CN107451402A - A kind of equipment health degree appraisal procedure and device based on alarm data analysis - Google Patents

A kind of equipment health degree appraisal procedure and device based on alarm data analysis Download PDF

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Publication number
CN107451402A
CN107451402A CN201710572591.3A CN201710572591A CN107451402A CN 107451402 A CN107451402 A CN 107451402A CN 201710572591 A CN201710572591 A CN 201710572591A CN 107451402 A CN107451402 A CN 107451402A
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alarm
mrow
msub
evaluation index
alarm event
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Inventor
郑宏云
胡敏
王巍巍
邵克松
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Beijing Ruiqihaodi Technology Co Ltd
Beijing Jiaotong University
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Beijing Ruiqihaodi Technology Co Ltd
Beijing Jiaotong University
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    • G16INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16ZINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
    • G16Z99/00Subject matter not provided for in other main groups of this subclass

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Abstract

The present invention principally falls into equipment health degree analysis field, and in particular to a kind of equipment health degree appraisal procedure and device based on alarm data analysis.The present invention determines index score and index weights with alarm feature, objectively the health degree of equipment is assessed, to instruct follow-up equipment fault management and maintenance work to provide foundation using alarm event as evaluation index.

Description

A kind of equipment health degree appraisal procedure and device based on alarm data analysis
Technical field
The present invention principally falls into equipment health degree analysis field, and in particular to a kind of equipment based on alarm data analysis is good for Kang Du appraisal procedures and device.
Background technology
Equipment health degree refers to the good degree of equipment overall operation, is comprehensive to one of the running status of whole equipment Close evaluation.Conventional health evaluating method is the health evaluating based on equipment running status parameter, i.e., operating by monitoring Equipment, obtain equipment status parameter and evaluation status is come with this.But equipment packages are in cabinet in practical application, to obtain operating Device parameter acquires a certain degree of difficulty, and is detected mostly by the way of manual discharge test and conductivity test, not only complicated, Specialty requires also very strong.
There is document report logical to assess based on the method for alarm data analysis using some in communication and network field Believe base station/system and/or network health degree.The reflection as equipment fault is alerted, alert analysis can be carried out to equipment state Effectively assess.The performance directly perceived as equipment state is alerted, data are easier to obtain by contrast, using alarm data as number According to collection, the correlated characteristic with equipment running status is excavated, health degree evaluation model is established, can avoid well based on equipment The problem of existing during state parameter.But the method in reporting is only applied to communication and network field, the selection of evaluation index and beats Divide because different assessment objects are different and different, its application has limitation.
The main thought assessed health degree is distinguished by assessing reflection the parameters of object performance state Assessment marking is carried out, each assessment result Weighted Fusion is obtained into final assessment result.The selection of evaluation index and marking because It is different and different that difference assesses object.Given a mark compared to assessing, determine that weight of the parameters in equipment performance assessment is more tired It is difficult.The method applied at present mainly includes:Fuzzy AHP, weigthed sums approach, PCA, fuzzy overall evaluation Method, entropy assessment, BP neural network method and SVMs method.Wherein, showing using Fuzzy AHP and weigthed sums approach There is work and weight is all determined with subjective method, the subjective factor of people may bring deviation.PCA is paid close attention to user Degree determines, still with subjective composition.The deficiency of Field Using Fuzzy Comprehensive Assessment is that can not solve commenting for ambiguity and stochastic interconnection Estimate problem.Entropy assessment is using the entropy of parameters as weight.Neural network and SVMs method are using fuzzy comprehensive Model is established by training after conjunction evaluation assessment acquirement assessed value, the quality of model is limited by Fuzzy Synthetic Evaluation.In a word, it is existing With the presence of the Weight Determination human factor of Evaluation Method, this certainly will influence whether the objectivity of assessment result.
