CN111717800B - Fault monitoring method and device for slewing mechanism of crane - Google Patents
Fault monitoring method and device for slewing mechanism of crane Download PDFInfo
- Publication number
- CN111717800B CN111717800B CN202010623925.7A CN202010623925A CN111717800B CN 111717800 B CN111717800 B CN 111717800B CN 202010623925 A CN202010623925 A CN 202010623925A CN 111717800 B CN111717800 B CN 111717800B
- Authority
- CN
- China
- Prior art keywords
- fault
- code
- vibration signal
- slewing mechanism
- type
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C15/00—Safety gear
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/028—Acoustic or vibration analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mechanical Engineering (AREA)
- Acoustics & Sound (AREA)
- Control And Safety Of Cranes (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a fault monitoring method and a device for a slewing mechanism of a crane, which comprises the following steps: collecting a fault sample set of the swing mechanism; inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range; collecting a current vibration signal source of the slewing mechanism; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; the fault of the slewing mechanism is determined according to the fault type, the slewing mechanism can be monitored, the fault type of the slewing mechanism can be rapidly identified, and the operation safety of the crane is improved.
Description
Technical Field
The invention relates to the technical field of cranes, in particular to a fault monitoring method and device for a slewing mechanism of a crane.
Background
The swing mechanism of the crane is driven by gears, and if the gears fail, the damage of the swing mechanism is aggravated. Among them, the failure of the gear is manifested by gear fracture, working tooth surface wear, pitting, gluing, plastic deformation, and the like.
When the gear is invalid, the operation manipulator can not know, and at the moment, the slewing mechanism still works, so that the damage of the slewing mechanism can be aggravated, and potential safety hazards are brought to the crane.
Disclosure of Invention
In view of the above, the present invention provides a method and a device for monitoring a fault of a swing mechanism of a crane, which can monitor the swing mechanism, quickly identify a fault type of the swing mechanism, and improve the operation safety of the crane.
In a first aspect, an embodiment of the present invention provides a fault monitoring method for a slewing mechanism of a crane, where the method includes:
collecting a fault sample set of the slewing mechanism;
inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range;
collecting a current vibration signal source of the slewing mechanism;
judging whether the current vibration signal source falls into the information entropy characteristic value range or not;
if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code;
determining a fault type according to the fault code;
and determining the fault of the slewing mechanism according to the fault type.
Further, the determining whether the current vibration signal source falls within the range of the information entropy characteristic value includes:
judging whether the current vibration signal source falls into a first fault state range in the multiple fault state ranges;
and if the fault codes fall into the first type of fault state range, taking the category corresponding to the first type of fault state range as the fault codes.
Further, the determining the fault type according to the fault code includes:
when the fault code is a first code, the fault type is that the speed reducer is not aligned;
when the fault code is a second code, the fault type is that the gear teeth of the speed reducer are broken;
when the fault code is a third code, the fault type is that the slewing support is not centered;
and when the fault code is a fourth code, the fault type is that the rotary support gear teeth break.
Further, the collecting a fault sample set of the slewing mechanism includes:
collecting a plurality of vibration signal sources;
carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and taking the plurality of vibration signal sources subjected to noise reduction as the fault sample set of the slewing mechanism.
In a second aspect, an embodiment of the present invention provides a fault monitoring device for a slewing mechanism of a crane, where the fault monitoring device includes: the device comprises a swing mechanism, an eddy current sensor and a classifier, wherein the swing mechanism is connected with the eddy current sensor, and the eddy current sensor is connected with the classifier;
the eddy current sensor is used for acquiring a current vibration signal source of the slewing mechanism;
the classifier is used for collecting a fault sample set of the slewing mechanism and inputting the fault sample set into an information entropy algorithm to obtain an information entropy characteristic value range; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; and determining the fault of the slewing mechanism according to the fault type.
Further, the information entropy feature value range includes a multi-class fault state range, and the classifier is specifically configured to:
judging whether the current vibration signal source falls into a first fault state range in the multiple fault state ranges;
and if the fault code falls into the first type fault state range, taking the category corresponding to the first type fault state range as the fault code.
