A system, apparatus and method of determining remaining life of a bearing is disclosed. The method includes generating a bearing model of the bearing. The bearing model is based on one of condition data associated with operation of the bearing, historical condition data of the bearing, bearing specification and technical specification of a technical system including the bearing. The method further includes predicting a defect in the bearing based on the bearing model and predicting the remaining life of the bearing based on the predicted defect.

Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

An integrated vehicle health management (IVHM) system to resolve equipment-fault related anomalies detected by cyber intrusion detection system (IDS). A benefit of the present system is that it can result in fewer alerts that need manual analysis. A combination of cyber and monitoring with integrated vehicle health management (IVHM) may be a high value differentiator. As a solution gets more mature through a learning loop, it may be customized for different customers in a cost-effective manner, something that might be expensive to develop on their own for most original equipment manufacturers (OEMs). An IVHM symptom pattern recognition matrix may link a pattern of reported symptoms to known equipment failures. This matrix may be initialized from the vehicle design data but its entries may get updated by a learning loop that improves a correlation by incorporating results of investigations.

A computer-implemented method for controlling and/or monitoring a production plant (110) is proposed. The production plant (110) comprises at least one process chain (112) comprising at least one batch process (114). The method comprises the following steps: a) at least one step of determining of input data (132), wherein the input data comprises at least one quality criterion and production plant layout data, wherein the step comprises retrieving the production plant layout data and receiving information relating to the quality criterion via at least one communication interface (158); b) at least one prediction step (134), wherein in the prediction step operating conditions for operating the production plant (110) are determined by applying at least one trained model (136) on the input data, wherein the trained model (136) is at least partially data-driven by being trained on sensor data from historical production runs; c) at least one control and/or monitoring step (140), wherein the operating conditions are provided.

A method for detecting abnormal operating states of a device includes obtaining model data to the device that is representative of operating states to be expected for at least one component of the device. The device collects measurement data that is representative of an actual operating state of the component of the device. The device ascertains comparison data on the basis of the model data and the measurement data, where the comparison data is representative of an expected operating state. The method includes using the comparison data and the measurement data as a basis for determining whether there is a discrepancy between the actual operating state and the expected operating state. The method further includes attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy.

An artificial intelligence diagnosis system includes a diagnosis model responsive to data received from a plurality of sensors. Each of the sensors is a part of an input channel further including a converter operative to process the received sensor data. A system manager is provided and is operative with an allocator to selectively distribute sensor data to the converters. The system manager operates with the converters such that a converter which is allocated with the sensor data processes the allocated sensor data and inputs the processed sensor data into the artificial intelligence diagnosis model. A converter which is not allocated with the sensor data generates virtual sensor data according to an instruction of the allocator and inputs the virtual sensor data into the artificial intelligence diagnosis model.

The application is directed to systems and methods of performing anomaly detection, predictive maintenance, and anomaly correction in an amusement park experience. A method may include receiving, via a sensor network, multiple layers of first sensor data indicative of characteristics of the experience and generating a profile of the experience based on the first sensor data, wherein the profile includes a baseline and a threshold. The method may also include receiving second sensor data and third sensor data via the sensor network, determining, in response to identifying characteristics of the second sensor data that deviate from the baseline but do not exceed the threshold, that the experience is operating properly, and performing a particular corrective action in response to identifying characteristics of the third sensor data that deviate from the baseline and exceed the threshold.

An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan.

An abnormality diagnostic device includes a diagnoser configured to diagnose a type of abnormality that occurs in an abnormality diagnostic target on the basis of differences between abnormality simulation results for each type of abnormalities obtained by simulating a plurality of types of abnormalities in the abnormality diagnostic target and a plurality of time-series observation results obtained by observing the abnormality diagnostic target in time series using a plurality of detectors.

Predictive maintenance methods and systems, including a method of applying transfer entropy techniques to find a causal link between parameters; a method of applying quality weighting to context data based on a priori knowledge of the accuracy of the context data; a method of detecting a maintenance action from parameter data by detecting a step and a process capability improvement; a method of managing unattended alerts by considering cost/benefit of attending to one or more alerts over time and assigning alert expiry time and/or ranking the alerts accordingly; a method of displaying components of a complex system in a functional way enabling improvements in system diagnostics; a method of determining the time of an event indicator in time series parameter data; a method of classifying an event associated with a fault condition occurring within a system; and a method of determining whether an event recorded in parameter data is attributable to an external factor.