CN107390661A - A kind of method for early warning of process flow industry process abnormal state - Google Patents
A kind of method for early warning of process flow industry process abnormal state Download PDFInfo
- Publication number
- CN107390661A CN107390661A CN201710750153.1A CN201710750153A CN107390661A CN 107390661 A CN107390661 A CN 107390661A CN 201710750153 A CN201710750153 A CN 201710750153A CN 107390661 A CN107390661 A CN 107390661A
- Authority
- CN
- China
- Prior art keywords
- mrow
- early warning
- trend
- msup
- mfrac
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 14
- 238000005070 sampling Methods 0.000 claims description 15
- 238000012544 monitoring process Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims 1
- 230000015556 catabolic process Effects 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 238000013024 troubleshooting Methods 0.000 abstract description 2
- 238000004364 calculation method Methods 0.000 description 5
- 230000005856 abnormality Effects 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000004821 distillation Methods 0.000 description 3
- 238000003786 synthesis reaction Methods 0.000 description 3
- 241001269238 Data Species 0.000 description 2
- 230000002547 anomalous effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000001568 sexual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Testing And Monitoring For Control Systems (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
Abstract
The invention discloses a kind of method for early warning of process flow industry process abnormal state, sets trend and the warning information of limit value first, trending early warning is divided into 4 classes according to operating mode trend, concurrently sets the bound section of duty parameter limit value early warning；Time shaft is then based on, choosing p sampled point forward using current point as starting point carries out recurrence calculating, obtains slope and coefficient correlation；Move forward and 1 sampled point and compute repeatedly n times altogether every time then according to above-mentioned steps；The last identification process according to trending early warning, the trending early warning classification of current working is determined, according to the identification process of limit value early warning, judge whether to produce limit value early warning, and the recognition result of integrative trend early warning and limit value early warning, judge the warning level of current operating condition.This method is practical, reliable, has both reduced the probability for causing erroneous judgement because of noise data, and and can gives warning in advance to unusual service condition.This method to timely troubleshooting, reduce breakdown loss, ensure that production safety has important application value.
Description
Technical field
The present invention relates to the fault detect in process flow industry process field and warning aspect, based on trend and the class early warning of limit value two
Information, establish real-time detection and the method for early warning of a kind of abnormality.
Background technology
Failure or abnormality in the process industry such as oil, petrochemical industry field, often reported using Distributed Control System (DCS)
It is alert.However, for DCS generally using fixed bound alarm, existing can not provide that early warning, alarm are not in time etc. many to ask at present
Topic.
Have the method for early warning of some failures or exception at present, be broadly divided into based on model and big based on data-driven two
Class.Method based on model needs profound understanding and grasps accurate technical process mechanism, is greatly limited in actual applications
System；Method based on data-driven is to gather real-time and historical data from DCS, then carries out data processing and judgement.But data
The method of driving remains in some problems in actual applications, for example, the data of measurement easily cause failure often with noise
Wrong report；And the filtering method used sometimes to eliminate noise is readily incorporated lag issues, alarm is caused not in time.Therefore,
A practicality, reliable abnormality early warning system how are established, balance can be found between accuracy, real-time, in recent years
Increasingly cause concern.
The content of the invention
It is extremely pre- to establish process status in view of the above-mentioned problems, combine trend and the early warning identification information of limit value by the present invention
Alert judgment rule and flow, detection and early warning in real time can be carried out to changing violent or gradual change sexual abnormality.