The content of the invention
In view of the above-mentioned problems, the present invention proposes a kind of equipment health degree appraisal procedure and dress based on alarm data analysis Put.The present invention determines index score and index weights, objectively to equipment using alarm event as evaluation index with alarm feature Health degree assessed, to instruct follow-up equipment fault management and maintenance work to provide foundation.
The present invention is achieved by the following technical solutions:
A kind of equipment health degree appraisal procedure based on alarm data analysis, the described method comprises the following steps:
Data acquisition:The alarm data of collecting device, according to alarm time of origin write into Databasce;
Assess data decimation:By alarm event title, alarm grade, alarm time of origin, alarm end time, alarm weight Again number selects from database is used as assessment data;
Evaluation index is chosen:The alarm event with different alarm event titles is chosen as evaluation index, is formed and assessed Index set;
Health degree calculates:Frequency is alerted by calculating each evaluation index within the time to be assessed and averagely alerts duration Each alarm event is given a mark, and different weights are assigned to alarm event according to alarm grade, the fraction of each evaluation index combines power Re-computation obtains equipment health degree.
Further, it is described to alert frequency by calculating each evaluation index within certain time and averagely alert duration It is specially to the marking of each alarm event:
21) by the time to be assessed, countershaft is divided into q marking moment, { t on time1,t2,…,ti,…,tq}(t0From the time Point), give a mark at intervals of { Δ1=t1-t02=t2-t1,…,Δj=tj-tj-1,…,Δq=tq-tq-1};To evaluation index collection In evaluation index, i.e. alarm event aiIn marking moment tj(j=1,2 ..., q) is given a mark, part a during alarmiAt time point tjIt is scored at
Wherein:
For evaluation index aiIn interval duration ΔjThe number of interior generation;
For evaluation index aiIn interval duration ΔjThe interior average alarm duration alerted,
WithIt is evaluation index (alarm event) a respectivelyiIn duration ΔjAlarm end time when interior kth time occurs And time of origin;
23) above-mentioned marking is repeated at other marking moment, obtains the evaluation index a within the time to be assessediScoring sequence
24) all evaluation indexes that evaluation index is concentrated are given a mark, obtains evaluation index and obtain diversity { X1,X2,…,Xi,… Xm, set element XiFor row or column vector, q moment obtained index score of giving a mark is contained, m is evaluation index number.
Further, it is described to be specially to the different weights of alarm event imparting according to alarm grade:
31) Judgement Matricies:
Utilize 1-9 ratio scales method (table 1) development of judgment matrix commonly used in analytic hierarchy process (AHP).By all evaluation indexes It is compared to each other two-by-two, relative importance between each index is provided by 1-9 ratio scale methods according to alarm grade, constructs judgement square Battle array A;
The 1-9 ratio scale methods of table 1
In the present invention, table 1 can also be described with following mathematical formulae;
bijThe element arranged for the i-th row jth in judgment matrix A, represents i-th of evaluation index relative to j-th of evaluation index Relative Link Importance, bij=1/bji
L is the difference of the alarm grade of i-th of evaluation index and j-th of evaluation index;
32) weight is calculated:
wiFor the weight of i-th of evaluation index;
Wherein
Mi=bi1×bi2×.....bim
M is index number.
Further, the correlation between each evaluation index is eliminated;
41) index Score Normalization is handled:
Evaluation index normalizes to obtain diversity { Y1,Y2,…,Yi,…Ym,
Wherein,
It is aiScore XiIn maximum score,It is XiIn minimum score;
M is evaluation index number;
42) correlation between index is eliminated using Glan nurse-Schmidt's orthogonalization, obtains one group between each other without correlation Orthogonal set { the β of propertyi, i=1,2 ..., m };
β1=Y1
β2=Y2-[Y21]/[β11]×β1, [Y21] it is Y2With β1Inner product, [β11] it is β1With β1Inner product;
To eliminate the orthogonal set { β obtained after correlationi, i=1,2 ..., m } score combination weight meter as evaluation index Calculation obtains equipment health degree, βiIt is vectorial for row or column, contain the score of the evaluation index at k marking moment.
