CN117906946A - Gear fault alarm method based on multi-scale peak searching - Google Patents
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Abstract
The application provides a gear fault alarm method based on multi-scale peak finding, which relates to the technical field of gear box alarm, and comprises the steps of extracting a peak value sequence of a vibration signal of target detection equipment according to a multi-scale peak finding algorithm, removing a linear trend of the vibration time domain signal, determining the maximum window width of the multi-scale peak finding algorithm, constructing a scale matrix and extracting the peak value sequence; extracting key features corresponding to the target detection equipment according to the peak value sequence, wherein the key features comprise peak value energy and peak value frequency; and carrying out fault alarm according to the key characteristics and the proximity degree of the required frequency of the target detection equipment. According to the application, the fault characteristics of the gear faults can be effectively and accurately extracted by the alarm method, so that the accurate alarm of the gear box faults is realized.
Description
Technical Field
The invention relates to the technical field of gear box alarm, in particular to a gear fault alarm method based on multi-scale peak finding.
Background
The steel industry has complex flow and various equipment, a large number of equipment is provided with speed-increasing or speed-reducing gearboxes for changing the rotation speed and torque output by a motor, and serious faults of gearbox fault bearings or gears are most likely to cause clamping stagnation of the whole gearbox, and the production line is stopped and even a serious safety accident is caused.
The main component of the vibration time domain signal is usually stable high-frequency gear meshing frequency under the normal working state of the gear box, when the gear box has a bearing or gear fault, the vibration time domain waveform has obvious modulating phenomenon of the fault source frequency signal, and along with the development of the fault, the signal modulating phenomenon becomes more and more obvious.
At present, an alarm threshold value is set for the fault alarm of a gear box, and a fault source is obtained through analysis and analysis from the vibration signal frequency domain of the gear box, so that fault alarm is carried out, but the deviation of the vibration signal of the gear box is large, so that the alarm mode cannot accurately alarm.
Disclosure of Invention
The application provides a gear fault alarm method based on multi-scale peak finding, which aims to solve the problem of inaccurate fault alarm for a gear box in the prior art.
The alarm method comprises the following steps:
Extracting a peak value sequence of a vibration signal of the target detection equipment according to a multi-scale peak finding algorithm, wherein the peak value sequence comprises the steps of removing a linear trend of a vibration time domain signal, determining the maximum window width of the multi-scale peak finding algorithm, constructing a scale matrix and extracting the peak value sequence;
Extracting key features corresponding to the target detection equipment according to the peak value sequence, wherein the key features comprise peak value energy and peak value frequency;
and carrying out fault alarm according to the key characteristics and the proximity degree of the required frequency of the target detection equipment.
Preferably, the step of removing the linear trend of the vibration signal includes:
setting an n-degree polynomial of the vibration signal to obtain a curve about the n-degree polynomial;
Fitting the curve, and enabling the residual error term of the curve to be the minimum value to obtain the trend term of the vibration signal.
Preferably, the step of determining the maximum window width of the multi-scale peaking algorithm includes:
Acquiring the minimum frequency of the modulation frequency of the vibration signal;
and determining the maximum window width according to the minimum frequency, the sampling frequency of the vibration signal and the length of the vibration signal.
Preferably, the step of constructing the scale matrix includes:
Constructing an original scale matrix, wherein the original scale matrix is a matrix with 1 element;
Updating the original scale matrix by utilizing a multi-scale peak searching updating formula to obtain a target scale matrix; the step of updating the original scale matrix by using the multi-scale peak searching updating formula comprises the steps of judging all elements in the original scale matrix by using the multi-scale peak searching updating formula and setting the elements meeting the multi-scale peak searching updating formula to 0.
Preferably, the step of extracting the peak sequence includes:
Summing the target scale matrix row by row to obtain a first matrix vector;
Acquiring an index of a minimum value of an element in the first matrix vector;
summing the rows from the 0 th row to the minimum value element in the target scale matrix row by row to obtain a second matrix vector;
and obtaining an index sequence with elements equal to 0 in the second matrix vector, and obtaining the peak value sequence according to the index sequence.
