WO2020029727A1

WO2020029727A1 - Fault monitoring and diagnosis system for port freight electric agv

Info

Publication number: WO2020029727A1
Application number: PCT/CN2019/094778
Authority: WO
Inventors: 刘成良; 黄亦翔; 赵路杰
Original assignee: 上海交通大学
Priority date: 2018-08-08
Filing date: 2019-07-05
Publication date: 2020-02-13
Also published as: CN109238735B; CN109238735A

Abstract

A fault monitoring and diagnosis system for a port freight electric automated guided vehicle (AGV), comprising an AGV and a server. The AGV operates on a port wharf site; the AGV and/or the port wharf site is mounted thereon with a sensor; the server generates a fault monitoring diagnosis signal according to a monitoring signal from the sensor on the AGV and/or the port wharf site. The fault monitoring and diagnosis system for the port freight electric AGV achieves online fault detection and a fault diagnosis function of a port unmanned container carrying a trolley, which greatly shortens the maintenance time of the carried vehicle, reduces maintenance costs, and better meets requirements for 24-hour efficient operations of the unmanned wharf.

Description

Fault monitoring and diagnosis system for port freight electric AGV

Technical field

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.

Background technique

With the continuous increase of the container throughput of the port and the continuous improvement of the level of port automation, unmanned container transport trolleys (hereinafter referred to as AGVs) have become more and more widely used in ports. In the port, 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. In order to ensure the normal operation of the AGV transportation system in automated ports, 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:

(1) At present, there is no online fault detection and diagnosis system for unmanned container carriers in the market. Generally, the overall fleet is checked off-line in turn according to the maintenance plan. Due to the large fluctuations in the life of the bearings and other components of the carrier, the port works The environment is complicated, and regular inspections not only consume a lot of time and money, but also affect the efficiency of operations. There is also no way to achieve real-time monitoring of faults, prepare spare parts inventory as needed in advance, and improve production efficiency.

(2) At present, most of the relevant data and inference methods about the reliability analysis and life of parts are based on the laboratory standard environment. However, there is no reliable model for the life estimation of each part in the complex environment of the terminal. The better method is artificial neural network. Training artificial neural network requires a large amount of training data. Traditional offline maintenance methods cannot provide the data needed to train artificial neural network.

(3) There are many existing fault inspection methods and equipment on the market, but most of them are applicable to fixed working conditions. In a complex and variable working environment such as a port, the fault diagnosis effect is often not ideal. AGV, as a large machine used in the complex situation of the dock, often has a lot of background noise doped into its vibration signal, which brings a lot of interference to the signal analysis. Among these noises, there is a type of signal due to the vehicle at the dock During the running process, it repeatedly starts and stops, that is, the linear frequency modulation signal LFM brought during acceleration and deceleration.

Summary of the invention

In view of the defects in the prior art, 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.

Preferably, the AGV vehicle includes a body module, a transmission module, a motor module, and a battery module;

In any of the following positions:

-Body module;

-Transmission module;

-Motor module;

-Battery module;

-Port terminal site,

Installed with any one or more of the following sensors:

--Vibration sensor;

--Humidity Sensor;

--Temperature Sensor;

--Voltage sensor;

--current sensor.

Preferably, 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. To the remote signal receiving module.

Preferably, 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.

Preferably, 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:

-Fault information of AGV cars;

-AGV vehicle health information;

--Remaining life information of parts on AGV cars.

Preferably, 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.

Preferably, the fault location and classification module includes a bearing fault diagnosis module, and 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.

Preferably, in the filtering module, 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:

Where: f _p (u) is the noise reduction vibration signal, p is the fractional order of the free variable, and p ∈ (-2, 2), 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;

f (t) is the original vibration signal;

A _α is the leading coefficient, α is the rotation angle, and α∈ (-2π, 2π];

j is the imaginary part symbol;

n is an integer;

δ () is a Dirac function;

sgn () is a symbolic function.

Preferably, in the reconstruction module, the inverse fractional Fourier transform is used to reconstruct the noise reduction vibration signal.

