CN107421738A - A kind of epicyclic gearbox method for diagnosing faults based on flow graph - Google Patents
A kind of epicyclic gearbox method for diagnosing faults based on flow graph Download PDFInfo
- Publication number
- CN107421738A CN107421738A CN201710719339.0A CN201710719339A CN107421738A CN 107421738 A CN107421738 A CN 107421738A CN 201710719339 A CN201710719339 A CN 201710719339A CN 107421738 A CN107421738 A CN 107421738A
- Authority
- CN
- China
- Prior art keywords
- flow graph
- epicyclic gearbox
- node
- fault diagnosis
- planetary gear
- 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
Classifications
-
- 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
- G01M13/02—Gearings; Transmission mechanisms
- G01M13/021—Gearings
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/901—Indexing; Data structures therefor; Storage structures
- G06F16/9024—Graphs; Linked lists
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The invention provides a kind of epicyclic gearbox method for diagnosing faults based on flow graph.The invention aims to solve the problems, such as that epicyclic gearbox diagnosis process is obscure low with diagnostic result accuracy rate.One：Fault diagnosis feature is extracted from typical epicyclic gearbox vibration signal, forms the fault diagnosis training example of epicyclic gearbox.Two：Planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm；Three：Remove redundancy or incoherent symptom attribute node using flow graph Algorithm for Reduction, obtain most simple planetary gear box fault diagnosis flow graph；Four：Fault diagnosis feature is extracted from follow-up failure epicyclic gearbox vibration signal, forms the fault diagnosis follow-up example of epicyclic gearbox；Five：The fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm.As a result show that the present invention can intuitively represent planetary gear box fault diagnosis knowledge, reduce computation complexity, improve arithmetic speed and accuracy rate.
Description
Technical field
The present invention relates to a kind of method for diagnosing faults, more particularly to a kind of planetary gear box fault diagnosis based on flow graph
Method.
Background technology
Epicyclic gearbox has the characteristics that compact to design, big speed ratio and bearing capacity are strong, so as to be widely used in wind-force
In the machine driven system of the equipment such as generator, vehicle or helicopter.The normal operation of epicyclic gearbox depends on gear, and
The precision-fit of gear and other parts, any small flaw can influence the normal operation of epicyclic gearbox.However, due to
In complicated adverse circumstances, epicyclic gearboxes such as high speed, heavy duty or high temperature the failures such as crackle or spot corrosion easily occur for longtime running；From
And cause huge economic loss, or even the catastrophic consequence such as fatal crass.Therefore, the fault diagnosis of epicyclic gearbox has
Highly important engineering significance and application value.
In recent years, the fault diagnosis of epicyclic gearbox has been increasingly becoming one of focus studied both at home and abroad.Many methods are
Applied in the fault diagnosis of epicyclic gearbox, as artificial neural network (Artificial Neural Networks, ANNs),
Evidence theory (D-S Evidence Theory, DST) and SVMs (Support Vector Machine, SVM) etc..
ANNs carries out thinking, study, memory, decision-making and identification by simulating the neural network structure of human brain.Because its is simple
Structure, quick training process and good extended capability, have been successfully applied in the fault diagnosis of epicyclic gearbox.But it is examined
Disconnected process is obscure and is difficult to understand, convergence rate is slower, the more difficult determination of structure and parameter of network.DST utilizes prior probability point
The evidence section of posterior probability, and the confidence level and likelihood ratio of quantified statement are obtained with function.Its maximum feature is in evidence
Introduce uncertainty.Therefore DST provides a kind of new resolving ideas for the fault diagnosis of epicyclic gearbox.However, in DST
Applied in the failure diagnostic process of epicyclic gearbox, the determination of the Basic probability assignment function of each proposition in fault diagnosis framework
Still it is difficult to solve.In the evidence of synthesis height conflict, obtained fault diagnosis result is often perverse.So as to limit
Application in DST Faults Diagnosis of Planetary Gearbox.In addition, SVM has developed into very effective planetary gear box fault diagnosis
Method.It uses structural risk minimization principle, and low-dimensional data is mapped into higher dimensional space, is divided data by optimal hyperlane
Class.It has high reasoning accuracy and good adaptability, and is especially suitable for handling Small Sample Database.But its is optimal super
Plane determination process needs the long period, and depends on the experience of operator and test repeatedly.
