CN115571585A - Belt conveyor intelligent diagnosis method based on characteristic value alarm - Google Patents
Belt conveyor intelligent diagnosis method based on characteristic value alarm Download PDFInfo
- Publication number
- CN115571585A CN115571585A CN202211462754.XA CN202211462754A CN115571585A CN 115571585 A CN115571585 A CN 115571585A CN 202211462754 A CN202211462754 A CN 202211462754A CN 115571585 A CN115571585 A CN 115571585A
- Authority
- CN
- China
- Prior art keywords
- frequency
- fault
- equipment
- alarm
- amplitude
- 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.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G43/00—Control devices, e.g. for safety, warning or fault-correcting
- B65G43/02—Control devices, e.g. for safety, warning or fault-correcting detecting dangerous physical condition of load carriers, e.g. for interrupting the drive in the event of overheating
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G15/00—Conveyors having endless load-conveying surfaces, i.e. belts and like continuous members, to which tractive effort is transmitted by means other than endless driving elements of similar configuration
- B65G15/30—Belts or like endless load-carriers
- B65G15/32—Belts or like endless load-carriers made of rubber or plastics
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G45/00—Lubricating, cleaning, or clearing devices
- B65G45/02—Lubricating devices
- B65G45/04—Lubricating devices for rollers
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G2203/00—Indexing code relating to control or detection of the articles or the load carriers during conveying
- B65G2203/02—Control or detection
- B65G2203/0266—Control or detection relating to the load carrier(s)
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a belt conveyor intelligent diagnosis method based on characteristic value alarming, which comprises the following steps: searching the frequency of the equipment, comparing the amplitude, and setting an alarm line; if the diagnosis amplitude value belongs to the equipment fault type, entering an equipment fault type diagnosis process if the diagnosis amplitude value belongs to the equipment fault type, and otherwise, entering a step S3; and if the frequency of the diagnostic equipment belongs to the bearing fault type, entering a bearing fault type diagnostic process if the frequency of the diagnostic equipment belongs to the bearing fault type, and if the frequency of the diagnostic equipment does not belong to the bearing fault type, judging that the equipment has no fault. The invention has the beneficial effects that: the problem that the automatic alarm of the equipment only represents the integral running state of the equipment is solved, and the intelligent alarm can automatically position the fault according to the unique fault characteristic frequency of the equipment component; the problem of alarm of some important parts in a fault building is avoided, and the alarm accuracy of the system is improved; meanwhile, the working efficiency of personnel performing fault diagnosis through frequency spectrum is improved.
Description
Technical Field
The invention belongs to the technical field of mechanical fault diagnosis, and particularly relates to a belt conveyor intelligent diagnosis method based on characteristic value alarming.
Background
In the prior art, mechanical fault diagnosis automatic alarm is still in an integral alarm state at present, and only total value alarm and effective value alarm are distinguished, the alarm principle is based on that a time domain waveform computer acquired by a sensor calculates all frequencies in whole data to obtain an integral characteristic quantity, and the characteristic quantity only can represent an integral state condition of equipment. The first case when there is a problem with the device: since this particular quantity is calculated from the entire piece of data with some rare faults and does not trigger an alarm of the system; in the second case: the system triggers an alarm, but only prompts that the equipment is in a bad state, the regulation severity degree of the equipment cannot be accurately reflected, and the fault position and the fault component of the equipment cannot be reflected.
Disclosure of Invention
In view of the above, the present invention is directed to a belt conveyor intelligent diagnosis method based on a characteristic value alarm, so as to solve the above-mentioned deficiencies of the prior art.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
a belt conveyor intelligent diagnosis method based on characteristic value alarming comprises the following steps:
s1, searching equipment frequency and comparing amplitude, and setting an alarm line;
s2, diagnosing whether the amplitude belongs to the equipment fault type or not according to the alarm line, if so, entering an equipment fault type diagnosis process, and if not, entering a step S3;
and S3, diagnosing whether the equipment frequency belongs to the bearing fault type according to the alarm line, if so, entering a bearing fault type diagnosis process, and if not, judging that the equipment has no fault.
