Detection of electric arcs in an electrical circuit of an aeronautical system by forming a pattern database

Abstract

The invention relates to a method for detecting electric arcs in an electrical circuit of an aeronautical system, which method comprises a first phase (P1) of identifying recurring patterns in the behaviour of the electric current, on an electric line of the electrical circuit, and storing the recurring patterns in a pattern database, and a second diagnostic phase (P2) which involves comparing the behaviour of the electric current on the electric line with recurring patterns stored in the pattern database, and detecting an electric arc when the behaviour does not correspond to a recurring pattern stored in the pattern database.

Description

Description Detection of electrical arcs in an electrical circuit of an aeronautical system by creating a pattern database

[0001] FIELD OF INVENTION

[0002] The present invention relates to the detection of faulty electrical arcs on an electrical line and is particularly applicable to the field of aeronautics and embedded systems.

[0003] An electric arc is a self-sustaining discharge with a high current.

[0004] Two types of electrical arcs can occur in an aeronautical environment: breaking arcs, present in contactors when a circuit is opened or closed, and fault arcs, which can appear unexpectedly on any component of the electrical system. The latter can cause severe damage to equipment, systems, and even the aircraft structure. This is therefore a critical point of vigilance.

[0005] Furthermore, fault arcs can be of two types depending on their position on the electrical circuit: parallel arcs and series arcs.

[0006] In current aircraft systems, these problems are partly resolved, or mitigated, by several factors: - The distributed voltage levels are generally at most 230V to 400V, which reduces the impact of electric arcs; - The distribution of an alternating (AC) waveform also helps to promote the self-extinction of the arc at each half-period, when the voltage passes through 0. - In continuous mode, voltage levels are generally lower, on the order of 28 V. - Passive protections can be used to limit the consequences of possible fault arcs: choice of materials resistant to these phenomena, distancing of the different elements, etc. Finally, active protection measures can also be implemented, such as arc detection and line opening mechanisms that activate when an arc is detected. Current active protection systems can detect most parallel arcs, but not series arcs, for which no active protection is currently deployed.

[0007] However, recent work on aircraft electrification suggests a distribution of direct current (DC) waveforms with high voltage levels, potentially reaching kilovolts. This paradigm shift obviously calls into question the passive protection strategy mentioned earlier.

[0008] Indeed, climate change is a major concern for many legislative and regulatory bodies worldwide. Various restrictions on carbon emissions have been, are being, or will be adopted by different countries. In particular, an ambitious standard applies to both new types of aircraft and those already in service, requiring the implementation of technological solutions to bring them into compliance with current regulations. Civil aviation has been actively working for several years now to contribute to the fight against climate change.

[0009] Technological research efforts have already led to significant improvements in the environmental performance of aircraft. The Applicant takes into account factors impacting all phases of design and development to obtain aeronautical components and products that are less energy-intensive, more environmentally friendly, and whose integration and use in civil aviation have moderate environmental impacts, with the aim of improving aircraft energy efficiency. Consequently, the Applicant is continuously working to reduce its climate impact by employing methods and operating virtuous development and manufacturing processes that minimize greenhouse gas emissions to the absolute minimum possible, thereby reducing the environmental footprint of its business.

[0010] This sustained research and development work thus focuses on new generations of aircraft engines, the weight reduction of aircraft, in particular through the materials used and lighter on-board equipment, aviation biofuels and the development of the use of electric technologies to provide propulsion.

[0011] Thus, in the context of increasing voltage and electrical power levels in aircraft networks, the series electrical arc must also be taken into account, just like the parallel arc, because the damage associated with the generation of higher power faults subjected to a continuous voltage risks damaging the aircraft and its on-board systems, and could even impact the safety of passengers and crew.

