CN116956202B - Engine fault detection method, system, equipment and storage medium - Google Patents
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- 238000001514 detection method Methods 0.000 title claims abstract description 78
- 238000005299 abrasion Methods 0.000 claims abstract description 95
- 239000002245 particle Substances 0.000 claims description 118
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 76
- 238000004364 calculation method Methods 0.000 claims description 49
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- 238000010183 spectrum analysis Methods 0.000 claims description 9
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- 238000012545 processing Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 239000010687 lubricating oil Substances 0.000 description 2
- 239000003921 oil Substances 0.000 description 2
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Abstract
The invention belongs to the technical field of fault detection, and particularly discloses an engine fault detection method, an engine fault detection system, an engine fault detection device and a storage medium. The invention can accurately detect the abrasion type and the abrasion degree in the engine, and improve the abrasion fault detection efficiency and the abrasion fault detection quality of the engine.
Description
Technical Field
The invention belongs to the technical field of fault detection, and particularly relates to an engine fault detection method, an engine fault detection system, an engine fault detection device and a storage medium.
Background
The engine is an important power machine and is also a core power component of mechanical equipment. The engine of the mechanical equipment is often in severe and complex environments such as high temperature, high rotating speed, high load and the like in the operation process, and is extremely easy to generate abrasion faults. Therefore, by monitoring the wear state of the engine, it is extremely important to diagnose the wear failure of the rolling bearing as early as possible. The engine lubricating oil system can play roles of lubricating, friction reduction and heat dissipation in the running process of the engine, mutual friction occurs between contact surfaces of all parts along with the running of the engine, abrasion is caused when the friction is severe, and fragments, particles and the like which are worn down are mixed into lubricating oil. The common wear detection method is to perform iron spectrum analysis on an engine oil sample of an engine to determine the quantity, the size, the characteristics and the like of particles in the engine oil sample, then rely on manual experience to judge the wear state, and the relationship between the correctness of the conclusion and the personal experience of an analyst is extremely large, sometimes the reliability is not high, and the detection efficiency and the detection accuracy are still to be improved.
Disclosure of Invention
The invention aims to provide an engine fault detection method, an engine fault detection system, engine fault detection equipment and a storage medium, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, an engine fault detection method is provided, including:
acquiring an engine oil sample detection result transmitted by a request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set;
extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from a ferrographic analysis data set, extracting pollution particle counts of various size intervals from the particle analysis data set, and extracting iron element content from a physicochemical analysis data set;
determining granularity level according to the pollution particle count of each size interval, determining iron element abnormal level according to iron element content, and determining abrasion intensity index according to the large abrasive particle reading and the small abrasive particle reading;
substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type;
according to each existing abrasion type, a corresponding abrasion degree calculation model is called;
substituting the granularity grade, the iron element abnormal grade and the abrasion intensity index into an abrasion degree calculation model for calculation to obtain the abrasion degree;
when the abrasion degree exceeds a set degree threshold, generating fault prompt information, and generating a fault detection result according to the abrasion degree and each existing abrasion type.
In one possible design, the determining the particle size class from the pollution particle count for each size interval includes:
substituting the pollution particle count of each size interval into a preset granularity grading standard table for matching, and determining granularity grades, wherein the granularity grading standard table comprises a plurality of granularity grades, and each granularity grade is respectively associated with a pollution particle number threshold value of the corresponding size interval.
In one possible design, the determining the abnormal grade of the iron element according to the content of the iron element includes:
dividing the content of the iron element by a set content threshold value to obtain a content ratio, substituting the content ratio into a preset iron element abnormal grade table for matching, and determining an iron element abnormal grade, wherein the iron element abnormal grade table comprises a plurality of iron element abnormal grades, and each iron element abnormal grade is respectively associated with a corresponding content ratio section.
In one possible design, the determining the wear severity index from the large and small abrasive particle readings includes:
the square of the large and small abrasive particle readings is calculated and subtracted to give the wear severity index.
In one possible design, the wear type reference table includes a plurality of wear types, and a number threshold value and a size threshold value of corresponding types of wear particles associated with each wear type, the substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and determining that each wear type exists includes:
substituting the number and the maximum size of the wear particles of each type into a wear type reference table, comparing the number threshold value and the size threshold value associated with the corresponding wear type, and judging that the corresponding wear type exists when the number of the wear particles of the corresponding type is larger than the number threshold value and/or the maximum size is larger than the size threshold value.
