WO2020029727A1 - Système de surveillance et de diagnostic de défaillance pour vag électrique de fret portuaire - Google Patents

Système de surveillance et de diagnostic de défaillance pour vag électrique de fret portuaire Download PDF

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Publication number
WO2020029727A1
WO2020029727A1 PCT/CN2019/094778 CN2019094778W WO2020029727A1 WO 2020029727 A1 WO2020029727 A1 WO 2020029727A1 CN 2019094778 W CN2019094778 W CN 2019094778W WO 2020029727 A1 WO2020029727 A1 WO 2020029727A1
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module
agv
signal
port
diagnosis
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PCT/CN2019/094778
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English (en)
Chinese (zh)
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刘成良
黄亦翔
赵路杰
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上海交通大学
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Publication of WO2020029727A1 publication Critical patent/WO2020029727A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/007—Wheeled or endless-tracked vehicles

Definitions

  • the invention relates to the technical field of automatic port equipment health management, in particular to a fault monitoring and diagnosis system for port freight electric AGV.
  • AGVs unmanned container transport trolleys
  • the AGV can move the container at the port according to the needs according to the planned path of the system and use guidance technology such as GPS and electromagnetic.
  • AGV as one of the main carriers of containers in automated ports, ensuring its safe operation is extremely important and critical to the entire port system.
  • the port container AGV has the characteristics of long working hours and harsh working environment, and has a short application time in China. It monitors its running status online and evaluates the performance status of AGV-related systems through data. Judging the type of failure has a very important role.
  • the AGV rotating system especially the bearings in the transmission system, has a great impact on the running status of the AGV.
  • it is extremely necessary to develop a reliable and effective online bearing fault diagnosis and prediction system. It can provide early warning of equipment failures and facilitate maintenance personnel to perform efficient maintenance of related equipment.
  • the highly efficient operation is of great significance.
  • the existing technology mainly has the following three types of defects:
  • an object of the present invention is to provide a fault monitoring and diagnosis system for a port freight electric AGV.
  • a fault monitoring and diagnosis system for a port freight electric AGV provided by the present invention includes an AGV vehicle and a server; the AGV vehicle runs on a port terminal site;
  • Sensors are installed on the AGV vehicle and / or the port terminal site;
  • the server generates a fault monitoring diagnosis signal according to the monitoring signals from the AGV vehicle and / or the sensors on the port terminal site.
  • the AGV vehicle includes a body module, a transmission module, a motor module, and a battery module;
  • the AGV vehicle is provided with a first signal processing module, and the port terminal site is provided with a second signal processing module;
  • the server includes a remote signal receiving module
  • the first signal processing module and the second signal processing module respectively process the monitoring signals from the sensors on the AGV vehicle and the monitoring signals from the sensors on the port terminal site to obtain the corresponding pre-processed data and send the corresponding pre-processed data respectively.
  • the server further includes a remote fault monitoring and diagnosis module and a data management module;
  • the remote fault monitoring and diagnosis module generates a fault monitoring diagnosis signal according to the preprocessed data received by the remote signal receiving module and / or the vehicle body running history data from the data management module.
  • the sensors on the AGV vehicle are connected to the second signal processing module through a bus;
  • the sensors on the port and dock site are connected to the second signal processing module through the bus;
  • Both the first signal processing module and the second signal processing module communicate with the remote signal receiving module through a wireless transmission form
  • the remote signal receiving module is connected to the remote fault monitoring and diagnosis module through a wired form;
  • the server further includes a display module
  • the display module displays any one or more of the following information according to the received fault monitoring diagnosis signal:
  • the remote fault monitoring and diagnosis module includes any one or more of the following modules:
  • Operation monitoring module monitor the overall operation of the AGV vehicle
  • Fault location and level classification module calculate the location and severity of faults on AGV vehicles
  • Component life estimation module estimate the remaining life of the components on the AGV vehicle.
  • the fault location and classification module includes a bearing fault diagnosis module
  • the bearing fault diagnosis module includes the following modules:
  • Filtering module filtering the original vibration signals contained in the preprocessed data to obtain noise reduction vibration signals
  • Reconstruction module Reconstruct the noise reduction vibration signal to obtain the reconstructed vibration signal
  • Diagnostic result acquisition module Diagnose the reconstructed vibration signal and obtain the bearing fault diagnosis result.
  • fractional Fourier transform filtering is performed on the original vibration signal to eliminate the chirp noise in the original vibration signal;
  • the fractional Fourier transform is implemented by the following formula:
  • f p (u) is the noise reduction vibration signal
  • p is the fractional order of the free variable
  • u is the parameter of the kernel function