Further, the classifier is specifically configured to:
when the fault code is a first code, the fault type is that the speed reducer is not aligned;
when the fault code is a second code, the fault type is the fracture of the gear teeth of the speed reducer;
when the fault code is a third code, the fault type is slewing bearing misalignment;
and when the fault code is a fourth code, the fault type is that the rotary support gear teeth break.
Further, the classifier is specifically configured to:
collecting a plurality of vibration signal sources;
carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and taking the plurality of vibration signal sources subjected to noise reduction as the fault sample set of the slewing mechanism.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements the method described above when executing the computer program.
In a fourth aspect, embodiments of the invention provide a computer readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method as described above.
The embodiment of the invention provides a fault monitoring method and a fault monitoring device for a slewing mechanism of a crane, wherein the fault monitoring method comprises the following steps: collecting a fault sample set of the slewing mechanism; inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range; collecting a current vibration signal source of the slewing mechanism; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; the fault of the slewing mechanism is determined according to the fault type, the slewing mechanism can be monitored, the fault type of the slewing mechanism can be rapidly identified, and the operation safety of the crane is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a fault monitoring method for a swing mechanism of a crane according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a step S106 in a method for monitoring a fault of a swing mechanism of a crane according to an embodiment of the present invention;
fig. 3 is a flowchart of step S101 in a method for monitoring a fault of a slewing mechanism of a crane according to an embodiment of the present invention;
fig. 4 is a schematic diagram of determining a fault type according to an embodiment of the present invention;
fig. 5 is a schematic view of a fault monitoring device for a swing mechanism of a crane according to a second embodiment of the present invention.
Icon:
1-a slewing mechanism; 2-an eddy current sensor; 3-a classifier; 4-display screen.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the understanding of the present embodiment, the following detailed description will be given of the embodiment of the present invention.
The first embodiment is as follows:
fig. 1 is a flowchart of a fault monitoring method for a swing mechanism of a crane according to an embodiment of the present invention.
Referring to fig. 1, the method includes the steps of:
step S101, collecting a fault sample set of a slewing mechanism;
step S102, inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range;
specifically, the fault sample set is used as the input of the information entropy algorithm, and the information entropy characteristic value range is obtained through output, wherein the information entropy characteristic value range comprises a plurality of fault state ranges.
The specific implementation process of the information entropy algorithm is as follows:
first, suppose the domain is U, U is the set of the studied objects, T is the set of a non-empty subset, if
There is a mapping, as shown in equation (1):
where μ defines a generalized subset of U, denoted G, and μ is a membership function of G, denoted μG(u) is an interval formed by membership degrees for G, and the power set of the generalized set is denoted as G (U).
And secondly, constructing an information entropy three-dimensional space coordinate, wherein a time domain is taken as an X axis of the three-dimensional space, a frequency domain is taken as a Y axis of the three-dimensional space, and a time-frequency domain is taken as a Z axis of the three-dimensional space, so that the singular spectrum information entropy is taken as the time domain corresponding to the X axis of the three-dimensional space, the power spectrum information entropy is taken as the frequency domain corresponding to the Y axis of the three-dimensional space, and the wavelet energy spectrum entropy is taken as the time-frequency domain corresponding to the Z axis of the three-dimensional space.
Third, setting i to 1,2,3,4 represents four failure categories, respectively. Respectively calculating to obtain a singular spectrum information entropy band value of a time domain, a power spectrum information entropy band value of a frequency domain and a wavelet energy spectrum entropy band value of a time-frequency domain in an information entropy three-dimensional space coordinate according to four fault categories;
most entropy values are shifted up and down within a small range of values, indicating the entropy band condition. By calculating the entropy band average value of the three information entropies, the central point of each information entropy band can be obtained, and further, the information entropy value central coordinate H (X) of each fault (i ═ 1,2,3, 4) in the three-dimensional space can be obtainedj)i。
Fourthly, setting discourse domain U ═ U { U } according to the generalized set1,u2,…,unIs a vibration signal to be decided. Taking 4 fault feature sets as fault domains G ═ mu1,μ2,μ3,μ4Mu determines a generalized subset of U, denoted G, and mu is a membership function of G, i.e. for each fault class muiAs a subset of the fault domain G. Therefore, a generalized set relation matrix between the fault symptom U and the fault domain G in the generalized set theory can be obtained. The identification of U is actually a matter of how much it belongs to which subset G, i.e. muG(u) degree of T, which is the entropy band of a certain fault.