In one possible design, the retrieving the corresponding wear-level calculation model according to each existing wear type includes:
determining the type numbers of the existing wear types according to the set numbering rules, and summarizing the type numbers of the existing wear types to obtain a type number set;
and calling the wear degree calculation models which are associated and matched from a calculation model library according to the type number sets, wherein a plurality of wear degree calculation models are prestored in the calculation model library, and each wear degree calculation model is respectively associated with the corresponding type number set.
In one possible design, the wear degree calculation model is
S=εK+σT+ωI
Wherein S represents the abrasion degree, K represents the granularity level, T represents the iron element abnormal level, I represents the abrasion intensity index, epsilon is a set first coefficient, sigma is a set second coefficient, and omega is a set third coefficient.
In a second aspect, there is provided an engine fault detection system including an acquisition unit, an extraction unit, a determination unit, a retrieval unit, a calculation unit, and a generation unit, wherein:
the acquisition unit is used for acquiring an engine oil sample detection result transmitted by the request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set;
the extraction unit is used for extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from the iron spectrum analysis data set, extracting the pollution particle count of each size interval from the particle analysis data set, and extracting the iron element content from the physicochemical analysis data set;
the determining unit is used for determining granularity grade according to the pollution particle count of each size interval, determining iron element abnormal grade according to iron element content, and determining abrasion intensity index according to the large abrasive particle reading and the small abrasive particle reading;
the judging unit is used for substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type;
the calling unit is used for calling a corresponding wear degree calculation model according to each existing wear type;
the calculation unit is used for substituting the granularity level, the iron element abnormal level and the abrasion intensity index into the abrasion degree calculation model to calculate so as to obtain the abrasion degree;
and the generating unit is used for generating fault prompt information when the abrasion degree exceeds a set degree threshold value and generating a fault detection result according to the abrasion degree and each existing abrasion type.
In a third aspect, there is provided an engine failure detection apparatus comprising:
a memory for storing instructions;
and a processor for reading the instructions stored in the memory and executing the method according to any one of the above first aspects according to the instructions.
In a fourth aspect, there is provided a computer readable storage medium having instructions stored thereon which, when run on a computer, cause the computer to perform the method of any of the first aspects. Also provided is a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of any of the first aspects.
The beneficial effects are that: according to the invention, the granularity level is determined according to the corresponding particle analysis data by extracting the corresponding iron spectrum analysis data, the particle analysis data and the physicochemical analysis data in the engine oil sample detection result, the iron element abnormal level is determined according to the corresponding physicochemical analysis data, the abrasion intensity index and each existing abrasion type are determined according to the corresponding iron spectrum analysis data, then the corresponding abrasion degree calculation model is called according to each existing abrasion type to substitute the granularity level, the iron element abnormal level and the abrasion intensity index for calculation, the abrasion degree is obtained, and finally the fault prompt information and the fault detection result are generated when the abrasion degree exceeds the standard, so that the abrasion fault detection of the engine is rapidly and efficiently completed. The invention can accurately detect the abrasion type and the abrasion degree in the engine by utilizing the corresponding oil detection data of the engine, efficiently and reliably judges whether the engine has abrasion fault hidden trouble or not, and improves the abrasion fault detection efficiency and quality of the engine.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram showing the steps of the method of example 1 of the present invention;
FIG. 2 is a schematic diagram showing the construction of a system in embodiment 2 of the present invention;
fig. 3 is a schematic view showing the constitution of the apparatus in embodiment 3 of the present invention.
Detailed Description
It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention. Specific structural and functional details disclosed herein are merely representative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It will be appreciated that the term "coupled" is to be interpreted broadly, and may be a fixed connection, a removable connection, or an integral connection, for example, unless explicitly stated and limited otherwise; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in the embodiments can be understood by those of ordinary skill in the art according to the specific circumstances.
In the following description, specific details are provided to provide a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, a system may be shown in block diagrams in order to avoid obscuring the examples with unnecessary detail. In other embodiments, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Example 1:
the present embodiment provides an engine fault detection method, which can be applied to a corresponding fault detection terminal, as shown in fig. 1, and includes the following steps:
s1, acquiring an engine oil sample detection result transmitted by a request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set.
During implementation, a detector can collect an engine oil sample in an engine, then a direct-reading iron spectrometer and an analysis iron spectrometer are utilized to perform corresponding iron spectrum analysis on the engine oil sample, an iron spectrum analysis data set is obtained, a particle size counter is utilized to detect the engine oil sample, a particle analysis data set is obtained, a physicochemical analyzer is utilized to perform component measurement on the engine oil sample, a corresponding physicochemical analysis data set is obtained, the iron spectrum analysis data set, the particle analysis data set and the physicochemical analysis data set are integrated into an engine oil sample detection result, finally the engine oil sample detection result is transmitted to a detection terminal through a request end (such as a computer and a mobile phone) and the detection terminal receives the engine oil sample detection result to perform subsequent analysis processing.