Further, the setting of the alarm line in step S1 includes the following steps:
s11, according to frequency groups in historical data frequency spectrums of the equipment database, a plurality of frequency groups with the maximum amplitude are selected;
s12, calculating the average value of a plurality of maximum amplitude values
Respectively set normal, early warning and alarmFour grades of risk;
s13, setting
In order to be normal, the operation of the device is carried out,
in order to provide an early warning,
an alarm is given out, and the alarm is given out,
setting the amplitude of the side frequency for danger
Alarm at center frequency amplitude, number of side frequencies
And alarming when the average of the maximum side frequency is added with 4, and alarming when the side frequency is copied to be more than or equal to 50% of the central frequency amplitude.
Further, the device fault type diagnosis process in step S2 includes the following steps:
s21, judging whether the amplitude value of 1X exceeds an alarm line, if so, entering a step S22, and if not, entering a step S25;
s22, judging whether the harmonic frequency amplitude exceeds an alarm line, if so, entering a step S23, and if not, judging that the output equipment has an unbalance fault;
s23, judging whether the 2X frequency amplitude is larger than or equal to 1X, if so, judging that the output equipment has a centering fault, otherwise, entering the step S24;
s24, judging whether the fundamental frequency and the harmonic frequency thereof have polar passing frequency sidebands, if so, judging that the rotor bars of the output equipment break and fail, and if not, judging that the output equipment has a structure loosening fault;
s25, judging whether the 2X frequency amplitude exceeds an alarm line, if so, judging that the output equipment has a centering fault, otherwise, entering a step S26;
s26, judging whether the passing frequency of the rotor bars exceeds an alarm line, the harmonic frequency exists and the frequency is 2XFL, if so, judging that the output equipment has an electrical fault, otherwise, entering the step S3.
Further, the bearing fault type diagnosis process in step S3 includes the steps of:
s31, judging whether an energy cluster exists in the high-frequency range of 2000-8000Hz or not and whether the overall total value exceeds an alarm line or not, if so, simultaneously entering the steps S32, S33, S34 and S35, and if not, judging that the equipment has no fault;
s32, judging whether the characteristic frequency error of the bearing outer ring fault is +/-4% or whether the harmonic frequency amplitude exceeds an alarm line, if so, outputting the bearing outer ring fault, otherwise, outputting poor lubrication of the bearing;
s33, judging whether the characteristic frequency or harmonic frequency amplitude of the inner ring fault exceeds an alarm line and a frequency conversion sideband exists, if so, outputting the bearing inner ring fault, and if not, outputting poor lubrication of the bearing;
s34, judging whether the fault characteristic frequency or harmonic frequency amplitude of the retainer exceeds an alarm line, if so, outputting a fault of a bearing rolling body, otherwise, outputting poor lubrication of the bearing;
s35, judging whether the characteristic frequency of the fault of the retainer or the harmonic frequency amplitude of the characteristic frequency exceeds an alarm line, if so, judging that the retainer of the output bearing has a fault, and if not, judging that the lubrication of the output bearing is poor.
Compared with the prior art, the belt conveyor intelligent diagnosis method based on characteristic value alarming has the following advantages:
the belt conveyor intelligent diagnosis method based on characteristic value alarming solves the problems that automatic alarming of equipment only represents the integral operation state of the equipment, field workers often do not have deep training for analyzing the equipment according to frequency spectrum, and the fault position of the equipment cannot be known in alarming information, so that the effect of an intelligent diagnosis system is reduced; the intelligent alarm can automatically locate the fault according to the unique fault characteristic frequency of the equipment component; the problem of alarm of a fault building of some important parts is avoided, and the alarm accuracy of the system is improved; meanwhile, the working efficiency of personnel performing fault diagnosis through frequency spectrum is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation of the invention. In the drawings:
fig. 1 is a schematic view of an overall method diagnosis process according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate a number of the indicated technical features. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, 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 meaning of the above terms in the present invention can be understood by those of ordinary skill in the art through specific situations.
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
As shown in fig. 1, a belt conveyor intelligent diagnosis method based on characteristic value alarm includes the following steps:
s1, searching equipment frequency and comparing amplitude, and setting an alarm line;
s2, diagnosing whether the amplitude belongs to the equipment fault type or not according to the alarm line, if so, entering an equipment fault type diagnosis process, and if not, entering a step S3;
and S3, diagnosing whether the equipment frequency belongs to the bearing fault type according to the alarm line, if so, entering a bearing fault type diagnosis process, and if not, judging that the equipment has no fault.