Flow graph is by a kind of Polish scholar Pawlak emerging representations of knowledge proposed first in 2002 and data point
Analysis instrument.It is mainly made up of node, oriented branch and stream function three parts.As the extension of rough set theory, its feature exists
In the distribution of oriented branch compactly description information, without any priori beyond processing data needed for offer, do not relate to
And the Probability Structure of information, there is patterned representation of knowledge characteristic and knowledge store characteristic, can be retouched in a manner of patterned
State decision process.Therefore, flow graph is widely used in the fields such as the representation of knowledge, data mining and pattern-recognition.But flow graph
Application in terms of mechanical fault diagnosis is also less.
The content of the invention
The invention aims to solve the problems, such as that epicyclic gearbox diagnosis process is obscure low with diagnostic result accuracy rate,
A kind of novel resolving ideas is provided for the fault diagnosis of epicyclic gearbox, and proposes a kind of planetary gear based on flow graph
Box fault diagnosis method.
A kind of epicyclic gearbox method for diagnosing faults based on flow graph, it is characterised in that this method comprises the following steps：
Step 1: extracting fault diagnosis feature from typical epicyclic gearbox vibration signal, the failure for forming epicyclic gearbox is examined
Disconnected training example；
Step 2: planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm：The sign of flow graph is built first
Million attribute nodes and decision attribute node, then the decision rule in decision table be sequentially connected each node from left to right, from
And oriented Bifurcation Set is formed, and number is flowed through in accumulative node and oriented branch, obtains node flow and oriented branch flow, and
And calculate node stream function and oriented branch's stream function, and be marked on respectively below node and above oriented branch, finally calculate
The confidence level and coverage of oriented branch, and be marked on above oriented branch, so as to obtain planetary gear box fault diagnosis flow direction
Figure；
Step 3: obtain most simple planetary gear box fault diagnosis flow graph using flow graph Algorithm for Reduction：Flow graph is calculated first
In all paths consistent sex factorγ, then delete first symptom attribute node, and the oriented branch being connected with it, structure
New flow graph is built, and calculates the consistent sex factor in all paths in new flow graphγ', if consistent sex factorγ≤γ', that
This symptom attribute node can be deleted, and otherwise this symptom attribute node unsuppressible-suppression, finally judges other symptom attributes successively
Node, until last symptom attribute node, deletes all deletable symptom attribute nodes, obtain most simple epicyclic gearbox
Fault diagnosis flow graph,So as to represent the causality between symptom attribute and decision attribute in a manner of most simple；
Step 4: extracting fault diagnosis feature from follow-up failure epicyclic gearbox vibration signal, the event of epicyclic gearbox is formed
Barrier diagnosis follow-up example；
Step 5: the fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm：It is real according to follow-up first
The fullpath of example calculates the confidence level of fullpath, then according to confidence level size, determines the fault type of follow-up example, its
Middle fault type is the fault type represented by the node of maximum confidence level, finally calculates the coverage of fullpath, will be complete
The confidence level and coverage in whole path are used for the quantitatively evaluating index of categorised decision.
Invention effect
Using a kind of epicyclic gearbox method for diagnosing faults based on flow graph of the present invention, examined with other epicyclic gearbox failures
Disconnected method is compared, and beneficial effects of the present invention are：
1. flow graph is a kind of patterned Knowledge Representation Model, can intuitively be represented in a manner of node symptom attribute value and
Decision attribute values, can quantitative information flow through the intensity of node and oriented branch, and the causality between node, with rough set
Decision table in theory is compared, and the Knowledge Representation Schemes of flow graph are more visual and understandable, is easy to user to understand and analyze；
2. flow graph Algorithm for Reduction is on the premise of flow graph categorised decision ability is not changed, remove redundancy or incoherent
Symptom attribute node, the causality between node is represented in a manner of most simple, so, the yojan of flow graph can reduce categorised decision
During input node quantity, reduce computation complexity, improve arithmetic speed and accuracy rate；
3. the confidence level of flow graph categorised decision algorithm passage path carries out categorised decision to example, by coverage to by road
The categorised decision that footpath is made carries out quantitative assessment, and the amount of calculation of the decision making algorithm is smaller, and the strategy of categorised decision is clear.