S2, extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from a ferrographic analysis data set, extracting pollution particle counts of various size intervals from the particle analysis data set, and extracting iron element content from a physicochemical analysis data set.
In the specific implementation, the detection terminal extracts required large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from a ferrograph analysis data set, the large abrasive particle readings and the small abrasive particle readings are detected by a direct-reading ferrograph, and the number of various types of abrasion particles and the maximum size of various types of abrasion particles are detected by an analysis ferrograph; extracting pollution particle counts of each size interval from the particle analysis dataset; and extracting the content of iron element from the physicochemical analysis data set.
S3, determining granularity grades according to the pollution particle counts of all the size intervals, determining abnormal grades of the iron elements according to the iron element content, and determining abrasion intensity indexes according to the large abrasive particle readings and the small abrasive particle readings.
In specific implementation, the detection terminal substitutes the pollution particle count of each size interval into a preset granularity grading standard table for matching, and determines granularity grades, wherein the granularity grading standard table comprises a plurality of granularity grades, each granularity grade is respectively associated with a pollution particle number threshold value of a corresponding size interval, and the granularity grading standard table can be MOOG granularity grading standard table. The detection terminal divides the content of the iron element by a set content threshold value to obtain a content ratio, the content ratio is substituted into a preset iron element abnormal grade table for matching, the iron element abnormal grade is determined, the iron element abnormal grade table comprises a plurality of iron element abnormal grades, and each iron element abnormal grade is respectively associated with a corresponding content ratio interval. The detection terminal firstly calculates the square of the large abrasive particle reading and the square of the small abrasive particle reading, and then subtracts the square of the small abrasive particle reading from the square of the large abrasive particle reading to obtain the abrasion intensity index.
S4, substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type.
In specific implementation, the number and the maximum size of each type of wear particles of the detection terminal are substituted into a preset wear type reference table for comparison, each existing wear type is obtained through judgment, and the judgment process comprises the following steps: substituting the number and the maximum size of the wear particles of each type into a wear type reference table, comparing the number threshold value and the size threshold value associated with the corresponding wear type, and judging that the corresponding wear type exists when the number of the wear particles of the corresponding type is larger than the number threshold value and/or the maximum size is larger than the size threshold value. The wear type reference table contains a number of wear types, and the number and size thresholds of corresponding types of wear particles associated with each wear type, the wear types in the wear type reference table may include adhesive wear, fatigue wear, cutting wear, erosion wear, etc., adhesive wear corresponding to adhesive wear particles, fatigue wear corresponding to fatigue wear particles, cutting wear corresponding to cutting wear particles, erosion wear corresponding to erosion wear particles, and so on.
S5, a corresponding abrasion degree calculation model is called according to each existing abrasion type.
In specific implementation, the detection terminal may determine the type numbers of the existing wear types according to a set numbering rule, and aggregate the type numbers of the existing wear types to obtain a type number set, where the numbering rule may be set as follows: adhesion wear corresponds to number 1, fatigue wear corresponds to number 2, cutting wear corresponds to number 3, and so on. Then, according to the type number set, the wear degree calculation model matched in a correlation manner is called from a calculation model library, a plurality of wear degree calculation models are prestored in the calculation model library, and the wear degree calculation models in the calculation model library are set as follows
S=εK+σT+ωI
Wherein S represents the wear degree, K represents the granularity level, T represents the iron element abnormal level, I represents the wear intensity index, epsilon is a set first coefficient, sigma is a set second coefficient, omega is a set third coefficient, the first coefficient epsilon, the second coefficient sigma and the third coefficient omega of different wear degree calculation models are different from each other, and editing setting can be performed according to actual conditions. Each wear-level calculation model is respectively associated with a corresponding type number set, for example, the type number set associated with a certain wear-level calculation model comprises a number 1, a number 2 and a number 3, and so on.
S6, substituting the granularity grade, the iron element abnormal grade and the abrasion intensity index into an abrasion degree calculation model for calculation to obtain the abrasion degree.
In the specific implementation, after the corresponding abrasion degree calculation model is called, the detection terminal substitutes the particle size grade, the iron element abnormal grade and the abrasion intensity index obtained through previous analysis into the abrasion degree calculation model for calculation, and finally the abrasion degree is obtained.
