A method for determining a dynamic response of a machine having at least one axis, including performing a measurement run for each axis of the machine over an entire work area of each respective axis, capturing and recording data associated with each measurement run, determining a time-frequency representation of recorded data using a data processing unit, and analyzing the time-frequency representation or a related representation using an image processing algorithm.

A method of monitoring movement of a robotic arm, the robotic arm being arranged to be moved by an actuator, the method comprising: determining an expected robotic arm condition based on a known robotic condition and a torque applied to the robotic arm by the actuator; measuring an actual robotic arm condition during movement of the arm caused by the applied torque; and determining whether a collision has occurred by comparing the actual robotic arm condition with the expected robotic arm condition and generating a collision signal if a difference between the actual robotic arm condition and the expected robotic arm condition exceeds a threshold.

Spectral machine condition energy peaks are graphically represented in a spectral plot using color coding, different line types, and/or filtering. This allows visual differentiation of spectral peaks associated with various fault frequency families from one another, whereby a machine condition analyst using computer-based analysis software can easily see each family of spectral peaks individually, without all the other spectral peaks, or in combinations of families that are relevant to a machine fault under investigation. In addition to current spectral data, the analyst can also view a historical trend of related scalar parameters plotted in conjunction with current spectral data, wherein the spectral data plot is synchronized with a time-based cursor on the trend plot.

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 failure prediction method of predicting a failure of a component of a robot including a robot arm having the component and a detection section that detects information on vibration characteristics when the robot arm moves, includes generating a failure prediction model for prediction of the failure of the component by machine learning based on the information on vibration characteristics, and predicting the failure of the component based on an estimated value of failure prediction output by the generated failure prediction model when the information on vibration characteristics is input to the generated failure prediction model.

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.

Spectral machine condition energy peaks are graphically represented in a spectral plot using color coding, different line types, and/or filtering. This allows visual differentiation of spectral peaks associated with various fault frequency families from one another, whereby a machine condition analyst using computer-based analysis software can easily see each family of spectral peaks individually, without all the other spectral peaks, or in combinations of families that are relevant to a machine fault under investigation. In addition to current spectral data, the analyst can also view a historical trend of related scalar parameters plotted in conjunction with current spectral data, wherein the spectral data plot is synchronized with a time-based cursor on the trend plot.

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.

An online monitoring system for industrial processes, such as nuclear power processes, including a data acquisition unit configured to sample output signals simultaneously from a plurality of process sensors, and a computing unit configured to record sampled output signals from the data acquisition unit and to cross-correlate the output signals from two or more of the process sensors to diagnose operation of the industrial process, identify loose parts and/or degradation of industrial plant equipment, enable virtual sensing, calculate sensor response time using the noise analysis technique, and to verify sensor calibration using the cross calibration method and/or empirical and/or physical modeling.

A control device includes a processor wherein the processor is configured to: receive designation of one or more frequency components, generate one or more second control signals obtained by reducing at least one of the frequency components from a first control signal, generate one or more third control signals obtained using two control signals among the first control signal and the one or more second control signals, output one control signal among the first control signal, the one or more second control signals, and the one or more third control signals, and generate and output a driving signal to drive a robot based on the one control signal.