning methods for the belt conveyor.

[0016] According to still another aspect of the embodiments of the present invention, there is also provided a fault warning system for a belt conveyor, including: a belt conveyor, which includes a conveying device; a target sensor, which includes a pulse and vibration sensor and a temperature sensor, the pulse and vibration sensor is used to collect the impact signal and vibration information of the transmission device within a predetermined time period, and the temperature sensor is used to collect the temperature value of the transmission device within the predetermined time period; a host computer, which includes a fault warning device for the belt conveyor, and the fault warning device is used to execute any one of the fault warning methods for the belt conveyor.

[0017] In the fault warning method for the belt conveyor according to the embodiments of the present invention, first, the impact signal, vibration signal, and temperature signal of the transmission device collected within a predetermined time period are received; then, time-domain analysis, frequency-domain analysis, and shaft center locus analysis are respectively performed on the impact signal and vibration signal to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters; finally, a fault warning is given to the transmission device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value. In the fault warning method of the present application, the host computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center locus analysis on the received impact signal and vibration signal respectively. That is to say, there is no need to perform spectrum analysis on the impact signal and vibration signal manually, which ensures a relatively high degree of automation. Compared with the prior art methods of manual inspection or on-line vibration monitoring, the present solution gives a fault warning to the transmission device at least according to the fault symptom domain obtained by analyzing the impact signal, vibration signal, and temperature signal. Since the fault symptom domain is automatically obtained without manual spectrum analysis and the fault analysis is carried out through the impact signal, vibration signal, and temperature signal, it ensures that the fault warning for the transmission device is relatively accurate, thus solving the problems of low accuracy and low degree of automation in the fault warning for the transmission device of the belt conveyor in the prior art. Description of the Drawings

[0018] The specification drawings constituting a part of the present application are used to provide a further understanding of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0019] Figure 1 Shows a flowchart of the fault warning method for the belt conveyor according to an embodiment of the present application;

[0020] Figure 2 Shows a schematic diagram of a bar chart of an embodiment of the present application;

[0021] Figure 3 Shows a schematic structural diagram of a fault warning device for a belt conveyor according to an embodiment of the present application;

[0022] Figure 4 Shows a schematic structural diagram of a fault warning system for a belt conveyor according to an embodiment of the present application.

[0023] Among them, the above-mentioned drawings include the following reference numerals:

[0024] 100, host computer; 101, terminal device; 102, belt conveyor; 103, target sensor; 104, edge data acquisition box. Specific embodiments

[0025] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so as to implement the embodiments of the present application described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] As mentioned in the background art, the accuracy of fault warning for the transmission device of the belt conveyor in the prior art is relatively low and the degree of automation is relatively low. To solve the above problems, in a typical implementation manner of the present application, a fault warning method, a fault warning device, a computer-readable storage medium, and a fault warning system for a belt conveyor are provided.

[0029] According to an embodiment of the present application, a fault warning method for a belt conveyor is provided.

[0030] Figure 1 It is a flowchart of a fault warning method for a belt conveyor according to an embodiment of the present application. The belt conveyor includes a transmission device. This fault warning method can be applied to a host computer, such as Figure 1 As shown, this fault warning method includes the following steps:

[0031] Step S101, receiving the impact signal, vibration signal, and temperature signal of the above-mentioned transmission device within a predetermined time period;

[0032] Step S102, respectively performing time-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal to obtain time-domain characteristic parameters, performing frequency-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal to obtain frequency-domain characteristic parameters, and performing shaft center locus analysis on the above-mentioned impact signal and the above-mentioned vibration signal to obtain shaft center characteristic parameters;

[0033] Step S103, constructing a fault symptom universe of discourse from the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the temperature value of the above-mentioned temperature signal, and performing fault warning on the above-mentioned transmission device at least according to the above-mentioned fault symptom universe of discourse.

[0034] In the above-mentioned fault warning method for a belt conveyor, first, receive the impact signal, vibration signal, and temperature signal of the transmission device collected within a predetermined time period; then, respectively perform time-domain analysis, frequency-domain analysis, and shaft center locus analysis on the impact signal and the vibration signal to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters respectively; finally, perform fault warning on the above-mentioned transmission device at least according to the fault symptom universe of discourse composed of time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature values. In the fault warning method of the present application, the host computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center locus analysis on the received impact signal and vibration signal respectively. That is to say, there is no need to perform spectrum analysis on the impact signal and vibration signal manually, ensuring a relatively high degree of automation. Compared with the method of manual inspection or online vibration monitoring in the prior art, this solution performs fault warning on the transmission device at least according to the fault symptom universe of discourse obtained by analyzing the impact signal, vibration signal, and temperature signal. Since the fault symptom universe of discourse is automatically obtained without manual spectrum analysis and fault analysis is performed through the impact signal, vibration signal, and temperature signal, it ensures that the fault warning for the transmission device is relatively accurate, thus solving the problems of low accuracy and low degree of automation in the fault warning for the transmission device of the belt conveyor in the prior art.

[0035] Specifically, in the above embodiments, the present application does not limit the size of the above-mentioned predetermined time period, which can be flexibly adjusted according to actual needs.

[0036] In a specific embodiment of the present application, the above-mentioned impact signal and the above-mentioned vibration signal can be collected by a pulse and vibration sensor, and the above-mentioned temperature signal can be collected by a temperature sensor. Of course, the collection of the above-mentioned impact signal and the above-mentioned vibration signal is not limited to the pulse and vibration sensors listed above, and can also be any feasible method in the prior art, as long as the above-mentioned impact signal and the above-mentioned vibration signal can be collected. In addition, the collection of the above-mentioned temperature signal is not limited to the temperature sensor, and can also be any feasible method in the prior art, as long as the above-mentioned temperature signal can be collected.

[0037] In the actual application process, the impact signal and the vibration signal are subjected to time-domain analysis to obtain time-domain characteristic parameters, and the above-mentioned time-domain characteristic parameters can be various types of parameters. In the present application, the specific types of the above-mentioned time-domain characteristic parameters are not limited, and can be flexibly adjusted according to the actual situation. Of course, when the computing power of the upper computer is relatively strong, all types of time-domain characteristic parameters can also be obtained when performing time-domain analysis on the impact signal and the vibration signal. For example, the time-domain characteristic parameters with dimensions can be effective value, peak-to-peak value, mean value, variance, etc., and the dimensionless time-domain characteristic parameters can be kurtosis, impulse index, skewness, etc.

[0038] Specifically, the impact signal and the vibration signal are subjected to frequency-domain analysis to obtain frequency-domain characteristic parameters, and the above-mentioned frequency-domain characteristic parameters can be various types of parameters. In the present application, the specific types of the above-mentioned frequency-domain characteristic parameters are not limited, and can be flexibly adjusted according to the actual situation. Of course, when the computing power of the upper computer is relatively strong, all types of time-domain characteristic parameters can also be obtained when performing time-domain analysis on the impact signal and the vibration signal. For example, FFT spectrum, auto-power spectrum, cross-power spectrum, etc., which will not be elaborated here one by one.

[0039] In the actual application process, before performing time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis on the above-mentioned impact signal and the above-mentioned vibration signal, the above-mentioned impact signal and the above-mentioned vibration signal can also be filtered to filter the interference of the interference signal to the useful signal.