S7, generating fault prompt information when the abrasion degree exceeds a set degree threshold, and generating a fault detection result according to the abrasion degree and each existing abrasion type.
In specific implementation, the detection terminal can compare the calculated wear degree with a set degree threshold, when the calculated wear degree exceeds the set degree threshold, the detection terminal generates fault prompt information, generates a fault detection result according to the wear degree and each existing wear type, and feeds the fault prompt information and the fault detection result back to the request terminal so that a detection person at the request terminal can check the fault prompt information and the fault detection result.
The method can accurately detect the abrasion type and the abrasion degree in the engine by utilizing the corresponding oil detection data of the engine, efficiently and reliably judges whether the engine has abrasion fault hidden trouble or not, and improves the abrasion fault detection efficiency and quality of the engine.
Example 2:
the present embodiment provides an engine failure detection system, as shown in fig. 2, including an acquisition unit, an extraction unit, a determination unit, a retrieval unit, a calculation unit, and a generation unit, wherein:
the acquisition unit is used for acquiring an engine oil sample detection result transmitted by the request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set;
the extraction unit is used for extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from the iron spectrum analysis data set, extracting the pollution particle count of each size interval from the particle analysis data set, and extracting the iron element content from the physicochemical analysis data set;
the determining unit is used for determining granularity grade according to the pollution particle count of each size interval, determining iron element abnormal grade according to iron element content, and determining abrasion intensity index according to the large abrasive particle reading and the small abrasive particle reading;
the judging unit is used for substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type;
the calling unit is used for calling a corresponding wear degree calculation model according to each existing wear type;
the calculation unit is used for substituting the granularity level, the iron element abnormal level and the abrasion intensity index into the abrasion degree calculation model to calculate so as to obtain the abrasion degree;
and the generating unit is used for generating fault prompt information when the abrasion degree exceeds a set degree threshold value and generating a fault detection result according to the abrasion degree and each existing abrasion type.
Example 3:
the present embodiment provides an engine failure detection apparatus, as shown in fig. 3, including, at a hardware level:
the data interface is used for establishing data butt joint between the processor and the request end;
a memory for storing instructions;
and a processor for reading the instructions stored in the memory and executing the engine fault detection method in embodiment 1 according to the instructions.
Optionally, the device further comprises an internal bus. The processor and memory and data interfaces may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc.
The Memory may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), flash Memory (Flash Memory), first-in first-out Memory (First Input First Output, FIFO), and/or first-in last-out Memory (First In Last Out, FILO), etc. The processor may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Example 4:
the present embodiment provides a computer-readable storage medium having instructions stored thereon that, when executed on a computer, cause the computer to perform the engine fault detection method of embodiment 1. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable system.
The present embodiment also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the engine fault detection method of embodiment 1. Wherein the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable system.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. An engine failure detection method, comprising:
acquiring an engine oil sample detection result transmitted by a request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set;
extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from a ferrographic analysis data set, extracting pollution particle counts of various size intervals from the particle analysis data set, and extracting iron element content from a physicochemical analysis data set;
substituting the pollution particle count of each size interval into a preset granularity grading standard table for matching, and determining granularity grades, wherein the granularity grading standard table comprises a plurality of granularity grades, and each granularity grade is respectively associated with a pollution particle number threshold value of a corresponding size interval; dividing the content of the iron element by a set content threshold value to obtain a content ratio, substituting the content ratio into a preset iron element abnormal grade table for matching, and determining an iron element abnormal grade, wherein the iron element abnormal grade table comprises a plurality of iron element abnormal grades, and each iron element abnormal grade is respectively associated with a corresponding content ratio interval; calculating the square of the large abrasive particle reading and the square of the small abrasive particle reading, and subtracting the square of the small abrasive particle reading from the square of the large abrasive particle reading to obtain an abrasion intensity index;
substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type;
according to each existing abrasion type, a corresponding abrasion degree calculation model is called;
substituting the granularity grade, the iron element abnormal grade and the abrasion intensity index into an abrasion degree calculation model for calculation to obtain the abrasion degree;
when the abrasion degree exceeds a set degree threshold, generating fault prompt information, and generating a fault detection result according to the abrasion degree and each existing abrasion type.