Preferably, the step of extracting key features corresponding to the target detection device according to the peak sequence includes:
calculating the peak energy of the vibration signal according to the peak sequence;
Calculating to obtain the peak frequency of the vibration signal according to the sampling frequency of the vibration signal, the length of peak training and the length of the vibration signal;
And calculating the absolute value of the difference value between the peak frequency and the required frequency to obtain the proximity degree of the peak frequency and the required frequency.
Preferably, the step of performing fault alerting according to the key feature and proximity to the desired frequency of the object detection device includes:
Setting a plurality of energy thresholds corresponding to the target detection equipment, wherein the energy thresholds comprise a first energy threshold and a second energy threshold, and the first energy threshold is smaller than the second energy threshold;
setting a proximity threshold corresponding to the target detection device;
And performing fault alarm according to a plurality of the energy thresholds, the proximity thresholds, the peak energy and the proximity.
Preferably, the step of performing fault alarm according to a plurality of the energy thresholds, the proximity thresholds, the peak energy and the proximity includes:
when the proximity degree is larger than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault category of slightly bad gear engagement on the target detection equipment;
And when the proximity degree is larger than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault alarming is that the gear engagement is seriously bad.
Preferably, the step of performing fault alarm according to a plurality of the energy thresholds, the proximity thresholds, the peak energy and the proximity further includes:
When the proximity degree is smaller than the proximity degree threshold value and the peak energy is smaller than the first energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is slightly fault;
when the proximity degree is smaller than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault class of the target detection equipment being a moderate fault of the gear tooth surface;
and when the proximity degree is smaller than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is seriously fault.
Preferably, the step of extracting the peak sequence of the vibration signal of the target detection device according to the multi-scale peak finding algorithm further includes:
and acquiring time series data of the running state of the target detection equipment, wherein the time series data comprises the vibration signal of the target detection equipment.
The application provides a gear fault alarm method based on multi-scale peak finding, which comprises the steps of extracting a peak value sequence of a vibration signal of target detection equipment according to a multi-scale peak finding algorithm, removing a linear trend of the vibration time domain signal, determining the maximum window width of the multi-scale peak finding algorithm, constructing a scale matrix and extracting the peak value sequence; extracting key features corresponding to the target detection equipment according to the peak value sequence, wherein the key features comprise peak value energy and peak value frequency; and carrying out fault alarm according to the key characteristics and the proximity degree of the required frequency of the target detection equipment. According to the application, the fault characteristics of the gear faults can be effectively and accurately extracted by the alarm method, so that the accurate alarm of the gear box faults is realized.
Drawings
In order to more clearly illustrate the technical solution of the present application, the drawings that are needed in the embodiments will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a gear failure alarm method based on multi-scale peak finding according to the present application;
FIG. 2 is a flowchart showing steps for removing the linear trend of vibration signals in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 3 is a flowchart of a step of determining a maximum window width of a multi-scale peak finding algorithm in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 4 is a flow chart of constructing a scale matrix in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 5 is a flow chart of peak sequence extraction in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 6 is a flowchart showing the steps for extracting key features in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 7 is a flow chart of a fault alarm in a gear fault alarm method based on multi-scale peak finding according to the present application;
FIG. 8 is a diagram of gear normal peak extraction and eigenvalue;
FIG. 9 is a graph showing peak extraction and eigenvalues for gear failure degradation;
FIG. 10 is a graph of gear failure degradation severity peak extraction and eigenvalue;
FIG. 11 is a graph of peak energy versus peak frequency;
FIG. 12 is a schematic diagram of peak energy versus peak frequency proximity alarm thresholds.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
FIG. 1 is a flow chart of a gear fault alarm method based on multi-scale peak finding.
Referring to fig. 1, the present embodiment provides a gear fault alarm method based on multi-scale peak searching, which includes:
S100, extracting a peak value sequence of a vibration signal of a target detection device according to a multi-scale peak finding algorithm, wherein the peak value sequence comprises removing a linear trend of a vibration time domain signal, determining a maximum window width of the multi-scale peak finding algorithm, constructing a scale matrix and extracting the peak value sequence, and particularly, in the embodiment, the vibration signal is from time sequence data of the target detection device, so that the time sequence data of an operation state of the target detection device needs to be acquired from the target detection device before the peak value sequence is extracted.
In the present embodiment, the sequence of the vibration signals is set as。
FIG. 2 is a flowchart showing the steps of removing the linear trend of the vibration signal in the gear fault alarm method based on multi-scale peak finding according to the present application.
As can be seen from fig. 2, further, in some embodiments, the step of removing the linear trend of the vibration signal includes:
s111, setting an n-degree polynomial of the vibration signal, and obtaining a curve about the n-degree polynomial;
And S112, fitting the curve, and enabling the residual error term of the curve to be the minimum value to obtain the trend term of the vibration signal.
Specifically, in this embodiment, the method for removing the signal trend term is not required, and the trend in the vibration signal can be effectively removed, and in this embodiment, the signal trend is removed by using a least square fitting polynomial:
Let the polynomial of degree n be:
；
Fitting a curve to The curve with the minimum value is the vibration signal trend term:
；
in this embodiment, the polynomial degree Taking 1.
FIG. 3 is a flowchart of a step of determining a maximum window width of a multi-scale peak finding algorithm in a gear fault alarm method based on multi-scale peak finding according to the present application.
As can be seen from fig. 3, further, in some embodiments, the step of determining the maximum window width of the multi-scale peaking algorithm includes:
S121, acquiring the minimum frequency of the modulation frequency of the vibration signal;
s122, determining the maximum window width according to the minimum frequency, the sampling frequency of the vibration signal and the length of the vibration signal.
Specifically, in the present embodiment, the maximum window width of the multi-scale peak finding algorithm is determinedThe minimum frequency/>, at which the modulation frequency may occur, in the time domain signal is determinedMaximum window width/>The method comprises the following steps:
；
In the above-mentioned method, the step of, The minimum frequency of the possible occurrence of modulation frequency in the time domain signal is 10Hz in this embodiment; The sampling frequency of the vibration signal is 25600 in this embodiment; /(I) Is a fixed coefficient, this embodiment is 5; /(I)For the length of the vibration signal, this embodiment is 50000.
FIG. 4 is a flow chart of constructing a scale matrix in a gear fault alarm method based on multi-scale peak finding.
As can be seen from fig. 4, further, in some embodiments, the step of constructing the scale matrix includes:
S131, constructing an original scale matrix, wherein the original scale matrix is a matrix with 1 element;
S132, updating the original scale matrix by using a multi-scale peak searching updating formula to obtain a target scale matrix; the step of updating the original scale matrix by using the multi-scale peak searching updating formula comprises the steps of judging all elements in the original scale matrix by using the multi-scale peak searching updating formula and setting the elements meeting the multi-scale peak searching updating formula to 0.
Specifically, in this embodiment, the original scale matrixIs element 1/>Is a matrix of (a):
；
A multiscale peak finding update formula:
，/>。
And performing 0 setting processing on the elements in the original scale matrix meeting the upper part formula of the multi-scale peak searching updating formula, wherein the rest elements are still 1.
Fig. 5 is a flowchart of a method for extracting peak sequences in a gear fault alarm method based on multi-scale peak finding.
As can be seen from fig. 5, further, in some embodiments, the step of extracting the peak sequence includes:
S141, summing the target scale matrixes row by row to obtain a first matrix vector;
s142, acquiring an index of the minimum value of the element in the first matrix vector;
s143, summing the rows from the 0 th row to the minimum value element in the target scale matrix row by row to obtain a second matrix vector;
s144, obtaining an index sequence with elements equal to 0 in the second matrix vector, and obtaining the peak value sequence according to the index sequence.
