CN118013401A - DAS-based belt conveyor vibration false alarm suppression method - Google Patents
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Abstract
The invention relates to the technical field of optical fiber sensing, in particular to a DAS-based belt conveyor vibration false alarm suppression method, which is used for monitoring different carrier roller faults of a belt conveyor by constructing a DAS system, and realizing carrier roller fault monitoring by carrying out time domain and frequency domain analysis on signals through an algorithm by utilizing the characteristics of high sensitivity, wide monitoring range and strong anti-interference performance of DAS equipment.
Description
Technical Field
The invention relates to the technical field of optical fiber sensing, in particular to a DAS-based belt conveyor vibration false alarm suppression method.
Background
A belt conveyor is a friction-driven machine that transports materials in a continuous manner, which can bring the materials on a conveyor line from an initial feed point to a final discharge point to form a material transport path. The device has the characteristics of strong conveying capacity, long conveying distance, reliable operation, simple structure, convenient maintenance, low cost, strong universality and the like, and is widely applied to the fields of metallurgy, coal, traffic, hydropower, chemical industry, ports and the like.
The existing distributed optical fiber acoustic wave sensing (DAS) monitoring system is used for roughly judging the position of a fault through an RMS curve in the aspect of identifying the fault azimuth of a belt conveyor, if the RMS peak exists in a larger section, the accurate position of the fault can only be judged through experience of an operator and even cannot be positioned, the performance types and working environments of different belt conveyors are obviously different, part of belt conveyors are large in vibration range during operation, the formed abnormal signal section is wide, the echo of a closed sound field of part of the working site of the machine is strong, abnormal signals at the fault are disordered, the abnormal points are many, and the fault scene under all complex sound fields is difficult to cover only by a single Root Mean Square (RMS) index or a phase waterfall diagram index.
The related algorithm adopted in the prior art only uses the acquired vibration signal, takes the mean square error or the phase waterfall diagram information of the time domain as a fault monitoring index, if the abnormal signal has a larger vibration range, the fault point is easily positioned inaccurately, and the technical problem that the traditional belt conveyor abnormal sound detection algorithm based on DAS is difficult to distinguish other carrier rollers nearby the fault carrier roller and simultaneously alarms is not solved, and meanwhile, the waste of manpower and material resources is easily caused.
Disclosure of Invention
The invention aims to provide a DAS-based belt conveyor vibration false alarm suppression method, which solves the technical problem that the traditional DAS-based belt conveyor abnormal sound detection algorithm is difficult to distinguish false alarms of other carrier rollers nearby a fault carrier roller and alarm at the same time.
In order to achieve the above purpose, the invention provides a belt conveyor vibration false alarm suppression method based on DAS, which comprises the following steps:
Step one: setting basic parameters, and setting various variables in advance before applying DAS equipment, wherein the variables comprise sampling rate, data acquisition time and initial point and end point of a positioning optical fiber;
Step two: collecting signal data, collecting one group of phase data through each trigger of a high-speed data collecting card in the DAS equipment, obtaining a plurality of groups of different phase data after multiple times of collection, then solving the phase difference between two adjacent groups in each group of data, and finally obtaining differential phase data;
Step three: filtering, namely filtering the acquired signal data by using a filter;
Step four: threshold comparison, namely comparing the acquired signal data with the signal data of a normal carrier roller by adopting a threshold algorithm;
Step five: analyzing the fault signals, obtaining a plurality of potential fault carrier roller positions after threshold comparison, and identifying the real positions of each fault carrier roller one by one to obtain a final judging result.
Wherein the sampling rate of the DAS device in the first step needs to be matched with the working performance of the belt conveyor.
The calculation formula of the phase difference in the second step is as follows: Wherein Representing the phase difference,/>And/>The phases at time t+δt and t are represented, respectively, and δt represents the time interval.
The filter in the third step is a high-pass filter.
