CN111379624A - Multi-working-condition and time-depth parallel diagnosis method - Google Patents
Multi-working-condition and time-depth parallel diagnosis method Download PDFInfo
- Publication number
- CN111379624A CN111379624A CN201811646833.XA CN201811646833A CN111379624A CN 111379624 A CN111379624 A CN 111379624A CN 201811646833 A CN201811646833 A CN 201811646833A CN 111379624 A CN111379624 A CN 111379624A
- Authority
- CN
- China
- Prior art keywords
- condition
- time
- diagnosis
- distance
- parallel
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000003745 diagnosis Methods 0.000 title claims abstract description 69
- 238000000034 method Methods 0.000 title claims abstract description 36
- 239000013598 vector Substances 0.000 claims abstract description 71
- 230000036541 health Effects 0.000 claims abstract description 46
- 230000008569 process Effects 0.000 claims abstract description 16
- 238000012360 testing method Methods 0.000 claims abstract description 8
- 238000012423 maintenance Methods 0.000 claims description 7
- 238000002405 diagnostic procedure Methods 0.000 claims description 5
- 238000013473 artificial intelligence Methods 0.000 claims description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000002474 experimental method Methods 0.000 description 2
- 230000003862 health status Effects 0.000 description 2
- 238000010977 unit operation Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003638 chemical reducing agent Substances 0.000 description 1
- 230000007717 exclusion Effects 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02B—INTERNAL-COMBUSTION PISTON ENGINES; COMBUSTION ENGINES IN GENERAL
- F02B77/00—Component parts, details or accessories, not otherwise provided for
- F02B77/08—Safety, indicating, or supervising devices
- F02B77/083—Safety, indicating, or supervising devices relating to maintenance, e.g. diagnostic device
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention relates to a multi-working-condition and time depth parallel diagnosis method, which comprises the following steps: acquiring a group of multivariate vectors in a factory test process as a health reference vector group; acquiring a group of multivariate vectors in the running process of a unit as a vector group to be detected; and calculating the distance between the health reference vector group and the vector group to be detected to perform parallel health condition diagnosis. The invention can carry out the parallel diagnosis of multiple working conditions and multiple time points on the basis of carrying out the parallel diagnosis of multiple parameter types; the multiple working conditions and the fault types are often associated, the faults are continuous in time depth, and the continuity of the faults in time and space can be utilized through the parallel diagnosis of the multiple working conditions and multiple time points, so that the diagnosis efficiency is greatly improved.
Description
[ technical field ] A method for producing a semiconductor device
The invention belongs to the technical field of intelligent energy, and particularly relates to a multi-working-condition and time-depth parallel diagnosis method.
[ background of the invention ]
The existing diagnosis can not effectively evaluate, perfect and expand the expert knowledge base; the reasoning function is not strong, and the problem of false report or missing report often occurs to a complex multivariable nonlinear system; in the diagnosis process, single-thread single-point diagnosis is often performed, and deep parallel diagnosis based on the change of working conditions and time cannot be performed, so that the diagnosis efficiency is low. The invention can carry out the parallel diagnosis of multiple working conditions and multiple time points on the basis of carrying out the parallel diagnosis of multiple parameter types; the multiple working conditions and the fault types are often associated, the faults are continuous in time depth, and the continuity of the faults in time and space can be utilized through the parallel diagnosis of the multiple working conditions and multiple time points, so that the diagnosis efficiency is greatly improved.
[ summary of the invention ]
In order to solve the above problems in the prior art, the present invention provides a multi-condition and time-depth parallel diagnosis method, which comprises:
step S1: obtaining a group of multivariate vectors in the process of factory test
As a health reference vector group;
step S2: obtaining a group of multivariate vectors in the running process of a unit
As a vector group to be detected;
step S3: and calculating the distance between the health reference vector group and the vector group to be detected to perform parallel health condition diagnosis.
Further, under the condition of multi-working-condition parallel diagnosis, a group of multivariate vectors corresponding to the multi-working conditions obtained in the factory test process are
Wherein: i is the ith working condition; for the case of time-depth diagnostics, i is the ith time point.
Further, under the condition of multi-working-condition parallel diagnosis, a group of multi-element vectors meeting low-threshold operation conditions corresponding to the multi-working conditions in the operation process of the unit are obtained as
Wherein: i is the ith working condition; for the case of time-depth diagnosis, i isThe ith time point.
