CN111523711A - Diagnosis method for leakage fault of hydraulic drive system of injection molding machine - Google Patents
Diagnosis method for leakage fault of hydraulic drive system of injection molding machine Download PDFInfo
- Publication number
- CN111523711A CN111523711A CN202010282999.9A CN202010282999A CN111523711A CN 111523711 A CN111523711 A CN 111523711A CN 202010282999 A CN202010282999 A CN 202010282999A CN 111523711 A CN111523711 A CN 111523711A
- Authority
- CN
- China
- Prior art keywords
- injection molding
- leakage
- data
- molding machine
- simulation
- 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
- 238000001746 injection moulding Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 32
- 238000003745 diagnosis Methods 0.000 title abstract description 11
- 238000004088 simulation Methods 0.000 claims abstract description 38
- 238000010801 machine learning Methods 0.000 claims abstract description 13
- 238000012360 testing method Methods 0.000 claims abstract description 12
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 10
- 238000002347 injection Methods 0.000 claims description 18
- 239000007924 injection Substances 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 15
- 238000012549 training Methods 0.000 claims description 9
- 238000006073 displacement reaction Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000005299 abrasion Methods 0.000 claims description 5
- 238000001816 cooling Methods 0.000 claims description 3
- 238000012795 verification Methods 0.000 claims description 3
- 230000003245 working effect Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 3
- 210000002569 neuron Anatomy 0.000 description 3
- 238000007689 inspection Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000002861 polymer material Substances 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
The invention provides a method for diagnosing leakage faults of a hydraulic drive system of an injection molding machine, which comprises the following steps: step 1, simulating a wear leakage fault and collecting parameter data; step 2, constructing a hydraulic simulation model in the injection molding process, and extracting simulation data; step 3, verifying the consistency of the simulation and test data; step 4, expanding sample data; step 5, obtaining a numerical prediction model through a machine learning algorithm; and 6, inputting sensing data and judging a specific fault mode. The method can diagnose the leakage fault type and the leakage degree of the hydraulic drive system of the injection molding machine, and provides a new method for solving the problem of leakage fault diagnosis of the hydraulic drive system of the injection molding machine.
Description
Technical Field
The invention relates to the technical field of injection molding machine fault diagnosis, in particular to a diagnosis method for leakage faults of a hydraulic drive system of an injection molding machine.
Background
The injection molding machine is high polymer material processing equipment, wherein the injection molding machine is driven by oil pressure to be used in large quantity, and the failure of the hydraulic driving system of the injection molding machine brings difficulty to troubleshooting and maintenance due to high failure frequency, strong concealment, unobvious reaction and delayed effect. The method has the advantages that a predictive model is established for the fault type and the fault degree of the injection hydraulic driving system of the injection molding machine, so that the hydraulic fault of the injection molding machine can be diagnosed and early warned in advance, and the conditions of quality reduction, production efficiency reduction and even forced shutdown in the injection molding production process are prevented.
The traditional diagnosis modes of the hydraulic fault of the injection molding machine comprise a traditional manual inspection method, an expert diagnosis system and an intelligent algorithm system. Traditional manual inspection has no predictability and only can be used for post-processing; the expert diagnosis system uses a knowledge base and an inference machine to complete fault positioning and diagnosis, but the knowledge base is updated slowly, and the system lacks generalization; the intelligent algorithm system can predict the failure cause in advance, has adaptability and needs a large amount of test sample data.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine. The fault early warning method based on the neural network can obviously improve the diagnosis efficiency and accuracy.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a diagnosis method for leakage faults of a hydraulic drive system of an injection molding machine comprises the following steps:
1) simulating a wear leakage fault and collecting parameter data;
a throttling valve is connected in parallel in a hydraulic circuit of the injection molding machine to simulate an abrasion leakage fault, the size of the throttling valve is adjusted, and a sensor is used for collecting parameter data in an injection molding oil way for executing a process action of injection molding; the sensor is an injection molding machine body controller and an external collection device of the injection molding machine; the parameter data is at least one description parameter in the description parameters of the injection molding oil circuit, including main oil circuit pressure, oil pump flow value, injection oil cylinder pressure value and injection displacement value, and the process action is executed into at least one action in the injection molding process (including mold closing, injection table forward movement, injection action, pressure maintaining, plasticizing, cooling, mold opening and product ejection) of the injection molding machine by executing the process action trigger marking time sequence data.
2) Constructing a hydraulic simulation model in the injection molding process, and extracting simulation data;
establishing a hydraulic circuit and a control system simulation model of the injection molding machine by AMEsim/Simulink combination, introducing a leakage model to simulate hydraulic leakage fault, and collecting simulation parameter data in the circuit; the parameter data comprises at least one of the characterizing variables (output pressure, flow rate and injection displacement data).