After the filtering processing is performed on the signal data in the third step, the environmental noise and the noise inside the system can be reduced, so that the influence of low-frequency noise on the system fault discrimination is reduced.
And when the threshold value in the fourth step is compared, the signal intensity of the position of the fault carrier roller is far greater than that of the position of the normal carrier roller.
The specific steps of analyzing the fault signal in the fifth step are as follows:
step one: setting a fault interval, and when the fault is confirmed to exist and cannot be ignored, judging whether all adjacent fault position numbers F i and F j meet the following conditions by the first standing horse:
Abs（Fi-Fj）＜Fth
Wherein Abs represents absolute value, F i is position number of i, F j is position number of j, F th is threshold value set according to arrangement condition of specific belt conveyor, and is used for judging whether carrier rollers are adjacent, if yes, it is indicated that two abnormal signals are too close, and if yes, it is caused by the same fault, F i and F j can be classified into the same fault section;
step two: performing time domain analysis on fault signals, extracting features of time domain impact based on the fault signals, extracting an envelope curve from the fault signals, thereby paying attention to low-frequency information of the signals, identifying fault signals with low frequency and high strength, calculating the ratio of mean value to integral mean value in different time windows in the time domain after obtaining the envelope curve, finally obtaining an extracted pulse diagram, and obtaining time domain features of the fault signals by comparing the extracted pulse diagram with pulse indexes of normal operation of a belt conveyor on the pulse diagram;
Step three: fault signal frequency domain analysis, converting the signal to time-frequency domain using short-time fourier transform (STFT), and performing variance curve calculation on each frequency by short-time fourier transform The frequency distribution information of the signal at different moments can be obtained, and then the variance/>, of the time axis where different frequencies are located, is calculated respectivelyObtaining a frequency variance curve, and summing the obtained frequency variance curve and a predicted fault frequency range difference value to obtain a frequency domain characteristic of a fault signal;
Step four: the real fault position is identified, the comprehensive strength information of time domain signals and frequency domain signals of all the fault positions in each fault interval is calculated, the comprehensive strength information of all the fault positions in the fault intervals is compared, the maximum comprehensive strength information is judged to be the real fault position, the real fault position is accurately identified by fusing the two signal characteristics of the time domain and the frequency domain through the formula and the steps, and the misjudgment caused by vibration spreading is eliminated, so that the accuracy and the reliability of fault monitoring are improved.
According to the method for suppressing the vibration false alarm of the belt conveyor based on the DAS, the DAS system is built to monitor the faults of different carrier rollers of the belt conveyor, the characteristics of high sensitivity, wide monitoring range and high anti-interference performance of the DAS are utilized, time domain and frequency domain analysis is carried out on signals through an algorithm, so that the fault monitoring of the carrier rollers is realized, compared with the traditional DAS system monitoring, the key point is that the vibration false alarm suppression algorithm is added, the specific occurrence point of the fault signal can be accurately positioned on a software level through time-frequency analysis of the fault signal and demarcation of the fault interval, and therefore the false alarm caused by the vibration spreading phenomenon of the belt conveyor can be suppressed, and the technical problem that the false alarm of other carrier rollers nearby the fault carrier rollers is difficult to distinguish by the traditional DAS-based belt conveyor abnormal sound detection algorithm is solved.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a flowchart of a method for suppressing vibration false alarms of a belt conveyor based on a DAS according to a first embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention, examples of which are illustrated in the accompanying drawings and, by way of example, are intended to be illustrative, and not to be construed as limiting, of the invention.