[0012] There are several proposals for detecting an electrical arc on a power line in order to isolate it (by cutting the circuit). However, these proposals must comply with certain constraints: - Reliability: dangerous electrical phenomena must be detectable systematically and under all circumstances. In other words, a high true positive rate, close to 100%, is required. - Robustness: the active protection mechanism must be immune to any event other than the one for which it was designed. In other words, the false positive rate must be as low as possible, i.e., close to 0%.

[0013] So far, these proposals show shortcomings either in terms of reliability or in terms of robustness.

[0014] For example, patent application EP3959525 proposes arc detection by combining two typical behaviors: a high scanning speed at the onset of the arc and a high-frequency behavior. This proposal can therefore detect electric arcs, but it can also mistake normal load behaviors associated with the power line for electric arcs. Consequently, the false positive rate is high.

[0015] The same applies to patent application WO202143027 which describes a method based on machine learning.

[0016] SUMMARY OF THE INVENTION

[0017] Therefore, there is a need to improve current state-of-the-art proposals.

[0018] The invention aims, in particular, to improve the performance of detecting electrical arc faults on a power line. Specifically, it allows these electrical arcs to be distinguished from transient phenomena related to the load, and thus significantly reduces the false alarm rate. In other words, the invention makes it possible to greatly increase the rate of true positives and to also greatly decrease the rate of false positives.

[0019] According to a first aspect, the present invention can be implemented by a method for detecting electric arcs in an electrical circuit of an aeronautical system, comprising: - a first phase of discovering recurring patterns in the behavior of electric current on an electrical line of said electrical circuit, and storing said recurring patterns in a pattern database, and, - a second diagnostic phase including a comparison of the behavior of said electric current on said electric line with recurring patterns stored in said pattern database, and a detection of an electric arc when said behavior does not correspond to a recurring pattern stored in said pattern database.

[0020] According to preferred embodiments, the invention comprises one or more of the following features which can be used separately or in partial combination with each other or in total combination with each other: - a time threshold triggers a switch from said first phase to said second phase. - said time threshold corresponds to a duration since the first power-up of said aeronautical system greater than or equal to 15 hours and less than or equal to 35 hours, preferably equal to 25 hours. - said first phase includes a step of acquiring measurement data relating to said electric current, and of storing said measurement data to form a time series, as well as a step of recognizing recurring patterns within said time series of measurements. - said step of recognizing recurring patterns implements a k-motiflets method. - said second phase includes a step of searching for a transient phenomenon in said electric current, and a step of comparing said transient phenomenon with said stored recurring patterns. - We extract a time window corresponding to the transient phenomenon, we calculate a distance between said time window and each of said stored recurring patterns, then we detect the presence of an electric arc as a function of all the distances, the calculated distance preferably being a normalized z-Euclidean distance.

[0021] Another aspect of the invention relates to a computer program comprising instructions for implementing a process as previously described when executed on an information processing platform.

[0022] Another aspect of the invention relates to a detection device for detecting electric arcs in an electrical circuit of an aeronautical system, comprising - a functional discovery module adapted for discovering recurring patterns in the behavior of electric current on a power line, and storing said recurring patterns in a pattern database, and, - a functional diagnostic module adapted for comparing the behavior of said electric current on said electric line with recurring patterns stored in said pattern database and for detecting an electric arc when said behavior does not correspond to a recurring pattern stored in said pattern database.

[0023] Another aspect of the invention relates to a monitoring device, in particular of the semiconductor power controller type, comprising a detection device as previously defined.

[0024] Another aspect of the invention relates to an aircraft comprising at least one detection device as previously defined.

[0025] According to preferred embodiments, this device may include one or more of the previously mentioned features in relation to the process, which may be used separately or in partial or total combination with each other.

[0026] Other features and advantages of the invention will become apparent from the following description of a preferred embodiment of the invention, given by way of example and with reference to the accompanying drawings.