Brief description of the drawings
Fig. 1 is a kind of flow chart of the epicyclic gearbox method for diagnosing faults based on flow graph of the present invention；
Fig. 2 is the flow chart of flow graph developing algorithm；
Fig. 3 is the flow chart of flow graph Algorithm for Reduction；
Fig. 4 is the flow chart of flow graph categorised decision algorithm；
Fig. 5 is four kinds of typical epicyclic gearbox vibration signal time domain beamformers；
Fig. 6 is planetary gear box fault diagnosis flow graph；
Fig. 7 is most simple planetary gear box fault diagnosis flow graph.
Embodiment
Embodiment one：Illustrate present embodiment with reference to Fig. 1, a kind of epicyclic gearbox failure based on flow graph is examined
Disconnected method, it is characterised in that this method comprises the following steps：
Step 1: extracting fault diagnosis feature from typical epicyclic gearbox vibration signal, the failure for forming epicyclic gearbox is examined
Disconnected training example；
Step 2: planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm；
Step 3: removing redundancy or incoherent symptom attribute node using flow graph Algorithm for Reduction, most simple planetary gear is obtained
Box fault diagnosis flow graph；
Step 4: extracting fault diagnosis feature from follow-up failure epicyclic gearbox vibration signal, the event of epicyclic gearbox is formed
Barrier diagnosis follow-up example；
Step 5: the fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm.
Embodiment two：Present embodiment is unlike embodiment one：Pass through stream in the step 2
Planetary gear box fault diagnosis flow graph is built to figure developing algorithm, illustrates present embodiment with reference to Fig. 2：
Step 2 one, structure flow to node of graphN=N C ∪N D , wherein,N C For symptom attribute set of node,N D For decision attribute node
Collection；
Step 2 two, according to training example be sequentially connected each node from left to right, so as to form oriented Bifurcation SetB；
Number is flowed through in step 2 three, accumulative node and oriented branch, obtains node flowψ(x) and oriented branch flowψ(x,y)；
Step 2 four, calculate node stream function σ (x) and oriented branch's stream function σ (x,y), and be marked on respectively below node and
Above oriented branch；
Step 2 five, calculate oriented branch (x,y) BConfidence level cer (x,y) and coverage cov (x,y), and be marked on
Above to branch, so as to obtain planetary gear box fault diagnosis flow graphG。
Embodiment three：Present embodiment is unlike embodiment one or two：It is sharp in the step 3
Remove redundancy or incoherent symptom attribute node with flow graph Algorithm for Reduction, obtain most simple planetary gear box fault diagnosis flow direction
Figure；Illustrate present embodiment with reference to Fig. 3：
Step 3 one, calculate planetary gear box fault diagnosis flow graphGIn all paths consistent sex factorγ；
Step 3 two, delete first symptom attribute noden, and the oriented branch being connected with itB, build new flow graphG’；
Step 3 three, calculate new flow graphG' in all paths consistent sex factorγ', if consistent sex factorγ≤γ', that
NodenIt can delete, otherwise nodenUnsuppressible-suppression；
Step 3 four, other symptom attribute nodes are judged successively, until last symptom attribute node, deletes all delete
Symptom attribute node, obtain most simple planetary gear box fault diagnosis flow graphG’’。
Embodiment four：Present embodiment is unlike embodiment one, two or three：In the step 5
The fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm, illustrates present embodiment with reference to Fig. 4：
Step 5 one, according to the fullpath of follow-up example [c i (x),d j ],i=1,2,…,mCalculate the confidence level of fullpath
cer[c i (x),d j ],j=1,2,…,n；
Step 5 two, according to confidence level cer [c i (x),d j ] size, determine the fault type of follow-up example.Fault type is most
Big cer [c i (x),d j ] noded j Represented fault type；
Step 5 three, the coverage for calculating fullpath, the confidence level of fullpath and coverage to be used for the amount of categorised decision
Change evaluation index.
