A prediction method of part surface roughness and tool wear based on multi-task learning belong to the file of machining technology. Firstly, the vibration signals in the machining process are collected; next, the part surface roughness and tool wear are measured, and the measured results are corresponding to the vibration signals respectively; secondly, the samples are expanded, the features are extracted and normalized; then, a multi-task prediction model based on deep belief networks (DBN) is constructed, and the part surface roughness and tool wear are taken as the output of the model, and the features are extracted as the input to establish the multi-task DBN prediction model; finally, the vibration signals are input into the multi-task prediction model to predict the surface roughness and tool wear.

An encoder apparatus includes an encoder and a processor. The encoder includes a scale portion, a light emitting portion, and a light receiving portion. The scale portion includes a first track and a second track. The processor obtains a plurality of candidate values of a gap between the light emitting portion and the scale portion on a basis of an amplitude of a first signal obtained by receiving light reflected on or transmitted through the first track by the light receiving portion. The processor determines a measured value from among the plurality of candidate values on a basis of an amplitude of a second signal obtained by receiving light reflected on or transmitted through the second track by the light receiving portion.

Example implementations described herein involve systems and methods for conducting feature extraction on a plurality of templates associated with vibration sensor data for a moving equipment configured to conduct a plurality of tasks, to generate a predictive maintenance model for the plurality of tasks, the predictive maintenance model configured to provide one or more of fault detection, failure prediction, and remaining useful life (RUL) estimation.

System, equipment, and procedure for monitoring, predictive maintenance, and operational optimization of vibrating screeners represented by an inventive solution in the industry and trade of vibrating equipment, with mechanically-driven vibration technology, with particular application to vibrating screening (Pe) equipment (Eq), aiming to monitor operational parameters, foresee the deterioration of the structural conditions of the equipment (Eq), so as to increase the interaction between maintenance and production engineering of the company, where, for such purpose, a system has been conceived whose architecture is composed of the following modules: hardware module (GHW), intelligence generation module (GI), data persistent layer module (CPD), and event management module (Ge), resulting in the conversion of the equipment (Eq)'s operational needs into a description of the performance parameters with functional analysis, synthesis, modeling, simulation, optimization, design, testing, and evaluation, integrating the performance parameters with the other requirements in the modeling process.

A control unit causes a sensor to start sampling at a plurality of start timings different from each other. The control unit combines sampling signals obtained by the start of the sampling at the different start timings, and detects a malfunction of an apparatus in accordance with the combined sampling signal.

A vibration analysis apparatus includes: a storage unit storing a regression equation indicating correspondence between rotation speed change of a rotation mechanism and a peak occurrence frequency of acceleration of vibration, for each acceleration peak; an analysis unit extracting a peak occurrence frequency of acceleration of vibration for each acceleration peak, based on vibration data of the rotation mechanism and calculating, for each acceleration peak, a waveform area of the acceleration peak by integrating the acceleration peak over a specific frequency section; and an anomaly determination unit determining whether operational anomaly occurs in the rotation mechanism for each acceleration peak. The analysis unit tracks, in accordance with the regression equation, change in peak occurrence frequency due to rotation speed change when vibration of the rotation mechanism is analyzed, and calculates, for each second acceleration peak, the waveform area of the second acceleration peak corresponding to the second vibration frequency tracked.

The present disclosure provides a system and method for determining condition of wafers during processing of the wafers. The system and method include detecting vibrations of a wafer transfer robot, generating signals based upon the vibrations, and processing the signals for determining a condition of the wafers held by the wafer transfer robot.

An information processing device is connected to a measurement device provided in the vicinity of a machine tool for machining a workpiece using a tool and a numerical control device configured to control the machine tool. The information processing device includes: a display unit configured to display information; a first acquisition unit configured to acquire, from the measurement device, measurement information measured by the measurement device; a second acquisition unit configured to acquire, from the numerical control device, state information indicating a state of the machine tool; and a display control unit configured to cause the display unit to display the measurement information and the state information.

A machining method for machining workpieces preferably consisting at least in sections of wood, wood materials, plastic or the like on a machining device, wherein a vibration state of the machining device is detected during a machining process, a closed-loop or open-loop control towards a lower or preferably optimal vibration state of the machining device is performed while the machining process is continued.

Methods, systems, and devices for analyzing vibration data and for identifying and tracking vibration anomalies in industrial machines are described. In various embodiments, the system described herein collects, transforms, and analyzes sensor data from one or more machines, such as industrial machines. The system may identify one or more sensors that are experiencing vibrational anomalies. In various embodiments, the system: collects and analyzes vibration data for a set of one or more vibration-related sensors of one or more industrial machines; determines an occurrence of one or more anomalies based on the vibration data as compared to a threshold; tracks anomalies in collected vibration data for the one or more industrial machines of the facility; generates a report of vibration data for the one or more vibration-related sensors of the facility; and reports industrial machines in the facility that may deviate from a target performance.