The present invention uses following technical scheme：
A kind of method for early warning of process flow industry process abnormal state, this method combine the early warning identification letter of trend and limit value
Breath, establishes the judgment rule and flow of process status abnormity early warning, has steps of：
(1) trending early warning is divided into 4 classes according to operating mode trend：Trend rapid increase, trend rapid decrease, the big amplitude wave of trend
Dynamic and trend is normal；
(2) the upper limit x in duty parameter normal operation section is sethWith lower limit xl；
(3) real-time running data is extracted from monitoring system, using current sampling point as starting point, p history samples point is returned
Return calculating, obtain slope k1And correlation coefficient r1；
(4) based on time shaft 1 sampled point of reach, again to p sampled point digital simulation slope k2And correlation coefficient r2, weight
This multiple process, until obtaining k1~knAnd r1~rn；
(5) foundation trending early warning identification process, the trending early warning classification of current working is determined：If trend is rapid increase, soon
Speed declines or fluctuation, then performs step (6)；If trend is normal, step is performed (7)；
(6), according to limit value early warning identification process, judge whether to produce limit value early warning；
(7) the recognition result of integrative trend and limit value, the warning level of the current operating condition of judgment means.
Specifically, process provides to trend rapid increase in current working and the identification process of rapid decrease early warning：
(1) r is counted1~rnIn coefficient correlation early warning range q1l≤|r|≤q1rRatio w1, and with threshold value m1Compare, if
w1＜ m1The trending early warning that fluctuation is then carried out to current working identifies；If w1≥m1Then go to step (2)；
(2) k is counted1~knIn slope early warning range ul≤|k|≤urRatio w2, and with threshold value m2Compare, if just oblique
Rate section and w2≥m2Then it is determined as trend rapid increase；If between negative slope region and w2≥m2Then it is determined as trend rapid decrease；
If w2＜ m2Then it is determined as that trend is normal.
Specifically, process provides the identification process to trend fluctuation early warning in current working：
(1) r is counted1~rnIn coefficient correlation early warning range q2l≤|r|≤q2rRatio w3, and with threshold value m3Compare, if
w3＜ m3Then it is determined as normal trend, if w3≥m3Then go to step (2)；
(2), using current sampling point as starting point, 2 are chosen forward based on time shaftpIndividual sampled point simultaneously calculates standard deviation v, with threshold value
m4Compare, if v >=m4Then it is judged as trend fluctuation, if v ＜ m4Then it is judged as normal trend.
Specifically, process provides the identification process to limit value early warning in current working, chosen forward based on time shaft
3 sampled points including current point are predicted to following 3 sampled points, are comprised the following steps that：
(1) current sampling point is x (p), and T is the sampling period, sets weight coefficient α, β and γ, makes alpha+beta+γ=1, extracts x
(p), x (p-1) and x (p-2) amount to 3 sampled points；
(2) K is calculated according to following formula(0) 1、K(0) 2And K(0) 3
According to formulaCalculateAccording to formulaMeter
Calculate
(3) K is calculated according to following formula(1) 1、K(1) 2And K(1) 3
According to formulaCalculateAccording to formulaMeter
Calculate
(4) K is calculated according to following formula(2) 1、K(2) 2And K(2) 3
According to formulaCalculateAccording to formula
Calculate
(5) ifWithIn have the limit value that 2 and the above exceed given duty parameter, that is, produce
Raw limit value early warning.
Preferably, identification information of this method based on trend and limit value early warning, the pre- of a set of process status exception is established
Alert judgment rule, wherein will determine that result is divided into three warning levels：Green, yellow and red, its Green are representing operating mode just
Often, yellow represents to point out, red for alarm state, and specific rules are as follows：
| Classification | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Trend rapid increase | It is | It is | It is no | It is no | It is no | It is no | It is no | It is no |
| Trend rapid decrease | It is no | It is no | It is | It is | It is no | It is no | It is no | It is no |
| Trend fluctuation | It is no | It is no | It is no | It is no | It is | It is | It is no | It is no |
| Trend is normal | It is no | It is no | It is no | It is no | It is no | It is no | It is | It is |
| Whether limit value is exceeded | It is | It is no | It is | It is no | It is | It is no | It is | It is no |
| Warning level | It is red | Yellow | It is red | Yellow | It is red | Yellow | It is red | Green |
Beneficial effect：
The present invention is directed to the actual conditions of abnormal state monitoring in real time and pre-alerting ability deficiency in process industry, based on trend
Early warning and the identification information of limit value early warning, the abnormal early warning judgment rule of a set of process status is established, and provide current operation
The warning level of operating mode.This method is practical, reliable, has both reduced the probability for causing erroneous judgement because of noise data, and can is to exception
Operating mode gives warning in advance.This method to timely troubleshooting, reduce breakdown loss, ensure that production safety has and important apply valency
Value.