[0040] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0041] In an embodiment of the present application, the above-mentioned transmission device is composed of multiple target components. At least according to the above-mentioned fault symptom domain, fault warning is performed on the above-mentioned transmission device, including: matching the fault symptoms between the above-mentioned fault symptom domain and the standard parameter domain to determine whether the above-mentioned transmission device has a fault trend. The above-mentioned standard parameter domain includes standard time-domain characteristic parameters, standard frequency-domain characteristic parameters, standard shaft center characteristic parameters, or standard temperature values; when it is determined that the above-mentioned transmission device has a fault trend, at least according to the fault symptom domain and the fault cause domain, fault location is performed on the above-mentioned transmission device. The above-mentioned fault cause domain includes target characteristic parameters for each of the above-mentioned target components to have a fault. The above-mentioned target characteristic parameter is at least one of the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, or the above-mentioned shaft center characteristic parameters. In this embodiment, by matching the fault symptoms between the fault symptom domain and the standard parameter domain, it can be relatively simply determined whether the transmission device has a fault trend. When it is determined that the transmission device has a fault trend, at least according to the fault symptom domain and the fault cause domain, fault location is performed on the transmission device, thus realizing automatic fault location of the transmission device and being able to timely detect the target components with faults in the transmission device, further ensuring a relatively high degree of automation of the fault warning method of the present application.

[0042] Specifically, in the above-mentioned embodiment, the target characteristic parameter in the above-mentioned fault cause domain is at least one of the time-domain characteristic parameter, the frequency-domain characteristic parameter, or the shaft center characteristic parameter. That is to say, the above-mentioned target characteristic parameter can be the characteristic parameter when the target component of the corresponding transmission device has a fault.

[0043] In order to more simply determine whether the transmission device has a fault trend, in another embodiment of the present application, matching the fault symptoms between the above-mentioned fault symptom domain and the standard parameter domain to determine whether the above-mentioned transmission device has a fault trend includes: when the above-mentioned fault symptom domain does not match the above-mentioned standard parameter domain, determining that the above-mentioned transmission device has a fault trend; when the above-mentioned fault symptom domain matches the above-mentioned standard parameter domain, determining that the above-mentioned transmission device does not have a fault trend.

[0044] In a specific embodiment of the present application, the above-mentioned fault symptom domain does not match the above-mentioned standard parameter domain. Specifically, it may be that there is one or more characteristic parameters in the above-mentioned fault symptom domain that do not match the corresponding characteristic parameters in the above-mentioned standard parameter domain. That is to say, whether there is a fault trend can be determined through the mismatched characteristic parameters.

[0045] In another specific embodiment of the present application, the above-mentioned fault symptom domain is matched with the above-mentioned standard parameter domain. Specifically, it can also be that one or more characteristic parameters in the above-mentioned fault symptom domain are matched with the corresponding characteristic parameters in the above-mentioned standard parameter domain. That is to say, whether there is a fault trend can be determined through the matched characteristic parameters.

[0046] In the actual application process, when the fault symptom domain and the standard parameter domain are matched, it is determined that the transmission device does not have a fault trend. In this case, health status information can also be sent to the terminal device so that the terminal device can display a color mark in the 3D model of the belt conveyor according to the health status information. For example, a green mark is displayed to prompt the user that the transmission device of the current belt conveyor is in a healthy state.

[0047] In another embodiment of the present application, when it is determined that the above-mentioned transmission device has a fault trend, at least based on the fault symptom domain and the fault cause domain, fault location of the above-mentioned transmission device is performed, including: determining the membership degrees of each target element in the above-mentioned fault symptom domain, and forming a fault symptom matrix from the membership degrees of each above-mentioned target element, where the above-mentioned target elements are the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the above-mentioned temperature value; determining the membership degrees of each above-mentioned target characteristic parameter in the above-mentioned fault cause domain, and forming a fault cause matrix from the membership degrees of each above-mentioned target characteristic parameter; performing fault location on the above-mentioned transmission device according to the above-mentioned fault symptom matrix, the above-mentioned fault cause matrix, and the fuzzy relationship matrix to obtain target fault components, where the above-mentioned target fault components are one or more of a plurality of target components. In this way, automatic fault location of the transmission device is realized, and relatively accurate and efficient fault definition of the transmission device is realized, further ensuring a relatively high degree of automation of the fault warning method of the present application.

[0048] In a specific embodiment of the present application, the fault symptom domain can be expressed as U = {x1, x2, …, x m}, and x1, x2, …, x m are the target elements in the fault symptom domain. If the membership degree of each target element x i is μ xi , then the fault symptom matrix X = [μ x1 , μ x2 , …, μ xm can be obtained. The fault cause domain can be expressed as V = [y1, y2, …, y n , and y1, y2, …, y n are the target characteristic parameters. If the membership degree of each target characteristic parameter y i is μ yi , then the fault cause matrix Y = [μy1 , μ y2 , …, μ yn . Among them, the fuzzy relation matrix R between the fault symptom domain and the fault cause domain can be expressed as:

[0049]

[0050] Then, according to the fuzzy relation matrix R and the fuzzy fault symptom matrix X, the fuzzy cause matrix Y can be obtained. The calculation formula is as follows:

[0051]

[0052] In order to enable users to promptly know the health status of the belt conveyor, in another embodiment of the present application, after performing fault location on the above-mentioned transmission device according to the above-mentioned fault symptom matrix, the above-mentioned fault cause matrix, and the fuzzy relation matrix to obtain the target fault component, the above-mentioned fault warning method further includes: performing a fast Fourier transform on the above-mentioned impact signal and the above-mentioned vibration signal to obtain the FFT spectrum; determining the fuzzy fault matrix at least according to the FFT spectrum, and determining the fuzzy cause matrix according to the fuzzy fault matrix and the above-mentioned fuzzy relation matrix; determining the fault probability of the above-mentioned target fault component according to the above-mentioned fuzzy cause matrix, and sending the above-mentioned target fault component and the fault probability of the above-mentioned target fault component to the terminal device, so that the terminal device performs visual display in the target model according to the above-mentioned fault probability, and the above-mentioned target model is the 3D model of the above-mentioned belt conveyor.

[0053] Specifically, after sending the target fault component and the fault probability of the target fault component to the terminal device, the terminal device can perform color marking on the target fault component in the 3D model of the belt conveyor. For example, in the case of a relatively high fault probability, the target fault component can be marked as red in the 3D model of the belt conveyor to prompt the user to process the target fault component in a timely manner.

[0054] In order to relatively simply determine the fuzzy fault matrix, in one embodiment of the present application, determining the fuzzy fault matrix at least according to the FFT spectrum includes: determining the amplitude of the characteristic frequency in the above-mentioned FFT spectrum, and forming a preset fuzzy fault matrix from the amplitude of the above-mentioned characteristic frequency, where the above-mentioned characteristic frequency is the frequency at intervals of the target time in the above-mentioned FFT spectrum; using to perform a fuzzification process on the above-mentioned preset fuzzy fault matrix to obtain the above-mentioned fuzzy fault matrix, where x is the amplitude of the above-mentioned characteristic frequency, a is 0, and k is a coefficient.

[0055] In a specific embodiment of the present application, fast Fourier transform is performed on the impact pulse and vibration signal to obtain the FFT spectrum. Then, at least based on the amplitudes of the characteristic frequencies in the FFT spectrum, the preset fuzzy fault matrix X1 of the transmission device is determined as X1 = [x1, x2, …, x l , where x1, x2, …, x l are the amplitudes of the respective characteristic frequencies. Then, the fuzzy membership function is used to perform fuzzy processing on the amplitudes corresponding to the characteristic frequencies to obtain the fuzzy fault matrix. Among them, the fuzzy membership function is:

[0056]

[0057] Among them, x (i.e., x i ) is the amplitude corresponding to the characteristic frequency, the coefficient a can be 0, and the value of k is determined by different target fault components. For transmission devices with different rotational speeds, the value of k is usually different. The fault cause matrix Y1 can be obtained by multiplying the fuzzy fault vector X1 and the fuzzy relation matrix R, that is, Y1 = X1R. According to the fuzzy cause matrix Y1, the fault probabilities of the respective target fault components can be determined.

