A plant operation data monitoring device comprises: an input section that receives operation data on a plant; and a calculator that includes databases storing the operation data received, and a computing section executing a program. The computing section stores the operation data received in a first database of the databases in time series. The computing section determines from peak values of the operation data stored whether gradients of the operation data are positive or negative, and then stores the gradients in a second database of the databases for positive gradients or in the second database of the databases for negative gradients in time series. The computing section determines threshold values for abnormality determination about the positive and negative gradients, divides the positive gradients and the negative gradients into normal values and abnormal values, and additionally stores the divided gradients in the second database for the positive or negative gradients.

A system of digitally representing equipment failure comprising: a testing assembly for testing a component of a manufacturing equipment wherein the component includes a defect in a critical area included in the component; a sensor in communications with the testing assembly for sensing a failure state of the component, a set of computer readable instructions adapted for: receiving a critical failure mode associated with the component, receiving a testing dataset from the sensor representing testing results produced by the testing assembly wherein the testing dataset includes initial data representing an undamaged component and a failure dataset representing a failed component, isolating a set of failure data representing a testing status of the component from initial testing to failure of the component determined by the critical failure mode, creating a usable lifetime model of the component according to the set of failure data.

The preset invention is directed to a building energy management system and method, more specifically to a building energy management system and method that can quickly detect and control an abnormal situation or an emergency situation that may occur within a building.

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.

Various embodiments include a system having: at least one computing device configured to monitor a combined-cycle (CC) power plant during a transient event by performing actions including: determining whether a change in an operating condition of a component of the CC power plant is unintentional, the determining including comparing control system instructions for the component of the CC power plant with a reference look-up table, the reference look-up table including correlation data for the control system instructions for the component and historical data about the operating condition of the component; and providing instructions to a control system of the CC power plant to modify the operating condition in the CC power plant in response to determining that the change in operating condition of the component is unintentional.

The present disclosure discloses a control method of industrial Internet of Things (IoT) based on abnormal identification. The IoT includes: an obtaining unit, which is configured to obtain a first machining parameter; a detection unit, which is configured to obtain real-time image data when the first machining parameter is abnormal; an extraction unit, which is configured to obtain a keyframe and obtain a second machining parameter; a judgment unit, which is configured to determine an abnormal cause based on the first machining parameter and the second machining parameter; and a communication unit, which is configured to transmit the abnormal cause to a user terminal through a service platform.

Provided is an embedding analysis-based facility predictive maintenance method performed by a server, including (a) collecting time-series operation data of at least one machine; (b) deriving abnormal state information to determine whether the collected time-series operation data deviates from a time-series threshold, deriving an embedding result pattern through embedding analysis on the collected time-series operation data, and mapping the embedding result pattern and the abnormal state information; (c) building an abnormal pattern analysis model by performing machine learning on each mapped information and analyzing whether the embedding result pattern indicates abnormality or normality; and (d) when new time-series operation data is collected, applying the new time-series operation data to the abnormal pattern analysis model to derive current state information and future prediction state information.

To provide a diagnostic service system and diagnostic method using a network. A factory monitoring system (100) comprises a factory monitoring system (100) which includes: a data acquisition unit (1011) that acquires data related to at least one machine, including time information; and a stored data management unit (1012) that stores data related to each machine acquired by the data acquisition unit in a storage unit (1002) together with identification information of each machine, wherein, based on past history data related to the machine and current data related to the machine, the diagnostic service system (1) predicts a possibility of abnormality occurrence in the machine, and provides preventative maintenance information related to the machine.

In one embodiment, a system may include a multi-purpose sensor coupled to a machine operating in an industrial environment. The multi-purpose sensor may include a camera that obtains a first and second set of image data including images of the machine and an environment surrounding the machine. The first set of image data is associated with a baseline of the machine and the environment, and the second set of image data is acquired subsequent to when the first set is acquired. The system may include a computing device that may include a processor to receive the first and second set of image data, determine baseline positions of objects in the first set, determine subsequent positions of the objects in the second set, determine whether the subsequent positions vary from the baseline positions, and perform an action when the subsequent positions vary from the baseline positions.

An automated prognostics system includes a sensor system configured to obtain measurement data by monitoring one or more parameters at one or more locations on each of one or more movable components of an object when the object is subjected to a first mode of operation. The system also includes a computing device having an input interface and a processor. The input interface receives input information such as spatial coordinates information associated with the one or more locations, the measurement data obtained by the sensor system, operational data associated with the first mode of operation, and structural data associated with the object. The processor processes the input information and generates a prognostics report on at least a first component of the one or more movable components, the prognostics report including at least one of a failure prognostic or a likelihood-of-failure prognostic of the first component.