The invention solves the problems that the automatic alarm of the equipment only represents the integral operation state of the equipment, field workers often do not have deep training for analyzing the equipment according to frequency spectrum and can not know the fault position of the equipment in alarm information, thereby reducing the effect of an intelligent diagnosis system; the intelligent alarm can automatically locate the fault according to the unique fault characteristic frequency of the equipment component; the problem of alarm of some important parts in a fault building is avoided, and the alarm accuracy of the system is improved; meanwhile, the working efficiency of personnel performing fault diagnosis through frequency spectrum is improved.
In a preferred embodiment of the present invention, the setting of the alarm line in step S1 includes the steps of:
s11, according to frequency groups in historical data frequency spectrums of the equipment database, a plurality of frequency groups with the maximum amplitude are selected;
s12, calculating the average value of a plurality of maximum amplitude values
Setting four grades of normal, early warning, alarming and danger respectively;
s13, setting
In order to be normal, the operation of the device is carried out,
in order to provide an early warning,
an alarm is given out, and the alarm is given out,
setting the amplitude of the side frequency for danger
Alarm at central frequency amplitude, when number of side frequency
And alarming when the average of the maximum side frequency is added with 4, and alarming when the side frequency is copied to be more than or equal to 50% of the central frequency amplitude.
In a preferred embodiment of the present invention, the device fault type diagnosis process in step S2 includes the following steps:
s21, judging whether the amplitude value of 1X exceeds an alarm line, if so, entering a step S22, and if not, entering a step S25;
s22, judging whether the harmonic frequency amplitude exceeds an alarm line, if so, entering a step S23, and if not, judging that the output equipment has an unbalance fault;
s23, judging whether the 2X frequency amplitude is larger than or equal to 1X, if so, judging that the output equipment has a centering fault, otherwise, entering the step S24;
s24, judging whether the fundamental frequency and the harmonic frequency thereof have polar passing frequency sidebands, if so, judging that the rotor bars of the output equipment break and fail, and if not, judging that the output equipment has a structure loosening fault;
s25, judging whether the 2X frequency amplitude exceeds an alarm line, if so, judging that the output equipment has a centering fault, otherwise, entering the step S26;
s26, judging whether the passing frequency of the rotor bars exceeds an alarm line, the harmonic frequency exists and the frequency is 2XFL, if so, judging that the output equipment has an electrical fault, otherwise, entering the step S3.
In a preferred embodiment of the present invention, the bearing fault type diagnosis process in step S3 includes the steps of:
s31, judging whether an energy cluster exists in the high-frequency range of 2000-8000Hz or not and whether the overall total value exceeds an alarm line (the total value calculation does not contain harmonic frequency components related to the masterwork), if so, simultaneously entering the steps S32, S33, S34 and S35, and if not, judging that the equipment has no fault;
s32, judging whether the characteristic frequency error of the bearing outer ring fault is +/-4% or whether the harmonic frequency amplitude exceeds an alarm line, if so, outputting the bearing outer ring fault, otherwise, outputting poor lubrication of the bearing;
s33, judging whether the characteristic frequency of the inner ring fault or the harmonic frequency amplitude exceeds an alarm line and a frequency conversion sideband exists, if so, outputting the bearing inner ring fault, otherwise, outputting poor lubrication of the bearing;
s34, judging whether the fault characteristic frequency or harmonic frequency amplitude of the retainer exceeds an alarm line, if so, outputting a fault of a bearing rolling body, and if not, outputting poor lubrication of the bearing;
s35, judging whether the characteristic frequency of the fault of the retainer or the harmonic frequency amplitude of the fault exceeds an alarm line, if so, judging that the retainer of the output bearing has a fault, and if not, judging that the lubrication of the output bearing is poor.