[0058] In the actual application process, the above-mentioned fuzzy cause matrix can be a 1×1-dimensional matrix. At the same time, for the same target fault component, there can be multiple fuzzy relation matrices, and thus multiple fault cause matrices can also be obtained. In the case where the above-mentioned fuzzy cause matrix can be a 1×1-dimensional matrix, the maximum value among the multiple 1×1-dimensional matrices can be determined as the fault probability of the target fault component.

[0059] In another embodiment of the present application, the above-mentioned time-domain characteristic parameters include probability density, autocorrelation function, cross-correlation function, and bar chart. The above-mentioned bar chart is composed of mean value, peak value, peak-to-peak value, effective value, and mean square deviation. The above-mentioned frequency-domain characteristic parameters include FFT spectrum, auto-power spectrum, cross-power spectrum, coherence spectrum, envelope spectrum, waterfall plot, and order spectrum. The above-mentioned shaft center characteristic parameter is the shaft center locus.

[0060] In a specific embodiment of the present application, the above-mentioned probability density represents the probability that the amplitude of the signal falls within a specified interval. When the signal changes, the waveform of its probability density also changes accordingly. Therefore, the signal can also be identified by analyzing the waveform of the probability density of the signal. Assuming that the acquired original signal is x(t), then the probability density P(i) of the signal is:

[0061]

[0062] Among them, x max is the maximum value in the signal, x minis the minimum value in the signal, m is the number of equal segments in the amplitude range, n is the length of the signal, and Num is the number of signals falling within the specified amplitude range.

[0063] Specifically, the above-mentioned autocorrelation function (i.e., the autocorrelation analysis of the signal) is a commonly used method in time-domain analysis. The autocorrelation function of a signal can highlight the periodic components of the signal and suppress the aperiodic components. Therefore, the autocorrelation function of a signal can also be used as a method to extract the periodic components of the signal. Assuming that the original signal collected is x(t), the autocorrelation function of the signal can be

[0064]

[0065] Specifically, the above-mentioned cross-correlation function is a commonly used method to determine the correlation between two signals. When the frequencies of the two signals are the same, the cross-correlation function is the periodic component of the same frequency; when the frequencies of the two signals are different, the cross-correlation function is zero, that is, the two signals are not correlated. If the two signals are respectively represented as x(t) and y(t), then the cross-correlation function R xy (τ) is

[0066]

[0067] Specifically, as Figure 2 shown, the above bar graph is composed of the mean value, peak value, peak-to-peak value, effective value, and mean square deviation. Among them, the calculation method of the peak value is as follows:

[0068]

[0069] Among them, X p is the single peak value of the signal, and X rms is the effective value of the signal. The calculation method of the above peak-to-peak value is as follows:

[0070] x p-p = x max - x min ,

[0071] Among them, x p-p is the peak-to-peak value, x max is the maximum value of the signal, and x min is the minimum value of the signal. The calculation method of the above effective value is as follows:

[0072]

[0073] Among them, RMS is the effective value of the signal, and x i is the amplitude of the signal at the i-th moment.

[0074] Specifically, the above FFT spectrum is the most commonly used method for frequency-domain analysis of signals. The FFT spectrum of a signal can be obtained by performing a Fourier transform on the time-domain signal of the collected original signal. The FFT spectrum of a signal can reflect the frequency components contained in the signal. For a belt conveyor, when the operating state of the drive device is abnormal, the frequency components contained in the impact signal and vibration signal will change. Therefore, the operating state of the drive device can be effectively judged through FFT spectrum analysis.

[0075] Specifically, the auto-power spectrum can reflect the energy of each frequency component in the signal. The auto-power spectrum of a signal can be determined from the FFT spectrum of the signal. If X(f) is the FFT spectrum of the signal, then the auto-power spectrum of the signal

[0076] Specifically, the cross-power spectrum is a frequency-domain description of the correlation between two signals, which can describe the amplitude and phase relationship between the two signals. Similar to the auto-power spectrum, the cross-power spectrum can also be determined from the FFT spectra of the two signals. If the FFT spectra of the two signals are X(f) and Y(f) respectively, then the cross-power spectrum

[0077] Specifically, the cepstrum is also a commonly used method for frequency-domain analysis of signals. The cepstrum can separate the sideband signals, making the periodic components that are difficult to distinguish in the auto-power spectrum become discrete line spectra in the cepstrum diagram. The cepstrum is widely used in analyzing gear fault signals with more sideband components. The calculation method of the cepstrum is as follows C x (τ) = |F -1 |logS x (f)||, where C x (τ) is the cepstrum obtained after transformation, and S x (f) is the auto-power spectrum of the original signal.

[0078] Specifically, the process of obtaining the envelope spectrum is as follows: The signal x(t) is subjected to a Hilbert transform to obtain Using the signal x(t) and the Hilbert transform, the obtained h(t) can be used to obtain the analytic signal z(t) = x(t) + jh(t). The amplitude function a(t) of the analytic signal z(t) is the envelope of the real signal s(t). Calculating the amplitude spectrum or power spectrum of a(t) is the envelope spectrum of the signal s(t).

[0079] Specifically, the waterfall plot is a commonly used method for analyzing the vibration signals during the speed-up and speed-down processes of rotating machinery. By calculating the spectra of the vibration signals collected at different speeds or different times and plotting them as a three-dimensional spectrogram, the waterfall plot can be obtained. Through the waterfall plot, the changes in the frequency and amplitude of each vibration component over time or speed can be clearly seen.

[0080] Specifically, the calculation method of the order spectrum is as follows: (a). Calculate the order tracking assuming that the rotational speed of the reference axis undergoes a uniformly accelerated motion within a small time period. On this premise, the angle θ of the reference axis can be expressed as θ(t) = b0 + b1t + b2t 2 , where b0, b1, and b2 are undetermined coefficients; t is the time point. (b). Determine the angular increment Δφ corresponding to the key phase pulse. If one key phase pulse is generated per revolution of the reference axis, then Δφ = 2π. The undetermined coefficients in the above formula can be obtained by fitting the arrival times t1, t2, and t3 of three consecutive pulses where, after the undetermined coefficients are obtained, the equally angular sampled time points can be calculated from the above formula where k is the interpolation coefficient; Δθ is the equally angular sampling interval. (c). Perform interpolation processing on the original signal at the sampling time point t k to obtain the equally angular sampled data. From the equally angular sampling interval Δθ, the sampling order ratio can be obtained (d). Perform amplitude spectrum analysis on the equally angular sampled data to obtain the order spectrum of the vibration signal during the acceleration and deceleration process.

[0081] Specifically, the above-mentioned shaft center orbit analysis is also a commonly used method for the vibration analysis of rotating machinery. In practice, two sensors with an included angle of 90° are usually used to measure the vibrations of the rotor in the X and Y directions respectively. Plotting with the signal in the X (Y) direction as the abscissa and the signal in the Y (X) direction as the ordinate can obtain the shaft center orbit of the rotor. When the operating state of the rotor changes, the shaft center orbit of the rotor usually also changes.

[0082] In a specific embodiment of the present application, the above-mentioned time-domain characteristic parameters may further include kurtosis, impulse index, skewness, margin, and the amplitudes corresponding to the 1x and 2x frequencies of the rotor power frequency.