Further, the calculating the distance between the healthy reference vector group and the vector group to be detected specifically includes: calculating the distance between each healthy reference vector in the healthy reference vector group and the vector group to be detected and the corresponding vector to be detected to obtain a distance set { dxi}。
Further, when the parallel health condition diagnosis is a multi-working condition parallel diagnosis; determining each distance dx in a set of distancesiWhether the distance thresholds under the corresponding working condition conditions are met, if so, judging that the health condition is good, otherwise, judging that the health condition is good; and pushing the working condition which does not meet the distance threshold value, the distance value and the corresponding operation data thereof to operation and maintenance personnel through the platform of the Internet of things.
Further, when the concurrent health condition diagnosis is a time-depth diagnosis concurrent diagnosis; set the distances { dx }i-performing a function fitting based on the start time and the end time to obtain a fitted distance function dx ═ f (t); obtaining a distance value corresponding to a maximum value point of the fitting distance function; and when the distance value exceeds a certain deviation value, sending out a potential fault alarm or a unit health early warning, otherwise, determining that the health diagnosis is passed.
Further, the starting time is the acquisition time of the first piece of operation data in the operation data; the end time is the acquisition time of the last operation data in the operation data.
Further, the distance is a squared distance.
Further, the deviation value is a preset value, and the preset value is obtained through an artificial intelligence algorithm;
further, the deviation value is a preset value, and the preset value is obtained through a big data intelligent algorithm.
The beneficial effects of the invention include: the parallel diagnosis of multiple working conditions and multiple time points can be carried out on the basis of the parallel diagnosis of multiple parameter types; the multiple working conditions and the fault types are often associated, the faults are continuous in time depth, and the continuity of the faults in time and space can be utilized through the parallel diagnosis of the multiple working conditions and multiple time points, so that the diagnosis efficiency is greatly improved.
[ description of the drawings ]
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, and are not to be considered limiting of the invention, in which:
FIG. 1 is a schematic diagram of a method for remote online health status diagnosis of a generator set according to the present invention.
FIG. 2 is a schematic diagram of a multi-condition and time-depth parallel diagnostic method according to another embodiment of the present invention.
[ detailed description ] embodiments
The present invention will now be described in detail with reference to the drawings and specific embodiments, wherein the exemplary embodiments and descriptions are provided only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
As shown in fig. 1, a method for diagnosing a remote online health status of a generator set according to the present invention is first described in detail;
preferably: the generator set is a multi-machine-electric coupling system and comprises an engine, a generator, a speed reducer, a charging system, an inlet/outlet system and the like;
preferably: the operational data includes: the method comprises the following steps of (1) three-phase voltage at a machine end, three-phase current, voltage frequency, active power, reactive power, rotating speed, oil pressure, water temperature, storage battery voltage, exhaust temperature, running time and starting times; the health state of the unit is jointly determined by the running state and the coupling condition of all equipment;
step S1: establishing a health reference vector; the method specifically comprises the following steps: collecting characteristic operation data of the unit in a factory test process, establishing a group of multidimensional space coordinates representing the unit under various operation conditions, namely forming a health reference vector of the unit by using the group of space coordinate points;
the characteristic operation data is acquired under a specific condition or acquired after specific operation is carried out on the operation data; for example: the specific condition defines the stage of experiment, the time length of experiment, etc.; the specific operation is to perform a preliminary exclusion operation and the like;
the establishing of a group of multidimensional space coordinates representing the unit under various operating conditions specifically comprises the following steps: taking the operation data of each parameter type of the unit at the first time as a unitary in the multivariate vector; correspondingly, the running data of each parameter type at the first time are organized together in an unordered/ordered form to form a multivariate vector; the multivariate vector is a multidimensional space coordinate;
preferably: organizing a plurality of multivariate vectors under various operating conditions to form a group of multidimensional space coordinates representing the unit under various operating conditions; comprehensive health diagnosis of multiple working conditions can be carried out through the most space coordinates; alternatively: organizing a plurality of multivariate vectors under a time point sequence under the same operation condition to form a group of multidimensional space coordinates representing the unit under various operation conditions; at the moment, the health diagnosis of time depth is carried out within the time length of one working condition;
preferably: on the premise of considering the difference of +/-5% of individuals, after the difference is compared with the standard point and accords with the error range, taking the group of spatial coordinate points as the health reference vector of the unit;