[0027] BRIEF DESCRIPTION OF THE FIGURES

[0028] The attached drawings illustrate examples of the invention: Figure 1 schematically illustrates an example of the implementation timeline of the detection process, showing a first and a second phase, according to one embodiment of the invention. Figures [FIG. 2a] and [FIG. 2b] schematically illustrate a functional architecture enabling the implementation of a detection device according to two embodiments of the invention. Figure 3 schematically illustrates a functional architecture of a detection device according to one embodiment of the invention. Figure 4 schematically illustrates a flowchart of a detection method according to one embodiment of the invention. Figures [FIG. 5a] and [FIG. 5b] represent two examples of electric current behavior in a supervised power line, according to implementations of the invention. Figures [FIG. 6a] and [FIG. 6b] illustrate two examples of transient phenomena introduced by electric arcs, according to implementations of the invention.

[0029] DETAILED DESCRIPTION OF METHODS OF IMPLEMENTING THE INVENTION

[0030] The proposed process is based on two distinct phases, linked together in time, as illustrated in Figure 1.

[0031] This principle itself is based on the idea that aircraft aeronautical systems function correctly during the first few hours of flight and that, over time, malfunctions are statistically more likely to occur.

[0032] Therefore, we can assume that during a time period [t0, t1] corresponding to the first hours of flight, the behavior of the electrical current in a monitored power line is normal, that is, free of fault arcs. These are only likely to occur in a second time period, beginning at time t1, forming a temporal threshold.

[0033] The instant to corresponds to the first power-up of the aeronautical system (which may be all or part of the aircraft). We can assume to=0.

[0034] According to this embodiment, this time threshold t1 therefore corresponds to a time elapsed since the instant to of the aircraft's commissioning.

[0035] The value of the time interval ti can depend on the type of aircraft and can be configurable. A possible value can be greater than or equal to 15 hours and less than or equal to 35 hours, and can be, for example, 25 hours, which corresponds to the time after which the aircraft must undergo preventive maintenance according to certain regulations.

[0036] The process includes a first phase Pi, which can correspond to this first time period, and during which recurring patterns are searched for and stored in a pattern database. Assuming the aircraft's behavior is free of malfunctions, these recurring patterns correspond to the nominal operation of the monitored power line, that is, the electrical behavior of the load (starting an engine, for example...).

[0037] This period [to; ti] must be long enough to allow the discovery of a wide variety of recurring patterns: some may indeed they appear only rarely and it is therefore necessary to study the electrical behavior long enough to identify them.

[0038] It should also be noted that, according to some embodiments, pattern identification is not performed on a single occurrence but on a plurality of occurrences, representative of a non-accidental electrical phenomenon and likely to occur again in the future. For this reason as well, the period [t0; ti] must be sufficiently long to allow the identification of several occurrences of the same pattern.

[0039] Thus, the method learns the behavior of the power line without making any presuppositions. It consists of determining all the recurring patterns occurring on the power line over time, so that the specific characteristics of the line (particularly its electrical load) can be taken into account. Therefore, it is not necessary to provide specific or parameterized identification mechanisms based on the type of line, or a type of load, etc. In particular, the proposed method avoids the need for laboratory training of each individual load and / or for modeling signals associated with a complex combination of loads in the electrical network of an aircraft operating under normal conditions (this load can also evolve over time, for example, depending on the flight phases). On the contrary, the method relies on a self-discovery mechanism.

[0040] A second phase, P2, can correspond to the time period beginning at time ti. It consists of detecting abnormal electrical behavior, particularly fault arcs. This detection is valid for both series arcs (current drops) and parallel arcs (current surges).

[0041] This P2 diagnostic phase involves comparing the behavior of the electrical current on the power line with recurring patterns stored in the pattern database. If this behavior does not correspond to a pattern stored in the pattern database, then the presence of a fault arc is detected.

[0042] Thus, the process allows learning a nominal behavior (in the Pi phase) in order to then (in the P2 phase) discriminate between normal and non-characteristic electrical artifacts of the occurrence of an electric arc, compared to artifacts corresponding to electric arcs.