[0083] Specifically, the calculation method of the above-mentioned kurtosis is K is the kurtosis index; is the mean value; σ is the standard deviation,

[0084] Specifically, the calculation method of the above-mentioned impulse index is I is the impulse index, x max is the maximum value, is the absolute average value.

[0085] Specifically, the calculation method of the above-mentioned skewness is is the average value, X rms is the effective value.

[0086] Specifically, the calculation method of the above-mentioned margin is X p is the single peak value of the signal, X ais the mean value of the absolute value of the signal,

[0087] Specifically, the FFT spectrum of the signal can be obtained by performing a Fourier transform on the signal, and the amplitudes corresponding to the power frequency 1x and 2x of the rotor can be searched for in the FFT spectrum.

[0088] The embodiment of the present application also provides a fault warning device for a belt conveyor. It should be noted that the fault warning device for the belt conveyor in the embodiment of the present application can be used to execute the fault warning method for the belt conveyor provided in the embodiment of the present application. The following introduces the fault warning device for the belt conveyor provided in the embodiment of the present application.

[0089] Figure 3 is a schematic diagram of the fault warning device for the belt conveyor according to the embodiment of the present application. As Figure 3 shown, the fault warning device includes:

[0090] A receiving unit 10, configured to receive the impact signal, vibration signal, and temperature signal of the above-mentioned transmission device within a predetermined time period;

[0091] An analysis unit 20, configured to perform time-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain time-domain characteristic parameters, perform frequency-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain frequency-domain characteristic parameters, and perform shaft center trajectory analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain shaft center characteristic parameters;

[0092] A warning unit 30, configured to form a fault symptom domain by the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the temperature value of the above-mentioned temperature signal, and perform fault warning on the above-mentioned transmission device at least based on the above-mentioned fault symptom domain.

[0093] In the above-mentioned fault warning device of the belt conveyor, the receiving unit is used to receive the impact signal, vibration signal and temperature signal of the driving device collected within a predetermined time period; the analysis unit is used to perform time-domain analysis, frequency-domain analysis and shaft center trajectory analysis on the impact signal and vibration signal respectively, and obtain time-domain characteristic parameters, frequency-domain characteristic parameters and shaft center characteristic parameters respectively; the warning unit is used to perform fault warning on the above-mentioned driving device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters and temperature value. In the fault warning device of the present application, the upper computer can automatically perform time-domain analysis, frequency-domain analysis and shaft center trajectory analysis on the received impact signal and vibration signal respectively. That is to say, there is no need to perform spectrum analysis on the impact signal and vibration signal manually, which ensures a relatively high degree of automation. Compared with the prior art methods of manual inspection or on-line vibration monitoring, the present solution performs fault warning on the driving device at least according to the fault symptom domain obtained by analyzing the impact signal, vibration signal and temperature signal. Since the fault symptom domain is automatically obtained without manual spectrum analysis and the fault analysis is performed through the impact signal, vibration signal and temperature signal, the fault warning of the driving device is ensured to be relatively accurate, thus solving the problems of low accuracy of fault warning and low degree of automation of the driving device of the belt conveyor in the prior art.

[0094] Specifically, in the above-mentioned embodiment, the present application does not limit the size of the above-mentioned predetermined time period, and it can be flexibly adjusted according to actual needs.

[0095] In a specific embodiment of the present application, the above-mentioned impact signal and the above-mentioned vibration signal can be collected by a pulse and vibration sensor, and the above-mentioned temperature signal can be collected by a temperature sensor. Of course, the collection of the above-mentioned impact signal and the above-mentioned vibration signal is not limited to the pulse and vibration sensors listed above, and any feasible method in the prior art can also be used, as long as the above-mentioned impact signal and the above-mentioned vibration signal can be collected. In addition, the collection of the above-mentioned temperature signal is not limited to the temperature sensor, and any feasible method in the prior art can also be used, as long as the above-mentioned temperature signal can be collected.

[0096] In the actual application process, time-domain analysis is performed on the impact signal and the vibration signal to obtain time-domain characteristic parameters, and the above time-domain characteristic parameters can be various types of parameters. In this application, the specific types of the above time-domain characteristic parameters are not limited, and they can be flexibly adjusted according to the actual situation. Of course, when the computing power of the host computer is relatively strong, all types of time-domain characteristic parameters can also be obtained when performing time-domain analysis on the impact signal and the vibration signal. For example, the time-domain characteristic parameters with dimensions can be the effective value, peak-to-peak value, mean value, variance, etc., and the dimensionless time-domain characteristic parameters can be kurtosis, impulse index, skewness, etc.

[0097] Specifically, frequency-domain analysis is performed on the impact signal and the vibration signal to obtain frequency-domain characteristic parameters, and the above frequency-domain characteristic parameters can be various types of parameters. In this application, the specific types of the above frequency-domain characteristic parameters are not limited, and they can be flexibly adjusted according to the actual situation. Of course, when the computing power of the host computer is relatively strong, all types of time-domain characteristic parameters can also be obtained when performing time-domain analysis on the impact signal and the vibration signal. For example, FFT spectrum, auto-power spectrum, cross-power spectrum, etc., which will not be elaborated one by one here.

[0098] In the actual application process, before performing time-domain analysis, frequency-domain analysis, and shaft center orbit analysis on the above impact signal and the above vibration signal, the above impact signal and the above vibration signal can also be filtered to filter the interference of the interference signal to the useful signal.

[0099] In an embodiment of the present application, the above transmission device is composed of multiple target components, and the above warning unit includes a matching module and a fault location module. Among them, the above matching module is used to perform fault symptom matching between the above fault symptom domain and the standard parameter domain to determine whether the above transmission device has a fault trend. The above standard parameter domain includes standard time-domain characteristic parameters, standard frequency-domain characteristic parameters, standard shaft center characteristic parameters, or standard temperature values; the above fault location module is used to, when it is determined that the above transmission device has a fault trend, perform fault location on the above transmission device at least according to the fault symptom domain and the fault cause domain. The above fault cause domain includes the target characteristic parameters of each of the above target components having a fault, and the above target characteristic parameters are at least one of the above time-domain characteristic parameters, the above frequency-domain characteristic parameters, or the above shaft center characteristic parameters. In this embodiment, performing fault symptom matching between the fault symptom domain and the standard parameter domain can relatively simply determine whether the transmission device has a fault trend. When it is determined that the transmission device has a fault trend, perform fault location on the transmission device at least according to the fault symptom domain and the fault cause domain, so as to realize automatic fault location of the transmission device and timely discover the target components with faults in the transmission device, further ensuring a relatively high degree of automation of the fault warning method of the present application.

[0100] Specifically, in the above embodiments, the target characteristic parameters in the above fault cause domain are at least one of time domain characteristic parameters, frequency domain characteristic parameters, or shaft center characteristic parameters. That is to say, the above target characteristic parameters can be the characteristic parameters when the target components of the corresponding transmission device fail.

[0101] In another embodiment of the present application, in order to more simply determine whether the transmission device has a fault trend, the above matching module includes a first matching sub-module and a second matching sub-module, wherein the first matching sub-module is used to determine that the transmission device has a fault trend when the above fault symptom domain does not match the above standard parameter domain; the second matching sub-module is used to determine that the transmission device does not have a fault trend when the above fault symptom domain matches the above standard parameter domain.

[0102] In a specific embodiment of the present application, the mismatch between the above fault symptom domain and the above standard parameter domain may specifically be that there is one or more characteristic parameters in the above fault symptom domain that do not match the corresponding characteristic parameters in the above standard parameter domain. That is to say, it is possible to determine whether there is a fault trend through the mismatched characteristic parameters.

