Belt conveyor condition monitoring method and system based on distributed optical fiber temperature-vibration coupling

Abstract

The present invention discloses a belt conveyor state monitoring method and system based on distributed optical fiber temperature-vibration coupling, the monitoring method comprising: obtaining belt conveyor DAS data through a single-mode optical fiber distributed acoustic wave sensor and a DAS demodulator, and obtaining belt conveyor DTS data through a multi-mode optical fiber distributed temperature sensor and a DTS demodulator; obtaining absolute temperature data through DTS data; obtaining low-frequency DAS data through filtering and denoising of DAS data; respectively deriving the absolute temperature function and the low-frequency DAS data with respect to time to obtain a distributed temperature gradient function based on DTS and a distributed temperature gradient function based on DAS; and comprehensively determining belt conveyor faults through DAS data, DTS data, and temperature gradient data for DTS and LF-DAS data. The present invention can combine distributed optical fiber temperature sensing technology and acoustic wave sensing technology, and obtain more accurate and timely belt conveyor fault identification results through the coupling of temperature information and vibration information.

Description

Technical Field

[0001] The invention relates to the technical field of belt conveyor state monitoring methods, and in particular to a belt conveyor state monitoring method using distributed optical fibers. Background Art

[0002] Belt conveyor (belt conveyor) is one of the main transportation equipment for comprehensive coal mining. It has the advantages of low cost, large conveying capacity and low maintenance cost. However, it is prone to safety problems under extreme conditions, bringing unpredictable risks to the safe production of coal mines. Therefore, it is very necessary to monitor the operating status of the belt conveyor.

[0003] One method of monitoring the state of a belt conveyor in the prior art is to use a point sensor to monitor the belt conveyor. Vibration data is collected by using a point vibration sensor or a single-mode optical fiber connected to a DAS demodulator next to the belt conveyor. By analyzing the vibration data to monitor the running state of the belt conveyor, it can be determined whether the belt conveyor has a major single fault type such as a broken roller shaft. However, this technology has the following defects: the high cost of point sensor deployment, the small number of sensors, and the large spatial intervals lead to low fault location accuracy and low accuracy; the use of point sensors is often not enough to monitor the entire structure of the belt conveyor, because the intervals within the sensor coverage range are inevitable; point sensor application scenarios require intrinsically safe sensors underground, and electric drive sensors require regular maintenance, which increases the safety hazards of coal mines; belt conveyor failures usually have not only major faults, but also other abnormalities and temperature abnormalities that affect the normal operating state. The prior art method is relatively simple in determining the fault type of the belt conveyor. Another method of monitoring the state of a belt conveyor in the prior art is to use distributed optical fiber temperature sensing technology to monitor the temperature of the conveyor belt (belt), motor, and reducer bearings, so as to detect hot spots that may cause fires in advance. However, this technology has a single monitoring parameter and can only determine whether a fire has occurred from the perspective of temperature, which may result in false alarms and cannot be cross-verified with other parameters to verify the authenticity of the fire. In addition, since a belt conveyor failure does not necessarily lead to a large temperature abnormality, and the existing DTS technology has a detection accuracy of ±0.1℃ / s for temperature changes, it does not meet the temperature change detection accuracy requirement of ±0.01℃ / s or even smaller. Therefore, the use of only a single parameter of absolute temperature cannot usually be directly used to identify belt conveyor bearing damage, belt deviation and other faults. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present invention is to propose a new belt conveyor state monitoring method and system. The monitoring method can combine distributed optical fiber temperature sensing technology and acoustic wave sensing technology, use distributed optical fiber sensing to collect vibration / acoustic wave signals and temperature changes at high frequency, and obtain more accurate and timely belt conveyor fault identification results through the coupling of temperature information and vibration information. It can solve the problem of belt conveyor safety production in a low-cost and better way, and detect the abnormal operating state of the belt conveyor at an early stage, and warn of faults at an early stage, thereby reducing the risk of accidents, reducing downtime, and ensuring the safety of personnel.

