Abstract: A method for determining vehicle component conditions via performance correlation is provided. A list of doors for maintenance on a transport vehicle is maintained. Measurements for one of the doors based on an inspection of that door are maintained. A determination is made as to whether maintenance is required for the door based on the measurements and a maintenance status is assigned to the door. The door measurements are compared to measurements for other doors of the transportation vehicle. Those other doors with measurements similar to the door are identified and the maintenance status of the door is assigned to the other doors identified.

Abstract: One embodiment can provide a system for determining a hybrid-manufacturing process plan for manufacturing a printed circuit board (PCB). During operation, the system can obtain a set of hybrid-manufacturing constraints. The set of hybrid-manufacturing constraints can include a set of primitives, a set of atoms, and an atom end-state vector. A primitive can represent an additive or a subtractive manufacturing process corresponding to one or more atoms of the PCB. An atom can correspond to a unit of spatial volume of the PCB. The system can determine a plurality of feasible hybrid-manufacturing plans based on the set of hybrid-manufacturing constraints. Each feasible hybrid-manufacturing plan can represent an ordering of the set of primitives satisfying the atom end-state vector. The system can determine costs for manufacturing the PCB using the plurality of feasible hybrid-manufacturing plans. The system can determine, based on the costs, an optimized hybrid-manufacturing plan for manufacturing the PCB.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A method and system for automated design of a physical system are provided. During operation, the system obtains a component library comprising a plurality of physical components, receives design requirements of the physical system, and constructs an initial system model based on physical components in the component library and the design requirements. The system topology associated with the initial system model can include a large number of links that are sufficiently coupled to one another, and a respective link comprises one or more physical components. The system further performs an optimization operation comprising a plurality of iterations, with the system topology being updated at each iteration. Updating the system topology includes removing links and components from the system topology. The system then generates a final system model based on an outcome of the optimization operation and outputs a design solution of the physical system according to the final system model.

Abstract: One embodiment of the present disclosure provides a system for determining a hybrid-manufacturing plan for manufacturing an integrated circuit (IC). During operation, the system can obtain a set of hybrid-manufacturing constraints for manufacturing the IC. The set of hybrid-manufacturing constraints can include a set of primitives, a set of atoms, and an atom end-state vector. An atom can correspond to a unit of spatial volume of the IC. A primitive can represent an additive, subtractive, or a mixed manufacturing process corresponding to one or more atoms of the IC. Next, the system can determine a plurality of feasible hybrid-manufacturing plans based on the set of manufacturing constraints. Each feasible hybrid-manufacturing plan can represent an ordering of the set of primitives that satisfies the atom end-state vector. The system can then determine costs for manufacturing the IC using the plurality feasible hybrid-manufacturing plans.

Abstract: One embodiment can provide a method and a system for diagnosing faults in a physical system. During operation, the system obtains a time-domain model of the physical system and converts the time-domain model to the frequency domain to obtain a frequency-domain model of the physical system. The time-domain model can include one or more model parameters having known values. The system also obtains time-domain input and output signals and converts the time-domain input and output signals to the frequency domain to obtain frequency-domain input and output signals. The system identifies at least one model parameter having an expected value that is different from a known value of the at least one model parameter based on the frequency-domain model and the frequency-domain input and output signals, and generates a diagnostic output indicating at least one component within the physical system being faulty based on the identified at least one model parameter.

Abstract: A target system is coupled to a diagnosis engine that uses a lumped parameter model of the system for diagnosis. A proximity search in is performed in a computer-aided design model of the system to find groups of components that may be affected by resistive or parasitic interactions between the individual components in the groups. The lumped parameter model is augmented by adding elements that emulate the resistive or parasitic interactions between the individual components in the groups. The augmented lumped model is used by the diagnosis engine to perform diagnosis on the system.

Abstract: The techniques discussed herein generally relate to a method and system for qualitative modeling of and reasoning about the behavior of spatio-temporal physical systems. In some embodiments, qualitative representations based on Tonti diagrams are used to describe lumped or distributed parameter systems. Using a topological structure of the physical system, some embodiments generate qualitative governing equations as symbolic constraints on qualitative state variables. The qualitative constraints may be used to produce a qualitative simulation of the physical system. The qualitative simulation may be used to guide conceptual design iterations with given design criteria, or for instantiation of quantitative or hybrid (qualitative and quantitative) models and simulations.

Abstract: An analog circuit for solving optimization algorithms comprises three voltage controlled current sources and three capacitors, operatively coupled in parallel to the three voltage controlled current sources, respectively. The circuit further comprises a first inductor, operatively coupled in series between a first pair of the capacitors and the voltage controller current sources and a second pair of the capacitors and the voltage controller current sources. The circuit further comprises a second inductor, operatively coupled in series between the second pair of the capacitors and the voltage controller current sources and a third pair of the capacitors and the voltage controller current sources.