[0103] In another specific embodiment of the present application, the match between the above fault symptom domain and the above standard parameter domain may specifically be that there is one or more characteristic parameters in the above fault symptom domain that match the corresponding characteristic parameters in the above standard parameter domain. That is to say, it is possible to determine whether there is a fault trend through the matched characteristic parameters.

[0104] In the actual application process, when the fault symptom domain and the standard parameter domain match, it is determined that the transmission device does not have a fault trend. In this case, it is also possible to send health status information to the terminal device so that the terminal device can display a color mark in the 3D model of the belt conveyor according to the health status information. For example, a green mark is displayed to prompt the user that the transmission device of the current belt conveyor is in a healthy state.

[0105] In another embodiment of the present application, the above-mentioned fault location module includes a first determination sub-module, a second determination sub-module, and a fault location sub-module. Among them, the first determination sub-module is used to determine the membership degrees of each target element in the above-mentioned fault symptom domain, and form a fault symptom matrix from the membership degrees of each above-mentioned target element. The above-mentioned target elements are the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the above-mentioned temperature value; the second determination sub-module is used to determine the membership degrees of each above-mentioned target characteristic parameter in the above-mentioned fault cause domain, and form a fault cause matrix from the membership degrees of each above-mentioned target characteristic parameter; the fault location sub-module is used to perform fault location on the above-mentioned transmission device according to the above-mentioned fault symptom matrix, the above-mentioned fault cause matrix, and the fuzzy relationship matrix, and obtain a target fault component. The above-mentioned target fault component is one or more of a plurality of target components. In this way, the automatic fault location of the transmission device is realized, and the more accurate and efficient fault definition of the transmission device is realized. Further, the higher automation degree of the fault warning method of the present application is ensured.

[0106] In a specific embodiment of the present application, the fault symptom domain can be expressed as U = {x1, x2, …, x m}, where x1, x2, …, x m are target elements in the fault symptom domain. If the membership degrees of each target element x i are μ xi , then the fault symptom matrix X = [μ x1 , μ x2 , …, μ xm can be obtained. The fault cause domain can be expressed as V = [y1, y2, …, y n , where y1, y2, …, y n are target characteristic parameters. If the membership degrees of each target characteristic parameter y i are μ yi , then the fault cause matrix Y = [μ y1 , μ y2 , …, μ yn can be obtained. Among them, the fuzzy relationship matrix R between the fault symptom domain and the fault cause domain can be expressed as:

[0107]

[0108] Then, according to the fuzzy relationship matrix R and the fuzzy fault symptom matrix X, the fuzzy cause matrix Y can be obtained, and its calculation formula is as follows:

[0109]

[0110] In order to facilitate users to timely know the health status of the belt conveyor, in another embodiment of the present application, the above-mentioned fault warning device further includes a transformation unit, a first determination unit, and a second determination unit. Among them, the above-mentioned transformation unit is used to perform fast Fourier transform on the above-mentioned impact signal and the above-mentioned vibration signal to obtain an FFT spectrum after performing fault location on the above-mentioned transmission device according to the above-mentioned fault symptom matrix, the above-mentioned fault cause matrix, and the fuzzy relationship matrix to obtain the target fault component; the above-mentioned first determination unit is used to determine a fuzzy fault matrix at least according to the FFT spectrum, and determine a fuzzy cause matrix according to the fuzzy fault matrix and the above-mentioned fuzzy relationship matrix; the above-mentioned second determination unit is used to determine the fault probability of the above-mentioned target fault component according to the above-mentioned fuzzy cause matrix, and send the above-mentioned target fault component and the fault probability of the above-mentioned target fault component to the terminal device, so that the terminal device performs visual display in the target model according to the above-mentioned fault probability, and the above-mentioned target model is a 3D model of the above-mentioned belt conveyor.

[0111] Specifically, after sending the target fault component and the fault probability of the target fault component to the terminal device, the terminal device can perform color marking on the target fault component in the 3D model of the belt conveyor. For example, in the case of a high fault probability, the target fault component can be marked as red in the 3D model of the belt conveyor to prompt the user to process the target fault component in time.

[0112] In order to more simply determine the fuzzy fault matrix, in an embodiment of the present application, the above-mentioned first determination unit includes a determination module and a processing module. Among them, the above-mentioned determination module is used to determine the amplitude of the characteristic frequency in the above-mentioned FFT spectrum, and form a preset fuzzy fault matrix from the amplitude of the above-mentioned characteristic frequency. The above-mentioned characteristic frequency is the frequency at intervals of the target time in the above-mentioned FFT spectrum; the above-mentioned processing module is used to use to perform fuzzy processing on the above-mentioned preset fuzzy fault matrix to obtain the above-mentioned fuzzy fault matrix, where x is the amplitude of the above-mentioned characteristic frequency, a is 0, and k is a coefficient.

[0113] In a specific embodiment of the present application, fast Fourier transform is performed on the impact pulse and the vibration signal to obtain an FFT spectrum. Then, at least according to the amplitude of the characteristic frequency in the FFT spectrum, the preset fuzzy fault matrix X1 = [x1, x2,..., x l of the transmission device is determined, and x1, x2,..., x l are the amplitudes of each characteristic frequency respectively. Then, the fuzzy membership function is used to perform fuzzy processing on the amplitude corresponding to the characteristic frequency to obtain the fuzzy fault matrix. Among them, the fuzzy membership function is:

[0114]

[0115] where x (i.e., x i ) is the amplitude corresponding to the characteristic frequency, the coefficient a can be 0, and the value of k is determined by different target fault components. For transmission devices with different rotational speeds, the value of k is usually different. The fault cause matrix Y1 can be obtained by multiplying the fuzzy fault vector X1 and the fuzzy relation matrix R, i.e., Y1 = X1R. The fault probability of each target fault component can be determined according to the fuzzy cause matrix Y1.

[0116] In the actual application process, the above-mentioned fuzzy cause matrix can be a 1×1 matrix. At the same time, for the same target fault component, there can be multiple fuzzy relation matrices, and thus multiple fault cause matrices can also be obtained. When the above-mentioned fuzzy cause matrix can be a 1×1 matrix, the maximum value among multiple 1×1 matrices can be determined as the fault probability of the target fault component.

[0117] In another embodiment of the present application, the above-mentioned time-domain characteristic parameters include probability density, autocorrelation function, cross-correlation function, and bar chart. The above-mentioned bar chart is composed of mean value, peak value, peak-to-peak value, effective value, and mean square deviation. The above-mentioned frequency-domain characteristic parameters include FFT spectrum, auto-power spectrum, cross-power spectrum, coherence spectrum, envelope spectrum, waterfall plot, and order spectrum. The above-mentioned shaft center characteristic parameter is the shaft center locus.

[0118] In a specific embodiment of the present application, the above-mentioned probability density represents the probability that the amplitude of the signal falls within a specified interval. When the signal changes, the waveform of its probability density also changes accordingly. Therefore, the signal can also be identified by analyzing the waveform of the probability density of the signal. Assuming that the original signal collected is x(t), the probability density P(i) of the signal is:

[0119]

[0120] where x max is the maximum value in the signal, x min is the minimum value in the signal, m is the number of equal segments of the amplitude interval, n is the length of the signal, and Num is the number of signals falling within the specified amplitude interval.

