Abstract: Example implementations described herein are directed to systems and methods for defect rate analytics to reduce defectiveness in manufacturing. In an example implementation, a method include determining, from data associated with each feature for a manufacturing process, the data feature indicative of process defects detected based on the feature, an estimated condition for the feature that reduces a defect rate of the process defects, the estimated condition indicating the data into a first group and second group; calculating the rate reduction of the defect rate based on a difference in defects between the first group and the second group; for the rate reduction meeting a target confidence level for a target defect rate, applying the estimated condition to the manufacturing process associated with each of the features. In example implementations, the defect rate analytics reduce defectiveness in manufacturing with independent processes and/or dependent processes.

Abstract: Example implementations described herein are directed to systems and methods for predictive maintenance with health indicators using reinforcement learning. An example implementation includes a method to receive sensor data, operational condition data, and failure event data and generate a model to determine health indicators that indicate equipment performance based on learned policies, state values, and rewards. The model is applied to external sensor readings and operating data for a piece of equipment to output a recommendation based on the model.

Abstract: In some examples, a computer system may receive sensor data and failure data for equipment. The system may determine, for the equipment, a plurality of time between failure (TBF) durations that are longer than other TBF durations for the equipment. The system may determine, from the sensor data corresponding to operation of the equipment during the plurality of TBF durations, a plurality of measured sensor values for the equipment. Additionally, the system may determine a subset of the measured sensor values corresponding to a largest number of the TBF durations of the plurality of TBF durations. The system may further determine at least one operating parameter value for the equipment based on the subset of the measured sensor values. The system may send a control signal for operating the equipment based on the operating parameter value and/or a communication based on the operating parameter value.

Abstract: A system is provided for operator profiling based on pre-installed sensor measurement. In example implementations, the system extracts a set of segmented time series data associated with a unit of operation and build models which distinguish the operators by machine learning algorithms. The system uses the models to output the evaluation score assigned to each operation, identify the key movements for skilled/non-skilled operators, and recommends appropriate actions to improve operation skill or adjust the scheduling of the operators.

Abstract: In some examples, a computing device may generate simulated data based on a physical model of equipment. For example, the simulated data may include a plurality of probability distributions of a plurality of degrees of failure, respectively, for at least one failure mode of the equipment. In addition, the computing device may receive sensor data indicating a measured metric of the equipment. The computing device may compare the received sensor data with the simulated data to determine a failure mode and a degree of failure of the equipment. At least partially based on the determined failure mode and degree of failure of the equipment, the computing device may send at least one of a notification or a control signal.

Abstract: Example implementations described herein are directed to a system and software for integrating model-based and data-driven diagnosis solutions, automatically generating residuals in dynamic systems and using these residuals for fault detection and isolation (FDI), and automatic fault identification. Through a combination of a model-based approach and a data-driven approach for generating residuals and applying the residuals to detect, isolate and identify faults in a physical system can be obtained.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: Example implementations described herein are directed to a system for lean angle estimation without requiring specialized calibration. In example implementations, the mobile device sensor data can be utilized without any additional specialized data or configuration to estimate the lean angle of a motorcycle. The lean angle is determined based on a determination of a base attitude of a mobile device and a measured attitude of the mobile device.

Abstract: Example implementations described herein are directed to predictive maintenance of equipment using data-driven performance degradation modelling and monitoring. Example implementations described herein detect degradation in performance over a period of time, and alert the user when degradation occurs. Through the example implementations, the operator of equipment undergoing predictive maintenance modeling can determine a more optimized time in repairing or replacing the equipment or its components.

Abstract: Example implementations described herein involve a system for maintenance predictions generated using a single deep learning architecture. The example implementations can involve managing a single deep learning architecture for three modes including a failure prediction mode, a remaining useful life (RUL) mode, and a unified mode. Each mode is associated with an objective function and a transformation function. The single deep learning architecture is applied to learn parameters for an objective function through execution of a transformation function associated with a selected mode using historical data. The learned parameters of the single deep learning architecture can be applied with streaming data from with the equipment to generate a maintenance prediction for the equipment.

