Patent application EP17193141A (EP), filed 2017-09-26
Publication number: EP3459810A1
Title: METHOD FOR PREDICTING A FAILURE OF A SENSOR
Abstract: The present invention relates to a method for predicting a failure (F) of at least one sensor (10, 12), especially, for measuring at least one parameter (P) of a mobile unit (14).  To provide a reliable prediction and thus a safely operating system the method comprises at least the following steps: Providing as input data (D) for a machine learning system (16) discrete conditional information (I) of the at least one sensor (10, 12) at several time points (t1, t2), wherein these time points (t1, t2) are such time points at which the discrete conditional information (I) of the at least one sensor (10, 12) has changed and evaluating the input data (D) by using the machine learning system (16) and thus predicting the failure (F) of the at least one sensor (10, 12) by using the machine learning system (16).
IPC codes: B61L  27/    00            A I
G06N  99/    00            A I
B61L  15/    00            A I
G05B  23/    02            A I
B61L  27/    57            A I
G06N  20/    00            A I
Claims: number: 1
text: Method for predicting a failure (F) of at least one sensor (10, 12), especially, for measuring at least one parameter (P) of a mobile unit (14),characterised in that the method comprises at least the following steps:
number: 2
text: Method according to claim 1, wherein the method comprises the further steps of:
number: 3
text: Method according to claim 1 or 2, wherein the method comprises the further steps of:
number: 4
text: Method according to any one of the preceding claims, wherein the discrete conditional information (I) refers to a signal strength (S) of the at least one sensor (10, 12), especially the discrete conditional information (I) refers to a signal strength (S) of the at least one sensor (10, 12) in an operational mode (M) of the at least one sensor (10, 12), especially, wherein the operational mode (M) is selected out of the group consisting of: a single mode, a low quality single mode, a high quality single mode, a dual mode, a low quality dual mode, a high quality dual mode and an unknown mode.
number: 5
text: Method according to any one of the preceding claims, wherein
the machine learning system (16) is trained by a method selected out of the group consisting of: Supervised learning, semi-supervised learning, unsupervised learning, reinforcement learning and active learning.
number: 6
text: Method according to any one of the preceding claims, wherein
the machine learning system (16) works according to an extreme gradient boosted tree predictor.
number: 7
text: Method according to any one of the preceding claims, wherein
the at least one sensor (10, 12) measures at least one parameter (P), wherein the at least one parameter (P) is a parameter selected out of the group consisting of: a velocity (V), an acceleration, a temperature, a pressure, humidity and visibility and a location, especially, the parameter is a velocity, and/or wherein the at least one sensor (10, 12) is a sensor selected out of the group consisting of: a radar sensor (10, 12), an IR-sensor, a UV-sensor, a magnetic sensor, a temperature sensor, a camera and a laser measurement device, especially, the at least one sensor (10, 12) is a radar sensor (10, 12).
number: 8
text: Method according to any one of the preceding claims, wherein
the at least one sensor (10, 12) is a sensor of a mobile unit (14) and/or wherein the mobile unit (14) is a track-bound vehicle (18), especially a train (20).
number: 9
text: Use of the method according to at least one of claims 1 to 8 for a failure prediction of at least one sensor (10, 12) of several sensors (10, 12) comprising a first sensor (10, 12) and at least a second sensor (10, 12), wherein the use comprises at least the steps of:
number: 10
text: Use of the method according to claim 9, comprising at least the further step of:
number: 11
text: Method for training the machine learning system (16) of the method according to at least one of claims 1 to 8,characterised in that the training method is trained by a supervised learning algorithm.
number: 12
text: Method according to claim 11, wherein at least one data sampling method is executed on training data, wherein the method is selected out of the group consisting of: Upsampling, especially via a SMOTE algorithm, random downsampling and preferential sample weighting.
number: 13
text: Computer program comprising instructions which, when the program is executed by a computer (22), cause the computer (22) to carry out the steps of the method of claim 1.
number: 14
text: Computer-readable storage medium comprising instructions which, when executed by a computer (22), cause the computer (22) to carry out the steps of the method of claim 1.
Office action: search_report, dated 2019-03-27
Prior art cited: ref_id: US6456908B1
ref_type: patent
severity: background
ref_id: - KAUSCHKE SEBASTIAN ET AL: "Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype", 21 September 2016, ECCV 2016 CONFERENCE; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER INTERNATIONAL PUBLISHING, CHAM, PAGE(S) 151 - 166, ISBN: 978-3-642-33485-6, ISSN: 0302-9743, XP047359213
ref_type: npl
severity: background
ref_id: US6456908
ref_type: patent
severity: background
ref_id: - KAUSCHKE SEBASTIAN ET AL, Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype, ECCV 2016 CONFERENCE; [LECTURE NOTES IN COMPUTER SCIENCE; LECT.NOTES COMPUTER], SPRINGER INTERNATIONAL PUBLISHING, CHAM, PAGE(S) 151 - 166, (20160921), ISSN 0302-9743, ISBN 978-3-642-33485-6, XP047359213 [I] 1-3,5-9,11-14 * the whole document *
ref_type: npl
severity: background
Final disposition: pending (2019-03-27)