Preferably, 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.

Compared with the prior art, the present invention has the following beneficial effects:

1. 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. .

2. 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.

3. 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. .

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

FIG. 1 is a structural diagram of a fault monitoring and diagnosis system for a port freight electric AGV provided by the present invention; FIG.

Figure 2 is a flowchart of bearing fault diagnosis;

FIG. 3 is a schematic diagram of a fractional Fourier transform. In the figure, 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;

β 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.

detailed description

The present invention is described in detail below with reference to specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

In the description of the present invention, it should be understood that the terms "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", The directions or positional relationships indicated by "bottom", "inside", "outside", etc. are based on the direction or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, and are not intended to indicate or imply the device referred to. Or the elements must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

As shown in FIG. 1, 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; 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. Communication; 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: 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.

As shown in FIG. 2, in the embodiment, the fault location and classification module includes a bearing fault diagnosis module, and 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.

In the filtering module, 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. As shown in FIG. 3, the fractional Fourier transform is implemented by the following formula:

Where f _p (u) is the noise reduction vibration signal, p is the fractional order of the free variable, and p ∈ (-2, 2), 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 _α is the leading coefficient, α is the rotation angle, and α∈ (-2π, 2π]; j is the symbol of the imaginary part; n is an integer; δ () 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 cotα. By changing the angle α, the basis of different tuning frequencies can be obtained. Once The chirp signal that needs to be filtered is consistent with the modulation frequency of a certain group of bases, then 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. Using these two points, the signal can be filtered in the fractional Fourier domain. Preferably, the inverse fractional Fourier transform is used to reduce the Noise and vibration signals are reconstructed. In practical applications, 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. In the embodiment, 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.

Preferred embodiment:

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. Specifically, 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. .

During the normal operation of the vehicle, the data collected by the sensor is sent to the on-board signal pre-processing module via the bus. After 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. To 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.

The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be arbitrarily combined with each other.

Claims

A fault monitoring and diagnosis system for a port freight electric AGV is characterized in that it 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 fault monitoring and diagnosis system for a port freight electric AGV according to claim 1, wherein the AGV vehicle comprises a body module, a transmission module, a motor module, and a battery module;

In any of the following positions:

-Body module;

-Transmission module;

-Motor module;

-Battery module;

-Port terminal site,

Installed with any one or more of the following sensors:

--Vibration sensor;

--Humidity Sensor;

--Temperature Sensor;

--Voltage sensor;

--current sensor.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 1, wherein a first signal processing module is provided on the AGV vehicle, and a second signal processing module is provided on the port terminal site;

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. To the remote signal receiving module.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 3, wherein the server further comprises 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 fault monitoring and diagnosis system for a port freight electric AGV according to claim 4, wherein 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:

-Fault information of AGV cars;

-AGV vehicle health information;

--Remaining life information of parts on AGV cars.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 4, wherein the remote fault monitoring and diagnosis module comprises 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 monitoring and diagnosis system for a port freight electric AGV according to claim 6, wherein the fault location and classification module comprises a bearing fault diagnosis module, and the bearing fault diagnosis module comprises 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.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 7, wherein the filtering module performs fractional Fourier transform filtering on the original vibration signal to eliminate linear frequency modulation noise in the original vibration signal;

The fractional Fourier transform is implemented by the following formula:

Where: f p (u) is the noise reduction vibration signal, p is the fractional order of the free variable, and p ∈ (-2, 2), 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;

f (t) is the original vibration signal;

A α is the leading coefficient, α is the rotation angle, and α∈ (-2π, 2π];

j is the imaginary part symbol;

n is an integer;

δ () is a Dirac function;

sgn () is a symbolic function.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 8, characterized in that, in the reconstruction module, an inverse fractional Fourier transform is used to reconstruct the noise reduction vibration signal.
The fault monitoring and diagnosis system for a port freight electric AGV according to claim 9, wherein the diagnosis result acquisition module comprises 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.