Beneficial effects of the present invention are verified using following examples：
Embodiment：
A kind of epicyclic gearbox method for diagnosing faults step based on flow graph of the present embodiment is as follows：
Step 1: extracting fault diagnosis feature from typical epicyclic gearbox vibration signal, the failure for forming epicyclic gearbox is examined
Disconnected training example：
Carried out on the present embodiment Faults Diagnosis of Planetary Gearbox testing stand, four kinds of typical epicyclic gearboxes are respectively normal planet
Gear-box, sun gear broken teeth failure, planetary gear broken teeth failure and ring gear broken teeth failure.Planet is gathered using acceleration transducer
The vibration signal of gear-box, sample frequency are 5120 Hz, sampling length 20,480 points.The input shaft rotating speed of epicyclic gearbox
75 r/min, 150 r/min, 300 r/min are respectively adjusted to, output shaft has two kinds of forms of loading and not loading, so as to
Simulate 6 kinds of different epicyclic gearbox operating conditions.Vibration signal is acquired when every kind of operating condition reaches stabilization,
Every kind of operating condition gathers 8 groups of samples.Therefore, 48 groups of samples, the epicyclic gearbox of four kinds of states can be obtained for every kind of gear
192 groups of samples can be obtained altogether.Fig. 5 is four kinds of typical epicyclic gearbox vibration signal time domain beamformers.The present embodiment is by 192 groups
144 groups of samples in sample regard training example as.Referred to using the kurtosis, waveform index, peak value of db8 wavelet packets extraction vibration signal
Mark, nargin factor, pulse index, amplitude spectral amplitude ratio and this 6 fault signatures, and symbol is used respectivelyxq, K, C, L, P, G
Represent.The fault signature of extraction is continuous variable, so sliding-model control need to be carried out, reduces computation complexity.After discretization,
This 6 fault signatures form failure symptom attribute, for judging four kinds of states of epicyclic gearbox.Decision attribute values are planet tooth
Four kinds of states of roller box, and respectively by symbolN, F 1, F 2, F 3Represent.
Step 2: planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm：
Can be according to flow graph developing algorithm, the training example structure planetary gear box fault diagnosis stream after being extracted using fault signature
Xiang Tu.First build flow graph symptom attribute node and decision attribute node, then the decision rule in decision table from
Left-to-right is sequentially connected each node, and so as to form oriented Bifurcation Set, and number is flowed through in accumulative node and oriented branch, is saved
Point flow and oriented branch flow, and calculate node stream function and oriented branch's stream function, and be marked on respectively below node
With above oriented branch, the confidence level and coverage of oriented branch are finally calculated, and is marked on above oriented branch, so as to obtain
Planetary gear box fault diagnosis flow graph, as a result as shown in Figure 6.In figurexq, K, C, L, P, GThis 6 layers expression planetary gear
The failure symptom attribute of case, each node therein represent a failure symptom property value.In figureDLayer represents epicyclic gearbox
Fault type, each node therein represent a kind of epicyclic gearbox state.The stream function of symptom attribute node is marked under node
Side.Due to the limitation of length, oriented branch's stream function, confidence level and coverage are omited.
Step 3: removing redundancy or incoherent symptom attribute node using flow graph Algorithm for Reduction, most simple planet is obtained
Fault Diagnosis of Gear Case flow graph：
From fig. 6 it can be seen that bulk redundancy or incoherent failure symptom attribute node are wherein included, therefore can be according to flow direction
Figure Algorithm for Reduction obtains most simple planetary gear box fault diagnosis flow graph.First calculate flow graph in all paths uniformity because
Sonγ, first symptom attribute node, and the oriented branch being connected with it are then deleted, builds new flow graph, and calculate
The consistent sex factor in all paths in new flow graphγ', if consistent sex factorγ≤γ', then this symptom attribute node can
To delete, otherwise this symptom attribute node unsuppressible-suppression, finally judges other symptom attribute nodes successively, until last sign
Million attribute nodes, all deletable symptom attribute nodes are deleted, obtain most simple planetary gear box fault diagnosis flow graph,So as to
The causality between symptom attribute and decision attribute is represented in a manner of most simple, as a result as shown in Figure 7.Due to the limit of length
System, oriented branch's stream function, confidence level and coverage are omited.Only it is left 5 failure symptom attribute layers and 14 as can be seen from Figure 7
Failure symptom attribute node, redundancy or incoherent failure symptom attribute node are rationally deleted.
Step 4: extracting fault diagnosis feature from follow-up failure epicyclic gearbox vibration signal, epicyclic gearbox is formed
Fault diagnosis follow-up example；
The epicyclic gearbox of four kinds of states can obtain 192 groups of samples altogether.The present embodiment, which will regard remaining 48 groups of samples as, to be treated
Examine example.Using the kurtosis of db8 wavelet packets extraction vibration signal, waveform index, peak index, nargin factor, pulse index, width
It is worth spectral amplitude ratio and this 6 fault signatures, and uses symbol respectivelyxq, K, C, L, P, GRepresent.The fault signature of extraction is company
Continuous variable, so sliding-model control need to be carried out, reduce computation complexity.After discretization, this 6 fault signatures form failure sign
Million attributes, for judging four kinds of states of epicyclic gearbox.Decision attribute values be epicyclic gearbox four kinds of states, and respectively by
SymbolN, F 1, F 2, F 3Represent.
