System and method for estimating the lifetime curve of a technical component

Abstract

The invention relates to a method for estimating a lifetime curve of at least one technical component, in particular a vehicle assembly or a vehicle component, comprising the following method steps: - Generating design data (350) for at least one technical component with a multitude of parameters and / or settings and / or properties of the technical component, - Transforming the design data (350) into a quantum state (450) by a quantum processor (430), wherein the quantum state (450) comprises each parameter, each parameter value, each property, each property value, each setting and each setting value of the engineering component as a single state (455); - Applying at least one quantum algorithm (470) to the quantum state (450), wherein the quantum algorithm (470) includes at least one quantum amplitude estimation; - Determining relevant states (490) from measurements of the quantum state (450), - Performing simulations with the relevant states (490) to model simulation results (570); - Analyzing the simulation results (570) and calculating a lifetime curve (580) for the technical component; - Output of the lifetime curve (580) as output data (750) for use in adapting the technical component.

Description

[0001] The invention relates to a system, a method and a computer program product for estimating a lifetime curve of a technical component, in particular a vehicle assembly or a vehicle component.

[0002] A motor vehicle consists of various assemblies, such as the drive system, the braking system, the lighting system, the interior fittings, the body, the tires, etc. The assemblies in turn contain a multitude of components.

[0003] The drive system comprises the engine, which generates power by burning fuel or electrical energy; the transmission, which transfers engine power to the drive wheels, either manually or automatically; the clutch, which engages and disengages the transmission from the engine; the drive shafts, which transmit power from the transmission to the wheels; the exhaust system (in combustion engines), which removes exhaust gases from the engine; and the fuel system, consisting of the tank, pump, lines, and injectors. In addition, most motor vehicles have a 12-volt battery, which starts the vehicle and powers various electrical components such as lights, radio, and air conditioning. Electric and hybrid vehicles also have a high-voltage battery to power the electric motor.

[0004] The braking system essentially consists of brake discs and brake pads. The brake discs support the brake pads and convert the kinetic energy into heat. The brake fluid transmits the force from the brake pedal to the brakes.

[0005] The lighting system includes the headlights at the front of the vehicle, the taillights, the turn signals, the brake lights and the fog lights.

[0006] The interior includes in particular the seats for the driver and front passenger, the steering wheel, the dashboard with instruments such as speedometer, tachometer and fuel gauge, the air conditioning system for regulating the temperature and air circulation in the passenger compartment, the entertainment and navigation system, and safety components such as airbags.

[0007] The body consists of the chassis, which forms the structure of the vehicle, the fenders that cover the wheels, the hood, the doors, the bumpers and the roof.

[0008] Furthermore, tires are crucial components of every vehicle. They represent the contact surface between the vehicle and the road and are therefore of particular importance for safety, handling, and driving comfort.

[0009] Each of these assemblies and components plays a crucial role in the functionality, safety, and overall driving experience of a vehicle. However, every assembly and component in a vehicle has a limited lifespan, as it is subjected to various stresses, wear, and aging processes over time. Components such as the engine, transmission, brakes, and drive shafts move and rub against other parts. This mechanical movement generates friction, which can lead to mechanical wear and eventual failure. Components subjected to constant vibration or repetitive stress, such as chassis components or drive shafts, can develop material fatigue, which can eventually lead to fractures or malfunctions. Thermal stresses also occur, as engines, brakes, and exhaust systems generate heat that alters material properties.Overheating can lead to premature wear or even failure. Corrosion and oxidation are further concerns, as metallic components such as vehicle bodies can rust over time, especially when exposed to moisture or salt. Additionally, components that come into contact with fluids like fuel, brake fluid, or coolant can be damaged by chemical reactions. The quality of these fluids can also deteriorate over time. Electrical components, such as the starter battery or the high-voltage battery in electric vehicles, are subject to electrical wear from repeated charging and discharging and can therefore lose their functionality. Furthermore, external stresses such as UV radiation, extreme temperatures, humidity, and dust contribute to a shortened lifespan of vehicle components.For example, plastic and rubber components, both indoors and outdoors, can become brittle due to UV radiation. Usage intensity also plays a role, as the frequency and intensity with which a vehicle is used influences wear and tear. A vehicle that is regularly driven on uneven roads or in extreme weather conditions will wear out faster than a vehicle that is used less intensively.

[0010] Tires are subject to natural wear and tear through contact with the road. This can be influenced by the quality of the road surface, driving style (e.g., rapid acceleration and hard braking), and tire pressure. As the tire tread wears down, the tire loses traction and grip. Tires are made of rubber and other materials that can harden or crack over time. This aging process is accelerated by environmental factors such as UV radiation, extreme temperatures, and ozone. Tires can also be damaged by stones, broken glass, potholes, or curbs on the road.

[0011] All these factors contribute to the need to replace or service vehicle components over time. Regular maintenance and inspection of motor vehicles is therefore essential to extend the lifespan of vehicle components and ensure safety.

[0012] Service life curves are tools for analyzing and evaluating the service life or reliability of technical components such as assemblies and parts of a vehicle. A service life curve is a diagram that represents the time until a component fails as a function of the failure probability or failure rate. It helps to assess the reliability and durability of technical components and to predict future failures, which is of great importance for vehicle development and maintenance. Based on this curve data, weaknesses can be identified and improvements can be made in design or production.

[0013] Monte Carlo simulation (MCS) is a method from stochastics or probability theory in which random samples are repeatedly drawn from a distribution to simulate a multitude of possible scenarios of a highly variable system and thus analyze the system's behavior. In the context of lifetime curves and the estimation of lifetime or reliability, Monte Carlo simulation is used to model and predict potential failure times or rates.

[0014] In the MCS method, random variables that influence the service life of an assembly or component are first randomly generated by a simulation model. These random variables can relate to material properties, environmental conditions, load types, operating modes, and other factors. Furthermore, each of these random variables is assigned a corresponding probability distribution, such as a normal distribution, exponential distribution, uniform distribution, etc. The simulation model is designed to generate random values ​​for each of these random variables based on the defined probability distributions. These random values ​​represent the various possible operating conditions that influence the service life of the assembly or component.

[0015] The simulation model uses the generated random values ​​to perform the simulation. It determines how these random values ​​affect the simulation result (e.g., the service life or the time of failure). This process is repeated multiple times, generating a large number of simulation results.