[0005] To achieve the above objectives, the present invention first proposes the following technical solutions:

[0006] The belt conveyor condition monitoring method based on distributed optical fiber temperature-vibration coupling includes the following steps:

[0007] S1: A single-mode optical fiber and a multi-mode optical fiber are laid out on the outer frames on both sides of the belt conveyor, or an optoelectronic composite cable composed of a single-mode optical fiber and a multi-mode optical fiber is laid out, wherein the single-mode optical fiber is used as a distributed acoustic wave sensor for distributed acoustic wave monitoring, and the multi-mode optical fiber is used as a distributed temperature sensor for distributed temperature monitoring; and the single-mode optical fiber is connected to a DAS demodulator to obtain demodulated DAS data of the belt conveyor through the DAS demodulator, and the multi-mode optical fiber is connected to a DTS demodulator to obtain demodulated DTS data of the belt conveyor through the DTS demodulator;

[0008] S2 obtains the absolute temperature function of the belt conveyor through the DTS data;

[0009] S3 performs filtering processing on the DAS data to obtain low-frequency DAS data, wherein the filtering processing includes: performing low-pass fidelity filtering on the DAS data with a filtering range of 0-1 Hz to obtain first filtered data; performing downsampling processing on the first filtered data to obtain second filtered data, and performing denoising processing on the second filtered data to obtain the low-frequency DAS data that can be used to characterize the temperature change of the belt conveyor;

[0010] S4: respectively deriving the absolute temperature function and the low-frequency DAS data with respect to time to obtain two distributed temperature gradient functions, namely, a distributed temperature gradient function based on DTS and a distributed temperature gradient function based on DAS;

[0011] S5: Determine the belt conveyor fault by combining at least three of the four data: the DAS data, the DTS data, the DTS-based distributed temperature gradient function, and the DAS-based distributed temperature gradient function.

[0012] According to some preferred embodiments of the present invention, the absolute temperature function is obtained from the DTS data by the following calculation model:

[0013]

[0014] Where R(T) represents the absolute temperature function; I F Represents the anti-Stokes light intensity; I S represents the Stokes light intensity; v F represents the center frequency of Stokes light; v S represents the center frequency of anti-Stokes light; e represents the natural constant; c represents the speed of light in vacuum; v represents the Raman frequency shift; h represents the Planck constant; K represents the Boltzmann constant; and T represents the absolute temperature.

[0015] According to some preferred embodiments of the present invention, the downsampling process includes downsampling from a sampling rate of 0.0001 seconds to a sampling rate of 1 second.

[0016] According to some preferred embodiments of the present invention, the denoising process includes: performing median filtering on the second filtered data to obtain third filtered data, performing low-pass filtering on the third filtered data with a corner frequency of 1 Hz to obtain fourth filtered data, and removing DC drift in the fourth filtered data to obtain the low-frequency DAS data.

[0017] According to some preferred embodiments of the present invention, the belt conveyor failure includes roller bearing breakage, drum shaft breakage, belt friction, belt deviation, motor abnormality and reducer abnormality.

[0018] According to some preferred embodiments of the present invention, the belt conveyor state monitoring method further includes:

[0019] After confirming that the belt conveyor is faulty, S6 locates the fault location using optical time domain reflectometry (OTDR).

[0020] According to some preferred embodiments of the present invention, the joint determination includes: determining whether any one of the four types of data, namely, the DAS data, the DTS data, the distributed temperature gradient function based on DTS, and the distributed temperature gradient function based on DAS, is abnormal; if abnormal, querying whether two or more of the other three types of data at the time point when the abnormality occurs also have abnormalities; if so, it is considered that the belt conveyor is faulty at that time point.

[0021] According to some preferred embodiments of the present invention, the abnormality determination includes: the root mean square of the amplitude obtained according to the DAS data is greater than 20, the absolute temperature obtained according to the DTS data is greater than 40°C, and the absolute value of the temperature gradient obtained according to the DTS-based distributed temperature gradient function or the DAS-based distributed temperature gradient function is greater than 10°C / s

[0022] According to the above monitoring method, the present invention can further obtain a monitoring system that applies the above-mentioned belt conveyor state monitoring method, which includes: the distributed acoustic wave sensor, the distributed temperature sensor, the DAS demodulator, the DTS demodulator, an analysis module for receiving, storing, and calculating sensor signals and demodulated data, and an early warning module for issuing early warnings based on the calculation results of the analysis module.