Specifically, in this embodiment, for the target scale matrixSumming line by line to obtain vectorObtain vector/>Index of element minimum in/>For the target scale matrix/>0 To/>Summing row by row to obtain vector/>Obtain vector/>Index sequence/>, element equal to 0，/>And/>Is a positive integer, the final peak sequence/>The method comprises the following steps:
。
Wherein the first matrix vector corresponds to the vector The second matrix vector corresponds to the vector/>。
The alarm method further comprises the following steps:
And S200, extracting key characteristics corresponding to the target detection equipment according to the peak value sequence, wherein the key characteristics comprise peak value energy and peak value frequency, and specifically, in the embodiment, the energy and the frequency in the target detection equipment are important parameters for judging whether the target detection equipment has faults or not. When the peak value of the data exceeds the safety range, it can be stated that the target detection device has a fault, so in this embodiment, the fault alarm can be accurately and timely performed by extracting the peak value energy and the peak value frequency contained in the peak value sequence.
FIG. 6 is a flowchart showing the steps of extracting key features in a gear fault alarm method based on multi-scale peak finding.
As can be seen from fig. 6, further, in some embodiments, the step of extracting key features corresponding to the target detection device according to the peak sequence includes:
s210, calculating the peak energy of the vibration signal according to the peak sequence;
s220, calculating the peak frequency of the vibration signal according to the sampling frequency of the vibration signal, the length of peak training and the length of the vibration signal;
S230, calculating the absolute value of the difference value between the peak frequency and the required frequency, and obtaining the proximity degree between the peak frequency and the required frequency.
Specifically, in the present embodiment, the peak energy of the vibration signal is calculatedThe peak energy may be calculated by means of an average, a root mean square value or other weighted summation, which is not required, and the root mean square value is used in this embodiment:
；
In the above formula: the length of training for the peak found.
Calculating peak frequency of vibration signal：
；
Calculating peak frequency of vibration signalRequired frequency/>Proximity/>The required frequency/>The present embodiment shows the process of damage and deterioration of the tooth surface of the gear at the high-speed shaft position of the reduction gearbox, so/>, the process is shownThe high-speed shaft of the gear box rotates at 30Hz.
Wherein the proximity of the peak frequency to the desired frequency is obtained by calculating an absolute value of a difference between the peak frequency and the desired frequency.
。
The alarm method further comprises the following steps:
S300, performing fault alarm according to the key characteristics and the proximity degree of the key characteristics to the required frequency of the target detection equipment, specifically, in the embodiment, comparing two different parameters in the key characteristics with the proximity degree of the required frequency of the target detection equipment, so as to realize real-time and accurate fault alarm on the fault of the gearbox, and positioning a fault source through different parameters, thereby improving the fault processing efficiency.
FIG. 7 is a flow chart of a fault alarm in a gear fault alarm method based on multi-scale peak finding according to the present application.
As can be seen from fig. 7, in further embodiments, the step of performing a fault alert based on the key feature and the proximity to the desired frequency of the object detection device includes:
s310, setting a plurality of energy thresholds corresponding to the target detection equipment, wherein the energy thresholds comprise a first energy threshold and a second energy threshold, and the first energy threshold is smaller than the second energy threshold;
s320, setting a proximity threshold corresponding to the target detection equipment;
S330, performing fault alarm according to a plurality of energy thresholds, the proximity thresholds, the peak energy and the proximity.
Peak energyProximity to peak frequency to desired frequency/>And carrying out fault alarm on the gear faults by the two characteristic indexes.