[0016] Each simulation uses random values ​​generated by the simulation model to simulate a specific outcome, such as the estimated service life or the time of failure. The results from the simulations are collected and statistically analyzed to determine key figures such as mean, median, standard deviation, and confidence intervals. These statistical measures provide information about the expected behavior and reliability of the assembly or component.

[0017] Although Monte Carlo simulation is an effective method for estimating lifetime curves, there are some challenges and limitations.

[0018] The MCS method requires a large number of random samples to achieve accurate results. This often leads to high computational costs and long calculation times. The need for a large sample size can introduce inaccuracies, especially if certain variables are not properly accounted for. Furthermore, the service life of vehicle components depends on many factors, including operating conditions, environmental influences, material fatigue, mechanical stress, and their interactions. Considering all these factors and their interactions in a simulation can be very complex and introduce further uncertainties into the results.

[0019] DE 10 2008 047 958 A1 discloses a method for the stress-appropriate design of a vehicle component, in which at least one time-dependent stress parameter set with at least one stress parameter is determined, a set of simulated components is generated by random selection of a large number of stress parameter values, a total load and a reference load, which is greater than the total load, are determined for the component, and the component is designed on the basis of the reference load.

[0020] One object of the present invention is therefore to create possibilities for improved estimation of a lifetime curve for the analysis of the performance and reliability of a technical component, in particular a vehicle assembly or a vehicle component, in order to arrive at simulation results with more accurate prediction results and lower computing resource consumption more quickly.

[0021] This problem is solved according to the invention with respect to a method by the features of claim 1, with respect to a system by the features of claim 8, and with respect to a computer program product by the features of claim 15. The further claims relate to preferred embodiments of the invention.

[0022] The present invention enables the determination of relevant scenarios for a simulation to estimate the lifetime curve of a technical component by employing quantum algorithms such as quantum amplitude estimation. The complexity of a Monte Carlo simulation is replaced by the quantum algorithm, particularly in conjunction with importance sampling, because from a probability distribution with various parameters, settings, and properties of a technical component, those relevant scenarios actually required for calculating a lifetime curve can be filtered out. This allows a lifetime curve to be calculated both faster and more accurately. Furthermore, the quantum algorithm allows for better consideration of interactions between the various parameters, settings, and properties.

[0023] Improved lifetime curve generation offers numerous advantages, particularly for planning, maintenance, and product development. Enhanced lifetime curves enable more accurate prediction of the service life of technical components. This helps to formulate more realistic expectations and plan product development accordingly. With a more accurate lifetime curve, maintenance schedules can be optimized to prevent failures and extend component lifespan. This, in turn, can lead to significant cost savings. Furthermore, optimized maintenance can help reduce unnecessary spare parts and energy consumption. The reliability of technical components can also be increased. In particular, improved lifetime curves can drive innovation, for example, through the use of new materials. Improved lifetime curves can also offer environmental benefits.When components last longer and need to be replaced less frequently, this reduces resource consumption and waste. All these aspects contribute to greater customer satisfaction.

[0024] According to a first aspect, the invention provides a method for estimating a service life curve of at least one technical component, in particular a vehicle assembly or a vehicle component. The method comprises the following process steps: - Generating design data for at least one technical component with a multitude of parameters and / or settings and / or properties of the technical component, wherein the parameters and / or properties and / or settings are physical parameters such as dimensions, weight, material and / or electrical parameters such as voltage, current, power and / or software settings such as a firmware version and / or environmental conditions such as a temperature range and / or safety properties such as certain limit values, and wherein these parameters, settings and properties of the technical component are random variables and the values ​​for the parameters, settings and properties represent random values; - Transferring the design data to a quantum module; - Transforming the design data into a quantum state by a quantum processor, where the quantum state includes each parameter, each parameter value, each property, each property value, each setting and each setting value of the engineering component as a single state; - Applying at least one quantum algorithm to the quantum state, wherein the quantum algorithm includes at least one quantum amplitude estimation; - Determining relevant states from measurements of the quantum state, wherein a relevant state represents a sample of the technical component with a specific selection of parameters, settings and properties of the technical component as random variables and a specific selection of parameter values, setting values ​​and property values ​​of the technical component as random values, and wherein the relevant states of the quantum state each have a high amplitude; - Passing the relevant states to a simulation model; - Conducting simulations with the relevant states to model simulation results; - Analyzing the simulation results and calculating a lifetime curve for the technical component; - Outputting the lifetime curve as output data for use in adjusting at least one parameter and / or property and / or setting of the technical component in order to extend the lifetime of the technical component.

[0025] In a training course, the output data is intended to include representations of the lifetime curve in the form of graphs, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc.

[0026] In an advantageous embodiment, an importance sampling algorithm is used to increase the probability of measuring relevant states in the quantum state.

[0027] In another embodiment, the importance sampling algorithm modifies the amplitudes of relevant states in such a way that the relevant states are measured more frequently when measuring the quantum state, thereby increasing the efficiency of determining relevant states.

[0028] In particular, the simulation model is intended to use special software and / or programming environments that support random number generation, statistical analysis and iterations, such as MATLAB and Python.

[0029] Advantageously, the input module and / or the database and / or the quantum module and / or the simulation module and / or the output module has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure or is / are integrated into the cloud computing infrastructure.

[0030] In particular, it is intended that the technical component is a bearing for reducing friction between moving parts, such as a ball bearing, a roller bearing, or a plain bearing; a shaft for transmitting torque, such as a drive shaft, a camshaft, or an axle; a coupling for connecting or disconnecting rotating parts in a drive system; a gearbox for changing speed or torque and for transmitting mechanical power; a spring, such as a coil spring or a leaf spring; a damper, such as a vibration damper; a valve for regulating the flow of liquids or gases; a pump; a compressor; a transformer; a coil; a capacitor; a resistor; a sensor; an actuator; a connector; and / or a cable; and / or that the technical component is an assembly, such as...a drive system, a braking system, a transmission, a clutch, a lighting system, an interior, a body, a tire or a component of an assembly, and / or that the technical component is a software program such as firmware or application software.