Abstract: An analog circuit for solving optimization algorithms comprises three voltage controlled current sources and three capacitors, operatively coupled in parallel to the three voltage controlled current sources, respectively. The circuit further comprises a first inductor, operatively coupled in series between a first pair of the capacitors and the voltage controller current sources and a second pair of the capacitors and the voltage controller current sources. The circuit further comprises a second inductor, operatively coupled in series between the second pair of the capacitors and the voltage controller current sources and a third pair of the capacitors and the voltage controller current sources.

Abstract: One embodiment provides a system and method for automated design of a computational system. During operation, the system obtains a component library comprising a plurality of computational components, receives design requirements, and builds a plurality of universal component cells. A respective universal component cell is configurable, by a selection signal, to behave as one of the computational components. The system further constructs a candidate computational system using the universal component cells, constructs a miter based on the design requirements and the candidate computational system, and converts the miter into a quantified satisfiability (QS) formula. The system generates a set of inputs that are a subset of all possible inputs of the QS formula, solves the QS formula by performing partial input expansion on the generated set of inputs to obtain at least one design solution, and outputs the at least one design solution to facilitate construction of the computational system.

Abstract: The following relates generally to analog circuit re-design. Some embodiments identify a candidate component of the circuit by determining that if the candidate component is adjusted or replaced, the circuit will satisfy a requirement metric. In some implementations, an optimization problem or Bayesian reasoning may be used to change parameters of the candidate component to create a replacement component. In some implementations, a replacement component of a different type than the candidate component may be selected by solving a mixed-integer optimization program or by using a non-linear program with continuous parameters.

Abstract: Embodiments described herein provide a system for facilitating a training system for a device. During operation, the system determines a system model for the device that can be based on empirical data of the device. The empirical data is obtained based on experiments performed on the device. The system then generates, from the system model, synthetic data that represents behavior of the device under a failure. The system determines uncertainty associated with the synthetic data and, from the uncertainty, determines a set of prediction parameters using an uncertainty quantification model. The system generates training data from the synthetic data based on the set of prediction parameters and learns a set of learned parameters associated with the device by using a machine-learning-based classifier on the training data.

Abstract: The disclosure following relates generally to complex simulations, and fault diagnosis. In some embodiments, a component that is causing a delayed simulation time of a system is determined. A component of reduced complexity is designed, and the component of reduced complexity is used to replace the original component in the system. Fault diagnosis may then be conducted using the updated system with the reduced complexity component, thus decreasing the time taken to diagnose the fault.

Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.

Abstract: The following relates generally to design and redesign of digital circuits. In one disclosed embodiment, a circuit is annotated by identifying at least one possible error location according to an error library; the at least one possible error location is localized; and the circuit is redesigned based on the localized at least one possible error location.

Abstract: The techniques disclosed herein help designers find interesting designs for small electrical, mechanical, and/or hydraulic mechanisms by exhaustively enumerating the design space given a library of components and a maximum number of components allowed per design. Some embodiments work by creating a design space grammar of designs, solving the equations associated with parts of the grammar, and putting the solutions into equivalence classes. This dramatically reduces the number of designs that have to be evaluated to see if they satisfy the design criteria. The result is often a small number of base designs that show the range of possible solutions to the design problem.

Abstract: One embodiment provides a method and a system for automated design of a computational system. During operation, the system obtains a component library comprising a plurality of computational components, receives design requirements of the computational system, and builds a plurality of universal component cells. A respective universal component cell is configurable, by a selection signal, to behave as one of the plurality of computational components. The system further constructs a candidate computational system using the plurality of universal component cells and encodes the received design requirements and the candidate computational system into a single logic formula. Variables within the single logic formula comprise at least inputs, outputs, and internal variables of the candidate computational system. The system solves the single logic formula to obtain at least one design solution for the computational system.

Abstract: A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

Abstract: A systematic approach to constructing process plans for hybrid manufacturing is provided. The process plans include arbitrary combinations of AM and SM processes. Unlike the suboptimal conventional practice, the sequence of AM and SM modalities is not fixed beforehand. Rather, all potentially viable process plans to fabricate a desired target part from arbitrary alternating sequences of pre-defined AM and SM modalities are explored in a systematic fashion. Once the state space of all process plans has been enumerated in terms of a partially ordered set of states, advanced artificial intelligence (AI) planning techniques are utilized to rapidly explore the state space, eliminate invalid process plans, for instance, process plans that make no physical sense, and optimize among the valid process plans using a cost function, for instance, manufacturing time and material or process costs.