CN107298485A - It is a kind of based on method of the data model to the fault detection and diagnosis of During Industrial Wastewater Treatment Process - Google Patents

It is a kind of based on method of the data model to the fault detection and diagnosis of During Industrial Wastewater Treatment Process Download PDF

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CN107298485A
CN107298485A CN201710621800.9A CN201710621800A CN107298485A CN 107298485 A CN107298485 A CN 107298485A CN 201710621800 A CN201710621800 A CN 201710621800A CN 107298485 A CN107298485 A CN 107298485A
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data
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wastewater treatment
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颜学峰
王蓓
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East China University of Science and Technology
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    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F3/00—Biological treatment of water, waste water, or sewage
    • C02F3/30—Aerobic and anaerobic processes
    • C02F3/302—Nitrification and denitrification treatment
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/08—Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/10—Solids, e.g. total solids [TS], total suspended solids [TSS] or volatile solids [VS]
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2209/00—Controlling or monitoring parameters in water treatment
    • C02F2209/40—Liquid flow rate
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2303/00—Specific treatment goals
    • C02F2303/14—Maintenance of water treatment installations

Abstract

The invention discloses a kind of based on method of the data model to the fault detection and diagnosis of During Industrial Wastewater Treatment Process.Input variable is used as using inhalant region dissolved oxygen amount, aerobic zone suspended solids content, water inlet flow, delivery port flow, COD and delivery port suspended solids content；It is normalized based on input variable, Holistic modeling is carried out to training data using PCA, is that each variable chooses pivot respectively according to the load vectors of generation, forms subspace；The T per sub-spaces is calculated respectively2Statistic, and construct SVDD models；Normalized real-time monitoring data is projected into each subspace, calculating obtains corresponding T2Statistic；Counting statistics amount in the higher dimensional space that SVDD is constructed apart from the center of circle distance, so as to judge whether the monitoring data at the moment normal.