Brief description of the drawings
Fig. 1 is the early warning overview flow chart of process flow industry process abnormal state
Fig. 2 is the regression figure that current sampling point is included in case 1
Fig. 3 is the Trend value prognostic chart of the trending early warning of case 1
Fig. 4 is the regression figure that current sampling point is included in case 2
Fig. 5 is the Trend value prognostic chart of the trending early warning of case 2
Specific implementation process
Below in conjunction with the accompanying drawings and two specific examples, detailed calculating process and concrete operations flow are provided, with right
The present invention is described further.The implementation case is implemented under premised on technical solution of the present invention, but the guarantor of the present invention
Shield scope is not limited to following case study on implementation.
Case 1
By taking primary distillation tower tower top pressure in the Atmospheric vacuum technique of certain Petrochemical Enterprises as an example, selection is the enterprise in November, 2016
25 days 23:25 to 26 days 1:Every the data of collection in 5 minutes in 30 periods, according to the abnormal state early warning rule and stream established
Journey, complete the anomalous identification of primary distillation tower tower top pressure.
The implementing procedure of case 1 is as shown in figure 1, specific implementation steps are as follows：
(1) trend and the warning information of limit value are set
Trending early warning is divided into 4 classes according to operating mode trend：Trend rapid increase, trend rapid decrease, trend fluctuation
And normal trend, setting duty parameter upper limit x is required according to site techniqueh=343kPa and lower limit xl=208kPa.
(2) slope calculations and coefficient correlation are returned
26 sample point datas are extracted first, and data are as shown in table 1：
The primary distillation tower tower top pressure data of table 1
The sampled point quantity p=7 of recurrence is set in present case, based on time shaft, selected forward using current sampling point as starting point
7 sampled points are taken to carry out least square regression.Regression result is as shown in Figure 2, it is seen that, regression equation y=-2.07x+208.93,
Fit slope k1=-2.07, correlation coefficient r1=-0.91.
1 sampled point of each reach simultaneously calculates, and finally gives the slope k collected1~k20And correlation coefficient r1~r20, specifically
Numerical value is as shown in table 2：
The regression coefficient of table 2 collects
| Slope (kPa/h) | Coefficient correlation | |
| 1 | -2.07 | -0.91 |
| 2 | -3.12 | -0.93 |
| 3 | -4.21 | -0.96 |
| 4 | -5.20 | -0.99 |
| 5 | -5.93 | -1.00 |
| 6 | -6.21 | -1.00 |
| 7 | -6.21 | -1.00 |
| 8 | -6.21 | -1.00 |
| 9 | -6.21 | -1.00 |
| 10 | -6.21 | -1.00 |
| 11 | -6.21 | -1.00 |
| 12 | -5.98 | -1.00 |
| 13 | -5.33 | -0.99 |
| 14 | -4.43 | -0.97 |
| 15 | -3.45 | -0.95 |
| 16 | -2.54 | -0.95 |
| 17 | -1.86 | -0.98 |
| 18 | -1.59 | -1.00 |
| 19 | -1.59 | -1.00 |
| 20 | -1.59 | -1.00 |
(3) trending early warning identifies
The coefficient correlation scope for the trending early warning that this case is set as 0.50≤| r |≤1.00, threshold value m1=0.60；Trend
The negative slope upper limit of early warning is -2.50kPa/h, threshold value m2=0.65.Correlation coefficient r1~r20Ratio in trending early warning section
Example：Slope k1~k20Belong to the ratio in trending early warning section：It is all higher than the threshold of setting
Value, and the coefficient correlation section of trending early warning is for minus zone between, therefore the result that trending early warning identifies is：Trend rapid decrease.