taking the operating parameters of the generator set in a full-power state as an example, the main characteristic parameters of the generator are three-phase voltage Uab，Ubc，UcaThree-phase current Ia，Ib，IcFrequency f, water temperature TwOil pressure PoExhaust temperature TgSpeed n, load factor η, battery voltage udc(ii) a Then the factory multivariate vector is represented as:
for the condition of multi-working condition parallel diagnosis, a group of multi-element vectors corresponding to the multi-working conditions are obtained as
Wherein: i is the firsti working conditions; in the case of time-depth diagnosis, i is the ith time point;
step S2: collecting real-time operation data; the method specifically comprises the following steps: collecting real-time operation data of the generator set in the operation process of the generator set, and establishing a group of space coordinates representing the generator set after the generator set operates for a period of time by taking various operation parameters as coordinate axes; the vector to be diagnosed of the unit is obtained;
the vector to be diagnosed is as follows:
preferably: judging whether the unit to be diagnosed reaches a full power condition, if so, acquiring real-time operation data of the generator set to obtain a vector to be diagnosed;
preferably: acquiring real-time operation data of the generator set, comparing each item in the real-time operation data with a low threshold operation condition during full-power operation, and determining that the generator set reaches a full-power condition when all the operation data meet the low threshold operation condition;
the low-threshold operation condition comprises the data size and the retention time of the operation data of each parameter type, the time sequence relation which needs to be retained when the operation data are in a specific data range and the like;
under the condition of multi-working-condition parallel diagnosis, acquiring a group of multi-element vectors corresponding to the multi-working conditions and meeting the low threshold operation condition as
Wherein: i is the ith working condition; in the case of time-depth diagnosis, i is the ith time point;
step S3: diagnosing the health condition of the unit; the method specifically comprises the following steps: calculating the distance between a vector to be diagnosed of the unit operation parameter and a reference vector in real time, wherein the farther the distance is, the worse the health state of the unit is represented;
preferably: when the distance exceeds a certain deviation value, sending out a potential fault alarm or a unit health early warning, and listing out the parameter type and the deviation value of main operation data causing deviation; inquiring overhaul data about the parameter type in a fault feature database based on the parameter type, and providing the overhaul data for a maintenance personnel reference;
preferably: the deviation value is a preset value; the preset value is obtained from a cloud server;
the distance between the vector to be diagnosed of the operation parameter of the computer set and the reference vector is specifically as follows: calculating a value obtained by adding and root-opening numbers of the squares of the difference values of the corresponding tuples of the vector to be diagnosed and the reference vector as the distance; i.e., the squared distance;
preferably; setting weight vectors of parameter types according to relevance of different parameter types and unit health states
Calculating the distance between the current state of the generator set and the reference vector based on the following formula:
the distance represents the health degree of the unit, and the higher the value, the worse the health state of the unit. When the distance reaches a set value, arranging main influence parameters as fault characteristic vectors, such as:
τ＝(δTw，δTg，δn′)T
preferably: storing the distance values, characteristic operation data corresponding to the distance values and corresponding relation items among the fault characteristics in a fault characteristic library; searching in a fault feature database by using the distance value, matching three most similar fault features, and pushing to operation and maintenance personnel through an Internet of things platform for reference during maintenance;
preferably: the characteristic operation data is as follows: the distance between the reference vector and the cluster center of the operation data set of which the distance value corresponds to the distance value is the distance value; that is, clustering the running data sets with the same distance value to obtain the characteristic running data;
the matching of the most similar three fault characteristics specifically includes: searching corresponding relation items corresponding to the most similar first quantity of distance values in a fault feature library by using the distance values; calculating the distance between each feature operation data in the corresponding relation items and the operation data to be diagnosed, and taking the fault features contained in the first three corresponding relation items with the minimum distance value as the matched three most similar fault features;
a parallel diagnosis step S4 may be further provided on the basis of the above steps S1 to S3, that is, a multi-operating-condition and time-depth parallel diagnosis method shown in fig. 2;
step S4: performing parallel health condition diagnosis on the unit; the method specifically comprises the following steps: obtaining a group of multivariate vectors in the process of factory test
As a health reference vector group, acquiring a group of multivariate vectors in the running process of the unit
As a vector group to be detected; calculating the distance between the health reference vector group and the vector group to be detected to perform parallel health condition diagnosis;
preferably: under the condition of multi-working-condition parallel diagnosis, a group of multi-element vectors corresponding to the multi-working conditions obtained in the factory test process are