[0023] The present invention has the following beneficial effects:

[0024] The method of the present invention obtains low-frequency DAS thermal strain data from original DAS vibration data and then obtains DTGS temperature gradient based on the thermal strain data. In essence, it obtains temperature change data through vibration data, realizes the coupling of temperature and vibration data, and the accuracy of DTGS temperature gradient obtained by low-frequency DAS thermal strain is higher than that of DTGS temperature gradient obtained by DTS temperature. Using it as a new parameter for fault judgment can achieve more precise and accurate fault identification.

[0025] The present invention can simultaneously use three kinds of temperature-vibration coupling data based on DAS vibration data, DTS absolute temperature data and DTGS temperature gradient obtained by transforming low-frequency DAS thermal strain data to identify mining belt conveyor faults with both vibration and temperature abnormal responses.

[0026] The present invention uses distributed optical fiber sensing technology for monitoring. The overall monitoring system has simple layout and low cost. It has the advantages of anti-electromagnetic interference, small size, high sensitivity, long transmission distance, easy networking, and inherent safety, which is suitable for use in coal mines.

[0027] The present invention can identify more fault types, has high fault identification accuracy and high fault location precision, and can realize timely and automatic monitoring of faults to warn relevant personnel to quickly take corresponding measures, eliminate faults in time, ensure safe production in coal mines, and reduce the maintenance cost of mining belt conveyors. It is of great significance to maintain safe production in coal mines and eliminate potential hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a technical architecture diagram of the monitoring method in a specific implementation method.

[0029] Figure 2It is a schematic diagram of the layout of the distributed optical fiber sensor in a specific implementation method, wherein 1-belt conveyor, 2-single-mode optical fiber as a distributed acoustic wave sensor, 3-multi-mode optical fiber as a distributed temperature sensor, 4-DAS demodulator, 5-DTS demodulator.

[0030] Figure 3 This is the data processing image obtained in Example 1.

[0031] Figure 4 This is the DTS waterfall chart obtained in Example 1 for determining the occurrence of an abnormal event.

[0032] Figure 5 The low-frequency DAS (LF-DAS) waterfall diagram obtained in Example 1 to determine the occurrence of an abnormal event

[0033] Figure 6 The distributed temperature gradient (DTGS) waterfall diagram obtained by low-frequency DAS transformation in Example 1

[0034] Figure 7 This is a distributed temperature gradient (DTGS) waterfall diagram obtained by low-frequency DAS transformation in Example 1. DETAILED DESCRIPTION

[0035] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0036] See attached Figure 1 In some specific implementations, the belt conveyor state monitoring method based on distributed optical fiber temperature-vibration coupling of the present invention comprises the following steps:

[0037] S1 lays a single-mode optical fiber and a multi-mode optical fiber or an optoelectronic composite cable composed of a single-mode optical fiber and a multi-mode optical fiber next to the belt conveyor, wherein the single-mode optical fiber is used as a distributed acoustic wave sensor for distributed acoustic wave monitoring, and the multi-mode optical fiber is used as a distributed temperature sensor for distributed temperature monitoring; and the single-mode optical fiber is connected to a DAS demodulator to obtain the demodulated DAS data of the belt conveyor through the DAS demodulator, and the multi-mode optical fiber is connected to a DTS demodulator to obtain the demodulated DTS data of the belt conveyor through the DTS demodulator.

[0038] Further, in some specific embodiments, refer to the attached Figure 2The optical fiber can be arranged as follows: a single-mode optical fiber 2 and a multi-mode optical fiber 3 are respectively arranged on the outer frames on both sides of the belt conveyor 1, wherein the single-mode optical fiber 2 is used as a distributed acoustic wave sensor, and the multi-mode optical fiber 3 is used as a distributed temperature sensor. The single-mode optical fiber 2 is connected to the DAS demodulator 4, and the multi-mode optical fiber 3 is connected to the DTS demodulator 5.