[0121] Specifically, the above-mentioned autocorrelation function (i.e., the autocorrelation analysis of the signal) is a commonly used method in time-domain analysis. The autocorrelation function of the signal can highlight the periodic components of the signal and suppress the non-periodic components. Therefore, the autocorrelation function of the signal can also be used as a method for extracting the periodic components of the signal. Assuming that the original signal collected is x(t), the autocorrelation function of the signal can be

[0122]

[0123] Specifically, the above cross-correlation function is a common method for determining the correlation between two signals. When the frequencies of the two signals are the same, the cross-correlation function is a periodic component with the same frequency; when the frequencies of the two signals are different, the cross-correlation function is zero, that is, the two signals are uncorrelated. If the two signals are represented as x(t) and y(t) respectively, then the cross-correlation function R xy (τ) is

[0124]

[0125] Specifically, as Figure 2 shown, the above bar graph is composed of the mean value, peak value, peak-to-peak value, effective value, and mean square deviation. Among them, the calculation method of the peak value is as follows:

[0126]

[0127] Among them, X p is the single peak value of the signal, and X rms is the effective value of the signal. The calculation method of the above peak-to-peak value is as follows:

[0128] x p-p = x max - x min ,

[0129] Among them, x p-p is the peak-to-peak value, x max is the maximum value of the signal, and x min is the minimum value of the signal. The calculation method of the above effective value is as follows:

[0130]

[0131] Among them, RMS is the effective value of the signal, and x i is the amplitude of the signal at the i-th moment.

[0132] Specifically, the above FFT spectrum is the most common method for frequency-domain analysis of signals. The FFT spectrum of the signal can be obtained by performing a Fourier transform on the time-domain signal of the collected original signal. The FFT spectrum of the signal can reflect the frequency components contained in the signal. For a belt conveyor, when the operating state of the drive device is abnormal, the frequency components contained in the impact signal and vibration signal will change. Therefore, the operating state of the drive device can be effectively judged through FFT spectrum analysis.

[0133] Specifically, the auto-power spectrum can reflect the energy of each frequency component in the signal. The auto-power spectrum of the signal can be determined from the FFT spectrum of the signal. If X(f) is the FFT spectrum of the signal, then the auto-power spectrum of the signal

[0134] Specifically, the cross-power spectrum is the frequency-domain description of the correlation between two signals, which can describe the amplitude and phase relationship between the two signals. Similar to the auto-power spectrum, the cross-power spectrum can also be determined by the FFT spectra of the two signals. If the FFT spectra of the two signals are X(f) and Y(f) respectively, then the cross-power spectrum

[0135] Specifically, the cepstrum is also a commonly used method for frequency-domain analysis of signals. The cepstrum can separate sideband signals, making the periodic components that are difficult to distinguish in the auto-power spectrum become discrete line spectra in the cepstrum diagram. The cepstrum is widely used in analyzing gear fault signals with more sideband components. The calculation method of the cepstrum is as follows: C x (τ) = |F -1 |logS x (f)||, where C x (τ) is the cepstrum obtained after transformation, and S x (f) is the auto-power spectrum of the original signal.

[0136] Specifically, the process of obtaining the envelope spectrum is as follows: The signal x(t) is subjected to Hilbert transform to obtain Using the signal x(t) and the Hilbert transform, the obtained h(t) can be used to obtain the analytic signal z(t) = x(t) + jh(t). The amplitude function a(t) of the analytic signal z(t) is the envelope of the real signal s(t). Calculating the amplitude spectrum or power spectrum of a(t) is the envelope spectrum of the signal s(t).

[0137] Specifically, the waterfall plot is a commonly used method for analyzing the vibration signals during the speed-up and speed-down processes of rotating machinery. By calculating the spectra of the vibration signals collected at different speeds or different times and plotting them as three-dimensional spectrograms, the waterfall plot can be obtained. Through the waterfall plot, the changes in the frequency and amplitude of each vibration component over time or speed can be clearly seen.

[0138] Specifically, the calculation method of the order spectrum is as follows: (a) Calculate the order tracking assuming that the rotational speed of the reference axis undergoes uniform acceleration within a small time period. Under this premise, the angular displacement θ of the reference axis can be expressed as θ(t) = b0 + b1t + b2t 2 , where b0, b1, and b2 are undetermined coefficients; t is the time point. (b) Determine the angular displacement increment Δφ corresponding to the key-phase pulse. If one key-phase pulse is generated per revolution of the reference axis, then Δφ = 2π. The undetermined coefficients in the above formula can be obtained by fitting the arrival times t1, t2, and t3 of three consecutive pulses Among them, after the undetermined coefficients are obtained, the equally angularly sampled time points can be calculated from the above formula Among them, k is the interpolation coefficient; Δθ is the equally angular sampling interval. (c) The sampling time t kDifferencing the original signal at this point can obtain equiangular sampling data. From the equiangular sampling interval Δθ, the sampling order ratio can be obtained. (d) Performing amplitude spectrum analysis on the equiangular sampling data can obtain the order spectrum of the vibration signal during the acceleration and deceleration processes.

[0139] Specifically, the above-mentioned axis orbit analysis is also a commonly used method for rotating machinery vibration analysis. In practice, two sensors with an included angle of 90° are usually used to measure the vibrations of the rotor in the X and Y directions respectively. Plotting with the signal in the X (Y) direction as the abscissa and the signal in the Y (X) direction as the ordinate can obtain the axis orbit of the rotor. When the operating state of the rotor changes, the axis orbit of the rotor usually also changes.

[0140] In a specific embodiment of the present application, the above-mentioned time-domain characteristic parameters may further include kurtosis, impulse index, skewness, margin, and the amplitudes corresponding to the fundamental frequency and the second harmonic frequency of the rotor power frequency.

[0141] Specifically, the calculation method of the above-mentioned kurtosis is K is the kurtosis index; is the mean value; σ is the standard deviation.

[0142] Specifically, the calculation method of the above-mentioned impulse index is I is the impulse index, x max is the maximum value. is the absolute average value.

[0143] Specifically, the calculation method of the above-mentioned skewness is is the average value, X rms is the effective value.

[0144] Specifically, the calculation method of the above-mentioned margin is X p is the single peak value of the signal, X a is the mean value of the absolute value of the signal.

[0145] Specifically, the FFT spectrum of the signal can be obtained by performing Fourier transform on the signal, and the amplitudes corresponding to the fundamental frequency and the second harmonic frequency of the rotor power frequency can be searched and found in the FFT spectrum.

[0146] The above-mentioned fault warning device for belt conveyors includes a processor and a memory. The above-mentioned receiving unit, analysis unit, warning unit, etc. are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to implement corresponding functions.

[0147] The processor contains a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and by adjusting the kernel parameters, the problems of low accuracy of fault warning and low automation degree of the driving device of the belt conveyor in the prior art can be solved.

[0148] The memory may include non-permanent memory in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

[0149] An embodiment of the present invention provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the above-mentioned fault warning method of the belt conveyor is implemented.

[0150] An embodiment of the present invention provides a processor, which is used to run a program, and when the program runs, the above-mentioned fault warning method of the belt conveyor is executed.

[0151] In a typical embodiment of the present application, a fault warning system for a belt conveyor is further provided. The fault warning system includes a belt conveyor, a target sensor, and a host computer. Among them, the above-mentioned belt conveyor includes a conveying device; the above-mentioned target sensor includes a pulse and vibration sensor and a temperature sensor. The pulse and vibration sensor is used to collect the impact signal and vibration information of the above-mentioned driving device within a predetermined time period, and the temperature sensor is used to collect the temperature value of the above-mentioned driving device within the above-mentioned predetermined time period; the above-mentioned host computer includes a fault warning device for the belt conveyor, and the fault warning device is used to execute any one of the above-mentioned fault warning methods for the belt conveyor.

