A system for automated reconfiguration of a commodity distribution system is provided. The system includes a plurality of nodes located in the distribution system and a plurality of node controllers. The node controllers control respective nodes in accordance with a first or second operating mode to affect system reconfiguration in response to a fault condition, loading, system optimization, system expansion and combinations thereof.

A method for providing an analytical artifact used for development and/or analysis of an investigated technical system of interest comprised of components having associated machine readable functional descriptions including port definitions and component failure modes processed to generate automatically the analytical artifact in response to at least one applied system evaluation criterion.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining causal models for controlling environments. One of the methods includes obtaining data specifying baseline probability distributions for each of a plurality of controllable elements; maintaining a causal model; repeatedly performing the following: selecting control settings for the environment based on the causal model and values for a particular internal parameter of the control system that are sampled from a range of possible values; selecting control settings for the environment based on the baseline probability distributions; monitoring environment responses to the control settings selected based on the causal model and the control settings selected based on the baseline probability distributions; determining, for each of the possible values, a measure of a difference between a current system performance and a baseline system performance; and updating how frequently each of the possible values is sampled.

Example implementations described herein are directed to management of a system comprising a plurality of apparatuses providing unlabeled sensor data, which can involve executing feature extraction on the unlabeled sensor data to generate a plurality of features; executing failure detection by processing the plurality of features with a failure detection model to generate failure detection labels, the failure detection model generated from a machine learning framework that applies supervised machine learning on unsupervised machine learning models generated from unsupervised machine learning; and providing extracted features and the failure detection label to a failure prediction model to generate failure prediction and a sequence of features.

A system and method are provided for determining a causal inference in a manufacturing process. During operation, the system can receive data associated with a processing system which includes a set of interconnected machines and an associated set of processes. The system can generate, based on the data, a graph indicating flows of outputs between the machines as part of the processes. The system can determine, based on a set of variables, one or more candidate clusters in the graph. The system can perform, based on one or more variables of interest, root cause analysis on the one or more candidate clusters by: applying an additive noise model to prune the one or more candidate clusters from the graph; and determining, based on the pruned graph, a candidate pathway likely to cause an issue in at least one process, thereby facilitating improved efficiency in the processing system.

An acquisition unit is configured to acquire measurement values of a target device. A likelihood calculation unit is configured to calculate an occurrence likelihood for each of a plurality of phenomena that are liable to occur to the target device based on the measurement values acquired by the acquisition unit. A table storage unit is configured to store a table in which the plurality of phenomena and occurrence causes of abnormalities of the target device are associated to each other. As estimation unit is configured to estimate the occurrence causes based on the occurrence likelihood and the table.

Provided is a computer-implemented method for generating a Component Fault and Deficiency Tree of a multi-component system the method including: a. modeling the multi-component system using a Component Fault and Deficiency Tree, b. the Component Fault and Deficiency Tree includes a plurality of component fault and deficiency tree elements associated with the respective components; c. each component fault and deficiency tree element includes at least one inport and at least one outport; d. each component fault and deficiency tree element includes at least two events as internal fault tree logic; e. at least one gate, f. each component fault and deficiency tree element includes at least one mitigation logic; g. at least one Boolean AND-Gate, configured to connect the internal fault tree logic and the at least one mitigation logic; and h. providing the generated Component Fault and Deficiency Tree of the multi-component system as output.

According to embodiments, a system, method and non-transitory computer-readable medium are provided to receive time series data associated with one or more sensors values of a piece of machinery at a first time period, perform a non-linear transformation on the time-series data to produce one or more nonlinear temporal embedding outputs, and projecting each of the nonlinear temporal embedding outputs to a different dimension space to identify at least one causal relationship in the nonlinear temporal embedding outputs. The nonlinear embeddings are further projected to the original dimension space to produce one or more causality learning outputs. Nonlinear dimensional reduction is performed on the one or more causality learning outputs to produce reduced dimension causality learning outputs. The learning outputs are mapped to one or more predicted outputs which include a prediction of one or more of the sensor values at a second time period.

The invention provides a dynamic risk analyzer (DRA) that periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRA assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.

A controller for controlling an operation of a system is disclosed. The controller receives an input signal indicative of the operation of the system and rotates a test signal multiple times with different circular shifts to produce different rotations of the test signal forming a matrix data structure with the input signal. The input signal and the test signal are time-series data having values monotonically measured over time. The controller is further configured to apply a sliding three-dimensional (3D) window method to the matrix data structure to produce statistics of the input signal with respect to the rotations of the test signal. The sliding 3D window method iteratively moves window over the matrix data structure to compute a value of the statistics for a segment of the matrix data structure within the window. Furthermore, the controller controls the operation of the system according to the statistics of the input signal.