Further, the score combination weight calculation of each evaluation index after the elimination correlation obtains equipment health degree Specially:By the score and weight of each evaluation index, it is weighted and obtains the health degree fraction of equipment, calculation formula is:
It is equipment health degree fraction, H is row or column Vector, contain the health degree fraction at all marking moment, βiIt is that i-th of evaluation index eliminates scoring sequence after correlation, Contain the score of the evaluation index at q marking moment, ωiIt is the weight of the score, m is the number of evaluation index.Each beat The health degree fraction that timesharing is carvedEqual to the evaluation index score at the moment and the sum of products of score weight.
Further, consistency check is carried out to weight distribution so that Consistency Ratio CR=CI/RI<0.10, otherwise, The element value (using the 1-9 ratio scales method shown in table 1) of judgment matrix will be adjusted, redistributes the value of weight coefficient, Until Consistency Ratio is less than 0.1;
Wherein coincident indicatorWherein λ max are the Maximum characteristic root of judgment matrix,A is judgment matrix, and W is weight matrix, and AW is the product of two matrixes, (AW)iIt is multiplying for matrix I-th long-pending of element, wiIt is the weight of i-th of index;
RI is Aver-age Random Consistency Index value.
Further, first alarm data is pre-processed before assessing data decimation, rejects the umber of defectives such as flash memory, repetition According to.
A kind of equipment health degree apparatus for evaluating based on alarm data analysis, the device uses above-mentioned appraisal procedure, described Device includes data acquisition module, assesses data decimation module, evaluation index selection module, health degree computing module;
The alarm data of the data collecting module collected equipment, according to alarm time of origin write into Databasce;
The assessment data decimation module is by the end of alarm event title, alarm grade, alarm time of origin, alarm Between, alarm number of repetition selects from database and is used as assessment data;
The evaluation index chooses module according to alarm event title, various alarm events is picked out, as assessment Index, form evaluation index collection;
The health degree computing module alerts frequency and is averaged by calculating each alarm event within the time to be assessed Alarm duration gives a mark to obtain alarm event fractional value to each alarm event, and eliminates correlation, obtains alarm event score value, and Different weights are assigned to alarm event according to alarm grade and obtain alarm event weighted value, by alarm event score value and alarm thing Part weighted value weighted sum obtains equipment health degree.
Further, the health degree computing module includes alarm event scoring modules, weight computation module, weighted sum modulo Block;
The number and average alarm that alarm event occurs the alarm event scoring modules within the time to be assessed occur Duration is multiplied to obtain alarm event fractional value;And the correlation between the fractional value of different alarm events is eliminated, obtain alerting thing Part score value;
The weight computation module utilizes the alarm grade development of judgment matrix of alarm event, is accused using judgment matrix Alert event weights value;
The weighted sum module, which corresponds to alarm event score value with alarm event weighted value, to be multiplied to obtain the alarm event Contribution component to health degree;The contribution component for each alarm event that evaluation index is concentrated is added, and obtains to be assessed set Standby health degree.
Further, the equipment is switch power supply equipment, and the alarm event includes battery powered alarm, direct current output Overtension alarm, the alarm of exchange input phase failure, rectification module fault warning, the too high alarm of exchange incoming frequency, exchange input Brownout alarm, the too high alarm of AC-input voltage, the exchange too low alarm of incoming frequency.
Further, the equipment is accumulator equipment, the alarm event include total voltage is too low, total voltage is too high, Certain monomer battery voltage is too high, certain monomer battery voltage is too low, battery pack middle point voltage is uneven.
The advantageous effects of the present invention:
1) present invention is based entirely on alarm data and equipment health degree is assessed, and almost without technical requirement, implements Get up easily;
2) using alarm event as evaluation index, index score determines the present invention with index weights by alarm feature, comments Valency result is very directly perceived, it is readily appreciated that;
3) present invention can not only obtain equipment real time health degree according to equipment health assessment model proposed by the present invention Scoring, and the change curve of following period of time equipment health degree can be drawn out.