And fifthly, determining a set T consisting of the non-empty subsets. According to the generalized set of the time-space field, entropy bands are respectively cut from the singular spectrum information entropy band value of the time domain, the power spectrum information entropy band value of the frequency domain and the wavelet energy spectrum entropy band value of the time-frequency domain, and the entropy bands and the wavelet energy spectrum entropy band value of the time-frequency domain enclose a cube, so that H (X) is formedj)iIs the center of a cube, where (i ═ 1,2,3, 4; j ═ 1,2,3), and the volume of the cube is made equal to an integer multiple of the unit 1 or 1. In addition, the conditions to be satisfied are: the cube is taken from the singular spectrum information entropy band value of the time domain, the power spectrum information entropy band value of the frequency domain and the wavelet energy spectrum entropy band value of the time-frequency domain
And is
Belonging to the range of entropy bands. From the space of entropy bands, T ═ V can be determinedj1,Vj2]。
Step S103, collecting a current vibration signal source of the slewing mechanism;
step S104, judging whether the current vibration signal source falls into the information entropy characteristic value range;
step S105, if the fault code falls into the information entropy characteristic value range, taking the category corresponding to the information entropy characteristic value range as the fault code;
specifically, the current vibration signal source of the collecting slewing mechanism is U ═ U { U ═1,u2,u3,....unGet it out ifThe current vibration signal source is positioned in the range of the ith fault state
And if so, attributing the current vibration signal source as a fault i. If the current vibration signal source U is equal to { U ═ U-1,u2,u3,....unDistance H (X)j)iThe more recent, the higher the degree of the current vibration signal source belonging to the fault i, and vice versa. Therefore, the fault code is determined to be i, the fault type can be determined according to the fault code, an operator can stop and check according to the fault type, and further operation is prevented from influencing the operation safety of the crane.
Step S106, determining the fault type according to the fault code;
and step S107, determining the fault of the slewing mechanism according to the fault type.
Further, the information entropy characteristic value range includes multiple types of fault state ranges, and step S104 includes:
judging whether the current vibration signal source falls into a first type fault state range in the multiple types of fault state ranges;
and if the fault code falls into the first type of fault state range, taking the category corresponding to the first type of fault state range as a fault code.
Specifically, the multiple types of fault condition ranges include the first type of fault condition range, and further include, but are not limited to, the second type of fault condition range, the third type of fault condition range, and the fourth type of fault condition range, and the four types of fault condition ranges are described herein. Referring to fig. 4, a plurality of cubes are constructed in an information entropy three-dimensional space coordinate system with singular spectral information entropy as a time domain corresponding to an X-axis of a three-dimensional space, power spectral information entropy as a frequency domain corresponding to a Y-axis of the three-dimensional space, and wavelet energy spectral entropy as a time-frequency domain corresponding to a Z-axis of the three-dimensional space, wherein fault samples are V1, V2, V3, and V4.
When the V1 falls into the cube of the first-class fault state range, taking the class corresponding to the first-class fault state range as a fault code, wherein the fault code is 1, and the corresponding fault type is that the speed reducer is not aligned;
when the V2 falls into the cube of the second type fault state range, taking the category corresponding to the second type fault state range as a fault code, wherein the fault code is 2, and the corresponding fault type is that the gear teeth of the speed reducer are broken;
when the V3 falls into the cube of the third type fault state range, taking the category corresponding to the third type fault state range as a fault code, wherein the fault code is 3, and the corresponding fault type is that the rotary support is not centered;
when the V4 falls into the cube of the fourth type fault state range, the category corresponding to the fourth type fault state range is used as a fault code, at this time, the fault code is 4, and the corresponding fault type is that the rotary support gear teeth are broken.
