A processing apparatus includes a holding table for holding a workpiece, a cutting unit including a spindle and a mount, a blade changer unit for mounting a cutting blade on or dismounting a cutting blade from the mount, and a control unit. The blade changer unit includes a blade chuck for holding the cutting blade and a moving unit. The cutting unit includes a vibration detecting sensor. The control unit includes a calculator for calculating the central axis of the mount from a position where the vibration detecting sensor detects vibrations caused upon contact between the mount and the blade chuck, and a mounting/dismounting controller for aligning the central axis of the blade chuck with the central axis of the mount and mounting the cutting blade on and dismounting cutting blade from the mount.

A disclosed system enables easy installation and removal of an in-machine robot for use in a machine tool and further enables easy replacement of a robot with another robot to suit an intended purpose. A machine tool system includes a machine tool with a cover that covers a machining space, and a robot unit on which an articulated robot is mounted. A portion of the cover of the machine tool has an opening to allow insertion of the articulated robot of the robot unit into the machining space through the opening. The articulated robot of the robot unit is inserted through the opening, and the opening is closed by a lid of the robot unit.

While monitoring the condition of a machine, vibration data is often collected for analysis by an experienced analyst. Systems and methods for analyzing vibration spectra associated with machine condition monitoring are disclosed herein. A system may be configured to collect vibration data from one or more vibration sensors, generate a vibration spectrum of the vibration data, and generate a spectral plot of the vibration spectrum. The system may receive a selection of a region of the spectral plot and generate a modifiable window of the vibration spectrum that is embedded within the spectral plot. The system may display a set of graphing tools along with the modifiable window that enable a user to make modifications to the window. The system may detect the modifications and update the modifiable window accordingly.

While monitoring the condition of a machine, vibration data is often collected for analysis by an experienced analyst. Systems and methods for analyzing vibration spectra associated with machine condition monitoring are disclosed herein. A system may be configured to collect vibration data from one or more vibration sensors, generate a vibration spectrum of the vibration data, and generate a spectral plot of the vibration spectrum. The system may receive a selection of a region of the spectral plot and generate a modifiable window of the vibration spectrum that is embedded within the spectral plot. The system may display a set of graphing tools along with the modifiable window that enable a user to make modifications to the window. The system may detect the modifications and update the modifiable window accordingly.

A method for automated condition monitoring whereby techniques of automated vibration analysis and signal processing are combined with deep learning/machine learning techniques for an enhanced system of automated anomaly detection, problem classification, and problem regression. The method may be implemented in software, firmware or hardware to run autonomously. Machines monitored and analyzed according to the disclosed method are typically found in industrial plants or commercial applications, but the disclosed invention may be applied to any rotating equipment such as motors, fans, pumps, compressors, and etc., in any environment where they are functioning.

A method for monitoring chatter in a machining process includes the following steps: collecting an original signal related to chatter in the machining process; for the original signal, obtaining a signal segment for calculation and analysis by updating data points in a sliding window with a set step-length, where the step-length refers to a number of data points updated every time in the sliding window, and is not greater than the size of the sliding window; calculating fractal dimensions of the signal segments in the sliding window by using a fractal algorithm; and comparing the calculated fractal dimension with an identification threshold to determine whether chatter occurs in the machining process. The measured signal does not need to be preprocessed by using the method, which can greatly improve calculation efficiency and can ensure accuracy of chatter identification.

A method for monitoring chatter in a machining process includes the following steps: collecting an original signal related to chatter in the machining process; for the original signal, obtaining a signal segment for calculation and analysis by updating data points in a sliding window with a set step-length, where the step-length refers to a number of data points updated every time in the sliding window, and is not greater than the size of the sliding window; calculating fractal dimensions of the signal segments in the sliding window by using a fractal algorithm; and comparing the calculated fractal dimension with an identification threshold to determine whether chatter occurs in the machining process. The measured signal does not need to be preprocessed by using the method, which can greatly improve calculation efficiency and can ensure accuracy of chatter identification.

A tool-mounting-state estimation system includes: a measuring tool including a body section having a tapered external surface common with a tapered external surface of a tool and a vibrating source that is mounted on the body section and that generates vibration; a sensor that is provided on a spindle for drawing the measuring tool thereinto so that the tapered external surface of the measuring tool comes into close contact with a tapered internal surface of the spindle and that detects the vibration generated by the vibrating source when the vibration propagates towards the spindle via the tapered external surface and the tapered internal surface; and an estimation unit for estimating a mounting state of the tool on the basis of the vibration detected by sensor.

A tool-mounting-state estimation system includes: a measuring tool including a body section having a tapered external surface common with a tapered external surface of a tool and a vibrating source that is mounted on the body section and that generates vibration; a sensor that is provided on a spindle for drawing the measuring tool thereinto so that the tapered external surface of the measuring tool comes into close contact with a tapered internal surface of the spindle and that detects the vibration generated by the vibrating source when the vibration propagates towards the spindle via the tapered external surface and the tapered internal surface; and an estimation unit for estimating a mounting state of the tool on the basis of the vibration detected by sensor.

Example implementations described herein are directed to systems and methods for extracting signal in the presence of noise for industrial IoT systems. Through the example implementations described herein, the high-frequency band signal of the sensor output can be maintained despite data compression, while also retaining information regarding the condition of the machine. Example implementations can also generate compressed data pairs to synchronized downsampled envelopes and spectrum data.