[0043] This allows us to improve the detection rate of fault arcs (true positive rate) but also to considerably reduce the false positive rate.

[0044] Phases Pi and P2 of the process can be implemented using a device that provides measurements of the electrical current transmitted on the line to be monitored. These measurements can be of the electrical current intensity.

[0045] Figures 2a and 2b illustrate two embodiments among several possible ones.

[0046] In the embodiment of Figure 2a, the measuring device 4 is positioned upstream of the power line 3, close to the current source 1. In the embodiment of Figure 2b, the measuring device 4 is positioned downstream of the power line 3, close to the load 2.

[0047] In general, the presented method can be integrated into monitoring devices commonly found in aircraft electrical systems, such as solid-state power controllers (SSPCs). An SSPC consists of one or more solid-state switching devices and associated solid-state circuits for protection, control signal actuation, and status information provision.

[0048] Figure 3 illustrates a high-level functional architecture of a detection device. As they are functional elements, the various functional modules shown can be arbitrarily combined or subdivided during a technical implementation.

[0049] The functions of this architecture will be explained in relation to Figure 4, which represents an example of a process flowchart. Again, the correspondence between these steps and the functional modules may vary depending on the specific implementations of the process principles and the proposed device.

[0050] According to one embodiment of the detection device 4, a functional measurement module 41 is provided to carry out measurements on the power line 3, for example measurements of the intensity of the electric current.

[0051] A functional routing module 42 can be provided to direct these measurements to a functional discovery module 43 or a functional search module 45, depending on the process phase. This determination can be based on a current time t, which can be compared to a time threshold ti between the discovery phase Pi and the diagnostic phase P2.

[0052] So, - if t < ti, then the discovery functional module 43 is active and the search functional module 45 is inactive, and - if t > ti, then the functional search module 45 is active, and the functional discovery module 43 is inactive.

[0053] In other words, the time threshold t1 triggers the switch from the first phase to the second phase.

[0054] The functional discovery module 43 can thus be designed to discover recurring patterns in the behavior of the electric current, as measured, on the power line. The discovered recurring patterns can then be stored in a pattern database 44.

[0055] More specifically, in the example in Figure 4, the proposed process may include a step S1 of acquiring measurement data (from the measurement functional module 41). This measurement data is stored in a memory, not shown.

[0056] These measurement data thus constitute a time series.

[0057] A time series, or chronological series, is a sequence of numerical values ​​representing the evolution of a specific quantity over time.

[0058] In step S2, the condition for switching to phase P2 is tested.

[0059] As long as the switching condition is not validated (i.e. it has not yet reached the time threshold ti), new measurements are acquired, and the process therefore loops back to step S1.

[0060] When the switching condition is reached (t>ti), an S3 step for processing the stored measurement stream can be implemented before switching to the diagnostic phase P2.

[0061] This S3 step aims to recognize recurring patterns within a time series of measurements.

[0062] A pattern can be defined as a time series that repeats itself (with a possible error rate) within a larger time series.

[0063] The concept of pattern discovery in a time series was proposed in the article by P. Patel, E. Keogh, J. Lin and S. Lonardi, "Mining motifs in massive time series database" in 2002 IEEE International Conference on Data Mining, 2002 Proceedings, pages 370-377.

[0064] We can define a time series T=(t1 , t2, ....tn) of length n as an ordered sequence of n real values.

[0065] We can also define a subsequence Si.i of a time series T, with 1 < i < n and 1 < i+l < n, as a time series of length I consisting of the I successive real values ​​belonging to T and starting at the index i: Si,i=(ti, ti+i , ... , ti+1-1).

[0066] Pattern discovery then becomes a matter of searching for longer subsequences that satisfy a minimum distance between them. The distance can be a Euclidean distance or, more specifically, a z-normalized Euclidean distance.

[0067] We can define a normalized Euclidean z-distance between two subsequences such that

[0068] We also define, for each time series, the average LI (2) respectively and the standard deviation respectively.