  • K p (u, t) is a Fourier transform kernel signal, and t is a time domain signal;
  • a ⁇ is the leading coefficient, ⁇ is the rotation angle, and ⁇ (-2 ⁇ , 2 ⁇ ];
  • j is the imaginary part symbol
  • n is an integer
  • ⁇ () is a Dirac function
  • sgn () is a symbolic function.
  • the inverse fractional Fourier transform is used to reconstruct the noise reduction vibration signal.
  • the diagnostic result acquisition module includes the following modules:
  • Module M1 find the Hilbert transform pair of the reconstructed vibration signal
  • Module M2 Construct the analytical signal with the reconstructed vibration signal as the real part and the Hilbert transform pair as the imaginary part;
  • Module M3 obtain the envelope signal by modulating the analytical signal
  • Module M4 Perform low-pass filtering and fast Fourier transform on the envelope signal to obtain the envelope spectrum, and obtain the modulation frequency, the higher harmonics of the modulation frequency, and the modulation function according to the envelope spectrum.
  • the present invention has the following beneficial effects:
  • the invention realizes the online fault detection and fault diagnosis function of the port unmanned container carrier trolley, which greatly shortens the maintenance time of the carrier vehicle, reduces the maintenance cost, and better meets the requirements of 24-hour efficient operation of the unmanned terminal. .
  • the invention can collect relevant equipment data and environment data in real time, provide data for training of artificial neural network, realize effective estimation of equipment life, and effective management of overall fleet quality.
  • the present invention uses a fractional Fourier transform to filter the signal, which can better remove the background noise in the vibration signal. By performing envelope spectrum analysis on the filtered reconstructed signal, efficient fault monitoring and diagnosis can be achieved. .
  • FIG. 1 is a structural diagram of a fault monitoring and diagnosis system for a port freight electric AGV provided by the present invention
  • Figure 2 is a flowchart of bearing fault diagnosis
  • FIG. 3 is a schematic diagram of a fractional Fourier transform.
  • f 0 is a center frequency of a chirp-type signal.
  • f m is the FM frequency of a chirp type signal
  • u 0 is the projection value of a chirp-like signal on the fractional Fourier domain
  • ⁇ is the angle between the time-frequency distribution of a chirp-like signal component in the signal under test and the time axis;
  • Chirp signals are chirp signals.
  • a fault monitoring and diagnosis system for a port freight electric AGV includes an AGV vehicle and a server; the AGV vehicle runs on a port terminal site; and the AGV vehicle and / or a port terminal site are installed with Sensors:
  • the server generates fault monitoring and diagnosis signals based on the monitoring signals from AGV vehicles and / or sensors on the port terminal site.
  • the AGV vehicle includes a body module, a transmission module, a motor module, and a battery module. Any one of the following positions: body module, transmission module, motor module, battery module, port and dock site. Any one or more of the following sensors are installed: vibration sensor, humidity sensor, temperature sensor, voltage sensor, current sensor.
  • the AGV vehicle is provided with a first signal processing module, and the port terminal site is provided with a second signal processing module; the server includes a remote signal receiving module; the first signal processing module and the second signal processing module will each come from the AGV vehicle
  • the monitoring signals of the upper sensors and the monitoring signals from the sensors on the port and dock site are processed to obtain the corresponding pre-processed data, and send the corresponding pre-processed data to the remote signal receiving module respectively.
  • the server further includes a remote fault monitoring and diagnosis module and a data management module; the remote fault monitoring and diagnosis module generates a fault monitoring and diagnosis signal according to the preprocessed data received by the remote signal receiving module and / or the vehicle body running history data from the data management module.
  • the sensors on the AGV vehicle are connected to the second signal processing module via the bus; the sensors on the port terminal are connected to the second signal processing module via the bus; both the first signal processing module and the second signal processing module are connected to the remote signal receiving module by wireless transmission.
  • the server further includes a display module; the display module displays any one or more of the following information according to the received fault monitoring diagnosis signal: fault information of the AGV vehicle; information on the health status of the AGV vehicle; parts on the AGV vehicle Remaining life information.
  • the remote fault monitoring and diagnosis module includes any one or more of the following modules: operation monitoring module: monitoring the overall operation of the AGV vehicle; fault location and classification module: calculating the location and severity of the fault on the AGV vehicle; component life Estimation module: estimate the remaining life of the parts on the AGV.
  • the fault location and classification module includes a bearing fault diagnosis module
  • the bearing fault diagnosis module includes the following modules: a filtering module: filtering the original vibration signal included in the preprocessed data to obtain a reduction Noise vibration signal; Reconstruction module: Reconstruct the noise reduction vibration signal to obtain the reconstructed vibration signal; Diagnostic result acquisition module: Diagnose the reconstructed vibration signal to obtain the bearing fault diagnosis result.
  • fractional Fourier transform filtering is performed on the original vibration signal to eliminate the chirp noise in the original vibration signal.
