Determination of health status of systems equipped with sensors
A method for determining a health status of a system of interest is proposed. The method comprises acquiring (S 1 ) a time series, extracting (S 2 ) subsequences, selecting (S 3 ) a set of subsequences, classifying (S 4 ) the subsequences of the set into several groups on the basis of at least one criterion of resemblance to at least one reference subsequence, and constructing (S 5 ) a normal operating model of the system of interest. The construction includes, for each group, a modeling (S 51 ) of a representative subsequence and a determination (S 52 ) of an associated weight. The normal model is defined by the modeled subsequences and the associated weights. The method further includes an attribution (S 6 ) of a normality score to each subsequence extracted by comparison with the normal model, an identification (S 7 ) of at least one abnormal subsequence, and a determination (S 8 ) of the health status of the system of interest.
1. A method of determining a health status of a system of interest equipped with at least one sensor, the method comprising:
an acquisition OBT T of a time series formed of a sequence of measurements from the sensor as a function of time,
an extraction EXTR of a plurality of subsequences from the time series, each extracted subsequence being formed of a plurality of measurements, consecutive in time, extracted from said sequence of measurements,
a selection SEL of a set of subsequences, the set forming a part of the plurality of extracted subsequences,
a classification CLASS SEL of the subsequences of the selected set, into several groups of subsequences, based on at least one similarity criterion between each subsequence of the selected set and at least one reference subsequence,
a construction CONST N M of a normal operating model of the system of interest, the construction comprising, for each group of subsequences: a modeling MODEL N M i of a subsequence representative of the subsequences of said group, and a determination DET w i of a weight associated with the modeled subsequence by comparing a collective distribution of the subsequences forming said group with a collective reference distribution,
the normal operating model of the system of interest being defined by the modeled subsequences and the associated weights,
an attribution SCOR of a normality score to each extracted subsequence, based on a comparison between said extracted subsequence and the normal operating model of the system of interest,
an identification ID of at least one abnormal subsequence, indicating an abnormality in the functioning of the system of interest, based on the assigned normality scores, and
based on said at least one identified abnormal subsequence, a determination DET SoH of the health status of the system of interest.
2. The method according to claim 1 , comprising, in conjunction with the selection SEL Ti, , an exclusion EXCL Ti, in which each subsequence with a proportion exceeding a predetermined threshold, is found in its entirety in at least one other subsequence, is excluded from the selected set.
3. The method according to claim 1 , wherein the selection SEL is a random selection of subsequences among the plurality of subsequences.
4. The method according to claim 1 , wherein the selection SEL is based on a comparison of the subsequences of the plurality of subsequences with each other, the set being formed such that each subsequence of the set has a degree of similarity exceeding a predetermined threshold with at least one other subsequence of the set.
5. The method according to claim 1 , wherein the similarity criterion between a given subsequence A and a reference subsequence B results of:
a determination of a distance dist(A,B) between the given subsequence and the reference subsequence, the distance dist(A, B) being defined as
dist
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where A i,1 and B i,1 denote a first measurement in time, μ A and μ B denote a mean, and σ A and σ B denote a standard deviation of the first subsequence A and the second subsequence B respectively, and
a comparison of the determined distance with a reference value.
6. The method according to claim 1 , wherein the classification CLASS SEL is based on a hierarchical clustering of the subsequences in the set, the hierarchical clustering being performed by repeating the following steps until a stopping criterion is reached:
determine DET DEG, for each pair of subsequences in the set, a degree of similarity,
form FORM GRP a group of level i, where i represents the number of subsequences in the set, based on the similarity criterion so that the group of level i consists of the pair of subsequences in the set with the highest degree of similarity determined,
generating GEN SS SEQ a subsequence representative of said level group i, intermediate between the subsequences of said level group i, and
reducing RED ENS the set by replacing the pair of subsequences forming said level group i with the generated subsequence representative of said level group i.
7. The method according to claim 6 , wherein:
during each iteration of the following steps, before performing each RED ENS reduction of the set, the subsequences forming the set are encoded and the total memory size of the encoded subsequences is determined, and
the stopping criterion is based on a comparison of the determined for two consecutive iterations of the following steps.
8. The method according to claim 1 , wherein during modeling MODEL N M i of a subsequence representative of subsequences of said group, the modeled subsequence is intermediate between subsequences of said group.
9. The method according to claim 1 , wherein the weight of each group is based on the number of subsequences forming said group.
10. The method according to claim 1 , wherein the weight of each group is based on a temporal coverage of said group.
11. The method according to claim 1 , wherein the weight of each group is based on a centrality of said group relative to several groups.
12. The method according to claim 11 , wherein the normality score of a given subsequence is obtained based on a comparison of the given subsequence with each subsequence of the normal model and based on a weighting of the results of said comparisons by the respective weights associated with each subsequence of the normal model.
13. The method according to claim 1 , wherein the system of interest is equipped with a plurality of sensors.
14. The method according to claim 1 , wherein the system of interest is an industrial site with a set of sensors.
15. The method according to claim 14 , wherein the industrial site has a sensor for temperature and/or a sensor for pressure.
16. The method according to claim 1 , wherein the system of interest is a human or an animal with an integrated sensor.
17. The method according to claim 16 , wherein the integrated sensor is an electrocardiograph.
18. A computer program comprising instructions for application of the method of claim 1 when said program is executed by a processor.
19. A device for non-transitory recording readable by a computer on which is recorded a program for the implementation of the method of claim 1 when such program is executed by a processor.
20. A processing circuit comprising a processor connected to the device for non-transitory recording of claim 19 .