(4) limit value early warning identifies
The current sampling point of this case is x (p)=208.04kPa, and the sampling period isWeight is set
Factor alpha=0.5, β=0.3 and γ=0.2, following three sampled points are predicted based on x (p-2), x (p-1) and x (p), it is specific to calculate
It is as follows：
1. calculate respectively
WithBand
Enter formula and obtain predicted value
2. calculate respectively
WithBand
Enter formula and obtain predicted value
3. calculate respectively
WithBand
Enter formula and obtain predicted value
The Trend value prediction calculated is as shown in Figure 3.
4. countWithIn beyond normal operation section have 3, produce limit value early warning.
(5) the early warning recognition result of integrative trend and limit value, the operating mode both trend rapid decrease, limit value early warning is produced again, according to
According to the judgment rule of process status abnormity early warning, current point in time 26 days 01 November in 2016 is drawn:30 operating mode warning levels are
It is red.
Case 2
Again by taking normal pressure column overhead temperatures in enterprise's Atmospheric vacuum technique as an example, choose on 2 4th, 2,017 11:15 to 13:20
Normal pressure column overhead temperatures data in period every sampling in 5 minutes once, it is complete according to the fault pre-alarming rule and flow established
Into the anomalous identification of normal pressure column overhead temperatures parameter, specific implementation steps are as follows：
(1) trend and the warning information of limit value are set
Trending early warning is divided into 4 classes according to operating mode trend, setting duty parameter upper limit x is required according to site techniqueh=146
DEG C and lower limit xl=128 DEG C.
(2) slope calculations and coefficient correlation are returned
26 sample point datas are extracted, as shown in table 3：
The normal pressure column overhead temperatures data of table 3
| Sequence number | Date | Time point | Temperature (DEG C) |
| 1 | 2017/2/4 | 11:15 | 141.79 |
| 2 | 2017/2/4 | 11:20 | 142.17 |
| 3 | 2017/2/4 | 11:25 | 142.29 |
| 4 | 2017/2/4 | 11:30 | 142.49 |
| 5 | 2017/2/4 | 11:35 | 142.58 |
| 6 | 2017/2/4 | 11:40 | 142.92 |
| 7 | 2017/2/4 | 11:45 | 143.33 |
| 8 | 2017/2/4 | 11:50 | 143.39 |
| 9 | 2017/2/4 | 11:55 | 143.53 |
| 10 | 2017/2/4 | 12:00 | 143.48 |
| 11 | 2017/2/4 | 12:05 | 143.30 |
| 12 | 2017/2/4 | 12:10 | 143.37 |
| 13 | 2017/2/4 | 12:15 | 143.42 |
| 14 | 2017/2/4 | 12:20 | 143.45 |
| 15 | 2017/2/4 | 12:25 | 143.60 |
| 16 | 2017/2/4 | 12:30 | 143.65 |
| 17 | 2017/2/4 | 12:35 | 143.69 |
| 18 | 2017/2/4 | 12:40 | 143.75 |
| 19 | 2017/2/4 | 12:45 | 143.71 |
| 20 | 2017/2/4 | 12:50 | 144.15 |
| 21 | 2017/2/4 | 12:55 | 144.18 |
| 22 | 2017/2/4 | 13:00 | 144.23 |
| 23 | 2017/2/4 | 13:05 | 144.27 |
| 24 | 2017/2/4 | 13:10 | 144.11 |
| 25 | 2017/2/4 | 13:15 | 144.79 |
| 26 | 2017/2/4 | 13:20 | 144.96 |
The sampled point quantity p=7 that setting returns, regression result are as shown in Figure 4.Digital simulation slope and coefficient correlation, are obtained
To k1~k20And r1~r20, concrete numerical value is as shown in table 4：
The regression coefficient of table 4 collects
(3) trending early warning identifies
The coefficient correlation scope for the trending early warning that this case is set as 0.50≤| r |≤1.00, threshold value m1=0.60；Trend
The positive slope lower limit of early warning is 0.80, threshold value m2=0.65.Correlation coefficient r1~r20Ratio in trending early warning section is
0.85, slope k1~k20Ratio in trending early warning section is 0.70, is all higher than the threshold value each set, and trending early warning
Coefficient correlation section is positive section, therefore the result of trending early warning identification is：Trend rapid increase.