Description

It is a kind of based on data model to the fault detection and diagnosis of During Industrial Wastewater Treatment Process Method
Technical field
The invention belongs to industrial chemical and process control crossing domain, it is related in During Industrial Wastewater Treatment Process, passes through measurement Data volume reflects the method for system running state in time.
Background technology
The construction of Industrial Wastewater Treatment engineering is mainly the problem of solution drainage system sewage is arbitrarily discharged, and improves and administers Sewage is to local ecological severe contamination problem.The displacement mass of industrial system is for the living environment of people, and social holds Supervention exhibition, suffers from vital influence.
It is food industrial wastewater for influent quality, its biodegradability is higher.Concentration of wastewater is higher, its biochemical oxygen demand (BOD) (Biochemical oxygen demand, BOD), COD (Chemical oxygen demand, COD), nitrogen always contain Amount (total nitrogen, TN), phosphorus total content (total phosphorous, TP) isoconcentration are the 3-4 of ordinary municipal sewage Times.For extensive sewage, treatment plant is using the bio-aeration technique that most technique is exactly traditional activated sludge process.This work Skill is to right using the aerobe (including bacterium and protozoa) cultivated in same artificial environment after physical treatment course Organic contamination matter in sewage is degraded.The technique is relatively simple, and the removal efficiency to COD, BOD and SS can reach expection It is required that.But, this method only has single biotic environment, it is impossible to play and the reinforcing biological nature of different microorganisms and excellent Gesture, can neither improve effective processing to high-concentration sewage, effective removal that can not be to compound fertilizer, it is therefore necessary to Expected processing target is met using biochemical two stage treatment.The approach that biologic treating technique is followed mainly has two：One is The microbial biomass of participation effect is improved, the probability that increase organic matter is contacted with microorganism, its means realized are to improve mixed liquor Concentration.Two be the metabolic characteristic for playing different microorganisms advantage, and there is provided matched with superior microorganism physiological property for screening strain Biotic environment.Make each quasi-microorganism try one's best " to divide labour with individual responsibility ", play sharpest edges to realize desired processing target. Biological phosphate-eliminating denitrification process is to take this approach.Its denitrogenation is, by extending aeration time, longer to be disappeared using generation time Change bacterium, be nitrate by mineralized nitrogen, recycle the amphimicrobian under anoxia condition is counter to digest bacterium nitrate nitrogen is converted into gaseous state Nitrogen is escaped, so as to reach the purpose of NH3-N in water removal.Dephosphorization is entered first with facultative under anaerobic condition and aerobic polyP bacteria Effective release of row phosphorus, recycle through anaerobism put the flora of phosphorus under aerobic condition everybody breed and inhale the characteristic of phosphorus, make raw sewage In phosphorus be converted into biological cell (activated sludge), separated eventually through precipitation, rich phosphorus excess sludge excluded to realize.By In this complete dephosphorization, denitrification process respectively anaerobism, and oxygen and it is aerobic under the conditions of carry out, so commonly referred to as A2/ O techniques (i.e. the aerobic Oxic techniques of anaerobism Anaerobic+ anoxics Anoxic+).On the basis of this technologic improvement, design whole Body sewage disposal system, its process flow diagram is shown in Fig. 1.
A in sewerage2The detailed process of/O parts, the pond body is made up of four parts, parallel operation.Per pond in the past extremely It is inhalant region, anaerobic zone, anoxic zone, aerobic zone successively afterwards.Quick-fried oxygen head is installed, remaining each area is respectively mounted stirs under water in aerobic zone Device, prevents mixed liquor formation precipitation.A2The running of/O reaction tanks has vital effect for wastewater treatment process. Such as when water intake velocity is too fast, make to react insufficient in reaction tank, or oxygen content is not in normal range (NR) in reaction tank so that nitrogen phosphorus Removal is insufficient, causes the water quality after processing not up to standard when serious, influence ecological environment.Therefore, how to determine to produce shape at present State, maintained equipment are safe and stable to be run with optimization, is one of Industrial Wastewater Treatment engineering major issue in the urgent need to address.
The process model building based on data model obtains a large amount of successfully applications in the industry in recent years, solves many fortune Row system status monitoring problem.Process monitoring based on data-driven is that process data is modeled during utilizing collection system, structure The monitoring statisticss amount of process observation is made, so that the state of monitoring system operation, finds system operation failure and diagnosed in time. A2Biochemical reaction in/O reaction tanks also generates mass data, and these data contain the feature letter of sewage disposal process Breath, therefore can be produced based on During Industrial Wastewater Treatment Process and analyze data, set up procedure correlation model.Present invention employs base Pivot analysis supporting vector test in data domain (the Loading-based Principal component of pivot are chosen in load selection for Principal component anaylsis integrated with Support Vector Data Description, PPCA-SVDD) modeling method, built using the process data being collected into During Industrial Wastewater Treatment Process Vertical mathematical modeling.Choose certain sensitive pivot for each variable and constitute and become vector subspace, the change to each variable is more sensitive, So as to possess more preferable procedure fault information extraction ability.Constructed by SVDD statistics so that process monitoring statistic has The ability of course of reaction state, so as to reach the purpose of procedure fault detection.After failure is detected, according to process data, do Go out corresponding variable contribution plot so that procedure fault reason is diagnosed, so as to further amendment failure so that process is extensive Multiple normal operation.