First embodiment:
Referring to fig. 1, fig. 1 is a flowchart of a method for suppressing vibration false alarms of a belt conveyor based on DAS according to a first embodiment of the present invention, and the method for suppressing vibration false alarms of a belt conveyor based on DAS provided by the present invention includes the following steps:
Step one: setting basic parameters, and setting various variables in advance before applying DAS equipment, wherein the variables comprise sampling rate, data acquisition time and initial point and end point of a positioning optical fiber;
Step two: collecting signal data, collecting one group of phase data through each trigger of a high-speed data collecting card in the DAS equipment, obtaining a plurality of groups of different phase data after multiple times of collection, then solving the phase difference between two adjacent groups in each group of data, and finally obtaining differential phase data;
Step three: filtering, namely filtering the acquired signal data by using a filter;
Step four: threshold comparison, namely comparing the acquired signal data with the signal data of a normal carrier roller by adopting a threshold algorithm;
Step five: analyzing the fault signals, obtaining a plurality of potential fault carrier roller positions after threshold comparison, and identifying the real positions of each fault carrier roller one by one to obtain a final judging result.
In the embodiment, different carrier roller faults of the belt conveyor are monitored by building the DAS system, the characteristics of high sensitivity, wide monitoring range and strong anti-interference performance of the DAS device are utilized, time domain and frequency domain analysis is carried out on signals through an algorithm, and the carrier roller fault monitoring is realized.
The sampling rate of the DAS device in the first step needs to be matched with the working performance of the belt conveyor, the data acquisition time of the DAS device in the first step determines how much data space is occupied by signal data in the process of monitoring the belt conveyor, the excessive data acquisition time can cause redundant waste of a memory, and the insufficient acquisition time can cause neglecting of potential fault hidden dangers.
Secondly, the calculation formula of the phase difference in the second step is as follows: Wherein Representing the phase difference,/>And/>The phases at time t+δt and t are represented, respectively, and δt represents the time interval.
Meanwhile, the filter in the third step is a high-pass filter, and after the signal data in the third step is subjected to filtering processing, environmental noise and system internal noise can be reduced, so that the influence of low-frequency noise on system fault discrimination is reduced.
In addition, when the threshold value in the fourth step is compared, the signal intensity of the position of the fault carrier roller is far greater than that of the position of the normal carrier roller, and the threshold algorithm in the fourth step is as follows: taking the intensity-based root mean square algorithm as an example, for the idler signal at position P, its root mean square V p is calculated and then compared to the threshold V th. If V p- Vth >0, then the idler at P is determined to be a suspected faulty idler.
Finally, the specific steps of analyzing the fault signal in the fifth step are as follows:
step one: setting a fault interval, and when the fault is confirmed to exist and cannot be ignored, judging whether all adjacent fault position numbers F i and F j meet the following conditions by the first standing horse:
Abs（Fi-Fj）＜Fth
Wherein Abs represents absolute value, F i is position number of i, F j is position number of j, F th is threshold value set according to arrangement condition of specific belt conveyor, and is used for judging whether carrier rollers are adjacent, if yes, it is indicated that two abnormal signals are too close, and if yes, it is caused by the same fault, F i and F j can be classified into the same fault section;
step two: performing time domain analysis on fault signals, extracting features of time domain impact based on the fault signals, extracting an envelope curve from the fault signals, thereby paying attention to low-frequency information of the signals, identifying fault signals with low frequency and high strength, calculating the ratio of mean value to integral mean value in different time windows in the time domain after obtaining the envelope curve, finally obtaining an extracted pulse diagram, and obtaining time domain features of the fault signals by comparing the extracted pulse diagram with pulse indexes of normal operation of a belt conveyor on the pulse diagram;
Step three: fault signal frequency domain analysis, converting the signal to time-frequency domain using short-time fourier transform (STFT), and performing variance curve calculation on each frequency by short-time fourier transform The frequency distribution information of the signal at different moments can be obtained, and then the variance/>, of the time axis where different frequencies are located, is calculated respectivelyObtaining a frequency variance curve, and summing the obtained frequency variance curve and a predicted fault frequency range difference value to obtain a frequency domain characteristic of a fault signal;
Step four: the real fault position is identified, the comprehensive strength information of time domain signals and frequency domain signals of all the fault positions in each fault interval is calculated, the comprehensive strength information of all the fault positions in the fault intervals is compared, the maximum comprehensive strength information is judged to be the real fault position, the real fault position is accurately identified by fusing the two signal characteristics of the time domain and the frequency domain through the formula and the steps, and the misjudgment caused by vibration spreading is eliminated, so that the accuracy and the reliability of fault monitoring are improved.