  • the fractional Fourier transform FRFT is a unified video transformation that reflects the signal in the time domain And frequency domain information, it uses a single variable to represent video information without interference from cross terms: compared with traditional Fourier transforms, it is more suitable for processing non-stationary signals due to the addition of a free parameter (transformation order p). And, due to the existence of more mature fast discrete algorithms, FRFT can obtain better analysis results with reasonable calculation limits.
  • the fractional Fourier transform is implemented by the following formula:
  • f p (u) is the noise reduction vibration signal
  • p is the fractional order of the free variable
  • u is the kernel function parameter
  • K p (u, t) is the Fourier transform Nuclear signal
  • t is the time domain signal
  • f (t) is the original vibration signal
  • a ⁇ is the leading coefficient
  • ⁇ is the rotation angle
  • j is the symbol of the imaginary part
  • n is an integer
  • ⁇ () is a Dirac function
  • sgn () is a sign function.
  • K p (u, t) is essentially a set of chirp signals with a tuning frequency of cot ⁇ .
  • the basis of different tuning frequencies can be obtained.
  • the signal will also form a delta function on the group of bases, and because the fractional Fourier transform is a linear transformation, the fractional order of the signal and noise are superimposed.
  • the Fourier transform is equal to the superposition of fractional transforms respectively.
  • the signal can be filtered in the fractional Fourier domain.
  • the inverse fractional Fourier transform is used to reduce the Noise and vibration signals are reconstructed.
  • similar algorithms such as batteries, motors, and variable speeds can also be used. And other parts of the specific diagnosis.
  • the specific working steps and inspection principles of the diagnostic result acquisition module are as follows.
  • the failure of rotating machinery such as rolling bearings generally has a periodic pulse impact force, which generates a modulation phenomenon of the vibration signal.
  • the modulation analysis method is used to extract modulation information from the signal and analyze its strength. And frequency can judge the degree and location of part damage.
  • the diagnosis result acquisition module includes the following modules: module M1: seeking a Hilbert transform pair of the reconstructed vibration signal; module M2: constructing an analytical signal with the reconstructed vibration signal as a real part and using the Hilbert transform pair as an imaginary part; module M3: modulate the analytical signal to obtain the envelope signal; module M4: perform low-pass filtering and fast Fourier transform on the envelope signal to obtain the envelope spectrum, and obtain the modulation frequency and higher harmonics of the modulation frequency based on the envelope spectrum And the modulation function.
  • Port freight electric AGV fault monitoring and diagnosis system includes sensors installed throughout the AGV body, sensors installed in the port, AGV vehicle signal pre-processing module, port site signal pre-processing module, remote signal receiving center and remote fault monitoring and diagnosis center.
  • the sensors on the body are mounted on the frame module, transmission module, motor module and battery module of the AGV.
  • the sensors mainly include vibration sensors, temperature sensors, current sensors, and voltage sensors. These sensors are used to collect the overall vibration data of the vehicle body, the vibration data and temperature data of the gearbox and bearings in the mechanical transmission module, the voltage, current, temperature and vibration data of the drive motor, and the current, voltage and temperature data of the power battery pack. .
  • the data collected by the sensor is sent to the on-board signal pre-processing module via the bus.
  • the signal is pre-processed, including amplification, filtering, and debugging, the pre-processed data is passed through the port.
  • the established wireless network transmits data to a remote signal receiving center located on the port.
  • a basic meteorological acquisition unit is arranged at a suitable location on the port, and a temperature and humidity sensor is arranged on the unit to measure the meteorological conditions of AGV work in the port.
  • the signal of the sensor is transmitted to the port site signal processing module through the bus.
  • This module performs basic preprocessing of the signal, including amplification, filtering, modulation, etc., and transmits the preprocessed data through the wireless network built on the port to transmit the data.
  • a remote signal receiving center located on the port.
  • the remote signal receiving center demodulates the received signal and sends the signal to the remote fault monitoring and diagnosis center.
  • the remote fault monitoring and diagnosis center simultaneously receives data sent from the remote data receiving center and data about the historical running track of AGV vehicles sent from the port AGV vehicle dispatch center, analyzes these data, and monitors the overall operation of the AGV at the port in real time. Situation, the health of the relevant equipment parts, whether there is a failure, and the location of the failure.
  • the real-time monitoring of the health status of the running vehicle is characterized in that the system displays the health status of each vehicle in the running process on the designed human-computer interaction interface in real time, and submits the faulty vehicle to the management personnel for processing or to other programs Automatically handled.
  • the fault location and classification are characterized in that the location and severity of the vehicle fault can be pointed out.
  • the estimation and prediction of the remaining life of the related parts is characterized in that the fault monitoring and diagnosis center generates a model through an intelligent algorithm based on the historical data in the server, estimates the service life model of the related parts, and uses the zero Part status to estimate the remaining life of the relevant part.

