The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.

Method, apparatus and computer program product for detecting vulnerability in an industrial control system, predicting maintenance in an industrial control system, and defining authorization rules for peripheral devices based on peripheral device categorization are described herein.

A device may receive equipment information, associated with a first equipment, including information associated with anomalies identified based on operational information collected during operation of the first equipment, and messages generated during the operation of the first equipment. The device may receive maintenance information, associated with the first equipment, that identifies one or more part failures associated with one or more equipment parts. The device may identify associations between the one or more part failures and the first equipment information. The device may receive equipment information, associated with a second equipment, including information associated with anomalies identified based on operational information collected during operation of the second equipment, and messages generated during the operation of the second equipment. The device may generate and provide a prediction, associated with a future failure of an equipment part of the second equipment, based on the second equipment information and the associations.

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.

An objective of the present invention is to reduce the downtime which occurs when changing a servo motor device. A servo motor device includes a motor section and a reduction gear configured to output a driving force by reducing a speed of rotation of the motor section, wherein a control device includes a detecting section configured to acquire detected information about operation of the motor section, and a computing section configured to generate an approximate curve based on a behavior for a time sequence of a parameter and to calculate predicted lifetime information of the servo motor device based on the approximate curve thus generated, wherein the parameter has been calculated by means of the detected information.

A method executed by an arithmetic circuit of one or a plurality of computers includes periodically collecting log information from an electronic device, inputting the log information collected before a current clock time from the electronic device to a prediction model, causing the prediction model to predict an abnormal or normal operating state of a recording medium after the current clock time, and transmitting replacement notification information that is based on a prediction result to the electronic device. The prediction model is generated by performing machine learning using, as training data, training log information collected within a predetermined period up to a reference clock time from a plurality of training electronic devices each including a training recording medium, and state information indicating an operating state of the training recording medium determined after the reference clock time.

Systems and methods for improving recordkeeping and analysis of an asset include creating and maintaining an integrated record about the asset. In some embodiments, the systems and methods include collecting data about an asset to form an asset data collection, recording the asset data collection in a record, analyzing at least a portion of the asset data collection to determine a characteristic of the asset, and recording the characteristic of the asset in the record. In some embodiments, recording the characteristic in the record includes adding the characteristic to the asset data collection.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

A state quantity acquisition unit acquires a state quantity of a target apparatus including a temperature of the target apparatus. A load specification unit specifies a load history of the target apparatus, based on the state quantity. A remaining life calculation unit calculates a parameter related to a remaining life of the target apparatus for each of a plurality of degradation types, based on the load history specified by the load specification unit.

A building management system includes connected equipment configured to measure a plurality of monitored variables and a predictive diagnostics system configured to receive the monitored variables from the connected equipment; generate a probability distribution of the plurality of monitored variables; determine a boundary for the probability distribution using a supervised machine learning technique to separate normal conditions from faulty conditions indicated by the plurality of monitored variables; separate the faulty conditions into sub-patterns using an unsupervised machine learning technique to generate a fault prediction model, each sub-pattern corresponding with a fault, and each fault associated with a fault diagnosis; receive a current set of the monitored variables from the connected equipment; determine whether the current set of monitored variables correspond with one of the sub-patterns of the fault prediction model to facilitate predicting whether a corresponding fault will occur; and determining the fault diagnosis associated with the predicted fault.