CN110595956A - Wear state mutation detection method based on fractal characteristics of abrasive particle groups - Google Patents
Wear state mutation detection method based on fractal characteristics of abrasive particle groups Download PDFInfo
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- CN110595956A CN110595956A CN201910733553.0A CN201910733553A CN110595956A CN 110595956 A CN110595956 A CN 110595956A CN 201910733553 A CN201910733553 A CN 201910733553A CN 110595956 A CN110595956 A CN 110595956A
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- 239000002245 particle Substances 0.000 title claims abstract description 84
- 238000001514 detection method Methods 0.000 title claims abstract description 21
- 230000035772 mutation Effects 0.000 title claims abstract description 20
- 238000009826 distribution Methods 0.000 claims abstract description 40
- 238000005070 sampling Methods 0.000 claims abstract description 4
- 239000006061 abrasive grain Substances 0.000 claims description 8
- 238000000034 method Methods 0.000 claims description 8
- 238000005299 abrasion Methods 0.000 claims description 3
- 238000004445 quantitative analysis Methods 0.000 abstract description 2
- 238000004458 analytical method Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N2015/0096—Investigating consistence of powders, dustability, dustiness
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N2015/1024—Counting particles by non-optical means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
- G01N15/10—Investigating individual particles
- G01N15/14—Optical investigation techniques, e.g. flow cytometry
- G01N2015/1486—Counting the particles
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Abstract
A wear state mutation detection method based on abrasive particle group fractal characteristics comprises the following steps: firstly, ensuring the stable flow velocity of oil in an oil sampling circulating system in mechanical equipment; periodically collecting oil samples mixed with abrasive particle groups with the same volume; then, carrying out quantitative analysis on the oil sample, and measuring different chord length intervals of the abrasive particle groups in the oil sample and the quantity of the abrasive particles corresponding to the different intervals; counting the chord length of the abrasive particle group and the corresponding number of normal distributions, and calculating the fractal dimension of the abrasive particle group distribution based on the normal distributions; and finally, judging whether the mechanical equipment has state mutation or not according to the abrasive particle group distribution fractal dimension value. The invention provides a wear state mutation detection method based on abrasive particle group fractal characteristics, which can more accurately detect wear state mutation points from the aspect of dynamics, can prevent sudden failure or serious damage of mechanical equipment in advance, and reduces economic loss.
Description
Technical Field
The invention relates to the field of frictional wear and mechanical equipment detection, in particular to a wear state mutation detection method based on abrasive particle group fractal characteristics.
Background
During the operation process of mechanical equipment, if relevant information about the equipment to be destroyed can be detected before sudden damage or serious damage occurs to the equipment, unnecessary economic loss can be effectively avoided. Therefore, a method for detecting the sudden change characteristic of the wear state of the mechanical equipment is indispensable.
At present, the detection of the operating state of mechanical equipment is usually performed by oil detection, and the number of the abrasive particles generated in different wear stages is different, the shape is different, the equivalent size is different, and the color is different. The existing detection means is to extract the area, the perimeter, the equivalent length and the fractal dimension of a single abrasive particle by image recognition through the combination of ferrography and a microscope. However, this approach has significant disadvantages. First, different abrasive particles have different shapes, sizes, and colors in the abrasive particle population generated in the same wear stage, and therefore, it is difficult to fully characterize the wear characteristics of that stage with a single abrasive particle characteristic. In addition, in the ferrographic analysis process, a spectrum sheet needs to be prepared, and the phenomena of overlapping or accumulation of a plurality of abrasive particles and the like are easily caused in the spectrum sheet preparation process, so that the abrasive particle characteristics are difficult to extract.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a wear state mutation detection method based on abrasive particle group fractal characteristics, which solves the problems of wear state mutation detection and abrasive particle group characteristic extraction, more accurately detects wear state mutation points from the aspect of dynamics, can prevent sudden failure or serious damage of mechanical equipment in advance, and reduces economic loss.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a wear state mutation detection method based on abrasive particle group fractal characteristics comprises the following steps:
(1) the stability of the flow speed of oil in the oil sampling circulating system in the mechanical equipment is ensured;
(2) periodically collecting oil samples mixed with abrasive particle groups with the same volume;
(3) quantitatively analyzing the abrasive particle groups in the oil sample, and measuring different chord length ranges of the abrasive particle groups in the oil sample and the quantity of abrasive particles in corresponding ranges;
(4) counting the chord length of the abrasive particle group and the normal distribution of the corresponding number;
(5) calculating a distribution fractal dimension of the abrasive particle group based on the normal distribution;
(6) and detecting whether the wear state of the mechanical equipment is or is about to change suddenly according to the change of the fractal dimension of the distribution of the abrasive particle group.