When the vibration false alarm suppression method of the belt conveyor based on the DAS is used, basic parameters are set first, various variables including sampling rate, data acquisition time and initial point and end point of positioning optical fiber are required to be set in advance before the DAS is applied, the sampling rate of the DAS determines whether details of acquired data are complete or not, and attention should be paid in practical application to enable the sampling rate of the DAS to be matched with the working performance of the belt conveyor; the data acquisition time of the DAS determines how much data space is occupied by signal data in the process of monitoring the belt conveyor, and excessive high data acquisition time can cause redundant waste of a memory and insufficient acquisition time can cause potential fault hidden dangers to be ignored.
And acquiring signal data again, wherein a high-speed data acquisition card in the DAS equipment acquires one group of phase data each time of triggering, and multiple groups of phase data with different time can be obtained after multiple times of acquisition. Then, the phase difference between two adjacent groups in each group of data is calculatedAnd finally obtaining differential phase data.
And then filtering, namely filtering the acquired signal data to reduce the influence of interference such as environmental noise, system internal noise and the like on signal acquisition, wherein the signal to noise ratio can be improved by applying a filter, and signals causing adverse effects on actual results are filtered. In practical application, a high-pass filter is often adopted to process the acquired data so as to reduce the influence of low-frequency noise on system fault discrimination.
A threshold comparison is then made, the threshold being an important loop in determining whether the DAS monitoring system is alarming. In general, the signal strength of the faulty idler location is much greater than that of a normal idler. There are currently a number of different fault monitoring threshold algorithms. Taking the intensity-based root mean square algorithm as an example, for the idler signal at position P, its root mean square V p is calculated and then compared to the threshold V th. If V p- Vth >0, then the idler at P is determined to be a suspected faulty idler. It should be noted that the algorithm is not only applicable to the root mean square based algorithm, but also applicable to the general threshold algorithm.
And finally, analyzing the fault signals, and comparing the fault signals by threshold values to obtain a plurality of potential fault carrier roller positions. In reality, vibrations at the faulty idler may be transmitted along the conveyor to several normal idlers in adjacent positions, resulting in their signal strengths also being large, possibly exceeding the established signal threshold V th. For this purpose, it is necessary to algorithmically distinguish between them, in particular by the following steps:
step one: setting a fault interval, and when the fault is confirmed to exist and cannot be ignored, judging whether all adjacent fault position numbers F i and F j meet the following conditions by the first standing horse:
Abs（Fi-Fj）＜Fth
wherein Abs represents absolute value, F i is position number of i, F j is position number of j, F th is threshold value set according to arrangement condition of specific belt conveyor, and is used for judging whether carrier rollers are adjacent, if yes, it is indicated that two abnormal signals are too close, and if yes, it is caused by the same fault, F i and F j can be classified into the same fault section;
step two: performing time domain analysis on fault signals, extracting features of time domain impact based on the fault signals, extracting an envelope curve from the fault signals, thereby paying attention to low-frequency information of the signals, identifying fault signals with low frequency and high strength, calculating the ratio of mean value to integral mean value in different time windows in the time domain after obtaining the envelope curve, finally obtaining an extracted pulse diagram, and obtaining time domain features of the fault signals by comparing the extracted pulse diagram with pulse indexes of normal operation of a belt conveyor on the pulse diagram;
Step three: fault signal frequency domain analysis, converting the signal to time-frequency domain using short-time fourier transform (STFT), and performing variance curve calculation on each frequency by short-time fourier transform The frequency distribution information of the signal at different moments can be obtained, and then the variance/>, of the time axis where different frequencies are located, is calculated respectivelyObtaining a frequency variance curve, and summing the obtained frequency variance curve and a predicted fault frequency range difference value to obtain a frequency domain characteristic of a fault signal;
step four: and identifying the real fault position, calculating the comprehensive strength information of time domain signals and frequency domain signals of all fault positions in each fault interval, finally comparing the comprehensive strength information of all fault positions in the fault interval, judging the maximum comprehensive strength information as the real fault position, and merging the two signal characteristics of the time domain and the frequency domain through the formula and the steps to accurately identify the real fault position so as to obtain a final judging structure, thereby eliminating misjudgment caused by vibration spreading and further improving the accuracy and the reliability of fault monitoring.