Abstract: Example implementations disclosed herein are directed to monitoring communications networks in real time to ensure that they are performing in a desired fashion. Metrics such as utilization and latency are monitored to ensure that customer SLAs (Service Level Agreements) are met in a timely fashion. To detect problems in advance, example implementations predict the future values of metrics and detect events if the future value violates certain conditions. Example implementations build models that can predict the future values of metrics and analyze historical, near real time and real time streaming data so as to build predictive models.

Abstract: Example implementations described herein are directed to systems and methods for estimating the remaining useful life of a component or equipment through the application of models for deriving functions that can express the remaining useful life over time. In an aspect, the failure acceleration time point is determined for a given type of component, and a function is derived based on the application of models on the failure acceleration time point.

Abstract: Equipment uptime is getting increasingly important across different industries which seek for new ways of increasing equipment availability. Detecting faults in the system by condition based maintenance (CBM) is not enough, because at the time of fault occurrence, the spare parts might not available or the needed resources (maintainers) are busy. Therefore, prediction failures and estimation of remaining useful life can be necessary. Moreover, not only predictions but also uncertainty in the predictions is critical for decision making. Example implementations described herein are directed to tuning parameters of deep learning network architecture by developing a mechanism to optimize for accuracy and uncertainty simultaneously, thereby achieving better asset availability, maintenance planning and decision making.

Abstract: Example implementations described herein are directed to a decision-support system for maintenance recommendation that uses analytics technology to evaluate the effectiveness of a maintenance action or a group of actions in improving the performance of equipment and its components, and provide recommendations on which maintenance actions or a group of maintenance actions should be pursued and which should be avoided.

Abstract: Example implementations described herein are directed to vehicle scheduling and management, and in particular for estimation of travel times and other activity times. Example implementations can be used to achieve improved vehicle scheduling and utilization based on the provision of accurate expected activity times. Example implementations are further directed to the integration of predictors to provide an estimation of activity time.

Abstract: A management server is coupled to a plurality of rig systems by a network, each of the rig systems having a plurality of sensors, a rig and a rig node. The management server stores data received from at least one of the plurality of rig systems, the data including values associated with one or more attributes of the rig. The management server derives a model signature for at least one phase from a timeline for at least one rig system based on analytics of information stored in the database and the data, where the model signature includes a set of attributes for the at least one of the plurality of rig systems. In addition, the management server generates a recommendation including one or more actions for planning rig system management operations corresponding to at least one attribute of the set of attributes.

Abstract: Systems and methods are directed to providing one or more interfaces to construct a flow and dashboard for data sources connected to an apparatus. In example implementations, a flow building interface and a dashboard designing interface is provided. The interfaces produce flow information and dashboard information, which is used by the apparatus to construct a dashboard, execute a plurality of flows and receive data from one or more external sources to execute the dashboard.

Abstract: In some examples, a computing device may generate simulated data based on a physical model of equipment. For example, the simulated data may include a plurality of probability distributions of a plurality of degrees of failure, respectively, for at least one failure mode of the equipment. In addition, the computing device may receive sensor data indicating a measured metric of the equipment. The computing device may compare the received sensor data with the simulated data to determine a failure mode and a degree of failure of the equipment. At least partially based on the determined failure mode and degree of failure of the equipment, the computing device may send at least one of a notification or a control signal.

Abstract: Example implementations described herein are directed to a decision-support system for maintenance recommendation that uses analytics technology to evaluate the effectiveness of a maintenance action or a group of actions in improving the performance of equipment and its components, and provide recommendations on which maintenance actions or a group of maintenance actions should be pursued and which should be avoided.

Abstract: Example implementations described herein are directed to predictive maintenance of equipment using data-driven performance degradation modelling and monitoring. Example implementations described herein detect degradation in performance over a period of time, and alert the user when degradation occurs. Through the example implementations, the operator of equipment undergoing predictive maintenance modeling can determine a more optimized time in repairing or replacing the equipment or its components.