Further, in the step (4), the total chord length interval is converted into 100 average chord length values, the number of the abrasive particles corresponding to each chord length value is counted and normalized, the comparability of the relation between the number of the abrasive particles and the chord length value in each oil sample is ensured, and then the statistical distribution graph of the abrasive particle group is drawn.
Further, in the step (5), the distribution fractal dimension of the abrasive grain group is calculated based on the normal distribution, and the calculation process is as follows:
lnN(l)＝(1-D')lnl+lnc (1)
in the formula, l is the chord length of the abrasive particles; n (l) is the number of abrasive particles with chord length larger than l; c is a proportionality constant; d' is the fractal dimension of the distribution of the abrasive particle group;
drawing a log-log curve of lnN (l) and lnl, selecting a part with better linearity, and calculating a slope value k of the part, wherein the distribution fractal dimension D' is as follows:
D'＝1-k (2)。
further, in the step (6), when the abrasive particle group distribution fractal dimension D' fluctuates back and forth in a certain small interval, the mechanical equipment is indicated to be in a good running state; when the fractal dimension D' of the abrasive particle group distribution is suddenly and greatly reduced, the abrasion state is suddenly changed, and the mechanical equipment enters a failure stage.
The invention has the following beneficial effects: according to the wear state mutation detection method, the abrasive particle group is taken as an analysis object, the abrasive particle group contains more abrasive particle characteristics and is more representative, the distribution fractal dimension of the abrasive particle group is calculated, errors caused by ferrographic analysis are made up, the state mutation points are better identified from the aspect of dynamics, and the wear state mutation detection method is high in accuracy and reliability.
Drawings
FIG. 1 is a flow chart of the operation of the present invention.
Fig. 2 is a statistical distribution of a population of abrasive particles according to the present invention.
Fig. 3 is a log-log plot of chord length versus number of abrasive particle populations according to the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a wear state mutation detection method based on fractal characteristics of abrasive particle groups includes the following steps:
(1) the stability of the flow speed of oil in the oil sampling circulating system in the mechanical equipment is ensured; the oil flow velocity is stable, and is the premise of validity of the measurement result of the abrasive particle group in the subsequent oil;
(2) periodically collecting oil samples mixed with abrasive particle groups with the same volume; collecting oil mixed with abrasive particle groups at intervals, wherein the volumes of the oil collected every time are the same, and the comparability of the calculation result of the distribution fractal dimension of each abrasive particle group is ensured;
(3) quantitative analysis of abrasive grain population in oil samples: generally, the chord length l of the abrasive grain group is divided into five intervals of 0-10 μm, 10-50 μm, 50-150 μm, 150-300 μm and 300-1000 μm, and the number of abrasive grains corresponding to each interval is measured; measuring chord length intervals corresponding to all abrasive particles contained in the abrasive particle group distributed in the oil sample and the quantity of the abrasive particles corresponding to each chord length interval;
(4) fig. 2 is a statistical distribution diagram of the abrasive particle group, the chord length of the statistical abrasive particle group and the normal distribution of the corresponding number: and (3) equating the chord length interval of 0-1000 mu m to 100 average chord length values, counting the quantity of the abrasive particles corresponding to each equivalent chord length value, carrying out normalization processing, and then drawing a statistical distribution graph of the abrasive particle group.
(5) Fig. 3 is a graph of a log-log relationship of the chord length and the number of the abrasive grain group, and the fractal dimension of the abrasive grain group is calculated based on the normal distribution diagram shown in fig. 2, and the calculation process is as follows:
lnN(l)＝(1-D')lnl+lnc (1)
in the formula, l is the chord length of the abrasive particles; n (l) is the number of abrasive particles with chord length larger than l; c is a proportionality constant; d' is the fractal dimension of the distribution of the abrasive grain group.