3) Verifying the consistency of simulation and test data;
and comparing the time-frequency domain curve characteristics of the parameter data obtained by the test and the simulation, evaluating the correlation of the parameter data, and verifying the consistency of the simulation and the test data. Dividing a time subsequence group according to the hydraulic working action of the injection molding machine, counting time domain characteristic parameters (expectation and standard deviation indexes) of the subsequence group, and calculating to obtain a Pearson product moment correlation coefficient of a data curve frequency domain: the time domain characteristic parameter error is less than 10%, the frequency domain correlation coefficient is more than 0.5, and the reliability and consistency of the simulation model are verified.
4) Expanding sample data;
and changing leakage parameters of a pump, a hydraulic cylinder and a valve element in the simulation model, acquiring output pressure, flow and injection displacement data in the simulation loop, and expanding the sample size.
5) Obtaining a numerical prediction model through a machine learning algorithm;
obtaining a numerical prediction model through a machine learning algorithm, wherein the data samples used in machine learning are the time sequence simulation data acquired in the step 4) and the parameter data acquired in the step 1); the machine learning method is a BP neural network, and specifically comprises the following steps:
classifying fault types and leakage degrees of the sample data, wherein the fault types comprise leakage of a pump, an output cylinder and a valve element, and the leakage degrees are classified into normal, mild and severe;
selecting a training set and a verification set to form an input matrix;
setting a target coding matrix according to the classification faults;
and defining a training network by the input matrix and the target matrix, and training to obtain a numerical prediction model.
6) And inputting sensing data and judging a specific fault mode.
And inputting the perception data to a numerical prediction model, and analyzing the fault type and the leakage degree according to a result matrix.
7) Compared with the prior art, the invention has the following obvious prominent substantive characteristics and remarkable technical progress: the method effectively solves the problems of low acquisition efficiency and high cost of training data samples of the hydraulic fault early warning of the existing injection molding machine; and secondly, a method for diagnosing the abrasion leakage fault of the hydraulic element of the injection molding machine is provided, the prediction precision of the algorithm is high, and the generalization is realized.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a schematic flow chart of the present invention
FIG. 2 is a schematic diagram of a simulated wear-leak fault of the present invention
FIG. 3 is a schematic diagram of a neural network topology of the present invention
Detailed Description
The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to the present invention will be further described with reference to the accompanying drawings and preferred embodiments.
The first embodiment is as follows: referring to fig. 1, a method for diagnosing a leakage fault of a hydraulic drive system of an injection molding machine, is characterized in that: the method comprises the following steps:
step 1, simulating a wear leakage fault and collecting parameter data;
step 2, constructing a hydraulic simulation model in the injection molding process, and extracting simulation data;
step 3, verifying the consistency of the simulation and test data;
step 4, expanding sample data;
and 6, inputting sensing data and judging a specific fault mode.
Example two: this embodiment is substantially the same as the embodiment, and is characterized in that:
step 1, simulating abrasion leakage fault and collecting parameter data
The principle of a method for simulating the abrasion leakage fault by connecting a throttle valve in parallel in a hydraulic loop of an injection molding machine is shown in figure 2, the throttle valve is connected in parallel between an oil inlet and an oil outlet, and the opening degree is used as the basis of the leakage degree; the sensor is an injection molding machine body controller and an external collector of the injection molding machine; the parameter data is description parameters of the injection molding oil circuit, including an injection oil cylinder pressure value and an injection displacement value, and the time sequence data is triggered and marked by executing process actions, wherein the executed process actions are six actions including mold closing, forward movement of an injection table, injection actions, cooling, mold opening, product ejection and the like in the injection molding process of the injection molding machine.
Step 2, constructing a hydraulic simulation model in the injection molding process, and extracting simulation data
A hydraulic circuit and a control system simulation model of the injection molding machine are established through AMEsim/Simulink in a combined mode, hydraulic leakage faults and leakage degrees are simulated by adjusting leakage model parameters, and output pressure, flow and injection displacement data in the circuit are collected.
Step 3, verifying the consistency of the simulation and test data
And comparing the time-frequency domain curve characteristics of the parameter data obtained by the test and the simulation, evaluating the correlation of the parameter data, and verifying the consistency of the simulation and the test data. Dividing a time subsequence group according to the hydraulic working action of the injection molding machine, counting time domain characteristic parameters (expectation and standard deviation indexes) of the subsequence group, and calculating to obtain a Pearson product moment correlation coefficient of a data curve frequency domain:
wherein, XiAnd YiAre frequency domain data of the experiment and simulation respectively,
and
are each XiAnd YiIs expected to
The time domain characteristic parameter error is less than 10%, the frequency domain correlation coefficient is more than 0.5, and the reliability and consistency of the simulation model are verified.