[0152] The above-mentioned fault warning system of the belt conveyor includes a host computer, and the host computer includes a fault warning device for the belt conveyor. The fault warning device is used to execute any one of the above-mentioned fault warning methods for the belt conveyor. In the above-mentioned fault warning method, first, impact signals, vibration signals, and temperature signals of the drive device collected within a predetermined time period are received; then, time-domain analysis, frequency-domain analysis, and shaft center locus analysis are respectively performed on the impact signals and vibration signals to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters; finally, a fault warning is performed on the drive device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value. In the fault warning method of the present application, the host computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center locus analysis on the received impact signals and vibration signals respectively. That is to say, there is no need to perform spectrum analysis on the impact signals and vibration signals manually, ensuring a relatively high degree of automation. Compared with the prior art methods of manual inspection or online vibration monitoring, this solution performs a fault warning on the drive device at least according to the fault symptom domain obtained by analyzing impact signals, vibration signals, and temperature signals. Since the fault symptom domain is automatically obtained without manual spectrum analysis and fault analysis is performed through impact signals, vibration signals, and temperature signals, it ensures that the fault warning for the drive device is relatively accurate, thus solving the problems of low accuracy and low automation degree of the fault warning for the drive device of the belt conveyor in the prior art.

[0153] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, it realizes at least the following steps:

[0154] Step S101: Receive the impact signal, vibration signal, and temperature signal of the above-mentioned drive device within a predetermined time period;

[0155] Step S102: Perform time-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain time-domain characteristic parameters, perform frequency-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain frequency-domain characteristic parameters, and perform shaft center locus analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain shaft center characteristic parameters;

[0156] Step S103: Construct a fault symptom domain from the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the temperature value of the above-mentioned temperature signal, and perform a fault warning on the above-mentioned drive device at least according to the above-mentioned fault symptom domain.

[0157] The device in this article can be a server, a PC, a PAD, a mobile phone, etc.

[0158] The present application also provides a computer program product, which, when executed on a data processing device, is adapted to execute a program initialized with at least the following method steps:

[0159] Step S101: Receive the impact signal, vibration signal, and temperature signal of the above-mentioned transmission device within a predetermined time period;

[0160] Step S102: Perform time-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain time-domain characteristic parameters, perform frequency-domain analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain frequency-domain characteristic parameters, and perform shaft center orbit analysis on the above-mentioned impact signal and the above-mentioned vibration signal respectively to obtain shaft center characteristic parameters;

[0161] Step S103: Construct a fault symptom universe of discourse from the above-mentioned time-domain characteristic parameters, the above-mentioned frequency-domain characteristic parameters, the above-mentioned shaft center characteristic parameters, and the temperature value of the above-mentioned temperature signal, and perform fault early warning on the above-mentioned transmission device at least based on the fault symptom universe of discourse.

[0162] To enable those skilled in the art to more clearly understand the technical solution of the present application, the technical solution and technical effects of the present application will be described below in conjunction with specific embodiments.

[0163] Embodiment

[0164] This embodiment relates to a fault early warning scheme for a belt conveyor, as Figure 4 shown. The above-mentioned fault early warning system for the belt conveyor may include a host computer 100, a terminal device 101, a belt conveyor 102, a target sensor 103, and an edge data acquisition box 104. Among them, the target sensor 103 is used to collect the impact signal, vibration signal, and temperature signal of the belt conveyor 102 within a predetermined time period. The edge data acquisition box 104 is used to perform data conditioning, preliminary filtering, data operation, data storage, data forwarding, etc. on the impact signal, vibration signal, and temperature signal sensed by the target sensor. The host computer 100 is used to receive the impact signal, vibration signal, and temperature signal, and perform time-domain analysis on the received impact signal and vibration signal to obtain time-domain characteristic parameters; perform frequency-domain analysis on the received impact signal and vibration signal to obtain frequency-domain characteristic parameters; and perform shaft center orbit analysis on the received impact signal and vibration signal to obtain shaft center characteristic parameters. Then, a fault symptom universe of discourse is constructed from the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value of the temperature signal, and a fault early warning is performed on the transmission device at least based on the fault symptom universe of discourse.

[0165] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0166] In several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the above division of units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

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

[0168] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0169] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the above methods in each embodiment of the present invention. The foregoing storage medium includes: USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs, and other various media that can store program codes.

[0170] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0171] 1) In the fault warning method of the belt conveyor of the present application, first, impact signals, vibration signals, and temperature signals of the drive device collected within a predetermined time period are received; then, time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis are respectively performed on the impact signals and vibration signals to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters; finally, a fault warning is given to the above drive device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value. In the fault warning method of the present application, the upper computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis on the received impact signals and vibration signals respectively. That is to say, there is no need to manually perform spectrum analysis on the impact signals and vibration signals, ensuring a relatively high degree of automation. Compared with the prior art methods of manual inspection or online vibration monitoring, this solution gives a fault warning to the drive device at least according to the fault symptom domain obtained by analyzing the impact signals, vibration signals, and temperature signals. Since the fault symptom domain is automatically obtained without manual spectrum analysis and the fault analysis is carried out through impact signals, vibration signals, and temperature signals, it ensures that the fault warning for the drive device is relatively accurate, thus solving the problems of low accuracy and low degree of automation in the fault warning of the drive device of the belt conveyor in the prior art.

[0172] 2) In the fault warning device of the belt conveyor of the present application, a receiving unit is used to receive impact signals, vibration signals, and temperature signals of the drive device collected within a predetermined time period; an analysis unit is used to perform time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis on the impact signals and vibration signals respectively to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters; a warning unit is used to give a fault warning to the above drive device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value. In the fault warning device of the present application, the upper computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis on the received impact signals and vibration signals respectively. That is to say, there is no need to manually perform spectrum analysis on the impact signals and vibration signals, ensuring a relatively high degree of automation. Compared with the prior art methods of manual inspection or online vibration monitoring, this solution gives a fault warning to the drive device at least according to the fault symptom domain obtained by analyzing the impact signals, vibration signals, and temperature signals. Since the fault symptom domain is automatically obtained without manual spectrum analysis and the fault analysis is carried out through impact signals, vibration signals, and temperature signals, it ensures that the fault warning for the drive device is relatively accurate, thus solving the problems of low accuracy and low degree of automation in the fault warning of the drive device of the belt conveyor in the prior art.

[0173] 3) The fault warning system of the belt conveyor of the present application includes a host computer. The host computer includes a fault warning device for the belt conveyor, and the fault warning device is used to execute any one of the above-mentioned fault warning methods for the belt conveyor. In the above-mentioned fault warning method, first, impact signals, vibration signals, and temperature signals of the drive device collected within a predetermined time period are received; then, time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis are respectively performed on the impact signals and vibration signals to obtain time-domain characteristic parameters, frequency-domain characteristic parameters, and shaft center characteristic parameters; finally, a fault warning is given to the above-mentioned drive device at least according to the fault symptom domain composed of the time-domain characteristic parameters, frequency-domain characteristic parameters, shaft center characteristic parameters, and temperature value. In the fault warning method of the present application, the host computer can automatically perform time-domain analysis, frequency-domain analysis, and shaft center trajectory analysis on the received impact signals and vibration signals respectively. That is to say, there is no need to perform spectrum analysis on the impact signals and vibration signals manually, ensuring a relatively high degree of automation. Compared with the prior art methods of manual inspection or on-line vibration monitoring, the present solution gives a fault warning to the drive device at least according to the fault symptom domain obtained by analyzing the impact signals, vibration signals, and temperature signals. Since the fault symptom domain is automatically obtained without manual spectrum analysis and the fault analysis is carried out through impact signals, vibration signals, and temperature signals, the fault warning for the drive device is ensured to be relatively accurate, thus solving the problems of low accuracy and low degree of automation in the fault warning of the drive device of the belt conveyor in the prior art.