Example 1
At present, the main principle of detecting equipment through vibration signal acquisition is that the amplitude of vibration of equipment (components) is higher than that of normal equipment when a fault occurs, and each component of the equipment has unique fault characteristic frequency, so that the amplitude and the structural change of the fault characteristic frequency of the component of the equipment are observed through frequency spectrum to judge the health condition of the equipment. It is particularly important to obtain the fault signature of the equipment components for each frequency and to identify the amplitude of these frequencies in the spectrum, as well as the spectral structure variations.
Setting and triggering conditions for automatic generation of alarm lines:
assuming that the device is operating normally for a sufficient period of time, the alarm line is set based on the frequency of the corresponding frequency group in the historical data spectrum (e.g. gear damage with a sideband of faulty axle standard, the meshing frequency plus the sideband frequency is a frequency group), and the alarm line is takenSeveral frequency groups (e.g. 10, 50, 100 groups) of maximum amplitude are obtained, and average value of several maximum amplitudes is obtained
Four grades of normal, early warning, alarming and danger are respectively set,
is normal,
Early warning,
An alarm is given,
And (4) danger. Simultaneous side-frequency amplitude
Alarm at central frequency amplitude, number of side frequencies
And adding 4 to the maximum side frequency average number for alarming. And when the sideband frequency replication is greater than or equal to 50% of the central frequency amplitude, an alarm is triggered.
The belt conveyor driving system can obtain information useful for vibration diagnosis in actual production, and comprises the following steps: motor stage number (converted into frequency output); the number of motor rotor bars (rotor bar passing frequency); the motor operating current frequency; motor speed (frequency); a slip frequency; motor bearing model, obtainable by the following bearing model:
1. the outer ring fault passing frequency of the characteristic coefficient of the outer ring of the bearing;
2. the failure passing frequency of the inner ring of the bearing inner ring characteristic coefficient;
3. bearing rolling element characteristic coefficient rolling element fault passing frequency;
4. bearing cage characteristic coefficient rolling element fault passing frequency.
The number of shafts of the speed reducer; the number of teeth of each level of the speed reducer (converted into meshing frequency output); the speed reducer speed ratio and the speed reducer output rotating speed (converted into the speed reducer input and output rotating frequency); the models (characteristic coefficients) of all levels of bearings of the speed reducer are the same as the models of bearings of the motor; the belt pulley drives the rotating speed of the roller; drive roller diameter (circumference); a belt length; the belt passing frequency can be obtained by the rotating speed of the belt pulley driving roller, the diameter (perimeter) of the driving roller and the length of the belt.
Detailed description:
1X: frequency conversion of a device driving part; 2X, 3X, 4X \8230: harmonic frequencies of the device driver component frequency transitions; LF: the current frequency at which the device operates normally; PPF: pole pass frequency; RBF: rotor bar pass frequency;
: outer ring failure frequency;
: inner ring failure frequency;
: frequency of rolling element failure;
: cage failure frequency.
1) Main fault frequency of motor and calculation method
Loosening: 1XHz is elevated with 2X, 3X 8230harmonic frequencies;
imbalance: 1XHz is increased and is much higher than 2X, 3X \8230 (judging basis is that 1X is more than 1.5 times 2X, 3X \8230);
centering: 1X and 2X are increased, and the amplitude of 2X is more than or equal to 70 percent of the amplitude of 1X;
and (3) breaking the rotor bars: 1X, 2X, 3X \8230, with polar pass frequency;
rotor bar loosening (electrical failure): 1X (number of rotor cages) with sidebands of LF X2 Hz;
bearing failure, see the diagnostic flow chart.
2) Main fault frequency of speed reducer and calculation method
Poor meshing and abrasion of gears at all stages:
bearing failure, see the diagnostic flow chart.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (7)
1. A belt conveyor intelligent diagnosis method based on characteristic value alarming is characterized in that: the method comprises the following steps:
s1, searching equipment frequency and comparing amplitude, and setting an alarm line;
s2, diagnosing whether the amplitude belongs to the equipment fault type or not according to the alarm line, if so, entering an equipment fault type diagnosis process, and if not, entering a step S3;
and S3, diagnosing whether the equipment frequency belongs to the bearing fault type according to the alarm line, if so, entering a bearing fault type diagnosis process, and if not, judging that the equipment has no fault.