(4) limit value early warning identifies
The current sampling point of this case is x (p)=144.96 DEG C, setting weight coefficient α=0.5, β=0.3 and γ=
0.2, following three sampled points are predicted based on x (p-2), x (p-1) and x (p), as shown in figure 5, being specifically calculated as follows：
1. slope calculations obtain K respectively(0) 1=2.04 DEG C/h, K(0) 2=8.16 DEG C/h and K(0) 3=5.10 DEG C/h, synthesis is
Obtain slope and weight coefficient calculatesBring formula into and obtain predicted value
2. slope calculations obtain K respectively(1) 1=4.44 DEG C/h, K(1) 2=2.04 DEG C/h and K(1) 3=3.24 DEG C/h, synthesis is
Obtain slope and weight coefficient calculatesBring formula into and obtain predicted value
3. slope calculations obtain K respectively(2) 1=3.48 DEG C/h, K(2) 2=4.44 DEG C/h and K(2)3=3.96 DEG C/h, synthesis is
Obtain slope and weight coefficient calculatesBring formula into and obtain predicted value
4. countWithIn exceed set interval and have 0, do not produce limit value early warning.
(5) integrative trend and the early warning identification information of limit value, the operating mode is trend rapid increase, but does not produce limit value early warning,
According to judgment rule, current point in time 2017 year 2 month is drawn 4 days 13:20 operating mode warning levels are yellow.
Claims (5)
1. a kind of method for early warning of process flow industry process abnormal state, it is characterised in that have steps of：
(1) trending early warning is divided into 4 classes according to operating mode trend：Trend rapid increase, trend rapid decrease, trend fluctuation and
Trend is normal；
(2) the upper limit x in duty parameter normal operation section is sethWith lower limit xl；
(3) real-time running data is extracted from monitoring system, using current sampling point as starting point, recurrence meter is carried out to p history samples point
Calculate, obtain slope k1And correlation coefficient r1；
(4) based on time shaft 1 sampled point of reach, again to p sampled point digital simulation slope k2And correlation coefficient r2, repeat this
Process, until obtaining k1~knAnd r1~rn；
(5) foundation trending early warning identification process, the trending early warning classification of current working is determined：If trend is rapid increase, it is quick under
Drop or fluctuation, then perform step (6)；If trend is normal, step is performed (7)；
(6), according to limit value early warning identification process, judge whether to produce limit value early warning；
(7) the recognition result of integrative trend and limit value, the warning level of the current operating condition of judgment means.
2. the method for early warning of a kind of process flow industry process abnormal state according to claim 1, it is characterised in that to current
Trend rapid increase and the early warning of trend rapid decrease use following identification process in operating mode：
(1) r is counted1~rnIn [q1l, q1r] ratio w1, q1lAnd q1rThe respectively lower and upper limit of the early warning of coefficient correlation first；
w1And with threshold value m1Compare, if w1＜ m1The trending early warning that trend fluctuation is then carried out to current working identifies；If w1≥m1Then
Go to step (2)；
(2) k is counted1~knIn [ul, ur] ratio w2, ulAnd urThe respectively lower and upper limit of slope early warning；w2And and threshold value
m2Compare, if between positive slope region and w2≥m2Then it is determined as trend rapid increase；If between negative slope region and w2≥m2Then judge
For trend rapid decrease；If w2＜ m2Then it is determined as that trend is normal.
3. the method for early warning of a kind of process flow industry process abnormal state according to claim 2, it is characterised in that to current
The early warning of trend fluctuation uses following identification process in operating mode：
(1) r is counted1~rnIn [q2l, q2r] ratio w3, q2lAnd q2rThe respectively lower and upper limit of the early warning of coefficient correlation second；
w3And with threshold value m3Compare, if w3＜ m3Then it is determined as normal trend, if w3≥m3Then go to step (2)；
(2), using current sampling point as starting point, choose 2p sampled point forward based on time shaft and calculate standard deviation v, v and threshold value m4Than
Compared with if v >=m4Then it is judged as trend fluctuation, if v ＜ m4Then it is judged as normal trend.
