A remaining life of a robot body is precisely estimated. A robot control apparatus 300 includes: a drive control unit 305 that controls drive of a robot body 200; a detection unit 306 that detects a feature amount quantitatively indicating a deterioration degree of the robot body 200 that is deteriorated over time as the robot body 200 is driven; a determination unit 303 that determinates presence/absence of a sign of malfunction of the robot body 200 based on the feature amount; and an estimation unit 304 that estimates a remaining life of the robot body 200 when presence of a sign of malfunction of the robot body 200 is determined.

A system includes a computer numerical control (CNC) machining system configured to perform a machining operation to define a feature on a workpiece and a machine edge controller disposed external of the machining system and in communication with the CNC machine system. The machine edge controller is configured to perform a machining evaluation during the machining operation. In executing the machining evaluation, the machine edge controller is configured to acquire data indicative of characteristics of the CNC machining system during the machining operation and compare the data with one or more machining baseline parameters associated with the machining operation to determine an abnormal operation of the CNC machining system. The one or more machining baseline parameters define a nominal response of the CNC machining system for performing the machining operation.

A data collection device includes: a setting information storage unit storing setting information respectively associated with a plurality of cooperation levels set in accordance with a degree of cooperation with a higher system; a life-and-death monitoring unit monitoring a state of the cooperation with the higher system; a cooperation level determination unit determining the level of the cooperation with the higher system; a setting switching unit switching an operation setting of each function in accordance with the setting information corresponding to the cooperation level; a data collection unit collecting data in accordance with the operation setting switched by the setting switching unit; a data processing unit executing predetermined processing with respect to the data; and a data output unit outputting the data to a designated output destination.

A diagnostic apparatus (10) diagnoses the existence of an abnormality in a tool for processing of a processing target by each of multiple working machines. The diagnostic apparatus (10) includes an acquirer (110) and a diagnoser (160). The acquirer (110) acquires program identification information identifying a program executed in each of the working machines, tool information indicating a type of the tool applied to processing, and transition information indicating transition of load in the working machine from the start to the end of the processing executed by execution of the program, at execution of the processing in the working machine. The diagnoser (160) diagnoses whether an index value of the load obtained from the transition information is out of a predetermined range corresponding to a combination of the program identification information, the tool information, and machine information identifying the working machine that transmits the transition information.

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.

A method and system utilizing multi-sensor analysis and data point correlation is provided for predictive monitoring and maintenance of a pressurized fluid cutting system. In a disclosed aspect, multiple sensed characteristics of system operation are correlated to determine a particular failure mode. Identification of the failure mode through active sensor data analysis and correlation facilitates predictive maintenance, minimizes system downtime, and optimizes system output.

There is provided a steam turbine startup support system capable of easily selecting a proper startup transition pattern from various startup transition patterns. In a steam turbine startup support system of the embodiment, an economic efficiency evaluation device performs economic efficiency evaluation regarding the various startup transition patterns recorded in a startup transition pattern recording device based on parameters recorded in a parameter recording device and information relating to a rotor lifetime recorded in a rotor lifetime recording device. Besides, a screen display device displays a result of the economic efficiency evaluation performed by the economic efficiency evaluation device.

The invention relates to a method for monitoring a milling process of a printed circuit board, having the steps of: (a) detecting (S1) the rotational speed of a milling head (2) of a milling machine (1) and at least one other operating parameter of the milling machine (1) during the milling process, wherein the other operating parameter is an electric supply current for operating the milling machine, and (b) analyzing (S2) the detected rotational speed and the detected operating parameter using a trained adaptive algorithm for detecting anomalies during the milling process.

A self-aware machine platform is implemented through analyzing operational data of machining tools to achieve machine tool damage assessment, prediction and planning in manufacturing shop floor. Machining processes are first identified by matching similar processes through an ICP algorithm. Machining processes are further clustered by Hotelling's T-squared statistics. Degradation of the machining tool is detected through a trend of the operational data within a cluster of machining processes by a monotonicity test, and the remaining useful life of the machining tool is predicted through a particle filter by extrapolating the trend under a first-order Markov process. In addition, process anomalies across machines are detected through a combination of outlier detection methods including SOMs, multivariate regression, and robust Mahalanobis distance. Warnings and recommendations are flexibly provided to manufacturing shop floor based on policy choice.

An operation management method for machine tools is provided for collectively managing a plurality of events generated by a series of events. The operation management method for the machine tools includes a collecting step of monitoring an operation state of the machine tools, and collecting events that are generated by operations thereof, and a grouping step of dividing into a plurality of groups a collected plurality of the events and storing the same in a storage medium. In the grouping step, at each time that a mode which is set in the machine tools is switched, one event or two or more events that are generated during the set mode are grouped together and stored.