Wherein: i is the ith working condition; in the case of time-depth diagnosis, i is the ith time point; for the condition of multi-working condition parallel diagnosis, the obtained group of multivariate vectors meeting the low threshold value operation condition corresponding to the multi-working conditions in the unit operation process is
Wherein: i is the firsti working conditions; in the case of time-depth diagnosis, i is the ith time point;
the calculating the distance between the health reference vector group and the vector group to be detected specifically comprises: calculating the distance between each healthy reference vector in the healthy reference vector group and the vector group to be detected and the corresponding vector to be detected to obtain a distance set { dxi}; (1) when the parallel health condition diagnosis is a multi-condition parallel diagnosis; determining each distance dx in a set of distancesiWhether the distance thresholds under the corresponding working condition conditions are met, if so, judging that the health condition is good, otherwise, judging that the health condition is good; the working condition which does not meet the distance threshold value, the distance value and the corresponding operation data are pushed to operation and maintenance personnel through the Internet of things platform for reference during maintenance; (2) when the concurrent health condition diagnosis is a time-depth diagnosis concurrent diagnosis; set the distances { dx }i-performing a function fitting based on the start time and the end time to obtain a fitted distance function dx ═ f (t); obtaining a distance value corresponding to a maximum value point of the fitting distance function; when the distance value exceeds a certain deviation value, sending out a potential fault alarm or a unit health early warning, otherwise, determining that the health diagnosis is passed; wherein the starting time is the starting time of the first piece of operation data in the operation data; the end time is the start time of the last operation data in the operation data;
in the embodiments provided in the present invention, it should be understood that the disclosed method and terminal can be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
In addition, the technical solutions in the above several embodiments can be combined and replaced with each other without contradiction.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of modules or means recited in the system claims may also be implemented by one module or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A multi-condition and time-depth parallel diagnosis method is characterized by comprising the following steps:
step S1: obtaining a group in the factory test processMultivariate vector
As a health reference vector group;
step S2: obtaining a group of multivariate vectors in the running process of a unit
As a vector group to be detected;
step S3: and calculating the distance between the health reference vector group and the vector group to be detected to perform parallel health condition diagnosis.
2. The multi-condition and time-depth parallel diagnosis method according to claim 1, wherein for multi-condition parallel diagnosis, a group of multivariate vectors corresponding to the multi-condition obtained in a factory test process are
Wherein: i is the ith working condition; for the case of time-depth diagnostics, i is the ith time point.
3. The multi-operating-condition and time-depth parallel diagnosis method according to claim 2, wherein for multi-operating-condition parallel diagnosis, a group of multivariate vectors meeting low-threshold operation conditions corresponding to the multi-operating conditions in the operation process of the unit are obtained as
Wherein: i is the ith working condition; for the case of time-depth diagnostics, i is the ith time point.
4. The multi-condition and time-depth parallel diagnosis method according to claim 3, wherein the calculating of the distance between the healthy reference vector group and the vector group to be detected is specifically: calculating each health reference direction in the health reference vector group and the vector group to be detectedDistance between the quantity and the corresponding vector to be detected to obtain a set of distances { dx }i}。
5. The multi-condition and time-depth parallel diagnosis method according to claim 4, wherein when the parallel health condition diagnosis is a multi-condition parallel diagnosis; determining each distance dx in a set of distancesiWhether the distance thresholds under the corresponding working condition conditions are met, if so, judging that the health condition is good, otherwise, judging that the health condition is good; and pushing the working condition which does not meet the distance threshold value, the distance value and the corresponding operation data thereof to operation and maintenance personnel through the platform of the Internet of things.
6. The multi-condition and time-depth parallel diagnosis method according to claim 5, wherein when the parallel health condition diagnosis is time-depth diagnosis parallel diagnosis; set the distances { dx }i-performing a function fitting based on the start time and the end time to obtain a fitted distance function dx ═ f (t); obtaining a distance value corresponding to a maximum value point of the fitting distance function; and when the distance value exceeds a certain deviation value, sending out a potential fault alarm or a unit health early warning, otherwise, determining that the health diagnosis is passed.
7. The multi-condition and time-depth parallel diagnostic method according to claim 6, wherein the start time is a collection time of a first piece of operation data in the operation data; the end time is the acquisition time of the last operation data in the operation data.
8. The multi-condition and time-depth parallel diagnostic method of claim 7, wherein the distance is a squared-off distance.
9. The multi-condition and time-depth parallel diagnostic method according to claim 8, wherein the deviation value is a preset value, and the preset value is obtained by an artificial intelligence algorithm.