[0039] Through the above process, the present invention can collect DAS data and DTS data at the same time, which has higher monitoring accuracy than single DTS or DAS. When an abnormality is found, the credibility of the abnormality report can be improved through cross-verification between the two types of data.

[0040] The above-mentioned distributed fiber temperature sensing (DTS) is a technology based on the principle of Raman scattering, which continuously measures the temperature of any point along the fiber by detecting the Raman scattered light in the fiber. Specifically, Raman scattered light is generated due to the thermal vibration of the fiber molecules and is divided into anti-Stokes light and Stokes light. The temperature of the fiber measurement point can be obtained based on the quantitative relationship between the ratio of the intensity of anti-Stokes light and Stokes light and the temperature.

[0041] The working principle of the distributed acoustic sensor (DAS) is as follows: when a light pulse is transmitted inside an optical fiber, backscattering occurs when the light encounters an inhomogeneous material in the optical fiber. External disturbances will affect the phase of the Rayleigh backscattered light, and the vibration event that occurred can be known by detecting the phase change. At the same time, since the speed of light is fixed, the location information of the vibration event can be obtained by calculating the time it takes for the scattered light to return. Distributed optical fiber acoustic sensing uses the phase of coherent Rayleigh scattered light rather than the light intensity to detect signals such as sound or vibration within the audio range. It can not only use the phase amplitude to provide information on the intensity of the sound or vibration event, but also use linear quantitative measurements to obtain the phase and frequency information of the sound or vibration event.

[0042] S2 obtains the absolute temperature function of the belt conveyor through the DTS data.

[0043] More specifically, the absolute temperature function is obtained from the DTS data by the following calculation model:

[0044]

[0045] Where R(T) represents the absolute temperature function; I F Represents the anti-Stokes light intensity; I S represents the Stokes light intensity; v F represents the center frequency of Stokes light; v S represents the center frequency of anti-Stokes light; e represents the natural constant; c represents the speed of light in vacuum; v represents the Raman frequency shift; h represents the Planck constant; K represents the Boltzmann constant; and T represents the absolute temperature.

[0046] S3 performs filtering processing on the DAS data to obtain low-frequency DAS data, wherein the filtering processing includes: performing low-pass fidelity filtering on the DAS data with a filtering range of 0-1 Hz to obtain first filtered data; performing downsampling processing on the first filtered data to obtain second filtered data, and performing denoising processing on the second filtered data to obtain the low-frequency DAS data that can be used to characterize the temperature change of the belt conveyor.

[0047] Preferably, the downsampling process includes downsampling from a sampling rate of 0.0001 seconds to a sampling rate of 1 second.

[0048] Preferably, the denoising process includes: performing median filtering on the second filtered data to obtain third filtered data, performing low-pass filtering on the third filtered data with a corner frequency of 1 Hz to obtain fourth filtered data, and removing the DC drift component (0.1 rad / s amplitude) in the fourth filtered data to obtain the low-frequency DAS data.

[0049] S4 is to respectively derive the absolute temperature function and the low-frequency DAS data with respect to time to obtain two distributed temperature gradient functions, namely, a distributed temperature gradient function based on DTS and a distributed temperature gradient function based on DAS.

[0050] Wherein, the derivation formula is as follows:

[0051]

[0052] in, represents the distributed temperature gradient, T d represents the absolute temperature function or the low-frequency DAS data, and t represents time.

[0053] The present invention can obtain distributed temperature gradient sensing data (DTGS) by obtaining the temperature gradient through the DTS absolute temperature function or the low-frequency DAS data. The DTGS distributed temperature gradient calculated by the low-frequency DAS data is essentially to analyze the temperature change through the vibration signal, which has higher resolution than the DTGS based on DTS and can be accurate to 0.01°C / s (the data collected by some DAS demodulators with excellent performance can reach ±0.001°C / s). It can more finely analyze the changes in temperature anomaly points and increase the credibility of anomaly reports.

[0054] S5: Determine the belt conveyor fault by combining at least three of the four data: the DAS data, the DTS data, the DTS-based distributed temperature gradient function, and the DAS-based distributed temperature gradient function.