Step 5: the fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm：
48 groups of follow-up examples are diagnosed below according to flow graph categorised decision algorithm, verifies that the present invention is a kind of and is based on flow graph
Epicyclic gearbox method for diagnosing faults accuracy.Epicyclic gearbox categorised decision result is as shown in table 1.As seen from Table 1,
Epicyclic gearbox method for diagnosing faults based on flow graph has high accuracy rate.The average standard of four kinds of states of epicyclic gearbox
True rate respectively reaches 93.75%, 95.83%, 95.83% and 97. 92%.Therefore, a kind of planetary gear based on flow graph of the present invention
Box fault diagnosis method can obtain satisfied diagnosis effect.
The epicyclic gearbox categorised decision result of table 1
Claims (4)
1. a kind of epicyclic gearbox method for diagnosing faults based on flow graph, it is characterised in that this method comprises the following steps：
Step 1: extracting fault diagnosis feature from typical epicyclic gearbox vibration signal, the failure for forming epicyclic gearbox is examined
Disconnected training example；
Step 2: planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm；
Step 3: removing redundancy or incoherent symptom attribute node using flow graph Algorithm for Reduction, most simple planetary gear is obtained
Box fault diagnosis flow graph；
Step 4: extracting fault diagnosis feature from follow-up failure epicyclic gearbox vibration signal, the event of epicyclic gearbox is formed
Barrier diagnosis follow-up example；
Step 5: the fault type of follow-up epicyclic gearbox is determined using flow graph categorised decision algorithm.
A kind of 2. epicyclic gearbox method for diagnosing faults based on flow graph according to claim 1, it is characterised in that institute
State in step 2 and planetary gear box fault diagnosis flow graph is built by flow graph developing algorithm；Concretely comprise the following steps：
Step 2 one, structure flow to node of graphN=N C ∪N D , wherein,N C For symptom attribute set of node,N D For decision attribute set of node；
Step 2 two, according to training example be sequentially connected each node from left to right, so as to form oriented Bifurcation SetB；
Number is flowed through in step 2 three, accumulative node and oriented branch, obtains node flowψ(x) and oriented branch flowψ(x,y)；
Step 2 four, calculate node stream function σ (x) and oriented branch's stream function σ (x,y), and be marked on respectively below node and
Above oriented branch；
Step 2 five, calculate oriented branch (x,y)∈BConfidence level cer (x,y) and coverage cov (x,y), and be marked on
Above to branch, so as to obtain planetary gear box fault diagnosis flow graphG。
A kind of 3. epicyclic gearbox method for diagnosing faults based on flow graph according to claim 1, it is characterised in that institute
State in step 3 and remove redundancy or incoherent symptom attribute node using flow graph Algorithm for Reduction, obtain most simple epicyclic gearbox
Fault diagnosis flow graph；Concretely comprise the following steps：
Step 3 one, calculate planetary gear box fault diagnosis flow graphGIn all paths consistent sex factorγ；
Step 3 two, delete first symptom attribute noden, and the oriented branch being connected with itB, build new flow graphG’；
Step 3 three, calculate new flow graphG' in all paths consistent sex factorγ', if consistent sex factorγ≤γ', that
NodenIt can delete, otherwise nodenUnsuppressible-suppression；
Step 3 four, other symptom attribute nodes are judged successively, until last symptom attribute node, deletes all delete
Symptom attribute node, obtain most simple planetary gear box fault diagnosis flow graphG’’。
A kind of 4. epicyclic gearbox method for diagnosing faults based on flow graph according to claim 1, it is characterised in that institute
State the fault type for determining follow-up epicyclic gearbox in step 5 using flow graph categorised decision algorithm；Concretely comprise the following steps：
Step 5 one, according to the fullpath of follow-up example [c i (x),d j ],i=1,2,…,mCalculate the confidence level of fullpath
cer[c i (x),d j ],j=1,2,…,n；
Step 5 two, according to confidence level cer [c i (x),d j ] size, determine the fault type of follow-up example；
Fault type for maximum cer [c i (x),d j ] noded j Represented fault type；
Step 5 three, the coverage for calculating fullpath, the confidence level of fullpath and coverage to be used for the amount of categorised decision