[0031] According to a second aspect, the invention provides a system for estimating a service life curve of at least one technical component, in particular a vehicle assembly or a vehicle component. The system comprises an input module, a database, a quantum module with a quantum processor, a simulation module with a simulation model, and an output module.The input module is designed to generate design data for at least one technical component with a multitude of parameters, settings, and / or properties of the technical component. These parameters, properties, and / or settings are physical parameters such as dimensions, weight, and material; electrical parameters such as voltage, current, and power; software settings such as a firmware version; environmental conditions such as a temperature range; and / or safety properties such as specific limits. These parameters, settings, and properties of the technical component are random variables, and their values ​​are random values. The input module is designed to transfer the design data to the quantum module.The quantum processor is configured to transform the design data into a quantum state, where the quantum state comprises each parameter, parameter value, property, property value, setting, and setting value of the technical component as a single state. The quantum processor is configured to apply at least one quantum algorithm to the quantum state, where the quantum algorithm includes at least one quantum amplitude estimation.The quantum algorithm is designed to determine relevant states from measurements of the quantum state. A relevant state represents a sample of the technical component with a specific selection of parameters, settings, and properties of the technical component as random variables and a specific selection of parameter values, setting values, and property values ​​of the technical component as random values. The relevant states of the quantum state each exhibit a high amplitude. The quantum module is designed to pass the relevant states to the simulation model. The simulation model is designed to perform simulations with the relevant states to model the simulation results. The simulation module is designed to analyze the simulation results and calculate a lifetime curve for the technical component.The output module is designed to output the lifetime curve as output data for use in adjusting at least one parameter and / or property and / or setting of the technical component in order to extend the lifetime of the technical component.

[0032] In a training course, the output data is intended to include representations of the lifetime curve in the form of graphs, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc.

[0033] In an advantageous embodiment, an importance sampling algorithm is used to increase the probability of measuring relevant states in the quantum state.

[0034] In another embodiment, the importance sampling algorithm is used to modify the amplitudes of the relevant states so that the relevant states are measured more frequently during the measurement of the quantum state, thereby increasing the efficiency of determining relevant states.

[0035] In particular, the simulation model is intended to use special software and / or programming environments that support random number generation, statistical analysis and iterations, such as MATLAB and Python.

[0036] Advantageously, the input module and / or the database and / or the quantum module and / or the simulation module and / or the output module has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure or is / are integrated into the cloud computing infrastructure.

[0037] In particular, it is intended that the technical component is a bearing for reducing friction between moving parts, such as a ball bearing, a roller bearing, or a plain bearing; a shaft for transmitting torque, such as a drive shaft, a camshaft, or an axle; a coupling for connecting or disconnecting rotating parts in a drive system; a gearbox for changing speed or torque and for transmitting mechanical power; a spring, such as a coil spring or a leaf spring; a damper, such as a vibration damper; a valve for regulating the flow of liquids or gases; a pump; a compressor; a transformer; a coil; a capacitor; a resistor; a sensor; an actuator; a connector; and / or a cable; and / or that the technical component is an assembly, such as...a drive system, a braking system, a transmission, a clutch, a lighting system, an interior, a body, a tire or a component of an assembly, and / or that the technical component is a software program such as firmware or application software.

[0038] According to a third aspect, the invention relates to a computer program product comprising an executable program code configured to perform the method according to the first aspect.

[0039] The invention will now be explained in more detail with reference to an embodiment shown in the drawing.

[0040] This shows: Fig. 1 a block diagram with a schematic representation of a system according to the invention for estimating a lifetime curve of a technical component; Fig. 2 a flowchart to explain the individual process steps of a process according to the invention; Fig. 3 a computer program product according to an embodiment of the third aspect of the invention.

[0041] Additional features, aspects and advantages of the invention or its embodiments become apparent from the detailed description in conjunction with the claims.

[0042] Lifetime curves are crucial for analyzing the performance and reliability of vehicle components. Classical methods such as Monte Carlo simulation can aid in estimating these curves by simulating a multitude of possible failure modes. However, due to their computationally intensive nature and the large number of influencing factors, inaccuracies or longer computation times can occur. The invention therefore provides for an improvement in the estimation of the lifetime curve of a technical component using a quantum processor with at least one quantum computer algorithm, as this can significantly increase the computational speed.Quantum algorithms and quantum computers make it possible to perform certain non-deterministic operations, which increase computing speed and thus achieve more accurate results than would be possible with classical computers in a reasonable amount of time.

[0043] Fig. Figure 1 shows a system according to the invention 100 system for improved estimation of a lifetime curve of a technical component, in particular a vehicle assembly or a vehicle component.

[0044] Technical components within the meaning of the invention are fundamental components used in a wide variety of vehicles, machines, devices, and systems. For example, a technical component may be a bearing for reducing friction between moving parts, such as a ball bearing, a roller bearing, or a plain bearing; a shaft for transmitting torque, such as a drive shaft, a camshaft, or an axle; a coupling for connecting or disconnecting rotating parts in a drive system; a transmission for changing speed or torque and for transmitting mechanical power; a spring, such as a coil spring or a leaf spring; a damper, such as a vibration damper; a valve for regulating the flow of liquids or gases; a pump; a compressor; a transformer; an inductor; a capacitor; a resistor; a sensor; an actuator; a connector; or a cable.

[0045] These common technical components are used in various fields such as automotive engineering, mechanical engineering, electrical engineering and other technical applications, e.g. in a motor vehicle, an autonomous vehicle, an agricultural vehicle such as a combine harvester, a robot in production or service facilities, a watercraft, an aircraft such as an airplane or a drone, a production machine, a scientific analysis instrument, a medical device for diagnosis, therapy and / or support of bodily functions, a household appliance such as a washing machine, etc., to name just a few examples.

[0046] In the case of a motor vehicle, a technical component refers both to an assembly, such as the drive system, brake system, lighting system, interior fittings, body, tires, etc., and to one or more components from which these assemblies are each built.

[0047] System 100 comprises an input module 200, a database 300, a quantum module 400 with a quantum processor 430, a simulation module 500 with a simulation model 550, and an output module 700. The input module 200, the database 300, the simulation module 500, and the output module 700 can each be equipped with a processor and / or a memory unit.

