A vibration data acquisition and analysis module is operable to be inserted directly into a distributed control system (DCS) I/O backplane, so that processed vibration parameters may be scanned directly by the DCS I/O controller. Because the process data and the vibration data are both being scanned by the same DCS I/O controller, there is no need to integrate numerical data, binary relay outputs, and analog overall vibration level outputs from a separate vibration monitoring system into the DCS. The system provides for: (1) directly acquiring vibration data by the DCS for machinery protection and predictive machinery health analysis; (2) direct integration of vibration information on DCS alarm screens; (3) acquisition and display of real time vibration data on operator screens; (4) using vibration data to detect abnormal situations associated with equipment failures; and (5) using vibration data directly in closed-loop control applications.

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 electromechanical joining system that uses an output force or output torque for performing a joining method and includes an electrical drive connected for driving a screw drive and is configured for generating actual values of force or torque that are provided as input variables to a monitoring device. The system includes a sensor configured for measuring the course of the forces or torques over time during the joining method and for detecting additional measurement values that are supplied to the monitoring device as input variables. Wherein the monitoring device links the supplied actual values with the supplied additional measurement values to detect upcoming wear of a wear-prone component of the electromechanical joining system. A method for analyzing the status of the electromechanical joining system is also disclosed.

A method includes generating a model for predicting a remaining service life of a gripping member of a torque wrench based in part on a number of connections of one or more types of tubulars that are made or broken using the torque wrench before slippage of the torque wrench occurs, counting a first number of connections of a first type of tubular that are made using the torque wrench, predicting, using the model, the remaining service life of the gripping member of the torque wrench based in part on the first number of connections of the first type of tubular that were made using the torque wrench, and performing maintenance on the torque wrench based on the remaining service life of the gripping member of the torque wrench that is predicted, before slippage of the torque wrench occurs.

A machine tool includes a first motor that receives load fluctuation when the workpiece is processed and a second motor that operates to change the plural kinds of tools. An information processing device takes out the current of the first motor measured by the first current sensor for each signal that occurs at the changing operation of the plural kinds of tools and is measured by the second current sensor, and relatively compares a non-negative function value that has a current value at each taken-out segment as a parameter for each number of times of processing on the workpiece, thereby detecting a tool abnormality for each kind of the tool.

Systems, methods, and computer program products for remaining useful life prediction. Operational data is collected from a test machine until a component fails, and a training dataset generated from the operational data. The training dataset is used to define and validate a prediction model. Operational data received from one or more field machines is provided to the prediction model. The prediction model then predicts the remaining useful life of the component of the field machine. To reduce the time-to-failure of the component in the test machine, the component may be repeatedly subjected to an accelerated wear cycle. The prediction model may be defined by extracting features from the training dataset. Like features may be extracted from the field dataset and provided to the prediction model as part of the prediction process. The operational data received from the field machines may be used to generate an updated prediction model.

Method for monitoring wear to cylinders of the cages of a rolling mill, in particular for bars or rods, including the following steps: reading by a neural network of a plurality of data relating to the initial conditions of one or more rolling cylinders, in particular one or more pairs of cylinders each belonging to a rolling cage, to the settings, and to the running of the process; and generation by the neural network of signals relating to the state of wear of the cylinders.

A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.

A numerical control device is intended for a machine tool that machines a workpiece using a multi-edge tool including a plurality of edges of different specifications, the numerical control device including: a tool information memory that stores edge type numbers in association with tool type numbers; a tool type-edge type selection command decoding unit that prefetches a plurality of blocks of a machining program, decodes a tool type selection command for selecting one of the tool types and/or an edge type selection command for selecting one of the edge types in the plurality of prefetched blocks, and generates internal information including the tool type selection command and/or the edge type selection command that have been decoded; and a tool selection unit that selects one tool with which the number of times of tool replacement is minimized during execution of at least the plurality of prefetched blocks.

A tool diagnostic device includes a data acquisition unit configured to acquire time-series data related to a deterioration state of a drilling tool when a hole is machined, a diagnostic section extraction unit configured to extract diagnostic section time-series data acquired when machining is performed in a diagnostic section from a middle position to a machining end position of the hole from the time-series data acquired by the data acquisition unit, and a deterioration diagnostic unit configured to diagnose deterioration of the drilling tool using the diagnostic section time-series data extracted by the diagnostic section extraction unit.