Compared with the traditional DAS system monitoring key point, the DAS-based belt conveyor vibration false alarm suppression method has the advantages that the vibration false alarm suppression algorithm is added, the specific occurrence point of the fault signal can be accurately positioned on the software level through time-frequency analysis of the fault signal and demarcation of the fault interval, and the technical problem that the fault position is roughly judged through an RMS curve when the fault position of the belt conveyor is identified by the traditional DAS monitoring system, and if the RMS peak has a larger interval, the accurate position of the fault can only be judged or even cannot be positioned through experience of an operator is solved.
The belt conveyor vibration false alarm suppression method based on the DAS has the following advantages:
The algorithm design of the invention has high flexibility and compatibility, and can be suitable for wide belt conveyor fault detection technology based on a distributed optical fiber sensing system (DAS). The algorithm not only can intelligently select and adjust parameters of the DAS system according to specific fault detection requirements so as to optimize the data processing and analysis process, but also can adapt to diversified belt conveyor designs and changeable operating conditions. The algorithm can ensure to provide accurate fault monitoring and real-time early warning no matter facing different running speeds, load weights or complex running environments, thereby playing a key role in the aspects of maintenance and running safety of the belt conveyor.
The method for suppressing the false alarm of the belt conveyor vibration based on the DAS has high fault recognition rate, can effectively reduce false alarm caused by vibration spreading, is difficult to judge the position of a specific fault point when the traditional DAS monitoring is applied to the belt conveyor, solves the problem of difficult positioning of fault signals on a software level by adopting a vibration spreading elimination algorithm, and greatly improves the accuracy of a monitoring system.
In summary, the invention monitors different carrier roller faults of the belt conveyor by building the DAS system, utilizes the characteristics of high sensitivity, wide monitoring range and strong anti-interference performance of the DAS equipment, analyzes signals in a time domain and a frequency domain by an algorithm, and realizes the fault monitoring of the carrier roller.
The foregoing disclosure is only illustrative of one or more preferred embodiments of the present application, and it is not intended to limit the scope of the claims hereof, as persons of ordinary skill in the art will understand that all or part of the processes for practicing the embodiments described herein may be practiced with equivalent variations in the claims, which are within the scope of the application.
Claims (7)
1. The method for suppressing the vibration false alarm of the belt conveyor based on the DAS is characterized by comprising the following steps of:
Step one: setting basic parameters, and setting various variables in advance before applying DAS equipment, wherein the variables comprise sampling rate, data acquisition time and initial point and end point of a positioning optical fiber;
Step two: collecting signal data, collecting one group of phase data through each trigger of a high-speed data collecting card in the DAS equipment, obtaining a plurality of groups of different phase data after multiple times of collection, then solving the phase difference between two adjacent groups in each group of data, and finally obtaining differential phase data;
Step three: filtering, namely filtering the acquired signal data by using a filter;
Step four: threshold comparison, namely comparing the acquired signal data with the signal data of a normal carrier roller by adopting a threshold algorithm;
Step five: analyzing the fault signals, obtaining a plurality of potential fault carrier roller positions after threshold comparison, and identifying the real positions of each fault carrier roller one by one to obtain a final judging result.
2. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
The sampling rate of the DAS device in the first step needs to be matched to the operation performance of the belt conveyor.
3. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
The calculation formula of the phase difference in the second step is as follows: wherein/> Representing the phase difference,/>And/>The phases at time t+δt and t are represented, respectively, and δt represents the time interval.
4. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
The filter in the third step is a high-pass filter.
5. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
After the signal data in the third step is subjected to filtering processing, environmental noise and system internal noise can be reduced, so that the influence of low-frequency noise on system fault discrimination is reduced.
6. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
And in the fourth step, the signal intensity of the position of the fault carrier roller is far greater than that of the position of the normal carrier roller when the threshold values are compared.
7. The method for suppressing vibration false alarm of a belt conveyor based on a DAS as in claim 1, wherein,
The specific steps of analyzing the fault signal in the fifth step are as follows:
step one: setting a fault interval, and when the fault is confirmed to exist and cannot be ignored, judging whether all adjacent fault position numbers F i and F j meet the following conditions by the first standing horse:
Wherein Abs represents absolute value, F i is position number of i, F j is position number of j, F th is threshold value set according to arrangement condition of specific belt conveyor, and is used for judging whether carrier rollers are adjacent, if yes, it is indicated that two abnormal signals are too close, and if yes, it is caused by the same fault, F i and F j can be classified into the same fault section;
step two: performing time domain analysis on fault signals, extracting features of time domain impact based on the fault signals, extracting an envelope curve from the fault signals, thereby paying attention to low-frequency information of the signals, identifying fault signals with low frequency and high strength, calculating the ratio of mean value to integral mean value in different time windows in the time domain after obtaining the envelope curve, finally obtaining an extracted pulse diagram, and obtaining time domain features of the fault signals by comparing the extracted pulse diagram with pulse indexes of normal operation of a belt conveyor on the pulse diagram;
Step three: fault signal frequency domain analysis, converting the signal to time-frequency domain using short-time fourier transform (STFT), and performing variance curve calculation on each frequency by short-time fourier transform The frequency distribution information of the signal at different moments can be obtained, and then the variance/>, of the time axis where different frequencies are located, is calculated respectivelyObtaining a frequency variance curve, and summing the obtained frequency variance curve and a predicted fault frequency range difference value to obtain a frequency domain characteristic of a fault signal;
Step four: the real fault position is identified, the comprehensive strength information of time domain signals and frequency domain signals of all the fault positions in each fault interval is calculated, the comprehensive strength information of all the fault positions in the fault intervals is compared, the maximum comprehensive strength information is judged to be the real fault position, the real fault position is accurately identified by fusing the two signal characteristics of the time domain and the frequency domain through the formula and the steps, and the misjudgment caused by vibration spreading is eliminated, so that the accuracy and the reliability of fault monitoring are improved.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106052842A (en) * | 2016-08-05 | 2016-10-26 | 上海交通大学 | Distributed fiber vibration sensing system capable of eliminating declining noises and demodulation method of system |