Step 4, expanding sample data
Leakage parameters of the hydraulic cylinder and the valve element are changed in the simulation model, output pressure data in the oil way are collected, data 400 groups of different leakage types and leakage degrees are obtained, and the sample size is expanded.
The data samples used by machine learning are the time sequence simulation data collected in the step 4) and the parameter data collected in the step 1); the machine learning method is a neural network, a schematic diagram of a network topological structure 3 is shown, a model of the BP neural network is composed of input layer neurons, hidden layer neurons and output layer neurons, and each node is i, j and k.
The method comprises the following specific steps:
classifying fault types and leakage programs of sample data, wherein the fault types comprise leakage faults of a hydraulic output cylinder and a valve element, and the leakage degree is divided into normal, mild and severe;
selecting a training set and a verification set to form an input matrix;
setting a target coding matrix table 1 according to the classification faults;
| fault marking | Kind of fault | Fault vector |
| Z1 | Normal output cylinder | 0001 |
| Z2 | Slight leakage of the output cylinder | 0010 |
| Z3 | Severe leakage of output cylinder | 0011 |
| Z4 | Valve element normal | 0100 |
| Z5 | Slight leakage of valve element | 1000 |
| Z6 | Heavy leakage of valve element | 1100 |
And defining a training network by the input matrix and the target matrix to obtain a numerical prediction model.
And inputting the perception data to a numerical prediction model, and analyzing the fault type and the leakage degree according to a result matrix.
Claims (7)
1. A method for diagnosing leakage faults of a hydraulic drive system of an injection molding machine is characterized by comprising the following steps:
step 1, simulating a wear leakage fault and collecting parameter data;
step 2, constructing a hydraulic simulation model in the injection molding process, and extracting simulation data;
step 3, verifying the consistency of the simulation and test data;
step 4, expanding sample data;
step 5, obtaining a numerical prediction model through a machine learning algorithm;
and 6, inputting sensing data and judging a specific fault mode.
2. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: in the step 1), a throttle valve is connected in parallel in a hydraulic circuit of the injection molding machine to simulate an abrasion leakage fault, the size of the throttle valve is adjusted, and a sensor is used for collecting parameter data in an injection molding oil way for executing technological actions of injection molding; the sensor is an injection molding machine body controller and an external collection device of the injection molding machine; the parameter data is at least one description parameter in the description parameters of the injection molding oil circuit, including main oil circuit pressure, oil pump flow value, injection oil cylinder pressure value and injection displacement value, and the process action is executed into at least one action in the injection molding process (including mold closing, injection table forward movement, injection action, pressure maintaining, plasticizing, cooling, mold opening and product ejection) of the injection molding machine by executing the process action trigger marking time sequence data.
3. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: in the step 2), an injection molding machine hydraulic circuit and a control system simulation model are established through AMEsim/Simulink combination, a leakage model is introduced to simulate hydraulic leakage faults, and simulation parameter data in the circuit are collected; the parameter data comprises at least one of the characterizing variables (output pressure, flow rate and injection displacement data).
4. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: in the step 3), the time-frequency domain curve characteristics of the parameter data obtained by the test and the simulation are compared, the correlation of the parameter data is evaluated, and the consistency of the simulation and the test data is verified. The time subsequence group is divided according to the hydraulic working action of the injection molding machine, time domain characteristic parameters (expectation and standard deviation indexes) of the subsequence group are counted, a Pearson product moment correlation coefficient of a data curve frequency domain is obtained through calculation, the time domain characteristic parameter error is less than 10%, the frequency domain correlation coefficient is more than 0.5, strong correlation is achieved, and the reliability and consistency of the simulation model are verified.
5. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: and 4) changing leakage parameters of a pump, a hydraulic cylinder and a valve element in the simulation model, acquiring output pressure, flow and injection displacement data in the simulation loop, and expanding the sample size.
6. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: in the step 5), a numerical prediction model is obtained through a machine learning algorithm, and the used data samples for machine learning are the time sequence simulation data acquired in the step 4) and the parameter data acquired in the step 1); the machine learning method is a BP neural network, and specifically comprises the following steps:
classifying fault types and leakage degrees of the sample data, wherein the fault types comprise leakage of a pump, an output cylinder and a valve element, and the leakage degrees are classified into normal, mild and severe;
selecting a training set and a verification set to form an input matrix;
setting a target coding matrix according to the classification faults;
and defining a training network by the input matrix and the target matrix, and training to obtain a numerical prediction model.