4. the method for early warning of a kind of process flow industry process abnormal state according to claim 1, it is characterised in that limit value is pre-
Alert identification process, choose 3 sampled points including current point forward based on time shaft and following 3 sampled points are carried out in advance
Survey, comprise the following steps that：
(1) current sampling point is x (p), and T is the sampling period, sets weight coefficient α, β and γ, makes alpha+beta+γ=1, extraction x (p), x
(p-1) and x (p-2) amounts to 3 sampled points；
(2) K is calculated according to following formula(0) 1、K(0) 2And K(0) 3
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>3</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mn>2</mn>
<mi>T</mi>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
According to formulaCalculateAccording to formulaCalculate
(3) K is calculated according to following formula(1) 1、K(1) 2And K(1) 3
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>3</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mn>2</mn>
<mi>T</mi>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
According to formulaCalculateAccording to formulaCalculate
(4) K is calculated according to following formula(2) 1、K(2) 2And K(2) 3
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>1</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>2</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
</mrow>
<mi>T</mi>
</mfrac>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<msup>
<mi>K</mi>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</msup>
<mn>3</mn>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<mover>
<mi>x</mi>
<mo>^</mo>
</mover>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>+</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mi>x</mi>
<mrow>
<mo>(</mo>
<mi>p</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<mn>2</mn>
<mi>T</mi>
</mrow>
</mfrac>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
According to formulaCalculateAccording to formulaCalculate
(5) ifWithIn have the limit value that 2 and the above exceed given duty parameter, that is, produce limit
It is worth early warning.
5. the method for early warning of a kind of process flow industry process abnormal state according to claim 1, it is characterised in that be based on
The early warning identification information of gesture and limit value, the abnormal early warning judgment rule of process status is established, wherein will determine that result is divided into 3 in advance
Alert rank：Green, yellow and red, its Green represent that operating mode is normal, and yellow represents to point out, red for alarm shape
State, specific rules are as follows：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710750153.1A CN107390661B (en) | 2017-08-28 | 2017-08-28 | A kind of method for early warning of process flow industry process abnormal state |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710750153.1A CN107390661B (en) | 2017-08-28 | 2017-08-28 | A kind of method for early warning of process flow industry process abnormal state |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107390661A true CN107390661A (en) | 2017-11-24 |
| CN107390661B CN107390661B (en) | 2019-05-24 |
Family
ID=60346651
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710750153.1A Active CN107390661B (en) | 2017-08-28 | 2017-08-28 | A kind of method for early warning of process flow industry process abnormal state |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107390661B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108132867A (en) * | 2018-01-11 | 2018-06-08 | 安徽容知日新科技股份有限公司 | A kind of device failure alert method and computing device |
| CN108765889A (en) * | 2018-04-17 | 2018-11-06 | 中国石油集团安全环保技术研究院有限公司 | Production of hydrocarbons operational safety method for early warning based on big data technology |
| CN109063261A (en) * | 2018-06-29 | 2018-12-21 | 深圳市科列技术股份有限公司 | A kind of judgment method and device of power battery aging tendency |
| CN110134079A (en) * | 2019-03-26 | 2019-08-16 | 石化盈科信息技术有限责任公司 | A kind of technological parameter method for early warning and system based on slope analysis |