2. The belt conveyor intelligent diagnosis method based on characteristic value alarming as claimed in claim 1, is characterized in that: the alarm line setting in step S1 includes the steps of:
s11, according to frequency groups in historical data frequency spectrums of the equipment database, a plurality of frequency groups with the maximum amplitude are selected;
s12, calculating the average value of a plurality of maximum amplitude values
Setting four grades of normal, early warning, alarming and danger respectively;
s13, setting
In order to be normal, the operation of the device is carried out,
in order to provide an early warning,
an alarm is given out, and the alarm is given out,
setting the amplitude of the side frequency for danger
Alarm at central frequency amplitude, when number of side frequency
And alarming when the average of the maximum sideband number is added with 4, and alarming when the sideband frequency is copied to be more than or equal to 50 percent of the central frequency amplitude.
3. The intelligent belt conveyor diagnosis method based on characteristic value alarm as claimed in claim 2, wherein: the device fault type diagnosis process in step S2 includes the steps of:
s21, judging whether the amplitude value of 1X exceeds an alarm line, if so, entering a step S22, and if not, entering a step S25;
s22, judging whether the harmonic frequency amplitude exceeds an alarm line, if so, entering a step S23, and if not, judging that the output equipment has an unbalanced fault;
s23, judging whether the 2X frequency amplitude is larger than or equal to 1X, if so, judging that the output equipment has a centering fault, otherwise, entering the step S24;
s24, judging whether the fundamental frequency and the harmonic frequency thereof have polar passing frequency sidebands, if so, judging that the rotor bars of the output equipment break and fail, and if not, judging that the output equipment has a structure loosening fault;
s25, judging whether the 2X frequency amplitude exceeds an alarm line, if so, judging that the output equipment has a centering fault, otherwise, entering the step S26;
s26, judging whether the passing frequency of the rotor bars exceeds an alarm line, the harmonic frequency exists and the frequency is 2XFL, if so, judging that the output equipment has an electrical fault, otherwise, entering the step S3.
4. The belt conveyor intelligent diagnosis method based on characteristic value alarming as claimed in claim 2, characterized in that: the bearing fault type diagnosis process in step S3 includes the steps of:
s31, judging whether an energy cluster exists in the high-frequency range of 2000-8000Hz and whether the overall total value exceeds an alarm line, if so, simultaneously entering the steps S32, S33, S34 and S35, and if not, judging that the equipment has no fault;
s32, judging whether the characteristic frequency error of the bearing outer ring fault is +/-4% or whether the harmonic frequency amplitude exceeds an alarm line, if so, outputting the bearing outer ring fault, otherwise, outputting poor lubrication of the bearing;
s33, judging whether the characteristic frequency or harmonic frequency amplitude of the inner ring fault exceeds an alarm line and a frequency conversion sideband exists, if so, outputting the bearing inner ring fault, and if not, outputting poor lubrication of the bearing;
s34, judging whether the fault characteristic frequency or harmonic frequency amplitude of the retainer exceeds an alarm line, if so, outputting a fault of a bearing rolling body, and if not, outputting poor lubrication of the bearing;
s35, judging whether the characteristic frequency of the fault of the retainer or the harmonic frequency amplitude of the fault exceeds an alarm line, if so, judging that the retainer of the output bearing has a fault, and if not, judging that the lubrication of the output bearing is poor.
5. An electronic device comprising a processor and a memory communicatively coupled to the processor and configured to store processor-executable instructions, wherein: the processor is used for executing the belt conveyor intelligent diagnosis method based on the characteristic value alarm in any one of claims 1-4.
6. A server, characterized by: the intelligent belt conveyor diagnosis method based on the characteristic value alarm comprises at least one processor and a memory which is in communication connection with the processor, wherein the memory stores instructions which can be executed by the at least one processor, and the instructions are executed by the processor, so that the at least one processor executes the intelligent belt conveyor diagnosis method based on the characteristic value alarm according to any one of claims 1-4.
7. A computer-readable storage medium storing a computer program, characterized in that: the computer program is used for realizing the intelligent belt conveyor diagnosis method based on characteristic value alarm in any one of claims 1-4 when being executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211462754.XA CN115571585B (en) | 2022-11-22 | 2022-11-22 | Belt conveyor intelligent diagnosis method based on characteristic value alarm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211462754.XA CN115571585B (en) | 2022-11-22 | 2022-11-22 | Belt conveyor intelligent diagnosis method based on characteristic value alarm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115571585A true CN115571585A (en) | 2023-01-06 |
| CN115571585B CN115571585B (en) | 2023-03-07 |
Family
ID=84588040
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211462754.XA Active CN115571585B (en) | 2022-11-22 | 2022-11-22 | Belt conveyor intelligent diagnosis method based on characteristic value alarm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115571585B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110779716A (en) * | 2019-11-01 | 2020-02-11 | 苏州德姆斯信息技术有限公司 | Embedded mechanical fault intelligent diagnosis equipment and diagnosis method |
| JP2020199017A (en) * | 2019-06-07 | 2020-12-17 | 株式会社ユニバーサルエンターテインメント | Game machine |
| CN113834657A (en) * | 2021-09-24 | 2021-12-24 | 北京航空航天大学 | Bearing fault early warning and diagnosis method based on improved MSET and frequency spectrum characteristics |
-
2022
- 2022-11-22 CN CN202211462754.XA patent/CN115571585B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2020199017A (en) * | 2019-06-07 | 2020-12-17 | 株式会社ユニバーサルエンターテインメント | Game machine |
| CN110779716A (en) * | 2019-11-01 | 2020-02-11 | 苏州德姆斯信息技术有限公司 | Embedded mechanical fault intelligent diagnosis equipment and diagnosis method |
| CN113834657A (en) * | 2021-09-24 | 2021-12-24 | 北京航空航天大学 | Bearing fault early warning and diagnosis method based on improved MSET and frequency spectrum characteristics |
Non-Patent Citations (1)
| Title |
|---|
| 张建文等: "基于证据理论的高压防爆电机转子故障在线诊断方法研究", 《华北电力大学学报(自然科学版)》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115571585B (en) | 2023-03-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9618037B2 (en) | Apparatus and method for identifying health indicators for rolling element bearings | |
| EP2585716B1 (en) | A method for performing condition monitoring in a wind farm | |
| US10495693B2 (en) | Wind turbine fault detection using acoustic, vibration, and electrical signals | |
| US11441940B2 (en) | Condition monitoring apparatus, condition monitoring system, and condition monitoring method | |
| EP3514376B1 (en) | System and method for monitoring a wind turbine pitch bearing | |
| CN110687896A (en) | Fault diagnosis method, device, equipment and readable medium | |
| CN103912448B (en) | A kind of regional wind power power of the assembling unit characteristic monitoring method | |
| CN110469462A (en) | A kind of Wind turbines intelligent condition monitoring system based on multi-template | |
| KR20190081933A (en) | Method for sensing and diagnosing abnormality of manufacture equipment | |
| CN110231166A (en) | The intelligent diagnosing method of three-level uni-drive gear box under a kind of variable speed operating condition | |
| CN213516341U (en) | Online vibration data monitoring system for wind turbine generator fault diagnosis | |
| EP3951167B1 (en) | Condition monitoring system | |
| CN115571585B (en) | Belt conveyor intelligent diagnosis method based on characteristic value alarm | |
| JP2017207435A (en) | Abnormality diagnostic method | |
| JP6824076B2 (en) | Condition monitoring system and wind power generator | |
| CN113280910A (en) | Real-time monitoring method and system for long product production line equipment | |
| JP6869156B2 (en) | Status monitoring device and status monitoring method | |
| CN108520093B (en) | Mechanical equipment fault diagnosis method and device based on knowledge base | |
| CN115962100A (en) | Wind turbine generator system state monitoring system | |
| JP7098399B2 (en) | Status monitoring device and status monitoring method | |
| CN112580586A (en) | Method and device for detecting bolt fault and computer readable storage medium | |
| CN112761900A (en) | Device and method for monitoring vibration reduction effect of elastic support of wind turbine generator gearbox | |
| CN113339280B (en) | Offshore centrifugal pump fault diagnosis method and system | |
| WO2019044575A1 (en) | State-monitoring device and state-monitoring method | |
| JP2017219325A (en) | Abnormality diagnostic device and abnormality diagnostic method of rotary part |
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 |