Change evaluation index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710719339.0A CN107421738A (en) | 2017-08-21 | 2017-08-21 | A kind of epicyclic gearbox method for diagnosing faults based on flow graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710719339.0A CN107421738A (en) | 2017-08-21 | 2017-08-21 | A kind of epicyclic gearbox method for diagnosing faults based on flow graph |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107421738A true CN107421738A (en) | 2017-12-01 |
Family
ID=60433766
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710719339.0A Pending CN107421738A (en) | 2017-08-21 | 2017-08-21 | A kind of epicyclic gearbox method for diagnosing faults based on flow graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107421738A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109506936A (en) * | 2018-11-05 | 2019-03-22 | 哈尔滨理工大学 | Bearing fault degree recognition methods based on flow graph and non-naive Bayesian reasoning |
| CN112766047A (en) * | 2020-12-29 | 2021-05-07 | 广东麦德克斯科技有限公司 | Fault diagnosis method for refrigeration system and refrigeration device |
-
2017
- 2017-08-21 CN CN201710719339.0A patent/CN107421738A/en active Pending
Non-Patent Citations (2)
| Title |
|---|
| 牛培路: "基于小波包特征提取和流向图故障决策的齿轮故障诊断", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 黄文涛等: "基于流向图的不完备故障诊断知识表示方法", 《哈尔滨理工大学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109506936A (en) * | 2018-11-05 | 2019-03-22 | 哈尔滨理工大学 | Bearing fault degree recognition methods based on flow graph and non-naive Bayesian reasoning |
| CN109506936B (en) * | 2018-11-05 | 2020-12-22 | 哈尔滨理工大学 | Bearing fault degree identification method based on flow chart and non-naive Bayes inference |
| CN112766047A (en) * | 2020-12-29 | 2021-05-07 | 广东麦德克斯科技有限公司 | Fault diagnosis method for refrigeration system and refrigeration device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107941537B (en) | A kind of mechanical equipment health state evaluation method | |
| Zhao et al. | Deep convolutional neural network based planet bearing fault classification | |
| CN109947086B (en) | Mechanical fault migration diagnosis method and system based on counterstudy | |
| CN109299705A (en) | Rotary machinery fault diagnosis method based on one-dimensional depth residual error convolutional neural networks | |
| Wang et al. | A practical chiller fault diagnosis method based on discrete Bayesian network | |
| CN102208028B (en) | Fault predicting and diagnosing method suitable for dynamic complex system | |
| CN106779069A (en) | A kind of abnormal electricity consumption detection method based on neutral net | |
| CN110297479B (en) | Hydroelectric generating set fault diagnosis method based on convolutional neural network information fusion | |
| CN108332970A (en) | A kind of Method for Bearing Fault Diagnosis based on LS-SVM and D-S evidence theory | |
| CN111046916A (en) | Motor fault diagnosis method and system based on void convolution capsule network | |
| Li et al. | Joint attention feature transfer network for gearbox fault diagnosis with imbalanced data | |
| Xu et al. | Deep coupled visual perceptual networks for motor fault diagnosis under nonstationary conditions | |
| Khuat et al. | Ensemble learning for software fault prediction problem with imbalanced data. | |
| CN104458250A (en) | Intelligent gearbox fault diagnosis method | |
| CN107421738A (en) | A kind of epicyclic gearbox method for diagnosing faults based on flow graph | |
| CN107976934A (en) | A kind of oil truck oil and gas leakage speed intelligent early-warning system based on wireless sensor network | |
| Hui et al. | A hybrid artificial neural network with Dempster-Shafer theory for automated bearing fault diagnosis | |
| Katasev et al. | Neuro-fuzzy model of complex objects approximation with discrete output | |
| Li et al. | Fault diagnosis for rolling bearings of a freight train under limited fault data: Few-shot learning method | |
| CN112329520A (en) | Truck bearing fault identification method based on generation countermeasure learning | |
| CN104483958A (en) | Adaptive data driving fault diagnosis method and device in complex refining process | |
| CN109583751A (en) | The failure decision-making technique of payload | |
| CN107766882A (en) | Epicyclic gearbox method for diagnosing faults based on the more granularities of data-driven quantization characteristic | |
| Yuan et al. | A recursive multi-head graph attention residual network for high-speed train wheelset bearing fault diagnosis | |
| CN115758561A (en) | Method for generating flight simulation parameter data of airplane |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| 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: 20171201 |