10. The multi-condition and time-depth parallel diagnostic method according to claim 9, wherein the deviation value is a preset value, and the preset value is obtained by a big data intelligent algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811646833.XA CN111379624A (en) | 2018-12-29 | 2018-12-29 | Multi-working-condition and time-depth parallel diagnosis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811646833.XA CN111379624A (en) | 2018-12-29 | 2018-12-29 | Multi-working-condition and time-depth parallel diagnosis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111379624A true CN111379624A (en) | 2020-07-07 |
Family
ID=71221098
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811646833.XA Pending CN111379624A (en) | 2018-12-29 | 2018-12-29 | Multi-working-condition and time-depth parallel diagnosis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111379624A (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050261837A1 (en) * | 2004-05-03 | 2005-11-24 | Smartsignal Corporation | Kernel-based system and method for estimation-based equipment condition monitoring |
| CN102279928A (en) * | 2011-07-20 | 2011-12-14 | 北京航空航天大学 | Product performance degradation interval prediction method based on support vector machine and fuzzy information granulation |
| CN102768115A (en) * | 2012-06-27 | 2012-11-07 | 华北电力大学 | Method for dynamically monitoring health status of wind turbine gearbox in real time |
| CN108460207A (en) * | 2018-02-28 | 2018-08-28 | 上海华电电力发展有限公司 | A kind of fault early warning method of the generating set based on operation data model |
-
2018
- 2018-12-29 CN CN201811646833.XA patent/CN111379624A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050261837A1 (en) * | 2004-05-03 | 2005-11-24 | Smartsignal Corporation | Kernel-based system and method for estimation-based equipment condition monitoring |
| CN102279928A (en) * | 2011-07-20 | 2011-12-14 | 北京航空航天大学 | Product performance degradation interval prediction method based on support vector machine and fuzzy information granulation |
| CN102768115A (en) * | 2012-06-27 | 2012-11-07 | 华北电力大学 | Method for dynamically monitoring health status of wind turbine gearbox in real time |
| CN108460207A (en) * | 2018-02-28 | 2018-08-28 | 上海华电电力发展有限公司 | A kind of fault early warning method of the generating set based on operation data model |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109218114B (en) | Decision tree-based server fault automatic detection system and detection method | |
| CN105427193B (en) | A kind of big data analysis device and method based on distributed time series data service | |
| CN105787584B (en) | Wind turbine group fault early warning method based on cloud platform | |
| CN109856299A (en) | A kind of transformer online monitoring differentiation threshold value dynamic setting method, system | |
| CN101876932A (en) | Method, system and device for monitoring internal storage database | |
| CN111817880A (en) | Oil and gas field production equipment health management system and implementation method | |
| CN113177357B (en) | Transient stability assessment method for power system | |
| CN113935562A (en) | Intelligent rating and automatic early warning method for health condition of power equipment | |
| CN108304350B (en) | Fan index prediction and fault early warning method based on big data set neighbor strategy | |
| CN112415331A (en) | Power grid secondary system fault diagnosis method based on multi-source fault information | |
| CN112561238A (en) | Pumped storage power station auxiliary equipment state health evaluation system and method | |
| Wang | Application of extension theory to vibration fault diagnosis of generator sets | |
| CN110992204A (en) | Emergency diesel generator set reliability evaluation method and system based on big data technology | |
| CN112084220B (en) | Abnormality diagnosis method and device for electric energy metering device and readable storage medium | |
| CN113360555A (en) | Fault diagnosis and analysis method and system based on big data of power grid secondary equipment | |
| CN111381161A (en) | Remote online health state diagnosis method for generator set | |
| Sun et al. | Data augmentation strategy for power inverter fault diagnosis based on wasserstein distance and auxiliary classification generative adversarial network | |
| Ye et al. | Bearing fault detection based on convolutional self-attention mechanism | |
| CN117312293B (en) | Electric power multisource heterogeneous data management and intelligent analysis method and system | |
| CN108647860A (en) | Power equipment state monitoring based on OpenStack and assessment cloud platform | |
| CN106649034B (en) | Visual intelligent operation and maintenance method and platform | |
| CN111379624A (en) | Multi-working-condition and time-depth parallel diagnosis method | |
| CN117009828A (en) | Hydropower equipment fault diagnosis method based on fault matching algorithm | |
| CN115146715A (en) | Power utilization potential safety hazard diagnosis method, device, equipment and storage medium | |
| CN111539642B (en) | Object-oriented power plant data acquisition and processing system and method thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200707 |