[0069] The normalized z-Euclidean distance ZED can then be written

[0070] However, other distances can also be used.

[0071] We can then define the notions of correspondence and trivial correspondences between two subsequences, based on a distance measure.

[0072] Two subsequences Si,i, Sj.i form a "matching" if and only if - Si,i, Sj,i do not form a trivial correspondence.

[0073] The radius r is a parameter that defines the proximity that two subsequences must have to be considered "matching". For this reason, the term "R-matching" is used in English to define this match.

[0074] Trivial correspondences are those that any subsequence forms with a sequence slightly offset in time. These correspondences are excluded in the search for patterns.

[0075] A possible definition of trivial correspondence can be: two subsequences Si,i, Sj.i of the same length I and of the same time series form a trivial match if and only if they share at least I / 2 common indices of the time series T: (il / 2) <j<(i+l / 2).

[0076] The aforementioned article proposes a first method called "K-motifs".

[0077] Given a time series T, a subsequence length n, and a radius R, the most significant pattern in T is the subsequence C1 that has the largest number of nontrivial matches. The K-pattern is the largest set of subsequences of length I in which every subsequence forms a match ("R-matching") with every other subsequence in the set.

[0078] Numerous other methods have been proposed to enable the discovery of patterns in time series depending on the nature or type of patterns to be discovered.

[0079] For example, a method described in the article by M. Linardi, Y. Zhu, T. Papanas and E. Keogh, “Valmod: A suite for easy and exact detection of cariable "length motifs in data series", in Proceedings of the 2018 International Conference on Management of Data, pages 1757-1760, 2018, consists of first determining a pair of sub-sequences forming a correspondence, then iteratively determining new sub-sequences forming a correspondence with members of a set that is thus gradually grown.

[0080] In one embodiment, a k-motiflet method is used. This process was described in the article by Patrick Schäfer and Ulf Leser, "Motiflets - Fast and Accurate Detection of Motifs in Time Series" in Woodstock '18: ACM Symposium on Neural Gaze Detection, June 3-5, 2018, Woodstock, New York, doi.org / 10.1145 / 1122445.1122456

[0081] The notion of extent of a set S of motifs is defined as the maximum value of the set of Euclidean distances between each motif in the set, taken two at a time. In other words: extent (

[0082] We can then define the best k-motiflet as the set S, of cardinality k, of subsequences of length I for which - All subsequences of S form pairwise correspondences, There is no set S' with extent(S') <extent(S) et qui remplit également ces contraintes.

[0083] The aforementioned article also describes algorithms for determining k-motiflets within a time series.

[0084] One advantage of an implementation based on a k-motif method is its independence from the radius r required for Valmod and k-motif methods. Current analysis can therefore be independent of the electrical monitoring system.

[0085] The article describes a possible method for automatically finding the values ​​of k and I. This method involves looking at the value of each extent(Sk) for the entire acquisition. By doing so, the elbow points of the extent(Sk) indicate the Pattern changes occur throughout an acquisition. The number of different inclinations of the extent(Sk) thus provides an estimate of the parameter k. To determine the optimal value of the parameter I (pattern length), one can look at the value of I for which the area under the extent(Sk) is minimal.

[0086] Figures 5a and 5b represent two examples of electric current behavior in the supervised power line.

[0087] This behavior is captured by measurement data, for example, the current intensity expressed in Amperes (A in the figures). All of this measurement data thus represents a time series where each instant t corresponds to a current value A.

[0088] In this example, the discovery phase reveals a pattern that appears at locations T1, T2, and T3. This recurring pattern corresponds to a transient current phenomenon caused by the loads in the electrical network. For example, it could be the regular or irregular ignition of an engine or other aircraft component. In any case, it corresponds to the aircraft's normal operation.

[0089] Figure 5b shows another example, in which a T4 motif is discovered.

