US-12620278-B2 - Method, apparatus and system for detecting abnormal operating states of a device

US12620278B2US 12620278 B2US12620278 B2US 12620278B2US-12620278-B2

Abstract

A method for detecting abnormal operating states of a device includes obtaining model data to the device that is representative of operating states to be expected for at least one component of the device. The device collects measurement data that is representative of an actual operating state of the component of the device. The device ascertains comparison data on the basis of the model data and the measurement data, where the comparison data is representative of an expected operating state. The method includes using the comparison data and the measurement data as a basis for determining whether there is a discrepancy between the actual operating state and the expected operating state. The method further includes attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy.

Inventors

Bernhard Schlegel
Philipp Reinisch
Christoph Weidner

Assignees

BAYERISCHE MOTOREN WERKE AKTIENGESELLSCHAFT

Dates

Publication Date: 20260505
Application Date: 20200923
Priority Date: 20191220

Claims (14)

1 . A method for detecting abnormal operating states of a motor vehicle, comprising the steps of: a) receiving model data at the motor vehicle from an external computing device, the model data representative of operating states to be expected for at least one component, the model data including parameters of an artificial neural network and generated at least in part based on operating data provided to the computing device by a plurality of motor vehicles, b) collecting measurement data, the measurement data representative of an actual operating state of the at least one component, c) ascertaining within the vehicle comparison data on the basis of the parameters of the artificial neural network of the model data and the measurement data, the comparison data representative of an expected operating state, d) using the comparison data and the measurement data as a basis for determining whether there is a discrepancy between the actual operating state and the expected operating state during operation of the motor vehicle, and e) responsive to determining the discrepancy, attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy.
2 . The method as claimed in claim 1 , further comprising specifying a vehicle function to be evaluated to the motor vehicle before step c), using the vehicle function to be evaluated and the measurement data as a basis for ascertaining filtered measurement data, and using the filtered measurement data to ascertain the comparison data in step c).
3 . The method as claimed in claim 1 , wherein steps b) to d) are each performed at multiple successive times, and if a discrepancy between the actual operating state and the expected operating state is ascertained at each of at least N successive times, the abnormal operating state is attributed to the at least one component in a manner corresponding to the N successive times in step e), N being a natural number greater than 1.
4 . The method as claimed in claim 3 , wherein N is greater than 5.
5 . The method as claimed in claim 3 , wherein N is greater than 10.
6 . The method as claimed in claim 1 , further comprising: f) if the abnormal operating state is attributed to the at least one component, storing and/or outputting the measurement data after step e).
7 . The method as claimed in claim 1 , wherein step a) further comprises: providing operating data of one or more motor vehicles to the computing device, specifying a vehicle function to be evaluated to the computing device and filtering the operating data on the basis of the vehicle function to be evaluated, ascertaining model data by training a model on the basis of the filtered operating data for the vehicle function to be evaluated, and providing the ascertained model data to the vehicle.
8 . The method as claimed in claim 1 , further comprising: f) transferring the measurement data to the computing device.
9 . The method as claimed in claim 1 , wherein the artificial neural network is a deep neural network.
10 . An apparatus for detecting abnormal operating states, wherein the apparatus is configured to perform the method as claimed in claim 1 .
11 . The method as claimed in claim 1 , wherein step c) further comprises: c) ascertaining the comparison data includes performing at least one of the group consisting of: a distance-based method; and a reconstruction-based method.
12 . A system for detecting abnormal operating states of a motor vehicle, comprising a computer center and a motor vehicle, the motor vehicle having an apparatus configured to perform the method as claimed in claim 1 .
13 . The system of claim 12 wherein the computer center is further configured to: obtain operating data of one or more motor vehicles to a computer center, specify a vehicle function to be evaluated to the computer center and filter the operating data on the basis of the vehicle function to be evaluated, ascertain model data by training a model on the basis of the filtered operating data for the vehicle function to be evaluated, and provide the ascertained model data to the motor vehicle.
14 . A non-transitory computer readable storage medium containing a computer program comprising instructions that, when the computer program is executed by way of a computer, cause said computer to perform the method as claimed in claim 1 .

Description

The present application is the U.S. national phase of PCT Application PCT/EP2020/076478 filed on Sep. 23, 2020, which claims priority of German patent application No. 102019135608.3 filed on Dec. 20, 2019, which is incorporated herein by reference in its entirety. TECHNICAL FIELD The disclosure relates to a method for detecting abnormal operating states of a device, in particular a motor vehicle, and to a corresponding device and a corresponding system. Furthermore, a corresponding computer program and storage medium are specified. BACKGROUND Motor vehicles today have a multiplicity of vehicle functions, including not only basic comfort functions, assistance settings or driving-dynamics settings but also safety-critical functions that permit for example the automated performance of driving maneuvers, in particular semiautonomous or fully autonomous driving. As the complexity of the vehicle functions increases and the degree of networking of said functions becomes greater and greater, the volume of data collected and interchanged during operation of the individual vehicle functions increases. When this happens, it becomes more and more difficult for abnormal operating states, i.e. errors and deviations from specification during the operation of the individual vehicle functions, to be detected and evaluated by way of manual modeling. Manual modeling in this case refers to the creation of a state machine on the basis of the vehicle specifications that is used to model all setpoint operating states of individual components of the motor vehicle. A component here and below refers both to individual software and hardware elements of the motor vehicle and to a combination of multiple software and/or hardware elements of the motor vehicle that each implement one or more vehicle functions. During the operation of the motor vehicle, the collected data interchanged on a bus system of the motor vehicle are normally recorded; depending on the vehicle function or the component, these may be diagnostic data such as status or error signals, control signals for controlling or regulating individual components, or data that are representative of recorded measured values such as for example speed or steering angle of the motor vehicle. All of the aforementioned collected or interchanged data are referred to as measurement data below. Following operation of the motor vehicle, the entire recorded volume of measurement data can then be read in order to check said data for discrepancies in respect of the setpoint operating states on the basis of one or more models. One object on which the disclosure is based is to provide an efficient and reliable method for detecting abnormal operating states of a motor vehicle. Furthermore, it is an aim to specify a corresponding apparatus, a corresponding system and a computer program and storage medium. SUMMARY The object, as well as others, are achieved by at least one embodiment disclosed herein. According to a first aspect, the disclosure relates to a method for detecting abnormal operating states of a motor vehicle. The method comprises the steps of: a) providing model data to the motor vehicle, which are representative of operating states to be expected for at least one component of the motor vehicle;b) collecting measurement data by way of the motor vehicle, which are representative of an actual operating state of the at least one component of the motor vehicle;c) ascertaining comparison data by way of the motor vehicle on the basis of the model data and the measurement data, which are representative of an expected operating state;d) taking the comparison data and the measurement data as a basis for checking whether there is a discrepancy between the actual operating state and the expected operating state; ande) attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy. The model data are in particular representative of a statistical model that in each case indicates a most probable next operating state of the at least one component on the basis of an initial state, or the operating states already encountered, of the at least one component. The most probable next operating state is also referred to as the expected operating state and represented by the comparison data ascertained in step c). The statistical model that can be considered is preferably a (deep) artificial neural network. An operating state can be understood to mean in particular any action by the respective component that is represented by the output of appropriate measurement data, and also the absence of an action by the respective component. The discrepancy can be ascertained in step d) by using for example a distance-based method such as “k-nearest neighbors” (kNN), “local outlier factor” (LOF), “[hierarchical]-density-based spatial clustering of applications with noise” ([H]-DBSCAN) or “ordering points t