Plotting the lnN (l) and lnl log-log curves shown in FIG. 3, selecting the part with better linearity in the graph, and calculating the slope value k, so that the distribution fractal dimension D' is:
D'＝1-k (2)
(6) and detecting whether the wear state of the mechanical equipment is or is about to change suddenly according to the change of the fractal dimension of the distribution of the abrasive particle group. When the operating state of mechanical equipment is stable, the fractal characteristics of the abrasive particle group in the oil sample are periodically quantified and kept relatively stable, if the abrasion state is suddenly changed, the distribution fractal dimension of the abrasive particle group is suddenly reduced, and at the moment, corresponding measures are taken immediately to prevent the impending serious damage.
The method takes the more representative abrasive particle group as an analysis object, only the chord length of the abrasive particle group and the corresponding abrasive particle number need to be counted, the fractal dimension of the abrasive particle group distribution is calculated on the basis of the normal distribution of the abrasive particle group distribution, and the state catastrophe point of the mechanical equipment is detected through the fractal characteristics of the abrasive particle group. The method realizes more accurate detection of the wear state mutation point from the aspect of dynamics, and improves the validity and reliability of the detection result.
The embodiments are only for illustrating the technical idea of the present invention, and the technical idea of the present invention is not limited thereto, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the scope of the present invention.
Claims (4)
1. A wear state mutation detection method based on abrasive particle group fractal characteristics is characterized by comprising the following steps:
(1) the stability of the flow speed of oil in the oil sampling circulating system in the mechanical equipment is ensured;
(2) periodically collecting oil samples mixed with abrasive particle groups with the same volume;
(3) quantitatively analyzing the abrasive particle groups in the oil sample, and measuring different chord length ranges of the abrasive particle groups in the oil sample and the quantity of abrasive particles in corresponding ranges;
(4) counting the chord length of the abrasive particle group and the normal distribution of the corresponding number;
(5) calculating a distribution fractal dimension of the abrasive particle group based on the normal distribution;
(6) and detecting whether the wear state of the mechanical equipment is or is about to change suddenly according to the change of the fractal dimension of the distribution of the abrasive particle group.
2. The wear state mutation detection method based on the fractal features of abrasive particle groups according to claim 1, is characterized in that: in the step (4), the total chord length interval is converted into 100 average chord length values, the number of abrasive particles corresponding to each chord length value is counted and normalized, the comparability of the relationship between the number of abrasive particles and the chord length value in each oil sample is ensured, and then a statistical distribution graph of abrasive particle groups is drawn.
3. The wear state mutation detection method based on the fractal features of abrasive particle groups according to claim 1, is characterized in that: in step (5), the distribution fractal dimension of the abrasive grain group is calculated based on the normal distribution, and the calculation process is as follows:
ln N(l)＝(1-D')ln l+ln c (1)
in the formula, l is the chord length of the abrasive particles; n (l) is the number of abrasive particles with chord length larger than l; c is a proportionality constant; d' is the fractal dimension of the distribution of the abrasive particle group;
drawing a log-log curve of lnN (l) and lnl, selecting a part with better linearity, and calculating a slope value k of the part, wherein the distribution fractal dimension D' is as follows:
D'＝1-k (2)。
4. the wear state mutation detection method based on the fractal features of abrasive particle groups according to claim 1, is characterized in that: in the step (6), when the abrasive particle group distribution fractal dimension D' fluctuates back and forth in a certain small interval, the mechanical equipment is indicated to be in a good running state; when the fractal dimension D' of the abrasive particle group distribution is suddenly and greatly reduced, the abrasion state is suddenly changed, and the mechanical equipment enters a failure stage.
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Cited By (3)
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| CN112182949A (en) * | 2020-11-30 | 2021-01-05 | 浙江中自庆安新能源技术有限公司 | Oil abrasive particle statistical method and system based on computer-aided technology |
| CN114220189A (en) * | 2021-12-15 | 2022-03-22 | 震坤行工业超市(上海)有限公司 | Monitoring method, prediction system, electronic device and storage medium |
| CN116008139A (en) * | 2023-03-27 | 2023-04-25 | 华中科技大学 | Evaluation method and evaluation system for fractal dimension of particles in dispersion system |
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Application publication date: 20191220 |