7. The method for diagnosing the leakage fault of the hydraulic drive system of the injection molding machine according to claim 1, characterized in that: and 6), inputting the sensing data to a numerical prediction model, and analyzing the fault type and the leakage degree according to the result matrix.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010282999.9A CN111523711A (en) | 2020-04-08 | 2020-04-08 | Diagnosis method for leakage fault of hydraulic drive system of injection molding machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010282999.9A CN111523711A (en) | 2020-04-08 | 2020-04-08 | Diagnosis method for leakage fault of hydraulic drive system of injection molding machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111523711A true CN111523711A (en) | 2020-08-11 |
Family
ID=71902328
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010282999.9A Pending CN111523711A (en) | 2020-04-08 | 2020-04-08 | Diagnosis method for leakage fault of hydraulic drive system of injection molding machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111523711A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926400A (en) * | 2021-01-29 | 2021-06-08 | 华南理工大学 | Intelligent diagnosis method and system for leakage fault in hydraulic cylinder based on data driving |
-
2020
- 2020-04-08 CN CN202010282999.9A patent/CN111523711A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112926400A (en) * | 2021-01-29 | 2021-06-08 | 华南理工大学 | Intelligent diagnosis method and system for leakage fault in hydraulic cylinder based on data driving |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111460728B (en) | Method and device for predicting residual life of industrial equipment, storage medium and equipment | |
| CN110110768A (en) | Fault Diagnosis of Roller Bearings based on Concurrent Feature study and multi-categorizer | |
| CN109918752A (en) | Mechanical failure diagnostic method, equipment and medium based on migration convolutional neural networks | |
| CN107272644B (en) | The DBN network fault diagnosis method of latent oil reciprocating oil pumping unit | |
| CN114048600A (en) | Digital twin-driven multi-model fusion industrial system anomaly detection method | |
| CN106649789A (en) | Integrated semi-supervised Fisher's discrimination-based industrial process fault classifying method | |
| CN103116306B (en) | Automatic stepping type ordered time interval dividing method | |
| CN102175282A (en) | Method for diagnosing fault of centrifugal air compressor based on information fusion | |
| CN110555230A (en) | rotary machine residual life prediction method based on integrated GMDH framework | |
| CN105574587A (en) | On-line condition process monitoring method for plastic injection moulding process | |
| CN110647911A (en) | Bearing fault diagnosis method based on principal component analysis and deep belief network | |
| CN113128338A (en) | Intelligent diagnosis method for printing machine roller fault under small sample | |
| CN111324110A (en) | Fermentation process fault monitoring method based on multiple shrinkage automatic encoders | |
| CN110348468A (en) | A kind of bearing inferior health recognition methods of the strong reconstruct edge noise reduction autocoder of Method Using Relevance Vector Machine optimization | |
| CN114091525A (en) | Rolling bearing degradation trend prediction method | |
| CN111523711A (en) | Diagnosis method for leakage fault of hydraulic drive system of injection molding machine | |
| CN113431925B (en) | Fault prediction method of electro-hydraulic proportional valve | |
| CN113378887B (en) | Emulsion pump fault grading diagnosis method | |
| CN111753465B (en) | Method for diagnosing leakage fault in buffering oil cylinder of anti-collision buffering hydraulic system of ship lift | |
| CN111241629B (en) | Intelligent prediction method for performance change trend of hydraulic pump of airplane based on data driving | |
| CN113029619A (en) | Underground scraper fault diagnosis method based on C4.5 decision tree algorithm | |
| CN114417704A (en) | Wind turbine generator health assessment method based on improved stack type self-coding | |
| CN109932904A (en) | Monitoring abnormal state and control system based on feature selecting and pivot control | |
| CN113191232B (en) | Electro-hydrostatic actuator fault identification method based on multi-mode homologous features and XGboost model | |
| CN108491878A (en) | A kind of failure modes diagnostic method generating model based on multiple errors |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| CB02 | Change of applicant information |
Address after: 9 / F, building C9, No.19, Lane 299, Guanghua Road, high tech Zone, Ningbo City, Zhejiang Province Applicant after: Ningbo Zhongke Information Technology Application Research Institute (Ningbo Artificial Intelligence Industry Research Institute) Applicant after: Ningbo Zhongke Integrated Circuit Design Center Co.,Ltd. Address before: 9 / F, building C9, No.19, Lane 299, Guanghua Road, high tech Zone, Ningbo City, Zhejiang Province Applicant before: NINGBO INSTITUTE OF INFORMATION TECHNOLOGY APPLICATION, CAS Applicant before: Ningbo Zhongke Integrated Circuit Design Center Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20200811 |
|
| WD01 | Invention patent application deemed withdrawn after publication |