[0055] In some more specific embodiments, the joint determination includes: determining whether any one of the four types of data, namely, the DAS data, the DTS data, the DTS-based distributed temperature gradient function, and the DAS-based distributed temperature gradient function, is abnormal; if so, querying whether two or more of the other three types of data at the time point when the abnormality occurs also have abnormalities; if so, it is considered that the belt conveyor is faulty at that time point.

[0056] Furthermore, the fault type can be determined based on the performance characteristics of different faults on these four types of data.

[0057] In some specific embodiments, the determination of the above-mentioned abnormality includes: the root mean square of the amplitude obtained according to the DAS data is greater than 20, the absolute temperature obtained according to the DTS data is greater than 40°C, and the absolute value of the temperature gradient obtained according to the DTS-based distributed temperature gradient function or the DAS-based distributed temperature gradient function is greater than 10°C / s.

[0058] In some preferred embodiments, the belt conveyor failure includes six types of failures, namely, roller bearing breakage, drum shaft breakage, belt friction, belt deviation, motor abnormality and reducer abnormality.

[0059] In some preferred embodiments, the belt conveyor state monitoring method based on distributed optical fiber temperature-vibration coupling of the present invention further includes:

[0060] After confirming that the belt conveyor is faulty, S6 locates the fault location using optical time domain reflectometry (OTDR).

[0061] Example 1

[0062] The belt conveyor status monitoring method and system of the present invention are used to monitor a belt conveyor in a coal mine. The data processing images obtained are as follows: Figure 3 As shown, it includes the DAS vibration root mean square amplitude curve, DTS absolute temperature curve and DTGS temperature gradient curve obtained by DTS transformation at the position of the belt conveyor roller. Among them, the dotted line position is the data when the roller bearing is locked. It can be seen that the vibration amplitude is greatly enhanced, the absolute temperature is greatly increased, and the temperature gradient fluctuates greatly. According to the data performance here, the root mean square amplitude greater than 20, the temperature greater than 40℃ and the absolute value of the temperature gradient greater than 10℃ / s can be set as the threshold indicators for judging faults based on temperature-vibration coupling data.

[0063] Furthermore, all the DTS data obtained from monitoring are plotted as a waterfall chart, as shown in the attached figure. Figure 4As shown, the waterfall chart is used to track whether the temperature changes within the normal range. When an abnormal value exceeding the set threshold appears, the DAS data is combined for abnormal matching. If the match is successful, it is determined that a fault event or a fire event has occurred, the belt conveyor is shut down urgently, and the relevant personnel in the fire point area are notified through the early warning module to evacuate in time.

[0064] In addition, the monitored DAS data is first low-frequency filtered with a filtering range of 0-1Hz to perform low-pass (0-1Hz) fidelity filtering on the original DAS data. The filtered data is downsampled from 0.0001 second sampling rate to 1 second sampling rate. The median filtered data is low-pass filtered again with the corner frequency set to 1Hz. The downsampled data is median filtered to remove impulse noise. Finally, the DC drift component (0.1rad / s amplitude) in the data is removed and its waterfall diagram is drawn, as shown in Figure 1. Figure 5 Analyzing the thermal strain response represented by the low-frequency DAS, the area with a strong energy response has a strong signal response between 40-60m, which is consistent with the temperature anomaly position of the DTS waterfall diagram.

[0065] Furthermore, the change of temperature gradient (DTGS) is obtained based on DTS temperature data and low-frequency DAS thermal strain data, respectively, as follows: Figure 6 and Figure 7 As shown, it can be seen that there are continuous and large changes in temperature gradients, and the area of ​​the signal response matches the area range of the DTS and DAS waterfall charts, which can confirm that an abnormal temperature event has occurred. It is worth noting that Figure 6 Displayed temperature gradient change ratio Figure 7 The displayed temperature gradient changes are more precise, with an accuracy of 0.01℃ / s.