[0048] In the context of the invention, a "module" is defined as a self-contained, specialized unit of software and / or hardware components. A module is designed to perform a specific function or task and is independent and self-contained; that is, it accepts specific inputs, performs internal processing, and then delivers specific outputs or results. A module can communicate with other modules or components via interfaces. These interfaces determine how data or commands are input into the module and how results or information are output.

[0049] In the context of the invention, a "processor" can be, for example, a machine or an electronic circuit. A processor can, in particular, be a central processing unit (CPU), a microprocessor, or a microcontroller, such as an application-specific integrated circuit or a digital signal processor, optionally in combination with a memory unit for storing program instructions. A processor can also be a virtualized processor, a virtual machine, or a soft CPU. It can, for example, also be a programmable processor equipped with configuration steps for executing the method according to the invention, or configured with configuration steps such that the programmable processor implements the features of the method, the modules, or other aspects and / or partial aspects of the invention.In particular, the processor can contain highly parallel computing units and powerful graphics modules.

[0050] In the context of the invention, a "storage unit" or "storage module" and the like can refer, for example, to volatile memory in the form of random access memory (RAM), permanent storage such as a hard drive or data carrier, or, for example, a replaceable storage module. The storage module can also be a cloud storage solution.

[0051] The term "database" refers to both a storage algorithm and the hardware in the form of a storage unit.

[0052] Furthermore, communication links are provided for the exchange and transmission of data between the individual modules, in particular as wireless communication links, e.g. as mobile communication links (e.g. 4G LTE, 5G, 6G) and / or as near-field communication links, e.g. Bluetooth. ® , Ethernet, NFC (Near Field Communication) or Wi-Fi ® are trained.

[0053] The Quantum Module 400 contains at least one Quantum Processor 430, which utilizes the principles of quantum mechanics to perform calculations. Unlike classical processors, which are based on binary states (0 or 1), a Quantum Processor 430 can use quantum bits (qubits), which can exist in states that are a superposition of 0 and 1. Furthermore, a Quantum Processor 430 uses wave interference to amplify or attenuate certain states, allowing it to perform certain calculations faster than a classical processor. The Quantum Processor 430 can execute quantum algorithms that, through the use of qubits, superposition, entanglement, and quantum interference, are more efficient than classical algorithms.

[0054] In particular, the Database 300 and / or the Quantum Module 400 and / or the Simulation Module 500 can be integrated into a cloud computing infrastructure. A cloud computing infrastructure offers the ability to scale resources up or down as needed, allowing computing power, storage space, or network resources to be easily adapted to changing requirements. This scalability enables cost optimization and efficient resource allocation without large investments in hardware. Furthermore, users can access applications and data from anywhere with internet access. Cloud computing also offers flexibility in software deployment, enabling applications to be deployed and updated quickly and without interruption. Cryptographic encryption methods can also be used to protect the connection to the cloud computing infrastructure via a mobile network.Since a fast calculation result is desired, the processing speed is crucial for the numerous calculations in the simulation module 500. To enable real-time calculations, the use of cloud computing infrastructure is therefore advantageous, as this ensures rapid computation.

[0055] The Input Module 200 and the Output Module 700 can be installed on various platforms such as desktop computers, laptops, tablet PCs, or smartphones. In particular, the hardware of the Input Module 200 and the Output Module 700 is identical. The Input Module 200 is an interface for communication with the Database 300, the Quantum Module 400, and the Simulation Module 500. Through this interface, users can interact with the Database 300, the Quantum Module 400, and the Simulation Module 500 to configure and adjust various technical aspects of the component for which a lifetime curve is to be generated, in the form of configuration data 350. Alternatively, the configuration data 350 of the component can be stored in the Database 300 and retrieved by the Quantum Module 400 and the Simulation Module 500 via the Input Module 200 using appropriate input commands.

[0056] The Input Module 200 features an intuitive user interface that facilitates interaction, particularly with the Quantum Module 400. Graphical symbols, clear labels, and a logical arrangement of elements are used to ensure ease of use. The Input Module 200 offers menus to help users select various options for a technical component. The interactive controls can include buttons, sliders, checkboxes, and radio buttons. These elements allow users to configure specific aspects of a component, such as selecting materials, dimensions, design, and so on. The Input Module 200 provides real-time feedback, allowing users to immediately see how their configuration affects the component by having an image of the technical component updated accordingly.

[0057] The configuration data 350 of a technical component comprises parameters, settings, and properties, as well as the respective parameter, setting, and property values ​​of the technical component, which determine its behavior and functionality. This configuration data 350 is crucial for the proper installation, operation, and maintenance of the component. The parameters, properties, or settings relevant to the component's configuration can include physical parameters such as dimensions, weight, and material; electrical parameters such as voltage, current, and power; software settings such as a firmware version; environmental conditions such as a temperature range; and safety properties such as specific limits. The various configuration parameters, properties, or settings can be defined by default values ​​or default settings.Some parameters, properties, and settings can have fixed values, while others can vary within certain ranges.

[0058] In particular, the input module 200, or another associated module, includes CAD software for creating the configuration data 350 of the technical component. CAD (Computer-Aided Design) software is a program for creating, modifying, analyzing, and optimizing 2D or 3D models of technical components. CAD software enables the creation of detailed technical drawings, models, and simulations used in product development and production. Typical functions include drawing geometries, adding annotations, performing analyses, and creating 3D models that can be used for further applications such as prototyping or manufacturing.

[0059] In one embodiment according to the prior art, simulation model 550 uses Monte Carlo simulation to simulate the service life curve of a technical component or a combination of several interacting technical components. Simulation model 550 includes random variables that influence the service life or failure behavior of the technical component. These random variables are a selection of parameters, properties, and settings from the design data 350 of the technical component, i.e., in particular, material properties, operating conditions, loads, environmental factors, and usage types. The random values ​​are the values ​​of the random variables, i.e., the parameter values, setting values, and property values ​​of the technical component specified for a simulation.The Simulation Model 550 defines the probability distribution for each random variable to account for its variability and uncertainty. Furthermore, the Simulation Model 550 describes causal relationships, i.e., how the various random variables interact and how these interactions affect lifetime. This can include aging, wear, fatigue, and other mechanisms. The Simulation Model 550 also defines how the simulation is run, how the random values ​​of the random variables are generated, and how the Simulation Model 550 predicts the lifetime or failure time of the technical component. The Simulation Model 550 performs numerous simulation runs and collects the simulation results to calculate statistical measures such as mean, median, standard deviation, and confidence intervals.

