An Asset Performance Monitoring (APM) based-system includes an APM workflow engine receiving measured data values for dependent process variables from a process. A process and control simulator includes a dynamic operator training simulations (OTS) model. The APM workflow engine initializes the OTS model at a defined operating point at values for independent process variables from the measured data values to synchronize to the OTS model. The OTS model simulates at the defined operating point to generate model predicted values for key dependent process variables used to generate a trained data model that generates trained model predicted values for the key dependent process variables. The trained model predicted values are compared to the measured data values to generate symptom inputs processed by fault models to identify a suspected fault with the processing equipment/process. The APM workflow engine triggers an alert relating to inspection or maintenance action regarding the processing equipment/process.

Various embodiments include a manufacturing system comprising: a communication module for receiving a three-dimensional model and control commands including manufacturing instructions for the manufacturing machine with respective reference values, tolerance values, and/or intervention tolerance values; a manufacturing module, wherein the model, the instructions, and the commands are used to manufacture an object; a calculating module using the three-dimensional model and the manufacturing instructions to calculate the control commands; and a measuring device having a communication module for receiving the three-dimensional model, a capture module using sensors to measure the manufactured object, captured for the reference values and/or the tolerance values and/or intervention tolerance values, and a checking module, wherein a divergence of the measured values from the applicable manufacturing reference values and an exceeding of the associated manufacturing tolerance values and/or the associated intervention tolerance values result in a control signal.

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

Methods, apparatus, and computer program products for analyzing, monitoring, and/or modeling the manufacture of a type of part by a manufacturing process. Non-destructive evaluation data and/or quality related data collected from manufactured parts of the type of part may be aligned to a simulated model associated with the type of part. Based on the aligned data, the manufacturing process may be monitored to determine whether the manufacturing process is operating properly; aspects of the manufacturing process may be spatially correlated to the aligned data; and/or the manufacturing process may be analyzed.

Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.

An information processing apparatus executes a simulation of a state of a process which is being performed in a semiconductor manufacturing apparatus, by using a simulation model of the semiconductor manufacturing apparatus. The information processing apparatus includes: a physical sensor data acquisition unit that acquires physical sensor data measured in the semiconductor manufacturing apparatus that is performing the process according to process parameters; a simulation execution unit that executes the simulation by the simulation model according to the process parameters, thereby outputting virtual sensor data; a simulation result determination unit that performs a pre-detection of a part of the semiconductor manufacturing apparatus that needs to be replaced, based on a difference between the physical sensor data and the virtual sensor data; and a part order unit that orders the part of the semiconductor manufacturing apparatus based on a result of the pre-detection.

Embodiments are directed to a computer-based tool that can identify an anomalous state of a component in a real-world environment, even if the component experiences gradual and/or seasonal trends. The tool receives data from sensors monitoring a component. The tool uses a trained machine learning model to calculate a predicted behavior of the monitored component. Actual behavior of the component, captured by current sensor readings, is compared to the predicted behavior of the component, calculated by the machine learning model, to compute a divergence. The computed divergence is used by a statistical learning method to determine if the component in the real-world environment is in an anomalous state.

This industrial system is characterized by being provided with: a storage unit that stores design data used when constructing an industrial line; a simulation execution unit that executes a simulation of movement of the industrial line, based on the design data; and a detection unit that compares a result of the simulation with the movement of the industrial line during operation and detects an abnormality in components of the industrial line. With such a configuration and movement, the present invention can be utilized for line operation support and high-resolution abnormality detection.