Brief description of the drawings
The daily variation tendency signal of Switching Power Supply health degree in Fig. 1, the embodiment of the present invention 1 be calculated one month Figure.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples The present invention is explained in further detail.It should be appreciated that specific embodiment described herein is used only for explaining the present invention, and It is not used in the restriction present invention.
On the contrary, the present invention covers any replacement done in the spirit and scope of the present invention being defined by the claims, repaiied Change, equivalent method and scheme.Further, in order that the public has a better understanding to the present invention, below to the thin of the present invention It is detailed to describe some specific detail sections in section description.Part without these details for a person skilled in the art Description can also understand the present invention completely.
Embodiment 1
Health degree assessment is carried out to the Switching Power Supply in certain communication base station using the method for the present invention.Target is to assess one The health degree variation tendency of the Switching Power Supply in the time of the moon, it is every other day that Switching Power Supply carries out a health degree marking, i.e., Interval time gave a mark as 1 day.
S1, the alarm data for gathering the communication base station Switching Power Supply, across when one month, by alarm occur time sequencing write Enter database.
S2, the invalid data such as missing, repetition, hit in alarm data is rejected, by alarm event title, alarm grade, accused The data attributes such as alert time of origin, alarm end time, alarm number of repetition extract.
Alarm missing refers to that alarm data information is imperfect, and some field values are sky.Alarm hit refers to alarm from generation To eliminating, the time is very short;Alarm repeats to refer to that some alarm record attribute values are completely the same.Alarm with these features Either data can not provide complete data attribute, missing data, or do not reflect correct alarm event, so entering Before row is assessed, rejected by pretreatment.
S3, choose health degree evaluation index.A total of eight kinds of different alarm events in alarm data, them are chosen to open Powered-down source health degree evaluation index, as shown in table 2.
S4, calculate power-supply device health degree.The time span of observation is one month (31 days), carries out a health degree daily Assess, that is, the time interval Δ given a mark is one day.
Marking interval can take uniform intervals, can also take non-uniform spacing.The size at marking interval can take dimension Shield person's time granularity interested;Simultaneously, it is ensured that marking includes a number of alarm data in interval.It is for example, to be evaluated It is 1 month to estimate time span, the daily health degree variation tendency of equipment to be investigated, then marking interval is chosen for 1 day.Between marking Every smaller time granularity, such as 6 hours, 1 hour etc. can also be chosen.Applicants have found that marking at intervals of 1 day, 6 Hour and 1 hour, equipment health degree variation tendency is roughly the same.
In every day, count the alarm number of each index in past one day and averagely alert duration, be calculated 31 The fraction of each index in time window, as shown in table 3.Given a mark the moment at first with evaluation index a1, i.e., exemplified by first day Illustrate the calculating process of index score.In first day, a1 altogether there occurs 5 times, each duration is respectively 29.05, 66.45th, 10.38,60.85 and 87.12 minutes, then
Minute,The value is the element that the 1st row the 1st arranges in table 3.
The evaluation index of the Switching Power Supply of table 2
The Switching Power Supply index score of table 3
Index score is normalized so that index score is between 0~1, as a result as shown in table 4.With X1In t1 The value at moment isNormalizing value calculate exemplified by.X1Maximum in sequenceFor 253.85, minimum valueFor 0, because This is in t1The normalized value at moment Should Value is the value of the column element of the 1st row the 1st of table 4.
Table 4 normalizes index score
Then, index score is handled using Schmidt's orthogonalization, eliminates the index score after correlation such as Shown in table 5.
Table 5 eliminates index related rear each index score
According to the alarm grade of each index of the Switching Power Supply of table 2, each index weights are determined using analytic hierarchy process (AHP).Judgment matrix It is as shown in table 6 with weights.Share eight kinds of alarm events, therefore m=8.Three kinds of different alarm grades are shared, so judge square Battle array value 1-3-5.By taking evaluation index a1 weight calculation as an example.In corresponding evaluation of estimate in judgment matrix, i.e. table 6 A line numerical value is 1,1,3,3,5,5,5,5, then the product M of the row element1=1 × 1 × 3 × 3 × 5 × 5 × 5 × 5= 5625, M18 powers are opened, its 8 th Root Similarly, the product of remaining seven row element can be obtained It is normalized, obtains weight:
Other weights can similarly be calculated.
Table 6. calculates weight using analytic hierarchy process (AHP)
To weight calculation coincident indicator.Coincident indicatorWherein λmaxIt is special for the maximum of judgment matrix Root is levied, i.e.,A is judgment matrix, and W is weight matrix, and AW is the product of two matrixes, (AW)iIt is it I-th of element, wiIt is the weight of i-th of index.
Consistency Ratio CR=CI/RI is calculated, wherein RI is Aver-age Random Consistency Index value, as shown in table 7, m in table It is the index number in judgment matrix.
The Aver-age Random Consistency Index RI values of table 7
As Consistency Ratio CR=CI/RI<When 0.10, it is believed that judgment matrix has better uniformity, it is believed that power Reassignment is rational.
In embodiment 1, judgment matrix
Weight matrix
Because evaluation index number is 8, tables look-up and 7 know RI=1.41, therefore CR=CI/RI=0.012395 ÷ 1.41= 0.008791<0.1 passes through consistency check.
Health degree of the Switching Power Supply at first day Similarly, the health degree of other days can be calculated.It is vertical using health degree Axle, corresponding observation day is transverse axis, draws the health degree change curve of daily Switching Power Supply in one month, as shown in Figure 1.The song Line reflects the variation tendency that Switching Power Supply health degree is daily in one month.
The present embodiment also includes a kind of equipment health degree apparatus for evaluating based on alarm data analysis simultaneously, and the device uses Above-mentioned appraisal procedure, described device include data acquisition module, assess data decimation module, evaluation index selection module, health Spend computing module;
After monitored power-supply device produces alarm, data are sent to by interfaces such as RS485, RS232 or Ethernets and adopted Collect module, equipment alarm data are carried out protocol format conversion by data acquisition module, according to alarm equipment place, alarm equipment ID, alarm equipment access IP address, alarm event title, alarm event type, alarm grade, alarm time of origin, alarm knot The form write into Databasce such as beam time, alarm number of repetition, alarm cause, alarm summary.
The alarm data of the data collecting module collected equipment, according to alarm time of origin write into Databasce;
The assessment data decimation module is by the end of alarm event title, alarm grade, alarm time of origin, alarm Between, alarm number of repetition selects from database and is used as assessment data;
The evaluation index chooses module according to alarm event title, various alarm events is picked out, as health Spend evaluation index, form evaluation index collection (alarm event in index set does not have sequence requirement);
The alarm event includes battery powered alarm, the too high alarm of VD, exchanges input phase failure alarm, be whole Flow module fault warning, the too high alarm of exchange incoming frequency, the too low alarm of AC-input voltage, the too high announcement of AC-input voltage Alert, the exchange too low alarm of incoming frequency.
The health degree computing module alerts frequency and is averaged by calculating each alarm event within the time to be assessed Alarm duration gives a mark to obtain alarm event fractional value to each alarm event, and eliminates correlation, obtains alarm event score value, and Different weights are assigned to alarm event according to alarm grade and obtain alarm event weighted value, by alarm event score value and alarm thing Part weighted value weighted sum obtains equipment health degree.
Further, the health degree computing module includes alarm event scoring modules, weight computation module, weighted sum modulo Block;
The number and average alarm that alarm event occurs the alarm event scoring modules within the time to be assessed occur Duration is multiplied to obtain alarm event fractional value, and eliminates correlation, obtains alarm event score value,;
The weight computation module utilizes the alarm grade development of judgment matrix of alarm event, is accused using judgment matrix Alert event weights value;
The weighted sum module, which corresponds to alarm event score value with alarm event weighted value, to be multiplied to obtain the alarm event Contribution component to health degree;The contribution component for each alarm event that evaluation index is concentrated is added, and obtains to be assessed set Standby health degree.

Claims (10)

1. a kind of equipment health degree appraisal procedure based on alarm data analysis, it is characterised in that methods described includes following step Suddenly:
Data acquisition:The alarm data of collecting device, according to alarm time of origin write into Databasce;
Assess data decimation:By alarm event title, alarm grade, alarm time of origin, alarm end time, alarm repetition time Number selects from database is used as assessment data;
Evaluation index is chosen:Each different alarm event is chosen as evaluation index, forms evaluation index collection;
Health degree calculates:Frequency is alerted by calculating each alarm event within the time to be assessed and averagely alarm duration obtains Alarm event fractional value, and assign different weights to alarm event according to alarm grade and obtain alarm event weighted value, each alarm Equipment health degree is calculated in the fractional value combination weighted value of event.
2. appraisal procedure as claimed in claim 1, it is characterised in that described to be accused by calculating each evaluation index within certain time Alert frequency and averagely alarm duration give a mark to obtain alarm event fractional value to each alarm event:
21) by the time to be assessed, countershaft is divided into q marking moment, { t on time1, t2..., ti..., tq, give a mark at intervals of {Δ1=t1-t0, Δ2=t2-t1..., Δj=tj-tj-1..., Δq=tq-tq-1};t0For start time;
The marking is at intervals of uniform intervals or non-uniform spacing, and marking interval is according to assessment selection of time;
22) the evaluation index a concentrated to evaluation indexiIn marking moment tjGiven a mark, alarm event aiIn time point tj It is divided intoM is evaluation index number, j=1,2 ..., q, q be the number at moment of giving a mark;
Wherein:
For evaluation index aiIn interval duration ΔjThe number of interior generation;
For evaluation index aiIn interval duration ΔjThe interior average alarm duration alerted,
WithIt is evaluation index a respectivelyiIn duration ΔjAlarm end time and time of origin when interior kth time occurs;
23) above-mentioned marking is repeated at other marking moment, obtains the evaluation index a within the time to be assessediScoring sequence
24) all alarm events that evaluation index is concentrated are given a mark, obtains evaluation index and obtain diversity { X1, X2..., Xi... Xm, collection Close element XiFor row or column vector, q moment obtained index score of giving a mark is contained, m is evaluation index number.
3. appraisal procedure as claimed in claim 1, it is characterised in that described to assign different power to alarm event according to alarm grade It is specially again:
31) Judgement Matricies:
All evaluation indexes are compared to each other two-by-two, drawn according to alarm grade, development of judgment matrix A;
<mrow> <msub> <mi>b</mi> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>2</mn> <mo>|</mo> <mi>L</mi> <mo>|</mo> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mi>L</mi> <mo>&gt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mn>1</mn> <mo>,</mo> <mi>L</mi> <mo>=</mo> <mn>0</mn> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <mo>|</mo> <mi>L</mi> <mo>|</mo> <mo>-</mo> <mn>1</mn> </mrow> </mfrac> <mo>,</mo> <mi>L</mi> <mo>&lt;</mo> <mn>0</mn> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
bijThe element arranged for the i-th row jth in judgment matrix A, represents phase of i-th of evaluation index relative to j-th of evaluation index To importance, bij=1/bji
L is the difference of the alarm grade of i-th of evaluation index and j-th of evaluation index;
32) weight is calculated:
<mrow> <msub> <mi>w</mi> <mi>i</mi> </msub> <mo>=</mo> <mover> <msub> <mi>W</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>/</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </msubsup> <mover> <msub> <mi>W</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>;</mo> </mrow>
wiFor the weight of i-th of evaluation index;
Wherein
<mrow> <mover> <msub> <mi>W</mi> <mi>i</mi> </msub> <mo>&amp;OverBar;</mo> </mover> <mo>=</mo> <mroot> <msub> <mi>M</mi> <mi>i</mi> </msub> <mi>m</mi> </mroot> </mrow>
Mi=bi1×bi2×.....bim
M is index number.
4. appraisal procedure as claimed in claim 1, it is characterised in that eliminate the correlation between each evaluation index;
41) index Score Normalization is handled:
Evaluation index normalizes to obtain diversity { Y1, Y2..., Yi... Ym,
Wherein,
It is aiScoring sequence XiIn maximum score,It is XiIn minimum score;
M is evaluation index number;
42) correlation between index is eliminated using Glan nurse-Schmidt's orthogonalization, obtains one group of mutual non-correlation Orthogonal set { βi, i=1,2 ..., m };
β1=Y1
β2=Y2-[Y2, β1]/[β1, β1]×β1, [Y2, β1] it is Y2With β1Inner product, [β1, β1] it is β1With β1Inner product;
<mrow> <msub> <mi>&amp;beta;</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>-</mo> <mfrac> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>Y</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>&amp;rsqb;</mo> </mrow> </mfrac> <mo>&amp;times;</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>-</mo> <mo>...</mo> <mo>-</mo> <mfrac> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow> <mrow> <mo>&amp;lsqb;</mo> <msub> <mi>&amp;beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;rsqb;</mo> </mrow> </mfrac> <mo>&amp;times;</mo> <msub> <mi>&amp;beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>i</mi> <mo>&gt;</mo> <mn>2</mn> <mo>;</mo> </mrow>
To eliminate the orthogonal set { β obtained after correlationi, i=1,2 ..., m } obtained as the score combination weight calculation of evaluation index Equipment health degree, βiIt is vectorial for row or column, contain the score of the evaluation index at q marking moment.
5. appraisal procedure as claimed in claim 1, it is characterised in that score combines after the elimination correlation of each evaluation index Weight calculation obtains equipment health degree, is specially:By the score and weight of each evaluation index, it is weighted and obtains equipment Health degree fraction, calculation formula is
<mrow> <mi>H</mi> <mo>=</mo> <msubsup> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </msubsup> <msub> <mi>w</mi> <mi>i</mi> </msub> <msub> <mi>&amp;beta;</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>
It is equipment health degree fraction, H is row or column vector, is contained in all marking The health degree fraction at moment, βiIt is that i-th of evaluation index eliminates Relevance scores sequence, contains the assessment at q marking moment The score of index, ωiIt is the weight of the score, m is the number of evaluation index;The health degree fraction at each marking momentDeng In the evaluation index score at the moment and the sum of products of score weight.
6. appraisal procedure as claimed in claim 3, it is characterised in that consistency check is carried out to weight distribution so that uniformity Ratio CR=CI/RI < 0.10, otherwise it is necessary to adjusting the element value of judgment matrix, the value of weight coefficient is redistributed, until Consistency Ratio is less than 0.1;
Wherein coincident indicatorWherein λmaxFor the Maximum characteristic root of judgment matrix,A is judgment matrix, and W is weight matrix, and AW is the product of two matrixes, (AW)iIt is multiplying for matrix I-th long-pending of element, wiIt is the weight of i-th of index;
RI is Aver-age Random Consistency Index value.
7. appraisal procedure as claimed in claim 1, it is characterised in that first located alarm data in advance before assessing data decimation Reason, reject missing, hit, repeat bad data.
8. a kind of equipment health degree apparatus for evaluating based on alarm data analysis, it is characterised in that described device is adopted including data Collect module, assess data decimation module, evaluation index selection module, health degree computing module;
The alarm data of the data collecting module collected equipment, according to alarm time of origin write into Databasce;
The data decimation module of assessing is by alarm event title, alarm grade, alarm time of origin, alarm end time, announcement Alert number of repetition selects from database is used as assessment data;
The evaluation index chooses module and chooses different alarm events as health degree evaluation index, forms evaluation index collection;
The health degree computing module alerts frequency and average alarm by calculating each evaluation index within the time to be assessed Duration gives a mark to obtain alarm event fractional value to each alarm event, and assigns different weights to alarm event according to alarm grade and obtain To alarm event weighted value, alarm event fractional value and alarm event weighted value weighted sum are obtained into equipment health degree.
9. device as claimed in claim 8, it is characterised in that the health degree computing module include alarm event scoring modules, Weight computation module, weighted sum module;
Duration occurs for the number and average alarm that alarm event occurs the alarm event scoring modules within the time to be assessed Multiplication obtains alarm event fractional value;The correlation between alarm event is eliminated, obtains alarm event score;
The weight computation module utilizes the alarm grade development of judgment matrix of alarm event, obtains alerting thing using judgment matrix Part weighted value;
The weighted sum module, which corresponds to alarm event score with alarm event weighted value, is multiplied to obtain the alarm event to health The contribution component of degree;The contribution component for each alarm event that evaluation index is concentrated is added, and obtains the strong of equipment to be assessed Kang Du.
10. device as claimed in claim 8, it is characterised in that the equipment is switch power supply equipment or accumulator equipment;
When equipment is switch power supply equipment, the alarm event include battery powered alarm, VD it is too high alarm, Exchange input phase failure alarm, rectification module fault warning, the too high alarm of exchange incoming frequency, AC-input voltage it is too low alert, The too high alarm of AC-input voltage, the exchange too low alarm of incoming frequency;
When equipment is accumulator equipment, the alarm event is including total voltage is too low, total voltage is too high, certain monomer battery voltage It is too high, certain monomer battery voltage is too low, battery pack middle point voltage is uneven.
CN201710572591.3A 2017-07-13 2017-07-13 A kind of equipment health degree appraisal procedure and device based on alarm data analysis Pending CN107451402A (en)

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CN108038624A (en) * 2017-12-26 2018-05-15 北京金风科创风电设备有限公司 Method and device for analyzing health state of wind turbine generator
CN108388503A (en) * 2018-02-13 2018-08-10 中体彩科技发展有限公司 Data-base performance monitoring method, system, equipment and computer readable storage medium
CN108683662A (en) * 2018-05-14 2018-10-19 深圳市联软科技股份有限公司 Separate unit online equipment methods of risk assessment and system
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CN109377017A (en) * 2018-09-27 2019-02-22 广东电网有限责任公司信息中心 A kind of information system is practical and data health degree evaluation method
CN112083689B (en) * 2019-06-12 2022-02-08 中国石油化工股份有限公司 Method and device for evaluating abnormal working condition of refining device and storage medium
CN112083689A (en) * 2019-06-12 2020-12-15 中国石油化工股份有限公司 Method and device for evaluating abnormal working condition of refining device and storage medium
CN112783102A (en) * 2019-11-06 2021-05-11 中国石油化工股份有限公司 Memory, refining device operation risk early warning method, system and device
CN111639842A (en) * 2020-05-20 2020-09-08 湖北博华自动化系统工程有限公司 Equipment health evaluation method, evaluation system and equipment health prediction method
CN111426949A (en) * 2020-06-11 2020-07-17 新誉轨道交通科技有限公司 Electromagnetic valve health assessment method, device and equipment and readable storage medium
CN112001295A (en) * 2020-08-19 2020-11-27 北京航天飞行控制中心 Performance evaluation method and device for high-speed rotor shafting, storage medium and processor
CN112001295B (en) * 2020-08-19 2023-12-08 北京航天飞行控制中心 Performance evaluation method and device of high-speed rotor shaft system, storage medium and processor
CN112203166A (en) * 2020-09-09 2021-01-08 中盈优创资讯科技有限公司 Multi-model user health record scoring method and device
CN112203166B (en) * 2020-09-09 2023-03-14 中盈优创资讯科技有限公司 Multi-model user health record scoring method and device
CN113671909A (en) * 2021-06-30 2021-11-19 云南昆钢电子信息科技有限公司 Safety monitoring system and method for steel industrial control equipment
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Application publication date: 20171208