The content of the invention
The present invention seeks to the process data using Industrial Wastewater Treatment factory there is provided the statistic of a condition monitoring come Running is monitored, the system failure is found in time and is diagnosed.The observational variable of selection has：Inhalant region dissolved oxygen amount DO (x1, Mg/L), aerobic zone suspended solids content SS (x2, mg/L), water inlet flow Qin(x3,m3/ s), delivery port flow Qef(x4,m3/s)、 COD CODin(x5, mg/L) and delivery port suspended solids content SSef(x6,mg/L).It regard this 6 variables as the work The raw monitored variable of industry Waste Water Treatment, using related measuring instrumentss, direct measurement or indirectly calculating obtain x1~x6。 Commercial plant sample data is gathered, data model is built, by calculating the process monitoring statistic obtained to wastewater treatment process Fault detection and diagnosis is realized, with the normal operation of safeguards system process.
1. the selection of raw monitored variable
In the technological process of Industrial Wastewater Treatment, A2/ O reaction tanks are the core cells in whole system.Influence reaction tank The key factor that oxidation reaction is normally run has：Oxygen content, actual oxygen demand, the flow of water inlet in reaction tank, the stream of water outlet Amount, reactant concentration.
It is characteristic of the invention that：
By using the sample data of process collection, the pivot analysis supporting vector data that pivot is chosen based on load are set up Domain describes (PPCA-SVDD) model, and construction process monitoring statisticss amount, realization is monitored in real time to whole service process, so that Procedure fault is monitored in time and is diagnosed.
Therefore, based on above analytic explanation, the monitored parameterses of the Industrial Wastewater Treatment engineering choose as follows：
(1) inhalant region dissolved oxygen amount DO (x1,mg/L)
(2) aerobic zone suspended solids content SS (x2,mg/L)
(3) water inlet flow Qin(x3,m3/s)
(4) delivery port flow Qef(x4,m3/s)
(5) COD CODin(x5,mg/L)
And delivery port suspended solids content SS (6)ef(x6,mg/L)
Six variables of the above can be directly obtained by measuring instrument.
2. the pretreatment of modeling sample
In order to eliminate the influence of dimension, pretreatment is normalized in the sample data to collection.Input variable utilizes formula (1) It is normalized：
(1) in formula, xiIt is the actual measured value of i-th of observational variable, μiRepresent observed quantity xiCorresponding average, σiTable Show observed quantity xiCorresponding variance,Represent the value after i-th of input variable normalization.
The representational commercial plant data of n groups are collected, wherein every group of packet [x containing input variable1,x2,x3,x4,x5, x6], obtained after formula (1) normalizedForm modeling sample.
For the data x in t real-time collectingt, place is normalized using the mean variance obtained in formula (1) to it Reason, it is as follows,
Wherein, μ=[μ1,μ2,…,μ6], and σ=[σ1,σ2,…,σ6]。
3. the During Industrial Wastewater Treatment Process monitoring model based on PPCA-SVDD
Assuming that the sample size of modeling sample is n, modeling data is normalized according to (1) formula first, using PPCA-SVDD Modeling method, sets up initial model；Then, by analyzing loading matrix P and each load vectors p that PCA modelings are producediIt is right The eigenvalue λ answeredi, the conversion weighted value calculated according to (2) formula is that each variable chooses sensitive pivot.
Wherein, pi,jFor (i, j) individual element in loading matrix.For a certain variableFor, calculate the institute corresponding to it There is the average of weighted valueBy comparing wi,jWithChoose the load vectors corresponding to the conversion weighted value more than average, structure Into variable xiProjector spaceMeanwhile, corresponding characteristic value also retains constitutive characteristic value matrixSo, each variable Produce corresponding new subspace.Data are projected to each space respectively, sub- statistic can be calculated according to (3) formula
Because the subspace corresponding to each variable is more sensitive for the change of the variable, therefore work as variable When failure changes, corresponding space faster can more accurately be detected.Normal training sample obtains corresponding to 6 changes The statistic of amount, will according to Support Vector data description domain SVDDUtilize functionNon-linear projection is to higher dimensional space, then finds can surround data point after projection Suprasphere as small as possible, wherein,Problem is converted to the following optimization of solution by SVDD Problem：
Wherein, R and τ represent radius and the center of suprasphere respectively, and parameter C represents spheroid volume size and normal sample one's duty Balance between wrong fraction, ξiCoefficient of relaxation is represented, that is, allows the misjudged probability of training sample.The antithesis of above-mentioned optimization problem Form can be expressed as,
Wherein βiFor corresponding Lagrange multiplier, SVDD chooses 0≤βiEach sample corresponding to≤C as support to Amount.So, the centre of sphere and radius of suprasphere can just be tried to achieve according to following formula,
Wherein,For any one in model supports vector.
There is the normalization target detection sample for including six variables for one that t is collectedIt projects to each son Space, calculates the statistic composition statistics moment matrix of each subspaceIts square distance for arriving the centre of sphere is calculated againIt can be expressed as,
When distance of the test sample to the center of circle in feature space is more than radius obtained by training sample, it is believed that the sample This is exceptional sample, conversely, the sample considered to be in normal condition.The design of process monitoring statistic is as follows,
When DR statistics are less than 1, it is believed that system is normal；When more than 1, that is, think that process monitoring statistic detects event Barrier, gives a warning.Then, it should just carry out corresponding fault diagnosis and carry out the basic reason that discovery procedure is broken down, so as to repair System.Here, using the pivot information in the subspace that can monitor failure, contribution rate is calculated, it is as follows
WhereinForIn j-th of element ForIn j-th of element,ForIn (i, j) it is individual Element, then can calculate and obtain variable xiFor scoreContribution rate conti,j.Then, variable xiTotal contribution rate can calculate ,
Because the corresponding sensitive pivot of variable contains more fault information volumes on process, therefore based on sensitive pivot choosing Made contribution plot is taken to provide a more accurate fault diagnosis result.
4. process monitoring statistic in line computation in industrial wastewater treatment system
The online calculation process of industrial wastewater treatment system process monitoring statistic is as shown in Figure 2.It is inhalant region dissolved oxygen amount, aerobic Area suspended solids content SS, water inlet flow, delivery port flow, COD and delivery port suspended solids content, based on above-mentioned Data are normalized by the direct measurement data of 6 input variables by (1) formula；Training data is carried out using PCA Holistic modeling, is that each variable chooses pivot respectively according to the load vectors of generation, forms subspace；Calculate empty per height respectively Between T2Statistic, and construct SVDD models；For the real time data of monitoring, the mean variance for utilizing (2) formula to produce is returned One change is handled；Normalized real-time monitoring data is projected into each subspace, calculating obtains corresponding T2Statistic；Counting statistics The distance apart from the center of circle in the higher dimensional space that SVDD is constructed is measured, so as to judge whether the monitoring data at the moment is normal.In addition, When faulty in discovery system, the contribution rate of variable is calculated with formula (11) (12), fault diagnosis is realized.
Brief description of the drawings
Fig. 1 technique for treating industrial wastewater FB(flow block)s.
The online calculation process of Fig. 2 During Industrial Wastewater Treatment Process monitoring statisticss amounts.
Embodiment
The invention will be further described by the following examples：
For six variables, inhalant region dissolved oxygen amount DO (x1, mg/L), aerobic zone suspended solids content SS (x2, mg/L), water inlet Mouth flow Qin(x3,m3/ s), delivery port flow Qef(x4,m3/ s), COD CODin(x5, mg/L) and delivery port suspension Body content SSef(x6, mg/L), the history normal data for gathering the system of one group of 100 sample is used as training data, and one group The real-time process being made up of 100 samples observes data.
1. pre-process sample
First group of data being made up of 200 samples to above-mentioned collection are normalized, and utilize (1) formula：x1's Average is 3.78, and variance is 1.37；x2Average be 4.00, variance is 1.27；x3Average be 3417.48, variance is 1290.31；x4Average be 4090.31, variance is 1274.90；x5Average be 24.82, variance is 3.58；x6Average be 19.58, variance is 33.53.Calculating is normalized：
To the data x of t real-time collectingt(8) formula of utilization is normalized
Wherein, mean μ=[3.78,4.00,3417.48,4090.31,24.82,19.58], variances sigma=[1.37, 1.27,1290.31,1274.90,3.58,33.53].Here there is the form of decimal, 2 significant digits, four houses five are retained without exception Enter to calculate.
2. the construction of statistic in the Monitoring Model based on PPCA-SVDD
Using PPCA-SVDD modeling methods, sample number is modeled for 100 training data, sample number is 100 number According to being tested.Its specific model parameter is as follows：
(1) PCA modelings are carried out to training data first, produces six pivots, it is specific such as to calculate weighting matrix by formula (3) Under：
According to weighted value be more than the variable to the averages of all weighted values, that is, choose the vectorial structure corresponding to the weighted value This principle of subspace is made, the pivot result that each variable is chosen is as follows：
Variable x1:p2,p3,p5
Variable x2:p2,p3
Variable x3:p1,p6
Variable x4:p1,p6
Variable x5:p1,p2,p3,p5
Variable x6:p1,p4
Thus, 6 variables of correspondence generate 6 sub-spaces, and decentralized supervisory control is realized to each variable.Conversion per sub-spaces Matrix is：
Space 1：
Space 2：
Space 3：
Space 4：
Space 5：
Space 6：
(2) in kernel function K (), s2=100；
(3) in SVDD models, parameter C=1.2；
(4) the SVDD models being made up of each subspace statistic, the parameter value of generation is as follows：
β value is 0.3006,0.4931,0.6545,0.8119,0.9374,0；Suprasphere radius R=0.216.
Described above by example, based on industrial wastewater treatment system, by measuring obtained inhalant region dissolved oxygen amount, good Oxygen area suspended solids content, water inlet flow, delivery port flow, COD and delivery port suspended solids content, in real time, Line monitoring system running status.
The model obtained by above-mentioned example, here is the test data sample that one group of real-time collecting is arrived：
Obtained through normalizing calculating：
By (10) formula, it is 0.3322 to calculate and obtain the statistic of the real-time sample.The value is less than 1, it is believed that be the moment System is in normal condition.DR normalized set results corresponding to 100 test samples are as follows：
Overstriking data represent that the point exceedes control limit 1 in result of calculation, are abnormal data.Statistic at 81st point is 15.5309, hence it is evident that more than control limit.The process data at the moment is used to do fault diagnosis, calculates the contribution rate of 6 variables Respectively：
0.315,2.031,0.929,2.743,46.401,0.972
From above diagnostic result, variable x5Contribution rate to this failure is maximum, therefore is the change in wastewater treatment process Learn oxygen demand and there occurs variable.

Claims (4)

1. it is a kind of based on fault detection method of the data model to During Industrial Wastewater Treatment Process, with inhalant region dissolved oxygen amount DO (x1,mg/ L), aerobic zone suspended solids content SS (x2, mg/L), water inlet flow Qin(x3,m3/ s), delivery port flow Qef(x4,m3/ s), change Learn oxygen demand CODin(x5, mg/L) and delivery port suspended solids content SSef(x6, mg/L) and it is used as input variable；Become based on input Amount is normalized, and Holistic modeling is carried out to training data using PCA, is each variable point according to the load vectors of generation Pivot is not chosen, forms subspace；The T per sub-spaces is calculated respectively2Statistic, and construct SVDD models；Will be normalized Real-time monitoring data projects to each subspace, and calculating obtains corresponding T2Statistic；The higher-dimension that Counting statistics amount is constructed in SVDD Apart from the distance in the center of circle in space, so as to judge whether the monitoring data at the moment is normal.
2. it is a kind of based on method for diagnosing faults of the data model to During Industrial Wastewater Treatment Process, it is the PPCA- based on claim 1 SVDD During Industrial Wastewater Treatment Process monitoring model, when DR statistics are less than 1, it is believed that system is normal；When more than 1, that is, recognize Failure is detected for process monitoring statistic, is given a warning.Then, it should just carry out corresponding fault diagnosis and carry out discovery procedure The basic reason of failure, so that repair system；Using the pivot information in the subspace that can monitor failure, contribution rate is calculated, It is as follows：
<mrow> <msub> <mi>cont</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfrac> <msubsup> <mi>t</mi> <mi>j</mi> <mo>*</mo> </msubsup> <msubsup> <mi>&amp;lambda;</mi> <mi>j</mi> <mo>*</mo> </msubsup> </mfrac> <msubsup> <mi>p</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> <mo>*</mo> </msubsup> <msub> <mover> <mi>x</mi> <mo>&amp;OverBar;</mo> </mover> <mi>i</mi> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>11</mn> <mo>)</mo> </mrow> </mrow>
WhereinForIn j-th of element ForIn j-th of element,ForIn (i, j) individual element, It can then calculate and obtain variable xiFor scoreContribution rate conti,j；Then, variable xiTotal contribution rate can calculate,
<mrow> <msub> <mi>CONT</mi> <mi>i</mi> </msub> <mo>=</mo> <msubsup> <mi>&amp;Sigma;</mi> <mi>j</mi> <mi>m</mi> </msubsup> <msub> <mi>cont</mi> <mrow> <mi>i</mi> <mo>,</mo> <mi>j</mi> </mrow> </msub> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>12</mn> <mo>)</mo> </mrow> </mrow>
The contribution rate of variable is calculated with formula (11), (12), so as to realize fault diagnosis.
3. it is according to claim 1 based on fault detection method of the data model to During Industrial Wastewater Treatment Process, its feature It is the inhalant region dissolved oxygen amount, aerobic zone suspended solids content, water inlet flow, delivery port flow, COD and to go out Mouth of a river suspended solids content, is directly measured by measuring instrumentss.
4. it is according to claim 1 based on fault detection method of the data model to During Industrial Wastewater Treatment Process, its feature It is that the modeling data is normalized using following formula：
<mrow> <msub> <mover> <mi>x</mi> <mo>&amp;OverBar;</mo> </mover> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>-</mo> <msub> <mi>&amp;mu;</mi> <mi>i</mi> </msub> </mrow> <msub> <mi>&amp;sigma;</mi> <mi>i</mi> </msub> </mfrac> <mo>,</mo> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>...</mo> <mo>,</mo> <mn>6</mn> </mrow>
Wherein,--- the value after i-th of input variable normalization,
μi--- observed quantity xiCorresponding average,
σi--- observed quantity xiCorresponding variance；
Real-time monitoring data xt, it is normalized using following formula：
<mrow> <msub> <mover> <mi>x</mi> <mo>&amp;OverBar;</mo> </mover> <mi>t</mi> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>x</mi> <mi>t</mi> </msub> <mo>-</mo> <mi>&amp;mu;</mi> </mrow> <mi>&amp;sigma;</mi> </mfrac> </mrow>
Wherein, μ --- include each element average, i.e. μ=[μ1,μ2,…,μ6]；
σ --- include each element variance, i.e. σ=[σ1,σ2,…,σ6]；
Gather commercial plant data, after normalization, using proposed based on the pivot analysis that load chooses pivot support to Measure test in data domain algorithm (PPCA-SVDD), building process monitoring mathematical modeling.Using loading matrix characteristic, become for each Amount selects pivot respectively, constitutes monitoring subspace, the normal condition T of generation2Statistic is used to build SVDD models.
CN201710621800.9A 2017-07-27 2017-07-27 It is a kind of based on method of the data model to the fault detection and diagnosis of During Industrial Wastewater Treatment Process Pending CN107298485A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110033105A (en) * 2019-04-18 2019-07-19 中国人民解放军国防科技大学 Suspension system fault detection method for unbalanced data set under complex working condition

