Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

A system for predicting machine failure may include a controller for controlling a machine, a plurality of sensors, a plugin device, a first computing device, a second computing device, and/or a third computing device. The sensors and/or the plugin device may be communicatively coupled to the controller. The first computing device may be communicatively coupled to the plugin device. The plugin device may transmit data associated with the machine to the first computing device. The first computing device may execute at least one low fidelity model to determine an interesting event associated with the machine. The second computing device may be communicatively coupled to the first computing device. The first computing device may transmit data associated with the interesting event to the second computing device. The second computing device may execute at least one high fidelity model to determine a machine failure prediction.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of cell configurations, determine an efficiency score by applying the feature data to a predictive model generated for predicting a production level of the manufacturing assembly line, determine at least one target cell configuration from the cell configurations based on the efficiency score, and apply the at least one target cell configuration to at least one cell by implementing each target cell configuration to a corresponding cell.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

In an information processing device according to the present invention, a statistics estimation unit estimates a value of a state quantity by using a statistical model constructed based on values of past state quantities of a target device. A physical estimation unit estimates a value of a state quantity by using a physical model constructed based on design data of the target device. A specification unit specifies a value to be used to manage the target device from the value estimated by the statistics estimation unit and the value estimated by the physical estimation unit based on deterioration of the target device with time.

A machine learning device included in a life predicting device observes, as a state variable, life related data related to a life of a consumable component, creates a probability model of a service life for replacement of the consumable component on the basis of the life related data, and predicts, using the created probability model, the service life for replacement of the consumable component based on the life related data.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data and line production data, determine one or more production associations between the cell data and the line production data; evaluate the one or more production associations to identify one or more critical production associations; retrieve the cell data and the line production data associated with the one or more critical production associations; and train a predictive model with the retrieved cell data and the retrieved line production data to predict the production level of the manufacturing assembly line.

An equipment inspection system capable of creating an inspection plan for inspecting a component part of an equipment at an appropriate timing is provided. An equipment inspection system includes an index acquisition unit, an importance level judgement unit, and an inspection time determination unit. The index acquisition unit acquires at least one influence index. The importance level judgement unit judges an importance level of an inspection on a component part based on the influence index. The inspection time determination unit determines the inspection time of the component part according to the importance level.

A system and method for unsupervised prediction of machine failures. The method includes monitoring sensory inputs related to at least one machine; analyzing, via at least unsupervised machine learning, the monitored sensory inputs, wherein the output of the unsupervised machine learning includes at least one indicator; identifying, based on the at least one indicator, at least one pattern; and determining, based on the at least one pattern and the monitored sensory inputs, at least one machine failure prediction.

Various systems and methods for modeling a manufacturing assembly line are disclosed herein. Some embodiments relate to operating a processor to receive cell data, extract feature data from the cell data, determine a plurality of faults, determine a priority level for each fault by applying the extracted feature data to a predictive model, determine at least one high priority fault, and generate at least one operator alert based on the at least one high priority fault.