An apparatus includes vibration data obtainer 30 obtaining data on vibration caused by a tool in cutting, a vibration frequency calculator 22 calculating a vibration frequency of the tool by analyzing the obtained vibration data, a regenerative chatter detector 23 detecting whether regenerative chatter occurs by comparing the calculated vibration frequency of the tool with a spindle rotation number, and a spindle rotation controller 24 gradually decreasing or increasing the spindle rotation number when occurrence of regenerative chatter vibration is detected, and a stable rotation number calculator 25 monitoring variation of the vibration frequency of the tool and determining a spindle rotation number when the variation of the vibration frequency exceeds a predetermined reference value to be a stable rotation number.

Systems and methods for data collection and processing are described, including a plurality of variable groups of industrial sensor inputs operationally coupled to an industrial environment and a multiplexer communicatively coupled to the industrial sensor inputs; and a controller configured to receive and monitor the data and adaptively schedule the data collector.

A sensor device is coupled to a mechanical machine. The sensor device detects vibrations of the mechanical machine and transmits the vibration data to a remote processing device. The vibration data may be compressed prior to transmission. The remote processing device receives the data and generates a reconstructed version of the vibration data. The remote processing device includes a machine learning model trained to examine vibration data and to identify a motion pattern associated with an error condition. The machine learning model is applied to the reconstructed vibration data and detects an occurrence of an error condition in the mechanical machine. An alert indicating that an error condition has been detected is transmitted to a human operator. The human operator verifies the status of the mechanical machine and confirms that an error condition has occurred. In response to receipt of the confirmation, the machine learning model is further trained on training data updated to include the vibration data generated by the mechanical machine.

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 device, system, and method are provided for providing vibration data for rotating machinery. A sensor device is provided as a one-piece unit that is mechanically mounted to a pump. The sensor includes a vibration sensor, a processor, a wireless communications interface for exchanging data with a user device, and an internal battery. The processor is configured to receive a measurement request from the user device via the wireless communications interface. In response, the processor is further configured to configure the vibration sensor, receive data samples for multiple axes from the vibration sensor, and calculate a component velocity root mean square (vRMS) value, from the data samples, for each of the multiple axes. The processor may combine the component vRMS values into a sample vRMS value, and send a final vRMS value, based on the sample vRMS value, to the user device via the wireless communication interface.

A smart tool system may include at least one assembly of a tool holder and a tool, and a tooling machine configured to rotate the at least one assembly to cut a workpiece. The tooling machine may have a spindle to which the tool holder may be selectively attachable, and a controller configured to rotate the spindle at a spindle speed. The smart tool system may also include at least one database configured to store vibrational data relating to at least one of the at least one assembly and the tooling machine. The smart tool system may further be configured to determine an optimum operating value and/or range of optimum operating values of at least one parameter for the tooling machine based on the vibrational data. The optimum operating value(s) provide for minimized or no chatter when cutting the workpiece.

The present disclosure describes systems and methods for data collection in an industrial environment having self-organization functionality. A method can include analyzing a plurality of sensor inputs, sampling data received from the sensor inputs, and self-organizing at least one of (i) a storage operation of the data, (ii) a collection operation of sensors that provide the plurality of sensor inputs, and (iii) a selection operation of the plurality of sensor inputs. The method may further include receiving instructions directing a mobile data collector unit to operate sensors at a target, transmitting a communication to one or more other mobile data collector units regarding the instructions, and self-organizing a distribution of the mobile data collector unit.

The present invention acquires, from control data for an apparatus to be controlled in a normal operation, a relationship between a noise included in a reception signal from a cable and the apparatus to be controlled. An information processing device (13) is provided with a correlation derivation unit (13212, 13212d) for deriving a correlation value between a variation of a time-series data (1331) of a noise included in a signal input through a cable in a network and a variation of a time-series data (1332) of control data of an apparatus to be controlled (11, 16) in the network.

Systems and methods for data collection and detection of noise patterns. A system may include a data collector communicatively coupled to a plurality of input channels, wherein at least one of the plurality of input channels is operatively coupled to a vibration detection facility structured to detect a noise pattern of an industrial machine, a library to store the detected noise pattern, an interface circuit structured to make the noise pattern available to a noise pattern marketplace, the noise pattern marketplace including a plurality of noise patterns from a plurality of industrial machines, and a user interface for accessing the plurality of noise patterns of the noise pattern marketplace.

Monitoring systems and methods for data collection in an industrial environment are described. A system can include a first and second data collector coupled to input channels and a data acquisition circuit to interpret detection values corresponding to the input channels, wherein the sensor data is acquired from a first route of input channels. A data storage may store sensor specifications for sensors corresponding to the input channels and a data analysis circuit may evaluate the sensor data with respect to stored anticipated state information including an alarm threshold level. When the threshold is exceeded, a communication circuit communicates with a second data collector which transmits a state message related to a first input channel. A response circuit changes a routing of the input channels from a first routing to an alternate routing based on the state message from the second data collector.