[0066] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A belt conveyor condition monitoring method based on distributed optical fiber temperature-vibration coupling comprises the following steps: S1: A single-mode optical fiber and a multi-mode optical fiber are respectively laid out at the outer frames on both sides of the belt conveyor, or an optoelectronic composite cable composed of a single-mode optical fiber and a multi-mode optical fiber is respectively laid out, wherein the single-mode optical fiber is used as a distributed acoustic wave sensor for distributed acoustic wave monitoring, and the multi-mode optical fiber is used as a distributed temperature sensor for distributed temperature monitoring; and the single-mode optical fiber is connected to a DAS demodulator to obtain demodulated DAS data of the belt conveyor through the DAS demodulator, and the multi-mode optical fiber is connected to a DTS demodulator to obtain demodulated DTS data of the belt conveyor through the DTS demodulator; S2 obtains the absolute temperature function of the belt conveyor through the DTS data; S3 performs filtering processing on the DAS data to obtain low-frequency DAS data, wherein the filtering processing includes: performing low-pass fidelity filtering on the DAS data with a filtering range of 0-1 Hz to obtain first filtered data; performing downsampling processing on the first filtered data to obtain second filtered data, and performing denoising processing on the second filtered data to obtain the low-frequency DAS data that can be used to characterize the temperature change of the belt conveyor; wherein the denoising processing includes: performing median filtering on the second filtered data to obtain third filtered data, performing low-pass filtering on the third filtered data with a corner frequency of 1 Hz to obtain fourth filtered data, and removing DC drift in the fourth filtered data to obtain the low-frequency DAS data; S4: respectively deriving the absolute temperature function and the low-frequency DAS data with respect to time to obtain two distributed temperature gradient functions, namely, a distributed temperature gradient function based on DTS and a distributed temperature gradient function based on DAS; S5: Determine the belt conveyor fault by combining at least three of the four data: the DAS data, the DTS data, the DTS-based distributed temperature gradient function, and the DAS-based distributed temperature gradient function.

2. The belt conveyor state monitoring method according to claim 1, characterized in that: The absolute temperature function is obtained from the DTS data by the following calculation model: Where R(T) represents the absolute temperature function; I F Represents the anti-Stokes light intensity; I S represents the Stokes light intensity; v F represents the center frequency of Stokes light; v S represents the center frequency of anti-Stokes light; e represents the natural constant; c represents the speed of light in vacuum; v represents the Raman frequency shift; h represents the Planck constant; K represents the Boltzmann constant; and T represents the absolute temperature.

3. The belt conveyor state monitoring method according to claim 1, characterized in that: The downsampling process includes downsampling from a 0.0001 second sampling rate to a 1 second sampling rate.

4. The belt conveyor state monitoring method according to claim 1, characterized in that: The belt conveyor failures include broken roller bearings, broken roller shafts, belt friction, belt deviation, motor abnormalities and reducer abnormalities.

5. The belt conveyor state monitoring method according to claim 1, characterized in that: It also includes: After confirming the belt conveyor fault, S6 locates the fault position through optical time domain reflectometry technology.

6. The belt conveyor state monitoring method according to claim 1, characterized in that: The joint determination includes: determining whether any one of the four types of data, namely, the DAS data, the DTS data, the distributed temperature gradient function based on DTS, and the distributed temperature gradient function based on DAS, is abnormal; if abnormal, querying whether two or more of the other three types of data at the time point when the abnormality occurs also have abnormalities; if so, it is considered that the belt conveyor is faulty at that time point.

7. The belt conveyor state monitoring method according to claim 6, characterized in that: The abnormality determination includes: the root mean square of the amplitude obtained according to the DAS data is greater than 20, the absolute temperature obtained according to the DTS data is greater than 40°C, and the absolute value of the temperature gradient obtained according to the DTS-based distributed temperature gradient function or the DAS-based distributed temperature gradient function is greater than 10°C / s.

8. A monitoring system using the belt conveyor state monitoring method according to any one of claims 1 to 7, comprising: The distributed acoustic wave sensor, the distributed temperature sensor, the DAS demodulator, and the DTS demodulator are an analysis module for receiving, storing, and calculating sensor signals and demodulated data, and an early warning module for issuing early warnings based on calculation results of the analysis module.

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