Further, referring to fig. 2, step S106 includes the following steps:
step S201, when the fault code is a first code, the fault type is that the speed reducer is not aligned;
step S202, when the fault code is a second code, the fault type is that the gear teeth of the speed reducer are broken;
step S203, when the fault code is a third code, the fault type is that the slewing support is not centered;
and step S204, when the fault code is a fourth code, the fault type is that the rotary supporting gear teeth are broken.
Further, referring to fig. 3, step S101 includes the steps of:
step S301, collecting a plurality of vibration signal sources;
step S302, carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and step S303, taking the plurality of vibration signal sources subjected to noise reduction as a fault sample set of the slewing mechanism.
The embodiment of the invention provides a fault monitoring method for a slewing mechanism of a crane, which comprises the following steps: collecting a fault sample set of the slewing mechanism; inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range; collecting a current vibration signal source of the slewing mechanism; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; the fault of the slewing mechanism is determined according to the fault type, the slewing mechanism can be monitored, the fault type of the slewing mechanism can be rapidly identified, and the operation safety of the crane is improved.
Example two:
fig. 5 is a schematic view of a fault monitoring device for a swing mechanism of a crane according to a second embodiment of the present invention.
Referring to fig. 5, the apparatus includes: the device comprises a slewing mechanism 1, an eddy current sensor 2, a display screen 4 and a classifier 3;
the rotary mechanism 1 is connected with an eddy current sensor 2, and the eddy current sensor 2 is connected with a classifier 3;
the eddy current sensor 2 is used for acquiring a current vibration signal source of the slewing mechanism;
the classifier 3 is used for collecting a fault sample set of the slewing mechanism and inputting the fault sample set into an information entropy algorithm to obtain an information entropy characteristic value range; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; determining the fault of the slewing mechanism according to the fault type;
and the display screen 4 is used for displaying the fault codes.
Further, the information entropy characteristic value range includes a multi-class fault state range, and the classifier 3 is specifically configured to:
judging whether the current vibration signal source falls into a first type fault state range in the multiple types of fault state ranges;
and if so, taking the category corresponding to the first-type fault state range as a fault code.
Further, the classifier 3 is specifically configured to:
when the fault code is the first code, the fault type is that the speed reducer is not aligned;
when the fault code is a second code, the fault type is the fracture of the gear teeth of the speed reducer;
when the fault code is a third code, the fault type is that the slewing support is not centered;
and when the fault code is a fourth code, the fault type is that the rotary supporting gear teeth are broken.
Further, the classifier 3 is specifically configured to:
collecting a plurality of vibration signal sources;
carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and taking the plurality of vibration signal sources subjected to noise reduction as a fault sample set of the slewing mechanism.
The embodiment of the invention provides a fault monitoring device for a slewing mechanism of a crane, which comprises: collecting a fault sample set of the slewing mechanism; inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range; collecting a current vibration signal source of the slewing mechanism; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; if the information entropy characteristic value falls into the range, the category corresponding to the information entropy characteristic value range is used as a fault code; determining a fault type according to the fault code; the fault of the slewing mechanism is determined according to the fault type, the slewing mechanism can be monitored, the fault type of the slewing mechanism can be rapidly identified, and the operation safety of the crane is improved.
The embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the steps of the method for monitoring the fault of the swing mechanism of the crane provided in the above embodiment are implemented.
The embodiment of the present invention further provides a computer readable medium having non-volatile program codes executable by a processor, where the computer readable medium has a computer program stored thereon, and the computer program is executed by the processor to perform the steps of the crane slewing mechanism fault monitoring method according to the above-mentioned embodiment.