| CN110349046A (en) * | 2019-05-29 | 2019-10-18 | 浙江陀曼云计算有限公司 | Produce method for early warning and device |
| CN110764975A (en) * | 2018-07-27 | 2020-02-07 | 华为技术有限公司 | Early warning method and device for equipment performance and monitoring equipment |
| CN112149957A (en) * | 2020-08-20 | 2020-12-29 | 汉威科技集团股份有限公司 | Risk trend deduction and grading early warning method based on online monitoring data |
| CN112508105A (en) * | 2020-12-11 | 2021-03-16 | 南京富岛信息工程有限公司 | Method for detecting and retrieving faults of oil extraction machine |
| CN112633561A (en) * | 2020-12-09 | 2021-04-09 | 北京名道恒通信息技术有限公司 | Production risk intelligent prediction early warning method based on industrial big data |
| CN113380411A (en) * | 2021-07-19 | 2021-09-10 | 苏州百孝医疗科技有限公司 | Method for improving concentration change real-time trend accuracy in continuous monitoring process of animal analyte concentration |
| WO2024077983A1 (en) * | 2022-10-14 | 2024-04-18 | 南京凯盛国际工程有限公司 | Method for improving early-warning advance performance and accuracy of evaluation model |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102117731A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method and device for monitoring measurement data in process production flow of semiconductor |
| CN103714238A (en) * | 2012-10-01 | 2014-04-09 | 福特全球技术公司 | System and method for rating computer model relative to empirical results for dynamic systems |
| CN103744389A (en) * | 2013-12-30 | 2014-04-23 | 中国石油天然气股份有限公司 | Early warning method for running state of oil and gas production equipment |
| CN105389648A (en) * | 2015-10-21 | 2016-03-09 | 南京富岛信息工程有限公司 | Distinguishing method for steady-state operating condition of atmospheric and vacuum distillation device |
| CN106061370A (en) * | 2013-12-16 | 2016-10-26 | 美敦力迷你迈德公司 | Use of electrochemical impedance spectroscopy (EIS) in continuous glucose monitoring |
-
2017
- 2017-08-28 CN CN201710750153.1A patent/CN107390661B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102117731A (en) * | 2009-12-31 | 2011-07-06 | 中芯国际集成电路制造(上海)有限公司 | Method and device for monitoring measurement data in process production flow of semiconductor |
| CN103714238A (en) * | 2012-10-01 | 2014-04-09 | 福特全球技术公司 | System and method for rating computer model relative to empirical results for dynamic systems |
| CN106061370A (en) * | 2013-12-16 | 2016-10-26 | 美敦力迷你迈德公司 | Use of electrochemical impedance spectroscopy (EIS) in continuous glucose monitoring |
| CN103744389A (en) * | 2013-12-30 | 2014-04-23 | 中国石油天然气股份有限公司 | Early warning method for running state of oil and gas production equipment |
| CN105389648A (en) * | 2015-10-21 | 2016-03-09 | 南京富岛信息工程有限公司 | Distinguishing method for steady-state operating condition of atmospheric and vacuum distillation device |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108132867B (en) * | 2018-01-11 | 2021-05-25 | 合肥科博软件技术有限公司 | Equipment fault alarm method and computing equipment |
| CN108132867A (en) * | 2018-01-11 | 2018-06-08 | 安徽容知日新科技股份有限公司 | A kind of device failure alert method and computing device |
| CN108765889A (en) * | 2018-04-17 | 2018-11-06 | 中国石油集团安全环保技术研究院有限公司 | Production of hydrocarbons operational safety method for early warning based on big data technology |
| CN109063261A (en) * | 2018-06-29 | 2018-12-21 | 深圳市科列技术股份有限公司 | A kind of judgment method and device of power battery aging tendency |
| CN109063261B (en) * | 2018-06-29 | 2022-11-29 | 深圳市科列技术股份有限公司 | Method and device for judging aging trend of power battery |
| CN110764975A (en) * | 2018-07-27 | 2020-02-07 | 华为技术有限公司 | Early warning method and device for equipment performance and monitoring equipment |
| CN110764975B (en) * | 2018-07-27 | 2021-10-22 | 华为技术有限公司 | Early warning method and device for equipment performance and monitoring equipment |
| CN110134079A (en) * | 2019-03-26 | 2019-08-16 | 石化盈科信息技术有限责任公司 | A kind of technological parameter method for early warning and system based on slope analysis |
| CN110134079B (en) * | 2019-03-26 | 2022-05-03 | 石化盈科信息技术有限责任公司 | Process parameter early warning method and system based on slope analysis |
| CN110349046A (en) * | 2019-05-29 | 2019-10-18 | 浙江陀曼云计算有限公司 | Produce method for early warning and device |
| CN112149957A (en) * | 2020-08-20 | 2020-12-29 | 汉威科技集团股份有限公司 | Risk trend deduction and grading early warning method based on online monitoring data |
| CN112633561A (en) * | 2020-12-09 | 2021-04-09 | 北京名道恒通信息技术有限公司 | Production risk intelligent prediction early warning method based on industrial big data |
| CN112508105A (en) * | 2020-12-11 | 2021-03-16 | 南京富岛信息工程有限公司 | Method for detecting and retrieving faults of oil extraction machine |
| CN112508105B (en) * | 2020-12-11 | 2024-03-19 | 南京富岛信息工程有限公司 | Fault detection and retrieval method for oil extraction machine |
| CN113380411A (en) * | 2021-07-19 | 2021-09-10 | 苏州百孝医疗科技有限公司 | Method for improving concentration change real-time trend accuracy in continuous monitoring process of animal analyte concentration |
| CN113380411B (en) * | 2021-07-19 | 2024-03-01 | 苏州百孝医疗科技有限公司 | Method for improving accuracy of concentration change real-time trend in continuous monitoring process of animal analyte concentration |
| WO2024077983A1 (en) * | 2022-10-14 | 2024-04-18 | 南京凯盛国际工程有限公司 | Method for improving early-warning advance performance and accuracy of evaluation model |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107390661B (en) | 2019-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107390661A (en) | A kind of method for early warning of process flow industry process abnormal state | |
| CN106447210B (en) | A kind of distribution net equipment health degree dynamic diagnosis method of meter and trust evaluation | |
| CN102175282B (en) | Method for diagnosing fault of centrifugal air compressor based on information fusion | |
| CN104298225B (en) | Chemical process unusual service condition causality inference pattern is modeled and graphical representation method | |
| CN104360677B (en) | Cigarette processing quality evaluation and diagnosis method | |
| CN102354116B (en) | Method for making omega event interval control chart for high quality process statistics control | |
| CN105955219A (en) | Distributed dynamic process fault detection method based on mutual information | |
| CN111441864A (en) | Engine health diagnosis method and engine diagnosis system | |
| CN102282552A (en) | System, method and computer program for pattern based intelligent control, monitoring and automation | |
| CN114239708B (en) | Combustion engine abnormity detection method based on quality control chart theory | |
| CN111930790B (en) | Valve inlet temperature prediction method of valve cooling equipment based on time sequence analysis | |
| CN104914850B (en) | Industrial process method for diagnosing faults based on switching linear dynamic system model | |
| CN108663995A (en) | A kind of industrial process variable trend anomaly detection method and device | |
| CN104808648A (en) | Online and real-time batch process monitoring method based on k nearest neighbor | |
| CN103310388A (en) | Method for calculating composite index of grid operation based on information source entropy | |
| CN101847004B (en) | Method for performance evaluation and failure diagnosis of coke oven multi-loop control system | |
| CN103389701A (en) | Plant-level process fault detection and diagnosis method based on distributed data model | |
| CN102789676A (en) | Method for designing industrial alarm on basis of alarm evidence fusion | |
| CN109978059B (en) | Early warning method for tower flushing faults of primary distillation tower in crude oil distillation process | |
| CN107515595B (en) | A kind of coal acetylene production information intelligent billboard with data analysis function | |
| CN106651065A (en) | Method for screening components of interest in lifetime management of nuclear power plant in operation | |
| CN114781547A (en) | Method and system for state monitoring and fault diagnosis of chemical equipment | |
| Chen et al. | Design of multivariate alarm systems based on online calculation of variational directions | |
| CN111338310B (en) | Industrial process steady-state working condition identification and classification method | |
| CN105760672B (en) | A kind of Trouble Diagnostic Method of Machinery Equipment |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |