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 PDFInfo
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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
Technical Field
The invention mainly belongs to the field of equipment health degree analysis, and particularly relates to an equipment health degree evaluation method and device based on alarm data analysis.
Background
The equipment health degree refers to the good degree of the overall operation of the equipment, and is a comprehensive evaluation of the operation state of the whole equipment. A commonly used health assessment method is health assessment based on equipment operating state parameters, that is, the state is assessed by monitoring the operating equipment and obtaining the equipment state parameters. However, in practical application, equipment is packaged in a cabinet, so that obtaining of equipment parameters in operation is difficult to achieve, and most of equipment adopts manual discharge testing and conductance testing modes for detection, so that the equipment is complex and has strong professional requirements.
There are reports in the literature that some methods based on alarm data analysis are used in the field of communications and networks to assess the health of communication base stations/systems and/or networks. The alarm is used as the reflection of equipment fault, and the alarm analysis can effectively evaluate the equipment state. The alarm is used as the visual representation of the equipment state, compared with the data which is easier to obtain, the alarm data is used as a data set, the relevant characteristics of the equipment running state are mined, a health degree evaluation model is established, and the problems existing based on the equipment state parameters can be well avoided. However, the method in the report is only applied to the communication and network fields, the selection and scoring of the evaluation indexes are different due to different evaluation objects, and the application of the method is limited.
The main idea of evaluating the health degree is that each parameter reflecting the performance state of an evaluation object is evaluated and scored respectively, and each evaluation result is weighted and fused to obtain a final evaluation result. The selection and scoring of the evaluation indexes are different due to different evaluation objects. Determining the weight of each parameter in the device performance assessment is more difficult than assessing the score. The currently applied method mainly comprises: a fuzzy analytic hierarchy process, a linear weighting process, a principal component analysis process, a fuzzy comprehensive evaluation process, an entropy weight process, a BP neural network process and a support vector machine process. In which the weights are determined subjectively using the existing work of the fuzzy analytic hierarchy process and the linear weighting process, human subjective factors may bring deviations. The principal component analysis method is determined according to the attention of a user and still has subjective components. The fuzzy comprehensive evaluation method has the defect that the evaluation problem of relevance of ambiguity and randomness cannot be solved. The entropy weight method uses the entropy of each parameter as a weight. The neural network method and the support vector machine method are used for establishing a model through training after obtaining an evaluation value by adopting a fuzzy comprehensive evaluation method, and the quality of the model is limited by the fuzzy comprehensive evaluation method. In summary, the weight determination method of the existing assessment method has human factors, which will influence the objectivity of the assessment result.
Disclosure of Invention
Aiming at the problems, the invention provides a method and a device for evaluating the health degree of equipment based on alarm data analysis. The invention takes the alarm event as an evaluation index, determines index score and index weight by alarm characteristics, objectively evaluates the health degree of the equipment and provides a basis for guiding subsequent equipment fault management and maintenance work.
The invention is realized by the following technical scheme:
an equipment health assessment method based on alarm data analysis, the method comprising the steps of:
data acquisition: collecting alarm data of equipment, and writing the alarm data into a database according to alarm occurrence time;
selecting evaluation data: selecting the alarm event name, the alarm level, the alarm occurrence time, the alarm ending time and the alarm repetition times from a database as evaluation data;
selecting evaluation indexes: selecting alarm events with different alarm event names as evaluation indexes to form an evaluation index set;
and (3) calculating the health degree: and scoring each alarm event by calculating the alarm occurrence frequency and the average alarm duration of each evaluation index in the time to be evaluated, giving different weights to the alarm event according to the alarm level, and calculating the score of each evaluation index in combination with the weight to obtain the equipment health degree.
Further, the scoring of each alarm event by calculating the alarm occurrence frequency and the average alarm duration of each evaluation index in a certain period of time specifically comprises:
21) dividing the time to be evaluated into q scoring moments on the time axis, { t1,t2,…,ti,…,tq}(t0As a time starting point), the scoring interval is { Δ }1＝t1-t0,Δ2＝t2-t1,…,Δj＝tj-tj-1,…,Δq＝tq-tq-1}; for evaluation indexes in the evaluation index set, i.e. alarm event aiAt the scoring time tj(j ═ 1,2, …, q) and a warning time piece aiAt a point in time tjIs scored as
Wherein:
to evaluate the index aiAt intervals of time deltajThe number of internal occurrences;
to evaluate the index aiAt intervals of time deltajThe average alarm duration within which the alarm occurred,
andrespectively, an evaluation index (alarm event) aiAt a time duration ofjThe alarm ending time and the occurrence time when the kth time occurs;
23) repeating the scoring at other scoring moments to obtain an evaluation index a in the time to be evaluatediScore sequence of (3)
24) Scoring all evaluation indexes in the evaluation index set to obtain an evaluation index diversity { X }1,X2,…,Xi,…Xm}, set element XiThe row or column vector includes index scores obtained at q scoring times, and m is the number of evaluation indexes.
Further, the giving of different weights to the alarm event according to the alarm level specifically includes:
31) constructing a judgment matrix:
the judgment matrix was constructed using the 1-9 ratio scaling method (Table 1) commonly used in the analytic hierarchy process. Comparing all the evaluation indexes with each other pairwise, giving out relative importance among the indexes according to the alarm level by a 1-9 ratio scaling method, and constructing a judgment matrix A;
TABLE 11-9 ratio scaling
In the present invention, table 1 can also be described by the following mathematical formula;
bijto determine the element in the ith row and the jth column of the matrix A, which represents the relative importance of the ith evaluation index with respect to the jth evaluation index, bij＝1/bji；
L is the difference value of the alarm levels of the ith evaluation index and the jth evaluation index;
32) calculating the weight:
withe weight of the ith evaluation index;
wherein
Mi＝bi1×bi2×.....bim；
m is the index number.
Further, the correlation between the respective evaluation indexes is eliminated;
41) index score normalization processing:
evaluation index normalization to diversity { Y }1,Y2,…,Yi,…Ym}，
Wherein,
is aiScore X ofiThe maximum score of (a) of (b),is XiMinimum score of;
m is the number of evaluation indexes;
42) utilizing the gram-Schmidt orthogonal method to eliminate the correlation between the indexes to obtain a group of orthogonal sets { β) without correlation between the indexesi,i＝1,2,…,m}；
β1＝Y1；
β2＝Y2-[Y2,β1]/[β1,β1]×β1，[Y2,β1]Is Y2And β1Inner product of [ β ]1,β1]Is β1And β1Inner product of (d);
to eliminate the orthogonal set β obtained after correlationiβ degree of health of the equipment was calculated by combining score of 1,2, …, m as evaluation index and weightiThe row or column vector contains the scores of the evaluation index at k scoring times.
Further, the calculating of the score of each evaluation index after the correlation elimination and the weight to obtain the health degree of the equipment specifically includes: and weighting and summing the scores and the weights of the evaluation indexes to obtain the health degree score of the equipment, wherein the calculation formula is as follows:
is the equipment health score, H is a row or column vector containing the health scores at all scored moments, βiIs the score sequence of the ith evaluation index after the correlation is eliminated, and comprises the scores of the evaluation indexes at q scoring moments, omegaiIs the weight of the score, and m is the number of evaluation indexes. Health score per scored timeEqual to the sum of the products of the evaluation index score and the score weight at that time.
Further, the weight distribution is subjected to consistency check, so that the consistency ratio CR is CI/RI <0.10, otherwise, the element values of the judgment matrix are adjusted (by using a 1-9 ratio scaling method shown in Table 1), and the values of the weight coefficients are redistributed until the consistency ratio is less than 0.1;
wherein the consistency indexWhere λ max is the largest characteristic root of the decision matrix,a is the decision matrix, W is the weight matrix, AW is the product of the two matrices, (AW)iIs the i-th element of the product of the matrices, wiIs the weight of the ith index;
RI is the average random consistency index value.
Furthermore, before the evaluation data is selected, the alarm data is preprocessed, and bad data such as flash memory and repetition are removed.
An equipment health degree evaluation device based on alarm data analysis uses the evaluation method, and comprises a data acquisition module, an evaluation data selection module, an evaluation index selection module and a health degree calculation module;
the data acquisition module acquires alarm data of the equipment and writes the alarm data into a database according to alarm occurrence time;
the evaluation data selection module selects the alarm event name, the alarm level, the alarm occurrence time, the alarm ending time and the alarm repetition times from the database as evaluation data;
the evaluation index selection module selects various alarm events according to the alarm event names as evaluation indexes to form an evaluation index set;
the health degree calculation module scores the alarm events by calculating the alarm occurrence times and the average alarm duration of the alarm events in the time to be evaluated to obtain alarm event score values, eliminates correlation to obtain alarm event score values, gives different weights to the alarm events according to the alarm grades to obtain alarm event weight values, and weights the alarm event score values and the alarm event weight values to obtain equipment health degrees.
Further, the health degree calculation module comprises an alarm event scoring module, a weight calculation module and a weighted sum module;
the alarm event scoring module multiplies the occurrence frequency of the alarm event in the time to be evaluated by the average alarm occurrence time length to obtain an alarm event score value; eliminating the correlation among the score values of different alarm events to obtain the score value of the alarm event;
the weight calculation module utilizes the alarm level of the alarm event to construct a judgment matrix, and utilizes the judgment matrix to obtain the weight value of the alarm event;
the weighting sum module correspondingly multiplies the alarm event score value and the alarm event weight value to obtain the contribution component of the alarm event to the health degree; and adding the contribution components of each alarm event in the evaluation index set to obtain the health degree of the equipment to be evaluated.
Further, the device is a switching power supply device, and the alarm event comprises a battery power supply alarm, a direct current output voltage overhigh alarm, an alternating current input phase loss alarm, a rectifier module fault alarm, an alternating current input frequency overhigh alarm, an alternating current input voltage overlow alarm, an alternating current input voltage overhigh alarm and an alternating current input frequency overlow alarm.
Further, the device is a storage battery device, and the alarm event includes that the total voltage is too low, the total voltage is too high, the voltage of a certain single battery is too low, and the voltage of the middle point of the battery pack is unbalanced.
The invention has the beneficial technical effects that:
1) the method and the system completely evaluate the health degree of the equipment based on the alarm data, almost have no technical requirements, and are very convenient to implement;
2) the invention takes the alarm event as the evaluation index, the index score and the index weight are both determined by the alarm characteristics, and the evaluation result is very visual and easy to understand;
3) according to the equipment health evaluation model provided by the invention, the score of the real-time health degree of the equipment can be obtained, and the change curve of the health degree of the equipment in a period of time can be drawn.
Drawings
Fig. 1 is a schematic diagram of the daily change trend of the health of the switching power supply in one month calculated in embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
Example 1
The method of the invention is used for evaluating the health of the switching power supply in a certain communication base station. The aim is to evaluate the health degree change trend of the switching power supply within one month, and the health degree of the switching power supply is scored once every other day, namely the scoring interval time is 1 day.
And S1, collecting alarm data of the communication base station switching power supply, and writing the alarm data into a database according to the time sequence of alarm occurrence in a one-month-span way.
S2, invalid data such as missing, repeated and instantaneous interruption in the alarm data are removed, and data attributes such as alarm event names, alarm levels, alarm occurrence time, alarm ending time, alarm repetition times and the like are extracted.
The alarm missing means that the alarm data information is incomplete, and some field values are null. The alarm instant interruption means that the time from generation to elimination of the alarm is very short; the alarm repetition means that the attribute values of a plurality of alarm records are completely consistent. Alarm data with these characteristics either fail to provide complete data attributes, are missing data, or do not reflect the correct alarm event, so they are rejected by preprocessing before being evaluated.
And S3, selecting a health degree evaluation index. There are eight different alarm events in the alarm data, and they are selected as the health evaluation index of the switching power supply, as shown in table 2.
And S4, calculating the health degree of the power supply equipment. The length of observation was one month (31 days) and the fitness assessment was performed once a day, i.e. the scored interval Δ was one day.
The scoring intervals may be uniformly spaced or non-uniformly spaced. The size of the scoring interval can be the granularity of time in which the maintainer is interested; meanwhile, a certain amount of alarm data is ensured to be contained in the scoring interval. For example, if the length of time to be evaluated is 1 month, and the daily health trend of the equipment is to be examined, the scoring interval is selected to be 1 day. The scoring interval may also be chosen to be of smaller temporal granularity, such as 6 hours, 1 hour, etc. The applicant studies found that the equipment health changes in the same general trend at the scoring intervals of 1 day, 6 hours and 1 hour.
In each day, the number of times of alarm and the average alarm duration of each index in the past day are counted, and the score of each index in 31 time windows is calculated, as shown in table 3. The calculation process of the index score is described by taking the evaluation index a1 as an example at the first scoring time, i.e., the first day. A1 occurred a total of 5 times during the first day, each time lasting 29.05, 66.45, 10.38, 60.85 and 87.12 minutes, thus
The time is as long as the reaction time is short,this value is the element in table 3, row 1, column 1.
TABLE 2 evaluation index of switching power supply
TABLE 3 switching power supply index score
The index scores are normalized, so that the index scores are all between 0 and 1, and the results are shown in table 4. With X1At t1The value of the moment of time isThe normalization value of (c) is calculated as an example. X1Maximum value in the sequenceIs 253.85, minimum valueIs 0, and is therefore at t1Normalized value of time This value is the value of the row 1, column 1 element of table 4.
TABLE 4 normalized index score
The index scores were then processed by the schmitt orthogonal method, and the index scores with the correlation removed are shown in table 5.
TABLE 5 score of each index after eliminating index correlation
And determining the weight of each index by using an analytic hierarchy process according to the alarm level of each index of the switching power supply in the table 2. The decision matrix and weighting results are shown in table 6. There are eight alarm events, so m is 8. Three different alarm levels are provided, so that the judgment matrix takes values of 1-3-5. The weight calculation of the evaluation index a1 is taken as an example. The corresponding evaluation value in the matrix, i.e. the first row in table 6, is determined to be 1, 1, 3, 3, 5, 5, 5, 5, so that the product M of the row elements1＝1×1×3×3×5×5×5×5＝5625，M1Root of 8 th power, 8 th power Similarly, the product of the other seven rows of elements can be obtained Normalization is carried out to obtain the weight:
other weights may be calculated similarly.
TABLE 6 calculation of weights by analytic hierarchy process
A consistency indicator is calculated for the weights. Index of consistencyWherein λmaxTo determine the maximum characteristic root of the matrix, i.e.A is the decision matrix, W is the weight matrix, AW is the product of the two matrices, (AW)iIs its i-th element, wiIs the weight of the i-th index.
The consistency ratio CR is calculated as CI/RI, where RI is the average random consistency index value, as shown in table 7, where m is the number of indices in the decision matrix.
TABLE 7 average random consistency index RI values
When the consistency ratio CR is CI/RI <0.10, the judgment matrix is considered to have relatively good consistency, and the weight assignment is considered to be reasonable.
In example 1, the judgment matrix
Weight matrix
Since the evaluation index number is 8, the RI is 1.41 as known from table 7, CR CI/RI 0.012395 ÷ 1.41 0.008791<0.1 passes the consistency test.
The health degree of the switching power supply on the first day Similarly, the health degree of other days can be calculated. The health degree is taken as the vertical axis, the corresponding observation day is taken as the horizontal axis, and the health degree change curve of the switching power supply every day in one month is drawn, as shown in fig. 1. The curve reflects the daily change trend of the health of the switching power supply in one month.
Meanwhile, the embodiment also comprises an equipment health degree evaluation device based on alarm data analysis, wherein the device adopts the evaluation method, and comprises a data acquisition module, an evaluation data selection module, an evaluation index selection module and a health degree calculation module;
after the monitored power supply equipment generates an alarm, the alarm is sent to a data acquisition module through interfaces such as RS485, RS232 or Ethernet, the data acquisition module carries out protocol format conversion on the equipment alarm data and writes the equipment alarm data into a database according to formats such as an alarm equipment location, an alarm equipment ID, an alarm equipment access IP address, an alarm event name, an alarm event type, an alarm level, alarm occurrence time, alarm ending time, alarm repetition times, alarm reasons, an alarm summary and the like.
The data acquisition module acquires alarm data of the equipment and writes the alarm data into a database according to alarm occurrence time;
the evaluation data selection module selects the alarm event name, the alarm level, the alarm occurrence time, the alarm ending time and the alarm repetition times from the database as evaluation data;
the evaluation index selection module selects various alarm events according to the alarm event names as health degree evaluation indexes to form an evaluation index set (the alarm events in the index set have no sequence requirement);
the alarm event comprises a battery power supply alarm, a direct current output voltage overhigh alarm, an alternating current input phase loss alarm, a rectifier module fault alarm, an alternating current input frequency overhigh alarm, an alternating current input voltage overlow alarm, an alternating current input voltage overhigh alarm and an alternating current input frequency overlow alarm.
The health degree calculation module scores the alarm events by calculating the alarm occurrence times and the average alarm duration of the alarm events in the time to be evaluated to obtain alarm event score values, eliminates correlation to obtain alarm event score values, gives different weights to the alarm events according to the alarm grades to obtain alarm event weight values, and weights the alarm event score values and the alarm event weight values to obtain equipment health degrees.
Further, the health degree calculation module comprises an alarm event scoring module, a weight calculation module and a weighted sum module;
the alarm event scoring module multiplies the occurrence frequency of the alarm event in the time to be evaluated by the average alarm occurrence time length to obtain an alarm event score value, and eliminates the correlation to obtain an alarm event score value;
the weight calculation module utilizes the alarm level of the alarm event to construct a judgment matrix, and utilizes the judgment matrix to obtain the weight value of the alarm event;
the weighting sum module correspondingly multiplies the alarm event score value and the alarm event weight value to obtain the contribution component of the alarm event to the health degree; and adding the contribution components of each alarm event in the evaluation index set to obtain the health degree of the equipment to be evaluated.
Claims (10)
1. An equipment health degree evaluation method based on alarm data analysis is characterized by comprising the following steps:
data acquisition: collecting alarm data of equipment, and writing the alarm data into a database according to alarm occurrence time;
selecting evaluation data: selecting the alarm event name, the alarm level, the alarm occurrence time, the alarm ending time and the alarm repetition times from a database as evaluation data;
selecting evaluation indexes: selecting different alarm events as evaluation indexes to form an evaluation index set;
and (3) calculating the health degree: the alarm event score value is obtained by calculating the alarm occurrence frequency and the average alarm duration of each alarm event in the time to be evaluated, different weights are given to the alarm events according to the alarm levels to obtain alarm event weight values, and the equipment health degree is obtained by combining the score value of each alarm event with the weight values.
2. The evaluation method according to claim 1, wherein the scoring of each alarm event by calculating the number of alarm occurrences and the average alarm duration for each evaluation index within a certain period of time to obtain the alarm event score value is specifically:
21) dividing the time to be evaluated into q scoring moments on the time axis, { t1，t2，...，ti，...，tqA scoring interval of { Δ }1＝t1-t0，Δ2＝t2-t1，…，Δj＝tj-tj-1，…，Δq＝tq-tq-1}；t0Is the starting point of time;
the scoring intervals are uniform intervals or non-uniform intervals, and are selected according to the evaluation time;
22) for the evaluation index a in the evaluation index setiAt the scoring time tjScore, alarm event aiAt a point in time tjIs scored asm is the number of evaluation indexes, j is 1, 2.., q, q is the number of scoring time;
wherein:
to evaluate the index aiAt intervals of time deltajThe number of internal occurrences;
to evaluate the index aiAt intervals of time deltajThe average alarm duration within which the alarm occurred,
andrespectively is an evaluation index aiAt a time duration ofjThe alarm ending time and the occurrence time when the kth time occurs;
23) repeating the scoring at other scoring moments to obtain an evaluation index a in the time to be evaluatediScore sequence of (3)
24) All alarm events in the evaluation index set are scored to obtain the evaluation index set { X }1，X2，…，Xi，…Xm}, set element XiThe row or column vector includes index scores obtained at q scoring times, and m is the number of evaluation indexes.
3. The evaluation method according to claim 1, wherein said assigning different weights to alarm events according to alarm level is specifically:
31) constructing a judgment matrix:
comparing all the evaluation indexes with each other pairwise, obtaining according to the alarm level, and constructing a 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>></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><</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>
bijto determine the element in the ith row and the jth column of the matrix A, which represents the relative importance of the ith evaluation index with respect to the jth evaluation index, bij＝1/bji；
L is the difference value of the alarm levels of the ith evaluation index and the jth evaluation index;
32) calculating the weight:
<mrow> <msub> <mi>w</mi> <mi>i</mi> </msub> <mo>=</mo> <mover> <msub> <mi>W</mi> <mi>i</mi> </msub> <mo>&OverBar;</mo> </mover> <mo>/</mo> <msubsup> <mi>&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>&OverBar;</mo> </mover> <mo>;</mo> </mrow>
withe weight of the ith evaluation index;
wherein
<mrow> <mover> <msub> <mi>W</mi> <mi>i</mi> </msub> <mo>&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 the index number.
4. The evaluation method according to claim 1, wherein the correlation between the respective evaluation indexes is eliminated;
41) index score normalization processing:
evaluation index normalization to diversity { Y }1，Y2，…，Yi，…Ym}，
Wherein,
is aiScore sequence X of (2)iThe maximum score of (a) of (b),is XiMinimum of (1)Scoring;
m is the number of evaluation indexes;
42) utilizing the gram-Schmidt orthogonal method to eliminate the correlation between the indexes to obtain a group of orthogonal sets { β) without correlation between the indexesi，i＝1，2，…，m}；
β1＝Y1；
β2＝Y2-[Y2，β1]/[β1，β1]×β1，[Y2，β1]Is Y2And β1Inner product of [ β ]1，β1]Is β1And β1Inner product of (d);
<mrow> <msub> <mi>&beta;</mi> <mi>i</mi> </msub> <mo>=</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>-</mo> <mfrac> <mrow> <mo>&lsqb;</mo> <msub> <mi>Y</mi> <mn>2</mn> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mn>1</mn> </msub> <mo>&rsqb;</mo> </mrow> <mrow> <mo>&lsqb;</mo> <msub> <mi>&beta;</mi> <mn>1</mn> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mn>1</mn> </msub> <mo>&rsqb;</mo> </mrow> </mfrac> <mo>&times;</mo> <msub> <mi>&beta;</mi> <mn>1</mn> </msub> <mo>-</mo> <mo>...</mo> <mo>-</mo> <mfrac> <mrow> <mo>&lsqb;</mo> <msub> <mi>Y</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&rsqb;</mo> </mrow> <mrow> <mo>&lsqb;</mo> <msub> <mi>&beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mi>&beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&rsqb;</mo> </mrow> </mfrac> <mo>&times;</mo> <msub> <mi>&beta;</mi> <mrow> <mi>i</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>,</mo> <mi>i</mi> <mo>></mo> <mn>2</mn> <mo>;</mo> </mrow>
to eliminate the orthogonal set β obtained after correlationiβ degree of health of the equipment was calculated by combining score of 1,2, …, m as evaluation index and weightiThe row or column vector contains the scores of the evaluation index at q scoring times.
5. The evaluation method according to claim 1, wherein the score obtained after the elimination of the correlation of each evaluation index is combined with the weight calculation to obtain the health degree of the equipment, specifically: the scores and the weights of all the evaluation indexes are weighted and summed to obtain the health degree score of the equipment, and the calculation formula is
<mrow> <mi>H</mi> <mo>=</mo> <msubsup> <mi>&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>&beta;</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>
Is the equipment health score, H is a row or column vector containing the health scores at all scored moments, βiIs the ith evaluation index elimination correlation score sequence which comprises q beatsScore, ω, of evaluation index at timeiIs the weight of the score, m is the number of evaluation indexes; health score per scored timeEqual to the sum of the products of the evaluation index score and the score weight at that time.
6. The evaluation method according to claim 3, wherein the weight distribution is subjected to a consistency check such that the consistency ratio CR ═ CI/RI <0.10, otherwise, the values of the elements of the decision matrix are adjusted and the values of the weight coefficients are redistributed until the consistency ratio is less than 0.1;
wherein the consistency indexWherein λmaxTo determine the largest root of the features of the matrix,a is the decision matrix, W is the weight matrix, AW is the product of the two matrices, (AW)iIs the i-th element of the product of the matrices, wiIs the weight of the ith index;
RI is the average random consistency index value.
7. The assessment method of claim 1 wherein the alarm data is pre-processed to remove missing, snap, duplicate bad data prior to assessment data selection.
8. An equipment health degree evaluation device based on alarm data analysis is characterized by comprising a data acquisition module, an evaluation data selection module, an evaluation index selection module and a health degree calculation module;
the data acquisition module acquires alarm data of the equipment and writes the alarm data into a database according to alarm occurrence time;
the evaluation data selection module selects the alarm event name, the alarm level, the alarm occurrence time, the alarm ending time and the alarm repetition times from the database as evaluation data;
the evaluation index selection module selects different alarm events as health degree evaluation indexes to form an evaluation index set;
the health degree calculation module scores alarm events by calculating the alarm occurrence times and the average alarm duration of each evaluation index in the time to be evaluated to obtain alarm event score values, gives different weights to the alarm events according to the alarm levels to obtain alarm event weight values, and weights the alarm event score values and the alarm event weight values to obtain the equipment health degree.
9. The apparatus of claim 8, wherein the health calculation module comprises an alarm event scoring module, a weight calculation module, a weighted sum module;
the alarm event scoring module multiplies the occurrence frequency of the alarm event in the time to be evaluated by the average alarm occurrence time length to obtain an alarm event score value; eliminating the correlation among the alarm events to obtain the score of the alarm event;
the weight calculation module utilizes the alarm level of the alarm event to construct a judgment matrix, and utilizes the judgment matrix to obtain the weight value of the alarm event;
the weighting sum module multiplies the alarm event score and the alarm event weight value correspondingly to obtain the contribution component of the alarm event to the health degree; and adding the contribution components of each alarm event in the evaluation index set to obtain the health degree of the equipment to be evaluated.
10. The apparatus of claim 8, wherein the device is a switching power supply device or a battery device;
when the equipment is switching power supply equipment, the alarm event comprises a battery power supply alarm, a direct current output voltage overhigh alarm, an alternating current input open-phase alarm, a rectifier module fault alarm, an alternating current input frequency overhigh alarm, an alternating current input voltage overlow alarm, an alternating current input voltage overhigh alarm and an alternating current input frequency overlow alarm;
when the equipment is storage battery equipment, the alarm events comprise that the total voltage is too low, the total voltage is too high, the voltage of a certain single battery is too low, and the voltage of the middle point of the battery pack is unbalanced.
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