An information processing device according to one aspect of the present invention includes a first acquirer configured to acquire measured data of a sensor, a second acquirer configured to acquire maintenance information related to maintenance performed on the sensor, a learner configured to learn teacher data in which the acquired maintenance information as label information is associated with the acquired measured data to generate a determination model, and a storage storing the generated determination model.

An analysis control device controls an analysis based on time series data for each of a plurality of sensors corresponding to a plurality of components that constitute a target device. The analysis control device acquires sensor data sets belonging to an analysis target time zone among the time series data of each of the plurality of sensors. Each sensor data set includes measurement values measured by a sensor. The analysis control device calculates an evaluation value according to a simple evaluation by using two or more sensor data sets corresponding to the sensor among the plurality of sensor data sets belonging to the analysis target time zone. The analysis control device sets an execution order of the analysis based on the measurement values of the sensor within a restricted time corresponding to the analysis target time zone in a descending order of the calculated evaluation value.

Event-related information is acquired, and a judgment is made as to whether a particular event is detected. When a particular event is detected, direction information indicating a first direction is acquired, and first recognition result information is acquired as to a result of a recognition process as to an object located outside a vehicle. Furthermore, a judgment is made as to whether the first recognition result information includes a result of object recognition in the first direction and as to whether existence of a specific object in the first direction is detected. In a case where it is judged that the first recognition result information indicates that object recognition in the first direction is performed and existence of a specific object in the first direction is not detected, process completion information indicating that the vehicle has performed the recognition process in the first direction is generated and output.

An embodiment includes duplicating an input dataset being input to a model predictive control (MPC) module for input to a first Hierarchical Temporal Memory (HTM) network. The embodiment also includes generating system behavior data using the MPC module for characteristic data of the input dataset. The embodiment also includes generating first HTM prediction data from the input dataset and the system behavior data using the first HTM network, the first HTM prediction data comprising predictions for respective dimensions of the system. The embodiment also includes generating second HTM prediction data from the first HTM prediction data and system output data using a second HTM network, the second HTM prediction data comprising a distinction between the first HTM prediction and the system output data. Finally, the embodiment includes determining that the distinction of the second HTM prediction data indicates an anomaly and adjusting system input data based on the anomaly.

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 monitoring apparatus of a plurality of equipment devices displays, on a state display screen, an operation state of each of the plurality of equipment devices in summary, and includes a display control unit configured to display, on the state display screen, information indicating each of the plurality of equipment devices and information relating to control that changed the operation state of the equipment device, in association with each other.

An abnormality diagnosis device diagnoses an abnormality of a plurality of speed reducers included in a robot in accordance with disturbance torque regarding a state of the respective speed reducers acquired from a sensor installed in the robot, and outputs a result of the diagnosis to a display unit, the abnormality diagnosis device including a maintenance history DB configured to store maintenance data on maintenance made for the respective speed reducers, and a control unit configured to detect an abnormality in the respective speed reducers in accordance with the disturbance torque. The control unit, when detecting an abnormality in one speed reducer in accordance with the disturbance torque, predicts an abnormality in another speed reducer caused in association with the abnormality in the one speed reducer in accordance with the maintenance data, and outputs information on the predicted abnormality to the display unit.

A system including a deep learning processor obtains response data of at least two data types from a set of process stations performing operations as part of a manufacturing process. The system analyzes factory operation and control data to generate expected behavioral pattern data. Further, the system uses the response data to generate actual behavior pattern data for the process stations. Based on an analysis of the actual behavior pattern data in relation to the expected behavioral pattern data, the system determines whether anomalous activity has occurred as a result of the manufacturing process. If it is determined that anomalous activity has occurred, the system provides an indication of this anomalous activity.

A device for outputting a future state of a central lubrication system includes at least one sensor for recording a parameter of the central lubrication system, a processing unit for processing the recorded parameter, determining a current state of the central lubrication system based on the processed parameter, and estimating a future state of the central lubrication system over a certain period of time based on the current state and stored data, and an output unit for outputting the future state of the central lubrication system.

A method of salvaging an output is provided. The method includes defining a condition for terminating a run of a tool, checking whether the condition is likely to be met during a running of the tool, terminating the running in an event the condition is likely to be met, checking a validity of an incomplete output of the tool generated during the running and finalizing the incomplete output in an event the incomplete output is valid.