2. The method for detecting engine failure according to claim 1, wherein the wear type reference table contains a plurality of wear types, and a number threshold value and a size threshold value of corresponding types of wear particles associated with each wear type, the substituting the number and the maximum size of each type of wear particles into a preset wear type reference table is compared, and the determining to obtain each existing wear type includes:
substituting the number and the maximum size of the wear particles of each type into a wear type reference table, comparing the number threshold value and the size threshold value associated with the corresponding wear type, and judging that the corresponding wear type exists when the number of the wear particles of the corresponding type is larger than the number threshold value and/or the maximum size is larger than the size threshold value.
3. The engine fault detection method according to claim 1, wherein the retrieving the corresponding wear degree calculation model according to each existing wear type includes:
determining the type numbers of the existing wear types according to the set numbering rules, and summarizing the type numbers of the existing wear types to obtain a type number set;
and calling the wear degree calculation models which are associated and matched from a calculation model library according to the type number sets, wherein a plurality of wear degree calculation models are prestored in the calculation model library, and each wear degree calculation model is respectively associated with the corresponding type number set.
4. The engine failure detection method according to claim 1, wherein the wear degree calculation model is
S=εK+σT+ωI
Wherein S represents the abrasion degree, K represents the granularity level, T represents the iron element abnormal level, I represents the abrasion intensity index, epsilon is a set first coefficient, sigma is a set second coefficient, and omega is a set third coefficient.
5. An engine fault detection system, comprising an acquisition unit, an extraction unit, a determination unit, a retrieval unit, a calculation unit and a generation unit, wherein:
the acquisition unit is used for acquiring an engine oil sample detection result transmitted by the request end, wherein the engine oil sample detection result comprises a ferrographic analysis data set, a particle analysis data set and a physicochemical analysis data set;
the extraction unit is used for extracting large abrasive particle readings, small abrasive particle readings, the number of various types of abrasion particles and the maximum size of various types of abrasion particles from the iron spectrum analysis data set, extracting the pollution particle count of each size interval from the particle analysis data set, and extracting the iron element content from the physicochemical analysis data set;
the determining unit is used for substituting the pollution particle count of each size interval into a preset granularity grading standard table for matching, and determining granularity grades, wherein the granularity grading standard table comprises a plurality of granularity grades, and each granularity grade is respectively associated with a pollution particle number threshold value of a corresponding size interval; dividing the content of the iron element by a set content threshold value to obtain a content ratio, substituting the content ratio into a preset iron element abnormal grade table for matching, and determining an iron element abnormal grade, wherein the iron element abnormal grade table comprises a plurality of iron element abnormal grades, and each iron element abnormal grade is respectively associated with a corresponding content ratio interval; calculating the square of the large abrasive particle reading and the square of the small abrasive particle reading, and subtracting the square of the small abrasive particle reading from the square of the large abrasive particle reading to obtain an abrasion intensity index;
the judging unit is used for substituting the number and the maximum size of each type of wear particles into a preset wear type reference table for comparison, and judging to obtain each existing wear type;
the calling unit is used for calling a corresponding wear degree calculation model according to each existing wear type;
the calculation unit is used for substituting the granularity level, the iron element abnormal level and the abrasion intensity index into the abrasion degree calculation model to calculate so as to obtain the abrasion degree;
and the generating unit is used for generating fault prompt information when the abrasion degree exceeds a set degree threshold value and generating a fault detection result according to the abrasion degree and each existing abrasion type.
6. An engine failure detection apparatus, characterized by comprising:
a memory for storing instructions;
a processor for reading the instructions stored in the memory and executing the engine fault detection method according to any one of claims 1-4 according to the instructions.
7. A computer readable storage medium having instructions stored thereon which, when executed on a computer, cause the computer to perform the engine fault detection method of any of claims 1-4.
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| CN110455546A (en) * | 2019-07-19 | 2019-11-15 | 广西大学 | Engine state monitor and method for diagnosing faults based on vibration and oil liquid information |
| CN114636555A (en) * | 2022-03-22 | 2022-06-17 | 南京航空航天大学 | Fuzzy fusion diagnosis method and system for abrasion fault of aircraft engine |
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| US9897582B2 (en) * | 2012-10-26 | 2018-02-20 | Pratt & Whitney Canada Corp. | Method and system for failure prediction using lubricating fluid analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110455546A (en) * | 2019-07-19 | 2019-11-15 | 广西大学 | Engine state monitor and method for diagnosing faults based on vibration and oil liquid information |
| CN114636555A (en) * | 2022-03-22 | 2022-06-17 | 南京航空航天大学 | Fuzzy fusion diagnosis method and system for abrasion fault of aircraft engine |
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