[0090] The patterns discovered by analyzing the stored time series are then stored in pattern database 44.

[0091] Thus, at the end of the discovery phase, this pattern database 44 contains a dictionary of patterns corresponding to the transient phases of the electric current during normal operation of the electrical circuit. As previously mentioned, the time threshold ti is chosen so that this dictionary contains all possible normal current transients during aircraft operation.

[0092] When the diagnostic phase is triggered, the discovery phase can be interrupted.

[0093] It is assumed that at this point there is no longer any guarantee that the electrical system will not exhibit arc faults, and the goal is then to detect them. with the best possible accuracy, using the previously compiled pattern dictionary.

[0094] When the switching condition is met, i.e. for example when the time threshold ti is crossed, the switching functional module 42 directs the measurement data taken by the measurement functional module 41 to the search functional module 45.

[0095] This functional research module 45 can implement an S4 step for searching for a transient phenomenon in the electric current.

[0096] It should be noted that when a fault arc occurs, it also generates transient current phenomena. This applies to both series arcs (current drop) and parallel arcs (current surge).

[0097] Figures 6a and 6b illustrate two examples of such transient phenomena. Figure 6a illustrates two transients T5, T6 related to series arcs (intensity drop A). ​​Figure 6b illustrates three transients T7, T8, T9 related to parallel arcs (intensity surges A).

[0098] Also, continuously (as represented by the loop on step S4 in figure 4), we search for transient phenomena within the time series constituted by the acquired measurement data.

[0099] To do this, we can define a sliding window to analyze the time series, and a current thresholding.

[0100] For example, if the increase or decrease in current exceeds a given threshold (e.g., 5%), then a transient phenomenon can be considered detected. The sliding window around this time point of crossing can be extracted for the subsequent step S5, which compares this detected phenomenon with the patterns stored in the pattern database 44. This step S5 can be implemented by a diagnostic functional module 46.

[0101] This S5 step aims to determine whether the detected transient phenomenon corresponds to normal behavior, related to a load connected to the power line, or to an arc fault.

[0102] To do this, we can measure a distance between the data contained in the sliding window and the data contained in each pattern in the pattern database.

[0103] The distance can be a Euclidean distance, a normalized Euclidean distance, a DTW distance, etc.

[0104] Dynamic Time Warping (DTW) is an algorithm that measures the similarity between two sequences that may vary over time.

[0105] The normalized Euclidean distance between the time window y and a pattern Xi contained in the pattern base can be described by dnEUC (. x i> y

[0106] In this expression, the tilde (~) signs indicate the normalization of quantities. For example:

[0107] In these equations: - N represents the total number of points in the pattern (i.e., the length of the subsequence that was determined as the pattern in the discovery phase P1); - Xi represents pattern i within pattern base 44. This same distance must therefore be estimated for each pattern Xi in the pattern base. - y represents the analysis window extracted in step S4. It is also a subsequence of a time series. - .x, py represent the means of, respectively, the pattern Xi and the analysis window y. ex, Gy represent the standard deviations of, respectively, the pattern Xi and the analysis window y.

[0108] Euclidean distance is sensitive to time shifts, dilation / contraction (of the pattern relative to the analysis window), outliers, and delays. However, normalization increases sensitivity to amplitude changes.

[0109] The analysis window can be designed to be the same length as the recurring patterns stored in the pattern database. In the case of an implementation based on a k-motiflet method, this length I is determined during the identification of recurring patterns.

[0110] In the case of a transient phase induced by a load longer than the time window, detection and identification will still work on the basis of a sub-part of this transient.

[0111] The dynamic time warp (DTW) algorithm is more robust against dilation / contraction.

[0112] Different distances and different mechanisms and algorithms can implement this S5 step.

[0113] For each time window extracted in step S4, we therefore calculate a distance with the set of patterns stored in the pattern database 44.

[0114] We can then determine a decision function based on this set of distances.