| CN106092305A (en) * | 2016-08-25 | 2016-11-09 | 上海交通大学 | Distributed optical fiber sensing system and vibration detection localization method thereof |
| CN205981438U (en) * | 2016-08-25 | 2017-02-22 | 上海交通大学 | Distributed optical fiber sensing system |
| CN108088548A (en) * | 2017-11-24 | 2018-05-29 | 安徽师范大学 | Distributed optical fiber vibration sensor high-precision locating method |
| US20180357542A1 (en) * | 2018-06-08 | 2018-12-13 | University Of Electronic Science And Technology Of China | 1D-CNN-Based Distributed Optical Fiber Sensing Signal Feature Learning and Classification Method |
| CN110793616A (en) * | 2019-10-25 | 2020-02-14 | 深圳第三代半导体研究院 | All-fiber distributed cable safety and reliability monitoring system |
| CN114114282A (en) * | 2022-01-24 | 2022-03-01 | 之江实验室 | Unit linear array and full-distributed optical fiber sonar linear array comprising same |
| CN114510960A (en) * | 2021-12-28 | 2022-05-17 | 齐鲁工业大学 | Method for recognizing distributed optical fiber sensor system mode |
| CN115479631A (en) * | 2022-09-07 | 2022-12-16 | 国网浙江省电力有限公司电力科学研究院 | Method and system for diagnosing mechanical fault and electrical fault of high-voltage alternating-current submarine cable |
| CN115539277A (en) * | 2022-09-27 | 2022-12-30 | 北京许继电气有限公司 | Fault early warning system and method based on hydroelectric machine voiceprint recognition |
| CN116772908A (en) * | 2021-12-28 | 2023-09-19 | 西安和其光电科技股份有限公司 | Signal data processing method applied to distributed optical fiber acoustic wave sensing system |
| CN116818080A (en) * | 2023-03-27 | 2023-09-29 | 电子科技大学 | Multi-dimensional depth feature extraction and identification method for DAS (data acquisition and distribution) signals |
-
2024
- 2024-04-10 CN CN202410429109.0A patent/CN118013401A/en active Pending
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106052842A (en) * | 2016-08-05 | 2016-10-26 | 上海交通大学 | Distributed fiber vibration sensing system capable of eliminating declining noises and demodulation method of system |
| CN106092305A (en) * | 2016-08-25 | 2016-11-09 | 上海交通大学 | Distributed optical fiber sensing system and vibration detection localization method thereof |
| CN205981438U (en) * | 2016-08-25 | 2017-02-22 | 上海交通大学 | Distributed optical fiber sensing system |
| WO2018035833A1 (en) * | 2016-08-25 | 2018-03-01 | 上海交通大学 | Distributed fibre sensing system and vibration detection and positioning method therefor |
| CN108088548A (en) * | 2017-11-24 | 2018-05-29 | 安徽师范大学 | Distributed optical fiber vibration sensor high-precision locating method |
| US20180357542A1 (en) * | 2018-06-08 | 2018-12-13 | University Of Electronic Science And Technology Of China | 1D-CNN-Based Distributed Optical Fiber Sensing Signal Feature Learning and Classification Method |
| CN110793616A (en) * | 2019-10-25 | 2020-02-14 | 深圳第三代半导体研究院 | All-fiber distributed cable safety and reliability monitoring system |
| CN114510960A (en) * | 2021-12-28 | 2022-05-17 | 齐鲁工业大学 | Method for recognizing distributed optical fiber sensor system mode |
| CN116772908A (en) * | 2021-12-28 | 2023-09-19 | 西安和其光电科技股份有限公司 | Signal data processing method applied to distributed optical fiber acoustic wave sensing system |
| CN114114282A (en) * | 2022-01-24 | 2022-03-01 | 之江实验室 | Unit linear array and full-distributed optical fiber sonar linear array comprising same |
| CN115479631A (en) * | 2022-09-07 | 2022-12-16 | 国网浙江省电力有限公司电力科学研究院 | Method and system for diagnosing mechanical fault and electrical fault of high-voltage alternating-current submarine cable |
| CN115539277A (en) * | 2022-09-27 | 2022-12-30 | 北京许继电气有限公司 | Fault early warning system and method based on hydroelectric machine voiceprint recognition |
| CN116818080A (en) * | 2023-03-27 | 2023-09-29 | 电子科技大学 | Multi-dimensional depth feature extraction and identification method for DAS (data acquisition and distribution) signals |
Non-Patent Citations (2)
| Title |
|---|
| 曹贯强;: "带式输送机托辊故障检测方法", 工矿自动化, vol. 46, no. 06, 30 June 2020 (2020-06-30) * |
| 梁堃 等: "基于分布式光纤声波传感器的带式输送机托辊故障监测方法", 激光与光电子学进展, vol. 60, no. 9, 31 May 2023 (2023-05-31) * |
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