The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
The functions may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present invention or a part thereof which substantially contributes to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method of fault monitoring a slewing mechanism of a crane, the method comprising:
collecting a fault sample set of the slewing mechanism;
inputting the fault sample set into an information entropy algorithm of a classifier to obtain an information entropy characteristic value range; the information entropy characteristic value range comprises a plurality of fault state ranges, different fault state ranges correspond to different cubes, and the cubes take singular spectrum information entropy as a time domain, take power spectrum information entropy as a frequency domain and take wavelet energy spectrum entropy as a time-frequency domain;
collecting a current vibration signal source of the slewing mechanism;
judging whether the current vibration signal source falls into the information entropy characteristic value range or not; the judging whether the current vibration signal source falls into the information entropy characteristic value range or not comprises the following steps: judging whether the current vibration signal source falls into the cube in the fault state range;
if the class fault state range falls into the fault code, taking the class corresponding to the class fault state range as the fault code;
determining a fault type according to the fault code;
and determining the fault of the slewing mechanism according to the fault type.
2. The method for monitoring the fault of the slewing mechanism of the crane as claimed in claim 1, wherein said determining whether the current vibration signal source falls within the cube of the information entropy characteristic value range comprises:
judging whether the current vibration signal source falls into the cube of a first type fault state range in the multi-type fault state ranges;
and if the fault codes fall into the first type of fault state range, taking the category corresponding to the first type of fault state range as the fault codes.
3. The method for fault monitoring of a slewing mechanism of a crane according to claim 1, wherein said determining a fault type based on said fault code comprises:
when the fault code is a first code, the fault type is that the speed reducer is not aligned;
when the fault code is a second code, the fault type is that the gear teeth of the speed reducer are broken;
when the fault code is a third code, the fault type is slewing bearing misalignment;
and when the fault code is a fourth code, the fault type is that the rotary support gear teeth break.
4. The crane slewing mechanism fault monitoring method as claimed in claim 1, wherein said collecting a set of fault samples of the slewing mechanism comprises:
collecting a plurality of vibration signal sources;
carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and taking the plurality of vibration signal sources subjected to noise reduction as the fault sample set of the slewing mechanism.
5. A fault monitoring device for a slewing mechanism of a crane, the device comprising: the device comprises a swing mechanism, an eddy current sensor and a classifier, wherein the swing mechanism is connected with the eddy current sensor, and the eddy current sensor is connected with the classifier;
the eddy current sensor is used for acquiring a current vibration signal source of the slewing mechanism;
the classifier is used for collecting a fault sample set of the slewing mechanism and inputting the fault sample set into an information entropy algorithm to obtain an information entropy characteristic value range; the information entropy characteristic value range comprises a plurality of fault state ranges, different fault state ranges correspond to different cubes, and the cubes take singular spectrum information entropy as a time domain, take power spectrum information entropy as a frequency domain and take wavelet energy spectrum entropy as a time-frequency domain; judging whether the current vibration signal source falls into the information entropy characteristic value range or not; the judging whether the current vibration signal source falls into the information entropy characteristic value range or not comprises the following steps: judging whether the current vibration signal source falls into the cube in the fault state range; if the fault code falls into the category, the category corresponding to the category fault state range is used as the fault code; determining a fault type according to the fault code; and determining the fault of the slewing mechanism according to the fault type.
6. The crane slewing mechanism fault monitoring device of claim 5, wherein the classifier is specifically configured to:
judging whether the current vibration signal source falls into the cube of a first type fault state range in the multi-type fault state ranges;
and if the fault codes fall into the first type of fault state range, taking the category corresponding to the first type of fault state range as the fault codes.
7. The crane slewing mechanism fault monitoring device of claim 5, wherein the classifier is specifically configured to:
when the fault code is a first code, the fault type is that the speed reducer is not aligned;
when the fault code is a second code, the fault type is that the gear teeth of the speed reducer are broken;
when the fault code is a third code, the fault type is slewing bearing misalignment;
and when the fault code is a fourth code, the fault type is that the rotary support gear teeth break.