It should be noted that the calculated peak energyThe characteristic index characterizes the energy level of a periodic impact signal or a periodic modulation signal in the vibration time domain signal; peak frequency/>Characterizing the periodic shock signal or periodic approximate frequency in the vibratory time domain signal; proximity of peak frequency to desired frequency/>The proximity of the periodic frequency to a fault signature frequency is characterized. The characteristic indexes can be combined and judged to give out fault alarm to the gear box, and the embodiment adopts the following modes:
is the peak energy Setting a multi-level alarm threshold/>Is the proximity/>, of the peak frequency to the desired frequencySetting a threshold/>And (3) judging the fault form of the gearbox by combining two characteristic indexes:
Threshold value of the embodiment Set to 15mm/s, threshold/>Set to 30mm/s, proximity/>Set to 10Hz.
Specifically, in the present embodiment, on the premise of maintaining the accuracy of the fault alarm, the calculation efficiency of the fault alarm still needs to be considered, and therefore, in the present embodiment, a plurality of thresholds are set, and the determination of the fault is divided into a case where the proximity degree is smaller than the proximity degree threshold and a case where the proximity degree is larger than the proximity degree threshold. The specific judging mode is as follows:
1. The proximity is greater than the proximity threshold:
When the proximity degree is larger than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault category of slightly bad gear engagement on the target detection equipment; and when the proximity degree is larger than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault alarming is that the gear engagement is seriously bad.
2. The proximity is less than the proximity threshold:
when the proximity degree is smaller than the proximity degree threshold value and the peak energy is smaller than the first energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is slightly fault; when the proximity degree is smaller than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault class of the target detection equipment being a moderate fault of the gear tooth surface; and when the proximity degree is smaller than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is seriously fault.
TABLE 1 fault characterization and fault manifestation
。
FIG. 11 is a graph of peak energy versus peak frequency.
FIG. 12 is a schematic diagram of peak energy versus peak frequency proximity alarm thresholds.
As shown in table 1, fig. 11 and fig. 12,
When (when)And/>When the gear normal peak value extraction and characteristic value are shown in figure 8, the signal has no obvious high-speed shaft rotation frequency modulation phenomenon, the peak frequency is close to the gear high-speed shaft meshing frequency, the peak energy amplitude is not overrun, and the gear box state is normal.
When (when)And/>When the vibration signal is in high-speed shaft rotation frequency modulation, the peak energy amplitude is not overrun, and the gear box has a mild gear tooth surface fault.
When (when)And/>When the vibration signal has no obvious high-speed shaft rotation frequency modulation phenomenon, the peak frequency is close to the gear high-speed shaft meshing frequency, but the peak energy amplitude exceeds the peak energy threshold/>Not exceed/>The gearbox has a slight gear underdrive failure.
When (when)And/>When the gear fault degradation development peak value extraction and characteristic values are shown in fig. 9, obvious high-speed shaft rotation frequency modulation phenomenon occurs, and the peak energy amplitude exceeds the peak energy threshold valueNot exceed/>The gearbox is subject to moderate gear tooth surface failure.
When (when)And/>When the vibration signal has no obvious high-speed shaft rotation frequency modulation phenomenon, the peak frequency is close to the gear high-speed shaft meshing frequency, but the peak energy amplitude exceeds the peak energy threshold/>The gearbox has a severe gear undermeshing failure.
When (when)And/>When the gear fault degradation serious peak value extraction and characteristic value are shown in fig. 10, the signal has obvious high-speed shaft rotation frequency modulation phenomenon, and the peak energy amplitude exceeds the peak energy threshold value/>There is a severe gear tooth surface failure in the gearbox.
This embodiment has the following advantages:
by the alarm method, not only can the rapid detection of the fault of the gear box be realized, but also the detection accuracy can be improved, thereby avoiding production accidents caused by the fault. In addition, the method can realize remote monitoring and early warning, is convenient for a manager to timely master the running state of the gear box, and provides powerful support for preventive maintenance.
Claims (10)
1. The gear fault alarm method based on multi-scale peak finding is characterized by comprising the following steps of:
Extracting a peak value sequence of a vibration signal of the target detection equipment according to a multi-scale peak finding algorithm, wherein the peak value sequence comprises the steps of removing a linear trend of a vibration time domain signal, determining the maximum window width of the multi-scale peak finding algorithm, constructing a scale matrix and extracting the peak value sequence;
Extracting key features corresponding to the target detection equipment according to the peak value sequence, wherein the key features comprise peak value energy and peak value frequency;
and carrying out fault alarm according to the key characteristics and the proximity degree of the required frequency of the target detection equipment.
2. The method of claim 1, wherein the step of removing the linear trend of the vibration signal comprises:
setting an n-degree polynomial of the vibration signal to obtain a curve about the n-degree polynomial;
Fitting the curve, and enabling the residual error term of the curve to be the minimum value to obtain the trend term of the vibration signal.
3. The gear failure warning method based on multi-scale peak finding according to claim 1, wherein the step of determining the maximum window width of the multi-scale peak finding algorithm includes:
Acquiring the minimum frequency of the modulation frequency of the vibration signal;
and determining the maximum window width according to the minimum frequency, the sampling frequency of the vibration signal and the length of the vibration signal.
4. A gear failure warning method based on multi-scale peak finding according to claim 3, wherein the step of constructing a scale matrix comprises:
constructing an original scale matrix, wherein the original scale matrix is an element 1 Is a matrix of (a);
Updating the original scale matrix by utilizing a multi-scale peak searching updating formula to obtain a target scale matrix; the step of updating the original scale matrix by using the multi-scale peak searching updating formula comprises the steps of judging all elements in the original scale matrix by using the multi-scale peak searching updating formula and setting the elements meeting the multi-scale peak searching updating formula to 0.
5. The method for gear failure warning based on multi-scale peaking as claimed in claim 4, wherein the step of extracting the peak sequence comprises:
Summing the target scale matrix row by row to obtain a first matrix vector;
Acquiring an index of a minimum value of an element in the first matrix vector;
summing the rows from the 0 th row to the minimum value element in the target scale matrix row by row to obtain a second matrix vector;
and obtaining an index sequence with elements equal to 0 in the second matrix vector, and obtaining the peak value sequence according to the index sequence.
6. The method of claim 5, wherein the step of extracting key features corresponding to the object detection device according to the peak sequence comprises:
calculating the peak energy of the vibration signal according to the peak sequence;
Calculating to obtain the peak frequency of the vibration signal according to the sampling frequency of the vibration signal, the length of peak training and the length of the vibration signal;
And calculating the absolute value of the difference value between the peak frequency and the required frequency to obtain the proximity degree of the peak frequency and the required frequency.
7. The multi-scale peaking-based gear fault alerting method of claim 6, wherein the step of performing fault alerting based on the key features and proximity to the desired frequency of the object detection device comprises:
Setting a plurality of energy thresholds corresponding to the target detection equipment, wherein the energy thresholds comprise a first energy threshold and a second energy threshold, and the first energy threshold is smaller than the second energy threshold;
setting a proximity threshold corresponding to the target detection device;
And performing fault alarm according to a plurality of the energy thresholds, the proximity thresholds, the peak energy and the proximity.
8. The multi-scale peaking-based gear fault alerting method of claim 7, wherein the step of performing fault alerting according to a number of the energy thresholds, the proximity thresholds, the peak energy and the proximity comprises:
when the proximity degree is larger than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault category of slightly bad gear engagement on the target detection equipment;
And when the proximity degree is larger than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault alarming is that the gear engagement is seriously bad.
9. The multi-scale peaking-based gear fault alerting method of claim 7, wherein the step of performing fault alerting according to a number of the energy thresholds, the proximity thresholds, the peak energy and the proximity further comprises:
When the proximity degree is smaller than the proximity degree threshold value and the peak energy is smaller than the first energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is slightly fault;
when the proximity degree is smaller than the proximity degree threshold value and the peak energy is in the interval of the first energy threshold value and the second energy threshold value, performing fault alarming with the fault class of the target detection equipment being a moderate fault of the gear tooth surface;
and when the proximity degree is smaller than the proximity degree threshold value and the peak energy is larger than the second energy threshold value, performing fault alarming on the target detection equipment, wherein the fault type of the fault alarming is that the gear tooth surface is seriously fault.
10. The gear fault alarm method based on multi-scale peak finding according to claim 1, wherein the step of extracting the peak sequence of the vibration signal of the target detection device according to the multi-scale peak finding algorithm further comprises:
and acquiring time series data of the running state of the target detection equipment, wherein the time series data comprises the vibration signal of the target detection equipment.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5895857A (en) * | 1995-11-08 | 1999-04-20 | Csi Technology, Inc. | Machine fault detection using vibration signal peak detector |
| CN111442927A (en) * | 2020-01-20 | 2020-07-24 | 内蒙古工业大学 | Rolling bearing fault diagnosis method based on multi-scale mathematical morphology |
| CN111812641A (en) * | 2020-07-22 | 2020-10-23 | 中国科学院空天信息创新研究院 | Radar altimeter waveform retracing method based on multi-scale peak monitoring |
| KR102348224B1 (en) * | 2020-11-23 | 2022-01-10 | 한국건설기술연구원 | Autonomous cable monitoring system based on vibration method using fully-automated peak-picking algorithm, and method for the same |
| CN114755010A (en) * | 2022-05-05 | 2022-07-15 | 厦门纵行信息科技有限公司 | Rotary machine vibration fault diagnosis method and system |
| CN115186707A (en) * | 2022-07-07 | 2022-10-14 | 郑州恩普特科技股份有限公司 | Simple gear box fault detection method |
| CN117370781A (en) * | 2023-10-18 | 2024-01-09 | 昆明理工大学 | RV reducer composite defect feature extraction method and device |
| CN117398626A (en) * | 2023-11-27 | 2024-01-16 | 湖南半岛医疗科技有限公司 | Equipment power output control method and related device based on pressure movement detection |
| CN117491015A (en) * | 2023-10-16 | 2024-02-02 | 安徽智寰科技有限公司 | Frequency conversion calculation method based on spectrum peak energy sum analysis |
-
2024
- 2024-03-20 CN CN202410316041.5A patent/CN117906946B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5895857A (en) * | 1995-11-08 | 1999-04-20 | Csi Technology, Inc. | Machine fault detection using vibration signal peak detector |
| CN111442927A (en) * | 2020-01-20 | 2020-07-24 | 内蒙古工业大学 | Rolling bearing fault diagnosis method based on multi-scale mathematical morphology |
| CN111812641A (en) * | 2020-07-22 | 2020-10-23 | 中国科学院空天信息创新研究院 | Radar altimeter waveform retracing method based on multi-scale peak monitoring |
| KR102348224B1 (en) * | 2020-11-23 | 2022-01-10 | 한국건설기술연구원 | Autonomous cable monitoring system based on vibration method using fully-automated peak-picking algorithm, and method for the same |
| CN114755010A (en) * | 2022-05-05 | 2022-07-15 | 厦门纵行信息科技有限公司 | Rotary machine vibration fault diagnosis method and system |
| CN115186707A (en) * | 2022-07-07 | 2022-10-14 | 郑州恩普特科技股份有限公司 | Simple gear box fault detection method |
| CN117491015A (en) * | 2023-10-16 | 2024-02-02 | 安徽智寰科技有限公司 | Frequency conversion calculation method based on spectrum peak energy sum analysis |
| CN117370781A (en) * | 2023-10-18 | 2024-01-09 | 昆明理工大学 | RV reducer composite defect feature extraction method and device |
| CN117398626A (en) * | 2023-11-27 | 2024-01-16 | 湖南半岛医疗科技有限公司 | Equipment power output control method and related device based on pressure movement detection |
Non-Patent Citations (1)
| Title |
|---|
| FELIX SCHOLKMANN 等: "An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals", 《ALGORITHMS》, 31 December 2012 (2012-12-31), pages 587 - 603 * |
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