Abstract

L'invention concerne un système de surveillance et de diagnostic de défaillance pour un véhicule autoguidé (VAG) électrique de fret portuaire, comprenant un VAG et un serveur. Le VAG fonctionne sur un site de quai de port; le VAG et/ou le site de quai de port sont montés sur celui-ci avec un capteur; le serveur génère un signal de diagnostic de surveillance de défaillance en fonction d'un signal de surveillance provenant du capteur sur le VAG et/ou le site de quai de port. Le système de surveillance et de diagnostic de défaillance pour le VAG électrique de fret portuaire réalise une détection de défaillance en ligne et une fonction de diagnostic de défaillance d'un conteneur sans équipage portuaire portant un chariot, ce qui raccourcit considérablement le temps de maintenance du véhicule transporté, réduit les coûts de maintenance, et répond mieux aux exigences pour des opérations efficaces en 24 heures du quai sans équipage.
PCT/CN2019/094778 2018-08-08 2019-07-05 Système de surveillance et de diagnostic de défaillance pour vag électrique de fret portuaire WO2020029727A1 (fr)

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CN201810897308.9A CN109238735B (zh) 2018-08-08 2018-08-08 港口货运电动agv的故障监测诊断系统

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CN109238735B (zh) * 2018-08-08 2019-10-08 上海交通大学 港口货运电动agv的故障监测诊断系统
CN110012105A (zh) * 2019-04-12 2019-07-12 青岛港国际股份有限公司 一种车载终端数据解码方法及解码装置
CN110186682B (zh) * 2019-07-08 2021-03-23 石家庄铁道大学 基于分数阶变分模态分解的滚动轴承故障诊断方法
CN110262362B (zh) * 2019-07-16 2021-01-12 上海快仓智能科技有限公司 一种agv工作温度监控方法、系统及装置
CN112183290B (zh) * 2020-09-22 2023-02-24 北京邮电大学 一种基于SAsFFT算法的机械故障诊断系统
CN112578794B (zh) * 2020-12-12 2023-09-01 云南昆船智能装备有限公司 基于机器学习的agv故障检测方法、存储介质及系统
CN113033663A (zh) * 2021-03-26 2021-06-25 同济大学 一种基于机器学习的自动化集装箱码头设备健康预测方法
CN113607413A (zh) * 2021-08-26 2021-11-05 上海航数智能科技有限公司 一种基于可控温湿度的轴承部件故障监测预测方法

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