[0174] The foregoing are only the preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A fault warning method for a belt conveyor, the belt conveyor including a transmission device, characterized in that, The described fault warning method includes: Receiving the impact signal, vibration signal, and temperature signal of the transmission device within a predetermined time period; Performing time-domain analysis on the impact signal and the vibration signal respectively to obtain time-domain characteristic parameters, performing frequency-domain analysis on the impact signal and the vibration signal respectively to obtain frequency-domain characteristic parameters, and performing center orbit analysis on the impact signal and the vibration signal respectively to obtain center characteristic parameters; Constructing a fault symptom universe of discourse from the time-domain characteristic parameters, the frequency-domain characteristic parameters, the center characteristic parameters, and the temperature value of the temperature signal, and performing fault warning on the transmission device at least according to the fault symptom universe of discourse; The transmission device is composed of multiple target components. Performing fault warning on the transmission device at least according to the fault symptom universe of discourse includes: matching the fault symptom universe of discourse with the standard parameter universe of discourse to determine whether the transmission device has a fault trend. The standard parameter universe of discourse includes standard time-domain characteristic parameters, standard frequency-domain characteristic parameters, standard center characteristic parameters, or standard temperature values; in the case of determining that the transmission device has a fault trend, performing fault location on the transmission device at least according to the fault symptom universe of discourse and the fault cause universe of discourse. The fault cause universe of discourse includes target characteristic parameters for each of the target components to have a fault, and the target characteristic parameters are at least one of the time-domain characteristic parameters, the frequency-domain characteristic parameters, or the center characteristic parameters; In the case of determining that the transmission device has a fault trend, performing fault location on the transmission device at least according to the fault symptom universe of discourse and the fault cause universe of discourse includes: determining the membership degrees of each target element in the fault symptom universe of discourse, and forming a fault symptom matrix from the membership degrees of each target element. The target elements are the time-domain characteristic parameters, the frequency-domain characteristic parameters, the center characteristic parameters, and the temperature value; determining the membership degrees of each of the target characteristic parameters in the fault cause universe of discourse, and forming a fault cause matrix from the membership degrees of each target characteristic parameter; performing fault location on the transmission device according to the fault symptom matrix, the fault cause matrix, and the fuzzy relation matrix to obtain target fault components, and the target fault components are one or more of the multiple target components.

2. The fault warning method according to claim 1, wherein Matching the fault symptom universe of discourse with the standard parameter universe of discourse to determine whether the transmission device has a fault trend includes: In the case where the fault symptom universe of discourse does not match the standard parameter universe of discourse, determining that the transmission device has a fault trend; In the case where the fault symptom universe of discourse matches the standard parameter universe of discourse, determining that the transmission device does not have a fault trend.

3. The fault warning method according to claim 1, characterized in that After performing fault location on the transmission device according to the fault symptom matrix, the fault cause matrix, and the fuzzy relation matrix to obtain target fault components, the fault warning method further includes: Performing fast Fourier transform on the impact signal and the vibration signal to obtain an FFT spectrum; Determine a fuzzy fault matrix at least according to the FFT spectrum, and determine a fuzzy cause matrix according to the fuzzy fault matrix and the fuzzy relation matrix; According to the fuzzy cause matrix, determine the fault probability of the target fault component, and send the target fault component and the fault probability of the target fault component to the terminal device, so that the terminal device performs visual display in the target model according to the fault probability, and the target model is a 3D model of the belt conveyor.

4. The fault warning method according to claim 3, characterized in that Determine a fuzzy fault matrix at least according to the FFT spectrum, including: Determine the amplitude of the characteristic frequency in the FFT spectrum, and form a preset fuzzy fault matrix from the amplitudes of the characteristic frequencies, where the characteristic frequency is the frequency at an interval of a target time in the FFT spectrum; Adopt , perform fuzzy processing on the preset fuzzy fault matrix to obtain the fuzzy fault matrix, where is the amplitude of the characteristic frequency, is 0, is the coefficient.

5. The fault warning method according to any one of claims 1 to 4, characterized in that The time-domain characteristic parameters include probability density, autocorrelation function, cross-correlation function, and bar chart, and the bar chart is composed of mean value, peak value, peak-to-peak value, effective value, and mean square deviation. The frequency-domain characteristic parameters include FFT spectrum, auto-power spectrum, cross-power spectrum, coherence spectrum, envelope spectrum, waterfall plot, and order spectrum. The shaft center characteristic parameter is the shaft center locus.

6. A fault warning device for a belt conveyor, the belt conveyor including a transmission device, characterized in that, The fault warning device includes: A receiving unit, configured to receive the impact signal, vibration signal, and temperature signal of the transmission device within a predetermined time period; An analysis unit, configured to perform time-domain analysis on the impact signal and the vibration signal respectively to obtain time-domain characteristic parameters, perform frequency-domain analysis on the impact signal and the vibration signal respectively to obtain frequency-domain characteristic parameters, and perform shaft center locus analysis on the impact signal and the vibration signal respectively to obtain shaft center characteristic parameters; A warning unit, configured to form a fault symptom universe of discourse from the time-domain characteristic parameters, the frequency-domain characteristic parameters, the shaft center characteristic parameters, and the temperature value of the temperature signal, and perform fault warning on the transmission device at least according to the fault symptom universe of discourse; The transmission device is composed of a plurality of target components, and the warning unit includes a matching module and a fault location module. The matching module is configured to perform fault symptom matching between the fault symptom universe of discourse and the standard parameter universe of discourse to determine whether the transmission device has a fault trend. The standard parameter universe of discourse includes standard time-domain characteristic parameters, standard frequency-domain characteristic parameters, standard shaft center characteristic parameters, or standard temperature values. The fault location module is configured to perform fault location on the transmission device at least according to the fault symptom universe of discourse and the fault cause universe of discourse when it is determined that the transmission device has a fault trend. The fault cause universe of discourse includes target characteristic parameters for each of the target components to have a fault, and the target characteristic parameter is at least one of the time-domain characteristic parameters, the frequency-domain characteristic parameters, or the shaft center characteristic parameters; The fault location module includes a first determination sub-module, a second determination sub-module, and a fault location sub-module. Among them, the first determination sub-module is used to determine the membership degrees of each target element in the fault symptom domain, and form a fault symptom matrix from the membership degrees of each target element. The target elements are the time-domain characteristic parameters, the frequency-domain characteristic parameters, the shaft center characteristic parameters, and the temperature value. The second determination sub-module is used to determine the membership degrees of each target characteristic parameter in the fault cause domain, and form a fault cause matrix from the membership degrees of each target characteristic parameter. The fault location sub-module is used to perform fault location on the transmission device according to the fault symptom matrix, the fault cause matrix, and the fuzzy relation matrix, and obtain a target fault component, where the target fault component is one or more of a plurality of target components.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, where the program executes the fault warning method of the belt conveyor according to any one of claims 1 to 5.

8. A fault warning system for a belt conveyor, characterized in that, Including: A belt conveyor, the belt conveyor includes a transmission device; Target sensors, the target sensors include a pulse and vibration sensor and a temperature sensor. The pulse and vibration sensor is used to collect the impact signal and vibration information of the transmission device within a predetermined time period, and the temperature sensor is used to collect the temperature value of the transmission device within the predetermined time period; An upper computer, the upper computer includes a fault warning device for the belt conveyor, and the fault warning device is used to execute the fault warning method of the belt conveyor according to any one of claims 1 to 5.

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