[0060] The simulation model 550 thus models the behavior of the technical component and enables the simulation of possible scenarios that influence the service life or failure behavior of the technical component. Within the scope of the invention, a scenario is understood to mean the selection of parameters, settings, and properties, as well as the determination of values ​​for these parameters, settings, and properties. The simulation model 550 therefore enables a mathematical and computer-aided representation of a specific scenario of the technical component and serves as a tool to understand the behavior of the technical component under different conditions, i.e., in different scenarios, and to predict possible outcomes.

[0061] Therefore, for the accuracy of the simulation results, it is crucial that the Monte Carlo simulation model 550 precisely defines the structure of the technical component with respect to its influencing factors, relationships, and dependencies. This structure can be linear or complex, depending on the type of technical component being simulated. Random variables are essential components of the Monte Carlo simulation model 550. They represent the variability of the technical component. Each random variable has a probability distribution that describes how the values ​​of that variable are distributed. Commonly used distributions include the normal distribution (bell curve), uniform distribution, exponential distribution, binomial distribution, and others.

[0062] The Simulation Model 550 contains algorithms that generate random values ​​for the random variables based on defined probability distributions. These random values ​​are used as inputs to the Simulation Model 550 to simulate various scenarios. The Simulation Model 550 defines how the generated random values ​​are integrated into the simulation process to describe how changes in the values ​​of the random variables affect the simulation results.

[0063] Monte Carlo simulations involve many repetitions or iterations. The 550 simulation model is capable of running the simulation multiple times, generating new random values ​​with each iteration. The results of these iterations are collected to perform statistical analyses such as calculating the mean, median, standard deviation, and confidence intervals. These results provide information about the expected outcomes or probabilities of various events.

[0064] The 550 simulation model uses specialized software and / or programming environments that support random number generation, statistical analysis, and iteration. Examples include MATLAB, Python, or dedicated simulation software.

[0065] The 550 simulation model is therefore a flexible and powerful tool for analyzing a technical component. However, Monte Carlo simulations generate a large number of scenarios, resulting in a large number of simulations. In a typical Monte Carlo simulation, random values ​​are generated for various random variables to represent possible scenarios. Each scenario consists of a specific combination of random variables and their random values, based on defined probability distributions.

[0066] Each random variable in a simulation can be varied in different ways. With each additional random variable, the number of possible scenarios increases exponentially. If each random variable can take on multiple possible values, this leads to a further multiplication of scenario combinations. Furthermore, different probability distributions influence how random values ​​are generated for each random variable. If different distributions can be used, the number of possible scenarios increases even further.

[0067] Even with just four random variables, each with ten possible random values ​​and three different probability distributions for each random variable, the number of possible scenarios increases rapidly. Therefore, to achieve complete coverage of all possible scenarios in this example, an enormous number of simulations are required.

[0068] The large number of scenarios is an advantage of Monte Carlo simulation, as it allows for a comprehensive consideration of different possibilities. On the other hand, it can lead to considerable computational effort and longer processing times.

[0069] To increase the efficiency of simulations for generating lifetime curves, it is therefore advisable to filter out the relevant scenarios with suitable random variables from the total set of possible scenarios and to run a simulation only with these. A sensitivity analysis can be used to determine which random variables or parameters have the greatest influence on the simulation results. By focusing on these critical variables, irrelevant or less important variables can be reduced. Selective sampling can also be used. Instead of simulating all possible scenarios, only those scenarios that are likely to have the greatest influence or are most relevant are selected.

[0070] By filtering and focusing on relevant scenarios and random variants, the efficiency of the simulations performed in Simulation Module 500 can be significantly increased without substantially compromising the accuracy of the results. However, this selection process must be carried out thoroughly and carefully to minimize the risk of incorrect decisions or insufficient coverage of relevant areas.

[0071] According to the invention, a quantum computing approach is used for this selection process to provide relevant scenarios for carrying out simulations and thus to replace the classical, very computationally intensive Monte Carlo simulations.

[0072] A quantum state is the mathematical description of a quantum mechanical system. In contrast to classical physics, where states are precisely defined (e.g., by position and velocity), quantum states are probabilistic in nature and represent superpositions of different states.

[0073] While classical bits in computer science are either 0 or 1, quantum bits (qubits) in quantum mechanics can exist in a superposition state, meaning they can simultaneously represent a combination of 0 and 1. A typical example of a superposition in a qubit is the state α|0〉+β|1〉, where |α|2 is the probability of state 0 and |β|2 is the probability of state 1.

[0074] Superposition means that a quantum state is a superposition of several states. For a quantum bit (qubit), this means that it can exist simultaneously in a combination of 0 and 1 with specific probabilities for each state. In quantum mechanics, this is mathematically represented as a wave function or state vector. When a quantum state is measured, the superposition collapses into one of the possible classical states. The probability that the quantum state collapses into 0 or 1 depends on the squares of the amplitudes of the wave function or state vector. Since the amplitudes are complex numbers, they must be converted to their magnitudes before squaring.

[0075] For example, if a qubit is in a superposition of 0 and 1, the probability of measuring 0 or 1 depends on the wavefunction. If, for example, the probability of 0 is 70%, this means that with repeated measurements, approximately 70% of the results will be 0.

[0076] The quantum mechanical wavefunction is a mathematical function that describes the quantum state of a system and can contain many superimposed states. Each of these states has an associated amplitude, which indicates the strength or weighting of that state in the wavefunction. The wavefunction is a linear combination of these states, with each state having a specific amplitude.

[0077] Mathematically, the wave function for a quantum state representing a superposition of n possible states can be described as follows: |ψ〉=∑i=1nαi|i〉 where | i) is the i-th state and α ithe corresponding amplitude. The amplitudes α i are complex numbers, and the probability that the state |i〉 is measured is equal to the square of the magnitude of the amplitude |α|. 2 .

[0078] When a quantum bit (qubit) is in a superposition of two states, the wave function can be described as follows: |ψ〉=α|0〉+β|1〉 where α and β are the amplitudes of states 10) and 11).

[0079] The Schrödinger equation describes how the wave function changes over time and is therefore comparable to the equations of motion in classical physics.

[0080] In quantum information science and quantum computing, quantum states are used to store and process information. The wavefunction describes how qubits interact with each other and how they are modified by quantum operations. The phase of complex amplitudes also plays a crucial role. Phase is the angle of a complex number in complex space. In quantum mechanics, the phase of an amplitude relative to other amplitudes determines how different states interfere or combine in a superposition. In quantum interference, states can be amplified (constructive interference) or attenuated (destructive interference) depending on their phase. Many quantum operations affect the phase of a qubit, which can lead to changes in its quantum state. It plays a vital role in many quantum algorithms, including quantum amplitude estimation and quantum phase estimation.

[0081] Quantum amplitude estimation (QAE) is a quantum algorithm used to accurately estimate the amplitude of a given state or the phase of a quantum mechanical wavefunction. The primary goal of quantum amplitude estimation is to determine the probability of measuring a particular state within a quantum state. This probability is called the "amplitude" in quantum mechanics.

[0082] Quantum amplitude estimation is based on the interference and periodic nature of quantum wavefunctions. It incorporates the quantum Fourier transform (QFT), a quantum analog of the classical discrete Fourier transform, and can be used to extract phase information from a quantum state. This is achieved by transforming the quantum state into a superposition state and performing a quantum operation multiple times. The resulting phase changes are then analyzed using QFT and subsequent measurements to estimate the phase or amplitude.

[0083] According to the invention, the possible parameters, settings, and properties, as well as the associated parameter values, setting values, and property values—that is, the possible random variables and random values ​​of a Monte Carlo simulation—are represented in a quantum state 450. The quantum state 450 is thus a superposition of all possible random variables and random values. The amplitudes or states 455 of the quantum state 450 can represent both random variables and random values, depending on how the quantum state 450 is structured. This superposition makes it possible to represent many different states 455 simultaneously in a single quantum state 450. Each amplitude within this superposition represents a specific probability for a particular state 455 or a combination of states 455.

[0084] In quantum mechanics, the term "state" refers both to the general quantum state 450 and to a specific individual state 455 within this quantum state 450. Within the scope of the invention, a quantum state 450 is thus the complete description of the technical component with all conceivable parameters, settings, properties, and associated parameter values, setting values, and property values ​​represented in the design data 350. An individual state 455 is a specific possible state within a quantum state 450 and can represent a specific parameter, setting, property, parameter value, setting value, or property value of the technical component. A specific state 455 thus corresponds to a specific scenario of a component.a component with a specific selection of parameters, settings and properties, as well as the definition of values ​​for the parameters, settings and properties.

[0085] This superposition of individual states 455 enables the quantum processor 430 to simultaneously represent multiple scenarios or possibilities of the technical components, which is a crucial difference from classical Monte Carlo simulation. In a Monte Carlo simulation, a multitude of possible scenarios are simulated sequentially in several iterations using random variables and values. This approach can be very computationally intensive, as each iteration requires a new run of the simulation. A quantum state 450, on the other hand, represents this multitude of states 455, i.e., scenarios, simultaneously by superimposing the various random variables and values. This can lead to a significant increase in efficiency compared to classical simulations.

[0086] The quantum processor 430 uses quantum algorithm 470, a quantum amplitude estimation, to extract relevant states 490 from quantum state 450. These states represent scenarios with a high probability of occurrence in reality, a process that would require many iterations using classical methods. The quantum amplitude estimation algorithm 470 analyzes the amplitudes of states 455 within quantum state 450 and can thus extract information about the distribution of random variables and random values. In this way, relevant states 490 can be generated from quantum state 450, which represents a superposition of various random variables and random values. These are representative samples of random variables and random values ​​from the technical component that have a significant influence on its lifetime curve.Quantum amplitude estimation thus integrates Monte Carlo simulation into the quantum algorithm 470 by directly generating a probability distribution through repeated measurements of the quantum state 450. This distribution represents the frequency of occurrence of all possible states 455 (or scenarios) in the quantum state 450. Since quantum amplitude estimations are exponentially faster than classical Monte Carlo simulations, especially for large and complex systems, relevant states 490 or scenarios that are important for calculating a lifetime curve can be determined in this way with significantly less computational effort.

[0087] These relevant states 490, determined by means of quantum amplitude estimation, are then used by the simulation model 550 to model simulation results 570.

[0088] Importance sampling is a technique in statistics and Monte Carlo simulation where certain samples are preferentially selected to increase estimation efficiency. This involves drawing samples from a probability distribution that differs from the original distribution by highlighting the important regions of the distribution. In this way, samples that are more probable or important can be favored, while samples that are less relevant can be avoided. This can improve the efficiency and accuracy of the estimation.

[0089] In the present invention, importance sampling is used in addition to amplitude estimation to extract relevant states 490 and to improve the accuracy of the extraction.

[0090] In importance sampling, the amplitudes of states 455 in the superposition state of quantum state 450 are modified by a specific operation so that the more relevant states 490 receive higher amplitudes. Since the amplitudes indicate the probability with which a particular state 455 is observed in a measurement, this increases the probability that these relevant states 490 will be measured when measuring quantum state 450; that is, they are measured more frequently and thus prioritized. Importance sampling therefore aims to increase the efficiency of measuring quantum state 450 by increasing the probability of relevant states 490 occurring in a measurement. Instead of a natural distribution where all states 455 are equally likely, a modified probability distribution is used in which the relevant states 490 are more likely.This adjustment allows the relevant states 490 to be measured more frequently when measuring quantum state 450, thereby reducing the variance of the estimate and thus enabling more accurate results with fewer samples.

[0091] An importance-sampling algorithm modifies the amplitudes of the relevant states 490 by appropriately modifying the quantum state 450. After the modification, repeated measurements of the quantum state 450 are performed, and the relevant states 490 are measured more frequently, leading to a more efficient estimation of the desired relevant scenarios.

[0092] In a quantum state 450, which is described as a superposition of the ground states |0〉 and 11) with |ψ〉=α|0〉+β|1〉 Where α and β are the amplitudes of the states |0〉 and |1〉, an importance-sampling algorithm can, for example, modify the amplitudes α and β such that the state |1〉 has a higher probability of being measured if this state |1〉 is more relevant to the question at hand. More relevant states 490, which are of greater interest or have greater significance for the final result, are assigned higher amplitudes. This increases the probability P that these relevant states 490 are measured, according to P(|0〉)=|α'|2 and P(|1〉)=|β'|2

[0093] In repeated measurements of the modified quantum state 450, the more relevant states 490 are measured more frequently, since states 490 with higher amplitudes (after the modification) have a higher probability of being measured. This allows important information regarding the relevant scenarios to be extracted more quickly, leading to a more efficient and accurate estimation, which is particularly advantageous given the large number of variables in lifetime curves. By prioritizing relevant states 490, the importance sampling algorithm can increase the efficiency of determining relevant scenarios.

[0094] By combining importance sampling and amplitude estimation, the quantum processor 430 is able to provide more accurate estimates and thus identify relevant states 490 or scenarios.

[0095] The relevant states 490 are now passed to the simulation model 550, which performs simulations with these relevant states 490 to model simulation results 570. Performing simulations only with relevant states 490 (scenarios) increases the efficiency and accuracy of the simulation results, as the results are no longer influenced by simulations with less relevant scenarios that have only a minor impact on the lifetime curve.

[0096] The simulation results 570 are then analyzed, and based on these results, a lifetime curve 580 is calculated for the technical component. Additional characteristic values ​​can also be derived from the simulation results. The lifetime curve 580 is output by the output module 700 as output data 750 and is used to modify at least one parameter and / or property and / or setting of the technical component in order to extend its lifetime. The output data 750 includes, in particular, representations of the lifetime curve in the form of graphs, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc.

[0097] In Fig. 2 shows the process steps for estimating a lifetime curve of at least one technical component, in particular a vehicle assembly or a vehicle component.

[0098] In step S10, design data 350 is generated for at least one technical component with a multitude of parameters and / or settings and / or properties of the technical component, wherein the parameters and / or properties and / or settings are physical parameters such as dimensions, weight, material and / or electrical parameters such as voltage, current, power and / or software settings such as a firmware version and / or environmental conditions such as a temperature range and / or safety properties such as certain limit values, and wherein in a Monte Carlo simulation these parameters, settings and properties of the technical component are random variables and the values ​​for the parameters, settings and properties represent random values.

[0099] In step S20, the design data 350 is transferred to a quantum module 400.

[0100] In step S30, the design data 350 is transformed into a quantum state 450 by a quantum processor 430, where the quantum state 450 includes each parameter, each parameter value, each property, each property value, each setting and each setting value of the technical component as a single state 455.

[0101] In step S40, at least one quantum algorithm 470 is applied to the quantum state 450, wherein the quantum algorithm 470 includes at least one quantum amplitude estimation.

[0102] In step S50, relevant states 490 are determined from measurements of the quantum state 450, wherein a relevant state 490 represents a sample of the technical component with a specific selection of parameters, settings and properties of the technical component as random variables and a specific selection of parameter values, setting values ​​and property values ​​of the technical component as random values, and wherein the relevant states 490 each have a high amplitude.

[0103] In step S60, the relevant states 490 are passed on to a simulation model 550.

[0104] In step S70, simulations are performed with the relevant states 490 to model simulation results 570.

[0105] In step S80, the simulation results 570 are analyzed and a lifetime curve 580 is calculated for the technical component.

[0106] In step S90, the lifetime curve 580 is output as output data 750 for use in adjusting at least one parameter and / or property and / or setting of the technical component in order to extend the lifetime of the technical component.

[0107] Fig. Figure 3 schematically represents a computer program product 900 comprising an executable program code 950 configured to execute the method according to the first aspect of the present invention when executed.

[0108] The present invention enables the optimized determination of relevant scenarios for generating a lifetime curve for a technical component through the use of quantum algorithms such as quantum amplitude estimation. The complexity of a Monte Carlo simulation is replaced by the quantum algorithm, particularly in conjunction with importance sampling, because from a probability distribution with various parameters, settings, and properties of a technical component, those relevant scenarios actually required for calculating a lifetime curve can be filtered out. This allows a lifetime curve to be calculated both faster and more accurately. Furthermore, the quantum algorithm allows for better consideration of interactions between the various parameters, settings, and properties.

[0109] Improved lifetime curve generation offers numerous advantages, particularly for planning, maintenance, and product development. Enhanced lifetime curves enable more accurate prediction of the service life of technical components. This helps to formulate more realistic expectations and plan product development accordingly. With a more accurate lifetime curve, maintenance schedules can be optimized to prevent failures and extend component lifespan. This, in turn, can lead to significant cost savings. Furthermore, optimized maintenance can help reduce unnecessary spare parts and energy consumption. The reliability of technical components can also be increased. In particular, improved lifetime curves can drive innovation, for example, through the use of new materials. Improved lifetime curves can also offer environmental benefits.When components last longer and need to be replaced less frequently, this reduces resource consumption and waste. All these aspects contribute to greater customer satisfaction. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] DE 10 2008 047 958 A1

[0019]

Claims

[1] Method for estimating a lifetime curve of at least one technical component, in particular a vehicle assembly or a vehicle component, comprising the following process steps: - Generating (S10) design data (350) for at least one technical component with a multitude of parameters and / or settings and / or properties of the technical component, wherein the parameters and / or properties and / or settings are physical parameters such as dimensions, weight, material and / or electrical parameters such as voltage, current, power and / or software settings such as a firmware version and / or environmental conditions such as a temperature range and / or safety properties such as certain limit values, and wherein these parameters, settings and properties of the technical component are random variables and the values ​​for the parameters, settings and properties are random values; - Transfer (S20) the construction data (350) to a quantum module (400); - Transforming (S30) the design data (350) into a quantum state (450) by a quantum processor (430), wherein the quantum state (450) includes each parameter, each parameter value, each property, each property value, each setting and each setting value of the engineering component as a single state (455); - Applying (S40) at least one quantum algorithm (470) to the quantum state (450), wherein the quantum algorithm (470) includes at least one quantum amplitude estimation; - Determining (S50) relevant states (490) from measurements of the quantum state (450), wherein a relevant state (490) represents a sample of the technical component with a specific selection of parameters, settings and properties of the technical component as random variables and a specific selection of parameter values, setting values ​​and property values ​​of the technical component as random values, and wherein the relevant states (490) of the quantum state (450) each have a high amplitude; - Passing (S60) the relevant states (490) to a simulation model (550); - Performing (S70) simulations with the relevant states (490) to model simulation results (570); - Analyzing (S80) the simulation results (570) and calculating a lifetime curve (580) for the technical component; - Output (S90) of the lifetime curve (580) as output data (750) for use in adjusting at least one parameter and / or property and / or setting of the technical component to extend the lifetime of the technical component. [2] Method according to claim 1, wherein the output data (750) comprise representations of the lifetime curve in the form of graphs, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc. [3] Method according to claim 1 or 2, wherein importance sampling is used to increase the probability of measuring relevant states (490) in the quantum state (450). [4] Method according to any one of claims 1 to 3, wherein the importance sampling algorithm is used to modify the amplitudes of the relevant states (490) such that the relevant states (490) are measured more frequently during the measurement of the quantum state (450), thereby increasing the efficiency of determining relevant states (490). [5] Method according to any one of claims 1 to 4, wherein the simulation model (550) comprises special software and / or programming environments that support random number generation, statistical analysis and iterations, such as in particular MATLAB and Python. [6] Method according to any one of claims 1 to 5, wherein the input module (200) and / or the database (300) and / or the quantum module (400) and / or the simulation module (500) and / or the output module (700) has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure or is / are integrated into the cloud computing infrastructure. [7] A method according to any one of claims 1 to 6, wherein the technical component is a bearing for reducing friction between moving parts such as a ball bearing, a roller bearing or a plain bearing, a shaft for transmitting torque such as a drive shaft, a camshaft or an axle, a coupling for connecting or disconnecting rotating parts in a drive system, a transmission for changing speed or torque and for transmitting mechanical power, a spring such as a coil spring or a leaf spring, a damper such as a vibration damper, a valve for controlling the flow of liquids or gases, a pump, a compressor, a transformer, a coil, a capacitor, a resistor, a sensor, an actuator, a connector and / or a cable; and / or wherein the technical component is an assembly such as, for example,a drive system, a braking system, a transmission, a clutch, a lighting system, an interior, a body, a tire or a component of an assembly; and / or wherein the technical component is a software program such as firmware or application software. [8] System (100) for estimating a lifetime curve (580) of at least one technical component, in particular a vehicle assembly or vehicle component, comprising an input module (200), a database (300), a quantum module (400) with a quantum processor (430), a simulation module (500) with a simulation model (550) and an output module (700), wherein the input module (200) is configured to generate design data (350) for at least one technical component with a plurality of parameters and / or settings and / or properties of the technical component, wherein the parameters and / or properties and / or settings are physical parameters such as dimensions, weight, material and / or electrical parameters such as voltage, current, power and / or software settings such as a firmware version and / or environmental conditions such as a temperature range and / or safety properties such as certain limit values, and wherein these parameters,The settings and properties of the technical component are random variables, and the values ​​for the parameters, settings, and properties are random values; wherein the input module (200) is configured to transfer the design data (350) to the quantum module (400); wherein the quantum processor (430) is configured to transform the design data (350) into a quantum state (450), wherein the quantum state (450) comprises each parameter, each parameter value, each property, each property value, each setting, and each setting value of the technical component as a single state (455); wherein the quantum processor (430) is configured to apply at least one quantum algorithm (470) to the quantum state (450), wherein the quantum algorithm (470) comprises at least one quantum amplitude estimation; wherein the quantum algorithm (470) is designed to determine relevant states (490) from measurements of the quantum state (450),where a relevant state (490) represents a sample of the technical component with a specific selection of parameters, settings and properties of the technical component as random variables and a specific selection of parameter values, setting values ​​and property values ​​of the technical component as random values,and wherein the relevant states (490) each have a high amplitude; wherein the quantum module (400) is configured to pass the relevant states (490) to a simulation model (550); wherein the simulation model (550) is configured to perform simulations with the relevant states (490) to model simulation results (570); wherein the simulation module (500) is configured to analyze the simulation results (570) and calculate a lifetime curve (580) for the technical component; wherein the output module (700) is configured to output the lifetime curve (580) as output data (750) for use in adjusting at least one parameter and / or property and / or setting of the technical component to extend the lifetime of the technical component. [9] System (100) according to claim 8, wherein the output data (750) comprise representations of the lifetime curve in the form of graphs, text messages, holograms, key figures, diagrams, and / or PowerPoint presentations, etc. [10] System (100) according to claim 8 or 9, wherein an importance sampling algorithm is used to increase the probability of measuring relevant states (490) in the quantum state (450). [11] System (100) according to one of claims 8 to 10, wherein the importance sampling algorithm is used to modify the amplitudes of the relevant states (490) such that the relevant states (490) are measured more frequently during the measurement of the quantum state (450), thereby increasing the efficiency of determining relevant states (490). [12] System (100) according to any one of claims 8 to 11, wherein the simulation model (550) uses special software and / or programming environments that support random number generation, statistical analysis and iterations, such as in particular MATLAB and Python. [13] System (100) according to any one of claims 8 to 12, wherein the input module (200) and / or the database (300) and / or the quantum module (400) and / or the simulation module (500) and / or the output module (700) has / have one or more technical interfaces and protocols for accessing a cloud computing infrastructure or is / are integrated into the cloud computing infrastructure. [14] System (100) according to any one of claims 8 to 13, wherein the technical component is a bearing for reducing friction between moving parts such as a ball bearing, a roller bearing or a plain bearing, a shaft for transmitting torque such as a drive shaft, a camshaft or an axle, a coupling for connecting or disconnecting rotating parts in a drive system, a transmission for changing speed or torque and for transmitting mechanical power, a spring such as a coil spring or a leaf spring, a damper such as a vibration damper, a valve for controlling the flow of liquids or gases, a pump, a compressor, a transformer, a coil, a capacitor, a resistor, a sensor, an actuator, a connector and / or a cable; and / or wherein the technical component is an assembly such as, for example,a drive system, a braking system, a transmission, a clutch, a lighting system, an interior, a body, a tire or a component of an assembly; and / or wherein the technical component is a software program such as firmware or application software. [15] Computer program product (900) comprising an executable program code (950) configured to perform the method according to any one of claims 1 to 7.

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