[0115] In particular, we can write a decision function f such that:

[0116] With :

[0117] d is a measure of the distance between the pattern Xi and the analysis window y. This distance can, for example, be the normalized Euclidean distance. described previously.

[0118] M is the number of recurring patterns stored in pattern database 44.

[0119] S is a detection threshold. Its value can be predefined.

[0120] The value taken by the decision function f directly gives the desired result: - If f=0, then the analysis window corresponds to a pattern Xi among those stored in the pattern database. This is therefore normal electrical behavior. In the example in Figure 4, the process then returns to step S4 to search for a new transient phenomenon. - If f=1, then the analysis window does not correspond to any pattern among those stored in the pattern database. This therefore indicates abnormal electrical behavior, i.e., an electric arc.

[0121] If an electric arc is detected, a detection processing step S6 can be triggered. This step S6 can be implemented by a functional processing module 47.

[0122] This step may include emergency responses such as shutting down and isolating the supervised power line: an electrical arc indicates abnormal behavior that can generate further electrical arcs in the future, each potentially damaging the system locally, including the aircraft, as previously discussed. It is therefore essential to prevent any potential damage by shutting down the power line as soon as the first electrical arc is detected.

[0123] Also, an alert can be triggered to notify a human operator who can compensate for this fault (activation of a possible redundant system following the shutdown of the faulty one, interventions to troubleshoot the fault, etc.)

[0124] Of course, the present invention is not limited to the examples and embodiments described and illustrated, but is defined by the claims. In particular, it is susceptible of numerous variations accessible to those skilled in the art.

Claims

Claims

1. Method for detecting electric arcs in an electrical circuit of an aeronautical system, comprising - a first phase (P1) of discovering recurring patterns in the behavior of the electric current on an electric line (3) of said electric circuit, and storing said recurring patterns in a pattern base (44), and, - a second diagnostic phase (P2) comprising a comparison of the behavior of said electric current on said electric line (3) with recurring patterns stored in said pattern base (44), and a detection of an electric arc when said behavior does not correspond to a recurring pattern stored in said pattern base (44).

2. Method according to the preceding claim, in which a time threshold (ti) triggers a switch from said first phase (P1) to said second phase (P2).

3. Method according to the preceding claim, in which said time threshold (ti) corresponds to a duration since the first powering up of said aeronautical system greater than or equal to 15 hours and less than or equal to 35 hours, preferably equal to 25 hours.

4. Method according to one of the preceding claims, in which said first phase (P1) comprises a step (S1) of acquiring measurement data relating to said electric current, and of storing said measurement data to form a time series, as well as a step (S3) of recognizing recurring patterns within said time series of measurements.

5. Method according to the preceding claim, in which said step (S3) of recognizing recurring patterns implements a k-pattern method.

6. Method according to one of the preceding claims, in which said second phase (P2) comprises a step of searching (S4) for a transient phenomenon in said electric current, and a step of comparing (S5) said transient phenomenon with said stored recurring patterns.

7. Method according to the preceding claim, in which a time window corresponding to said transient phenomenon is extracted, a distance between said time window and each of said stored recurring patterns is calculated, then the presence of an electric arc is detected as a function of all the distances, the calculated distance preferably being a z-normalized Euclidean distance.

8. Computer program comprising instructions for implementing a method according to one of the preceding claims when executed on an information processing platform.

9. Detection device (4) for detecting electric arcs in an electrical circuit of an aeronautical system, comprising - a functional discovery module (43) adapted for the discovery of recurring patterns in the behavior of the electric current on a power line (3), and the storage of said recurring patterns in a pattern base (44), and - a functional diagnostic module (46) adapted for comparing the behavior of said electric current on said electric line with recurring patterns stored in said pattern base (44) and for detecting an electric arc when said behavior does not correspond to a recurring pattern stored in said pattern base (44).

10. Aircraft comprising at least one detection device (4) according to claim 9.

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