Abstract: A system and method utilize a first rotation sensor that provides a first signal indicative of a first rotation of a first end of a rotating coupling. The rotating coupling has an articulating member mechanically coupling the first end to a second end. A second rotation sensor provides a second signal indicative of a second rotation of the second end of the rotating coupling. A processor is coupled to the first and second rotation sensors and is operable to determine a relationship between the first and second rotations based on the first and second signals. Based on the relationship, the processor estimates at least one of a degradation of and a remaining useful life of the rotating coupling.

Abstract: One embodiment provides a method and a system for diagnosing faults in a physical system. During operation, the system can create a fault-augmented model of the physical system by considering various potential faults, and it can generate a machine-learning model to predict an operation mode of the physical system using the outputs of the physical system. A respective operation mode corresponds to normal operation or a potential fault in the physical system. The system can generate a plurality of training samples based on the fault-augmented model, use the training samples to train the machine-learning model to learn a sequence of inputs and model parameters that minimizes an uncertainty of the predicted operation mode, and then apply the learned sequence of inputs and the trained machine-learning model on the physical system to determine the operation mode of the physical system.

Abstract: A flexible device mounting kit allows to securely attach a sensor or another device to an arbitrary surface, including a surface that is very uneven. The kit includes a scaffolding assembly which includes a scaffolding guide and scaffolding teeth attached to the guide in a way that allows some of the teeth to move relative to the guide when the bottom of the teeth is pressed against uneven surface. When the assembly is pressed against the surface, the positions of the teeth adjust, forming, together with the surface, a cavity into which a gluing compound can be filled. A device mount to which a sensor (or another device) can be attached is pressed into the gluing compound before the gluing compound solidifies. As the gluing compound securely connects the sensor mount to the surface, the device can be securely placed within the mount regardless of how uneven the surface is.

Abstract: One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain.

Abstract: One embodiment provides a method and a system for automated design of a physical system. During operation, the design system obtains qualitative and quantitative design requirements associated with the physical system and inputs the qualitative design requirements to a trained machine-learning model to generate a topology of the physical system. The topology specifies a number of components and connections among the components. The design system then determines parameters of the components based on the quantitative design requirements.

Abstract: A system and method for performing hybrid reasoning to predict remaining useful life of a target system. During operation, the system measures, via a set of sensors associated with the target system, sensor signals before a prediction start time. The system updates, based on the measured sensor signals, a first set of parameters of a physics-based model associated with the target system. The system in response to determining that the target system current time is less than a prediction start time: apply a machine-learning model to estimate a second aspect of the health of the target system; and update a second set of parameters of the physics-based model. The system can perform a time simulation of the updated physics-based model to predict a wear/degradation pattern of the target system after the prediction start time; and determine, based on the predicted wear/degradation pattern, a remaining useful life of the target system.

Abstract: One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain.

Abstract: A condition of an asset including one or more subsystems connected in series is monitored. Each subsystem includes one or more components connected in parallel. The asset has one or more jobs. A probability of the asset surviving a predetermined amount of time is determined based on the monitoring and one or more shared resources. The one or more shared resources are configured to be shared between the subsystems. A model is established using a threshold based heuristic maintenance policy. The model is configured to maximize a number of successful jobs that the asset is able to complete based on the determined probability. The one or more shared resources are allocated to the one or more subsystems based on the model.

Abstract: One embodiment provides a method and a system for diagnosing faults in a physical system. During operation, the system can obtain a model of the physical system comprising a plurality of components and can perform a structural analysis on the model to decompose the model into multiple independent subsystem models. A subsystem model corresponds to a subsystem comprising a subset of the plurality of components. The system can then perform, in parallel, a fault-diagnosis operation on each subsystem based on the corresponding subsystem model and can generate a diagnostic output indicating one or more components within the physical system being faulty based on outcomes of the fault-diagnosis operation on each subsystem.