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101403923A (en) * 2008-10-31 2009-04-08 浙江大学 Course monitoring method based on non-gauss component extraction and support vector description
CN102487293A (en) * 2010-12-06 2012-06-06 中国人民解放军理工大学 Satellite communication network abnormity detection method based on network control
CN103472820A (en) * 2013-09-18 2013-12-25 哈尔滨工业大学 Method for diagnosing propulsion system failure based on partial least squares algorithm
CN103645249A (en) * 2013-11-27 2014-03-19 国网黑龙江省电力有限公司 Online fault detection method for reduced set-based downsampling unbalance SVM (Support Vector Machine) transformer
CN205528209U (en) * 2016-01-26 2016-08-31 临沂高新区鲁润净水设备有限公司 Ozone purifier intelligence control system based on fuzzy neural network

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101403923A (en) * 2008-10-31 2009-04-08 浙江大学 Course monitoring method based on non-gauss component extraction and support vector description
CN102487293A (en) * 2010-12-06 2012-06-06 中国人民解放军理工大学 Satellite communication network abnormity detection method based on network control
CN103472820A (en) * 2013-09-18 2013-12-25 哈尔滨工业大学 Method for diagnosing propulsion system failure based on partial least squares algorithm
CN103645249A (en) * 2013-11-27 2014-03-19 国网黑龙江省电力有限公司 Online fault detection method for reduced set-based downsampling unbalance SVM (Support Vector Machine) transformer
CN205528209U (en) * 2016-01-26 2016-08-31 临沂高新区鲁润净水设备有限公司 Ozone purifier intelligence control system based on fuzzy neural network

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110033105A (en) * 2019-04-18 2019-07-19 中国人民解放军国防科技大学 Suspension system fault detection method for unbalanced data set under complex working condition

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Application publication date: 20171027