8. The crane slewing mechanism fault monitoring device of claim 5, wherein the classifier is specifically configured to:
collecting a plurality of vibration signal sources;
carrying out noise reduction processing on the plurality of vibration signal sources to obtain a plurality of vibration signal sources subjected to noise reduction;
and taking the plurality of vibration signal sources subjected to noise reduction as the fault sample set of the slewing mechanism.
9. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-4 when executing the computer program.
10. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010623925.7A CN111717800B (en) | 2020-06-30 | 2020-06-30 | Fault monitoring method and device for slewing mechanism of crane |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010623925.7A CN111717800B (en) | 2020-06-30 | 2020-06-30 | Fault monitoring method and device for slewing mechanism of crane |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111717800A CN111717800A (en) | 2020-09-29 |
| CN111717800B true CN111717800B (en) | 2022-05-17 |
Family
ID=72571058
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010623925.7A Active CN111717800B (en) | 2020-06-30 | 2020-06-30 | Fault monitoring method and device for slewing mechanism of crane |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111717800B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115448188B (en) * | 2022-08-25 | 2024-05-14 | 杭州大杰智能传动科技有限公司 | Comprehensive monitoring control system for slewing mechanism of tower crane |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107628539B (en) * | 2017-11-11 | 2019-02-19 | 福建省特种设备检验研究院 | The runabout crane fault diagnosis system of case-based reasioning |
| CN109297699A (en) * | 2018-12-07 | 2019-02-01 | 中南大学 | A kind of intelligent rotating mechanical failure diagnostic method of mixed decomposition and extraction |
| CN111307438B (en) * | 2020-03-11 | 2021-09-14 | 上海纵行实业有限公司 | Rotary machine vibration fault diagnosis method and system based on information entropy |
-
2020
- 2020-06-30 CN CN202010623925.7A patent/CN111717800B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111717800A (en) | 2020-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102466566B (en) | Power equipment abnormality detection device and detection method thereof | |
| Mortada et al. | Diagnosis of rotor bearings using logical analysis of data | |
| CN111717800B (en) | Fault monitoring method and device for slewing mechanism of crane | |
| JP2017129379A (en) | Rotary machine abnormality detection device and method and rotary machine | |
| CN110068435B (en) | Vibration analysis system and method | |
| WO2007130380A2 (en) | Rotating bearing analysis and monitoring system | |
| JP6615963B1 (en) | Abnormal sign diagnostic apparatus and abnormal sign diagnostic method | |
| CN102854006A (en) | Severity analysis apparatus and method for shafts of rotating machinery | |
| CN110068789B (en) | Method and circuit for monitoring service life of electric energy meter | |
| CN115828145B (en) | Online monitoring method, system and medium for electronic equipment | |
| JP2018059821A (en) | Rotary machine abnormality detecting device and method, and rotary machine | |
| CN115524002B (en) | Operation state early warning method, system and storage medium of power plant rotating equipment | |
| EP2110778B1 (en) | Determining corrective actions using a geometrically-based determination of sufficient confidence | |
| CN116050930A (en) | Monitoring disc system, monitoring disc method, storage medium and electronic equipment | |
| CN111831517A (en) | Data processing method and device, electronic equipment and storage medium | |
| JP7383207B1 (en) | Motor abnormality determination system | |
| CN108683662A (en) | Separate unit online equipment methods of risk assessment and system | |
| Medina et al. | Gearbox fault classification using dictionary sparse based representations of vibration signals | |
| CN106368972B (en) | A kind of fan-status monitoring method, device and web camera | |
| JP6869156B2 (en) | Status monitoring device and status monitoring method | |
| CN115314305A (en) | Network security situation sensing system and method based on artificial intelligence | |
| US20180151043A1 (en) | Interactive alerting method and system for an electronic apparatus | |
| Xue et al. | A new mechanical compound fault diagnosis method based on morphological filter and RLS | |
| US20230134638A1 (en) | Method and systems for vibration-based status monitoring of electric rotary machines | |
| CN117742303B (en) | Production automation equipment detection method, system and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |