CN114034476A - Method and device for identifying scaling and corrosion faults of rotary machine rotor - Google Patents
Method and device for identifying scaling and corrosion faults of rotary machine rotor Download PDFInfo
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- CN114034476A CN114034476A CN202111358688.7A CN202111358688A CN114034476A CN 114034476 A CN114034476 A CN 114034476A CN 202111358688 A CN202111358688 A CN 202111358688A CN 114034476 A CN114034476 A CN 114034476A
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N17/00—Investigating resistance of materials to the weather, to corrosion, or to light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
Abstract
The invention discloses a method and a device for identifying scaling and corrosion faults of a rotary machine rotor, which are used for acquiring real-time data of a unit; judging the type of the equipment; if the equipment conforms to the type of scaling or corrosion equipment, taking a vibration measuring point and a data sweet area within 5 time periods under the equipment; calculating a characteristic value X corresponding to each vibration measuring point in each data sweet spot; judging whether a single measuring point meets a red rule or a green rule; if yes, calculating the probability P and outputting a conclusion. According to the method, based on the data acquired by the SG8000 system, online identification and diagnosis of the rotor scaling (or corrosion) fault are realized under the condition that the operation of equipment is not influenced by processing the data, searching the data sweet area and calculating the fault characteristic value, the equipment management personnel are helped to judge the fault to a certain extent, and the production influence and economic loss caused by shutdown disassembly and inspection are avoided.
Description
Technical Field
The invention relates to the technical field of process industry rotating equipment, in particular to a method and a device for identifying scaling and corrosion faults of a rotating machine rotor.
Background
Most of process industrial raw material media have adsorbability or corrosivity, and the rotor balance state can be destroyed in a long-term and long-term accumulation, so that the operation efficiency is reduced, and the safe and stable operation of equipment is damaged. The flow industry has the problem that once key equipment fails, the production of the whole device is influenced due to the particularity of the production mode, hundreds of millions of devices are lost every day, and therefore scaling or corrosion faults can be found on line in time. In the traditional method, the equipment needs to be stopped and disassembled when the surface of a rotor in the equipment is required to be scaled or corroded, the maintenance period is relatively long, and the production is stopped during the stoppage, so that great economic loss is caused.
Disclosure of Invention
Therefore, the embodiment of the invention provides a method and a device for identifying the scaling and corrosion faults of a rotor of a rotary machine, which are used for solving the problems that in the prior art, equipment needs to be shut down and disassembled for checking whether the surface of the rotor inside the equipment is scaled or corroded, the overhaul period is relatively long, and the production is stopped during the shutdown, so that great economic loss is caused.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
in a first aspect, a method of identifying fouling and corrosion failure of a rotor of a rotary machine, comprising:
acquiring real-time data of a unit;
judging the type of the equipment;
if the equipment conforms to the type of scaling or corrosion equipment, taking a vibration measuring point and a data sweet area within 5 time periods under the equipment;
calculating a characteristic value X corresponding to each vibration measuring point in each data sweet spot according to the formula (1);
α=m/n （2）
in the formula: alpha is a truncation coefficient, n is the number of data, and m is the number of removed data;
judging whether a single measuring point meets a red rule or a green rule;
wherein the red rule is: X5-X4 > 2.4 holds and at least 2 inequalities of X1-X0 > 2.4, X2-X1 > 2.4, X3-X2 > 2.4 and X4-X3 > 2.4 hold;
the green rule is: X5-X4 < -2.4 holds and at least 2 inequalities of X1-X0 < -2.4, X2-X1 < -2.4, X3-X2 < -2.4 and X4-X3 < -2.4 hold;
if the current value is satisfied, calculating the probability P according to P =25% + (the number of measuring points satisfying the red rule-1) × 10% + 5% of the number of measuring points satisfying the green rule or P =20% + (the number of measuring points satisfying the green rule-1) × 5%, and outputting a conclusion.
Further, the number of the vibration measuring points is 4.
Further, when the red rule and the green rule in the 4 vibration measuring points are both satisfied, calculating the probability P according to P =25% + (the number of measuring points satisfying the red rule-1) × 10% + the number of measuring points satisfying the green rule × 5%;
and when the 4 vibration measuring points all meet the green rule, calculating the probability P according to P =20% + (the number of measuring points meeting the green rule is-1) × 5%.
Further, the length of each of the 5 time periods is 3 days.
Further, the device types include: is not easy to occur, is easy to occur and is very easy to occur.
Further, each addition of an inequality in the red rule and the green rule holds true, and the weight value is increased by 3%.
Further, the characteristic value X is calculated every six days.
In a second aspect, a rotary machine rotor fouling, corrosion failure identification apparatus comprises:
the acquisition module is used for acquiring real-time data of the unit;
the first judging module is used for judging the type of the equipment;
the computing module is used for computing a characteristic value X corresponding to each vibration measuring point in each data sweet spot;
the second judgment module is used for judging whether a single measuring point meets a red rule or a green rule;
and the probability calculation module is used for calculating the probability P.
In a third aspect, a computer apparatus comprises a memory storing a computer program and a processor implementing the steps of a method for identifying fouling and corrosion failure of a rotor of a rotating machine when the computer program is executed by the processor.
In a fourth aspect, a computer readable storage medium has stored thereon a computer program, wherein the computer program, when executed by a processor, performs the steps of a method for rotary machine rotor fouling, corrosion failure identification.
The invention has at least the following beneficial effects: the invention provides a method and a device for identifying scaling and corrosion faults of a rotary machine rotor, which are used for acquiring real-time data of a unit; judging the type of the equipment; if the equipment conforms to the type of scaling or corrosion equipment, taking a vibration measuring point and a data sweet area within 5 time periods under the equipment; calculating a characteristic value X corresponding to each vibration measuring point in each data sweet spot; judging whether a single measuring point meets a red rule or a green rule; if yes, calculating the probability P and outputting a conclusion. According to the method, based on the data acquired by the SG8000 system, online identification and diagnosis of the rotor scaling (or corrosion) fault are realized under the condition that the operation of equipment is not influenced by processing the data, searching the data sweet area and calculating the fault characteristic value, the equipment management personnel are helped to judge the fault to a certain extent, and the production influence and economic loss caused by shutdown disassembly and inspection are avoided.
Drawings
In order to more clearly illustrate the prior art and the present invention, the drawings which are needed to be used in the description of the prior art and the embodiments of the present invention will be briefly described. It should be apparent that the drawings in the following description are merely exemplary, and that other drawings may be derived from the provided drawings by those of ordinary skill in the art without inventive effort.
The structures, proportions, sizes, and other dimensions shown in the specification are for illustrative purposes only and are not intended to limit the scope of the present invention, which is defined by the claims, and it is to be understood that all such modifications, changes in proportions, or alterations in size which do not affect the efficacy or objectives of the invention are not to be seen as within the scope of the present invention.
FIG. 1 is a flow chart of a method for identifying fouling and corrosion faults of a rotor of a rotary machine according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for identifying fouling and corrosion faults of a rotor of a rotary machine according to an embodiment of the present invention;
fig. 3 is a schematic diagram of searching a data sweet spot according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the description of the present invention, "a plurality" means two or more unless otherwise specified. The terms "first," "second," "third," "fourth," and the like in the description and claims of the present invention and in the above-described drawings (if any) are intended to distinguish between referenced items. For a scheme with a time sequence flow, the term expression does not need to be understood as describing a specific sequence or a sequence order, and for a scheme of a device structure, the term expression does not have distinction of importance degree, position relation and the like.
Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, product, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements specifically listed, but may include other steps or elements not expressly listed that are inherent to such process, method, product, or device or that are added to a further optimization scheme based on the present inventive concept.
Referring to fig. 1 and 2, an embodiment of the present invention provides a method for identifying fouling and corrosion failure of a rotor of a rotary machine, including:
s1: acquiring real-time data of a unit;
specifically, the real-time data of the unit is acquired based on the data acquired by the SG8000 device.
S2: judging the type of the equipment;
specifically, the equipment is classified into a type that is not easily generated, and very easily generated, etc., according to the characteristics of the equipment and the degree of scaling or corrosion.
S3: if the equipment conforms to the type of scaling or corrosion equipment, taking a vibration measuring point and a data sweet area within 5 time periods under the equipment;
specifically, the weight probability is not calculated by the devices which are not easy to occur, and other types of devices need to continue calculating the weight probability and endow the weight probability with corresponding probability coefficients which are manually input in advance.
When searching for a data sweet spot (a "sweet spot" refers to a time period with a relatively high quality in a data segment), the following steps are specifically performed: defining 5 time periods T0, T1, T2, T3, T4 and T5; each time period has a length of 3 days, and the end time of each time period is t0, t1, t2, t3, t4 and t5 respectively, wherein t4= t5-14 days, t3= t4-14 days, t2= t3-14 days, t1= t2-14 days, and t0= t1-14 days.
The specific process of finding t5 is as follows: in the time periods of T0, T1, T2, T3, T4 and T5, taking the minimum rotating speed of each time period, recording the time point of the minimum rotating speed, and if the minimum rotating speed is greater than 95% of the working rotating speed, meeting the time period condition and recording as T5; otherwise, a first time point with the rotating speed greater than 95% of the working rotating speed is found forward according to the minimum rotating speed time point, three-day data are continuously obtained forward from the time point, and the rule judgment is continued until t5 meeting the conditions is found. If the condition is not met, the data are sequentially shifted to historical data, and the maximum detection range is 180 days before t 4.
Referring to fig. 3, core data C0, C1, C2, C3, C4 and C5 in T0, T1, T2, T3, T4 and T5 time periods are taken, wherein C0, C1, C2, C3, C4 and C5 are data sweet spots in T0, T1, T2, T3, T4 and T5 time periods, respectively.
S4: calculating a characteristic value X corresponding to each vibration measuring point in each data sweet spot;
specifically, the characteristic value X is historical data of the unit, and characteristic values X0, X1, X2, X3, X4 and X5 of T0, T1, T2, T3, T4 and T5 are respectively calculated according to C0, C1, C2, C3, C4 and C5.
When the characteristic value X is calculated, the following is specifically performed:
the first method comprises the following steps: calculating characteristic values in each time period according to the formulas (1) and (2):
α=m/n （2）
in the formula: alpha is the truncation coefficient, n is the data number, and m is the removed data number.
The second method comprises the following steps: after the data are arranged in an ascending order, calculating a characteristic value in each time period according to an expression (3) in the exccel:
TRIMMEAN(array, percent) （3）
in the formula, array is a group of data or data area which needs to be sorted and averaged;
percentage is the proportion of data points to be removed in the calculation, and in the present invention, percentage is preferably 0.4.
S5: judging whether a single measuring point meets a red rule or a green rule;
in a single station, either of the following two cases, red and green, must be satisfied:
red rule (hereinafter all abbreviated as "red"): X5-X4 > 2.4 and X1-X0 > 2.4, X2-X1 > 2.4, X3-X2 > 2.4 and X4-X3 > 2.4, at least 2 of the 4 being true;
green rule (hereinafter all abbreviated as "green"): at least 2 of X5-X4 < -2.4 and X1-X0 < -2.4, X2-X1 < -2.4, X3-X2 < -2.4, X4-X3 < -2.4, 4.
S6: if yes, calculating the probability P and outputting a conclusion;
in particular, it should be noted that in each station, only either red or green may be present, and neither may be present at the same time.
When each channel is independently calculated, if 1 measuring point meets a red key factor, the basic weight is obtained by 25 percent;
a base weight of 20% was obtained with 1 station satisfying the green key factor.
Referring to table one and table two, when recording the weight values, the first red is 25%, and the second and subsequent reds are 10%; the first green is denoted as 20% and the second and subsequent greens are denoted as 5%, red being the first when there are channels satisfying both the red and green rules.
Table one:
| channel | Status of state | Status of state | Status of state | Status of state | Status of state | Status of state |
| 1 | Red by 25% | Green 5% | Green 5% | Green 20% | Green 20% | Red by 25% |
| 2 | Red by 10% | Green 5% | Red by 25% | Green 5% | ||
| 3 | Red by 10% | Green 5% | Green 5% | |||
| 4 | Red by 10% | Red by 25% | ||||
| Total weight | 55% | 40% | 30% | 30% | 20% | 25% |
Table two:
remarking: the maximum value of 4 is taken because the qualified equipment has to have only 4 measuring points
Finally, the following results are obtained:
the first method comprises the following steps: when red and green in the four measuring points are all satisfied, the probability P is as follows: 25% + (number of stations-1 satisfied by red) × 10% + number of stations satisfied by green × 5%;
and the second method comprises the following steps: when only green satisfies red among the four measuring points, the probability P is: 20% + (number of spots satisfied by green-1) × 5%.
Example 1: there are 4 measuring points under a certain device, wherein 2 measuring points satisfy the red key factor, and 2 measuring points satisfy the green key factor, and the probability should be:
P=25% + (2-1)*10% + 2*5% = 45%
example 2: there are 4 measuring points under a certain device, wherein 0 measuring point satisfies the red key factor, and 3 measuring points satisfy the green key factor, and the probability should be:
P=20% + (3-1)*5% = 30%
and finally, comprehensively analyzing according to each characteristic value, red rule or green rule and the calculated probability P in the data sweet spot to obtain the equipment scaling or corrosion probability.
It should be noted that when the probability coefficient is manually input, the following situations need to be noted:
(1) the probability of the unbalanced fault of the equipment (all channels are recorded once) is increased by 10% from t4 to t 5;
(2) the probability of a smoke machine, a nitrogen oxide compressor, a cracking gas compressor, melamine and a carrier gas compressor is increased by 8 percent;
(3) the probability of a steam turbine, a coke oven gas compressor and a rich gas compressor is increased by 5 percent;
(4) the probability of the recycle gas compressor, the synthesis gas compressor and the air compressor is increased by 3 percent;
(5) increasing 3% of weight when an inequality is increased on the basis of meeting the probability P, and not distinguishing red and green rules;
in other words, on the premise that red or green is satisfied under a single measuring point, 2 inequalities of red or green are satisfied, the weight is increased by 0, 3 inequalities are satisfied, the weight is increased by 3%, 4 inequalities are satisfied, the weight is increased by 6%, and the single measuring point can only be increased by 6% at most.
(6) There is a 7% improvement of greater than 0.8 fold in X0, X1, X2, X3, X4 and X5.
One embodiment of the present invention provides a device for identifying fouling and corrosion faults of a rotor of a rotary machine, comprising:
the acquisition module is used for acquiring real-time data of the unit;
the first judging module is used for judging the type of the equipment;
the computing module is used for computing a characteristic value X corresponding to each data sweet spot;
the second judgment module is used for judging whether a single measuring point meets a red rule or a green rule;
and the probability calculation module is used for calculating the probability P.
For specific limitations of the device for identifying fouling and corrosion failure of the rotor of the rotary machine, reference may be made to the above limitations of the method for identifying fouling and corrosion failure of the rotor of the rotary machine, and details are not repeated here.
One embodiment of the present invention provides a computer apparatus comprising a memory storing a computer program and a processor implementing the steps of the method for identifying fouling and corrosion failure of a rotor of a rotating machine when the processor executes the computer program.
One embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for rotary machine rotor fouling, corrosion failure identification.
The above specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
All the technical features of the above embodiments can be arbitrarily combined (as long as there is no contradiction between the combinations of the technical features), and for brevity of description, all the possible combinations of the technical features in the above embodiments are not described; these examples, which are not explicitly described, should be considered to be within the scope of the present description.
The present invention has been described in considerable detail by the general description and the specific examples given above. It should be noted that it is obvious that several variations and modifications can be made to these specific embodiments without departing from the inventive concept, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for identifying fouling and corrosion faults of a rotor of a rotary machine is characterized by comprising the following steps:
acquiring real-time data of a unit;
judging the type of the equipment;
if the equipment conforms to the type of scaling or corrosion equipment, taking a vibration measuring point and a data sweet area within 5 time periods under the equipment;
calculating a characteristic value X corresponding to each vibration measuring point in each data sweet spot according to the formula (1);
α=m/n （2）
in the formula: alpha is a truncation coefficient, n is the number of data, and m is the number of removed data;
judging whether a single measuring point meets a red rule or a green rule;
wherein the red rule is: X5-X4 > 2.4 holds and at least 2 inequalities of X1-X0 > 2.4, X2-X1 > 2.4, X3-X2 > 2.4 and X4-X3 > 2.4 hold;
the green rule is: X5-X4 < -2.4 holds and at least 2 inequalities of X1-X0 < -2.4, X2-X1 < -2.4, X3-X2 < -2.4 and X4-X3 < -2.4 hold;
if the current value is satisfied, calculating the probability P according to P =25% + (the number of measuring points satisfying the red rule-1) × 10% + 5% of the number of measuring points satisfying the green rule or P =20% + (the number of measuring points satisfying the green rule-1) × 5%, and outputting a conclusion.
2. The method for identifying fouling and corrosion failure of a rotor of a rotary machine according to claim 1, wherein the number of vibration measuring points is 4.
3. The method of identifying fouling and corrosion faults in a rotor of a rotary machine according to claim 2,
when the red rule and the green rule in the 4 vibration measuring points are both satisfied, calculating a probability P according to P =25% + (the number of measuring points satisfying the red rule-1) × 10% + the number of measuring points satisfying the green rule × 5%;
and when the 4 vibration measuring points all meet the green rule, calculating the probability P according to P =20% + (the number of measuring points meeting the green rule is-1) × 5%.
4. The method of claim 1, wherein the 5 time periods are each 3 days in length.
5. The method of identifying fouling and corrosion faults in a rotor of a rotary machine according to claim 1, wherein the equipment types include: is not easy to occur, is easy to occur and is very easy to occur.
6. A method for identifying fouling and corrosion failure of a rotor of a rotary machine according to claim 1, wherein each of the red rule and the green rule with an added inequality holds that the weight value is increased by 3%.
7. A method for identifying fouling and corrosion faults in a rotor of a rotary machine according to claim 1, wherein the characteristic value X is calculated every six days.
8. A rotary machine rotor fouling, corrosion failure identification apparatus, comprising:
the acquisition module is used for acquiring real-time data of the unit;
the first judging module is used for judging the type of the equipment;
the computing module is used for computing a characteristic value X corresponding to each vibration measuring point in each data sweet spot;
the second judgment module is used for judging whether a single measuring point meets a red rule or a green rule;
and the probability calculation module is used for calculating the probability P.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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