This document has description of acoustic and vibration data of defect cases of centrifugal pump,  Test rig facility, sensor, and data acquisition device located at Precision Metrology Laboratory, Mechanical Engineering Department of Sant Longowal Institute of Engineering and Technology Longowal, India.  

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Recently, there has been a notable shift to increasingly more sedentary lifestyles, yet it remains unclear whether inexpensive accelerometers, such as those contained in Actigraph devices, can provide consistent measurements of low magnitude accelerations. This study sought to explore the ability of two Actigraph models to differentiate low frequency oscillations, in reference to higher-end accelerometers with the idle sleep mode disabled (ISM) (Part 1) and enabled (Part 2) in a controlled environment.

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The measurement and diagnosis of the severity of failures in rotating machines allow the execution of predictive maintenance actions on equipment. These actions make it possible to monitor the operating parameters of the machine and to perform the prediction of failures, thus avoiding production losses, severe damage to the equipment, and safeguarding the integrity of the equipment operators. This paper describes the construction of a dataset composed of vibration signals of a rotating machine.

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The dataset includes the sweep scanning paths and measured points in two experiments.

 

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Delta 3D printers can significantly increase throughput in additive manufacturing by enabling faster and more precise motion compared to traditional serial-axis 3D printers. Further improvements in motion speed and part quality can be realized through model-based feedforward vibration control, as demonstrated on several serial-axis 3D printers. However, delta 3D printers have not benefited from model-based controllers because their position-varying, coupled nonlinear dynamics are difficult to model accurately.

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The PHM Data Challenge is a competition open to all potential conference attendees. This dataset is from the challenge and focused on RUL estimation for a high-speed CNC milling machine cutters using dynamometer, accelerometer, and acoustic emission data.

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This is a dataset of 32 five-second-long vibration recordings. One human used a metal tool to perform one of two tool-mediated surface interactions (tapping or dragging) on the following four different surfaces: sandpaper (hard and rough), acrylic plastic (hard and smooth), rough paper (soft and rough), and rubber (soft and smooth). Each of the eight combinations of interaction and surface were recorded four times.

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<p>The technique of electrical impedance tomography (EIT) has been recognized as a promising method to design tactile sensors with continuous sensing capability over a large area. The mechanism of electrical impedance tomography allows reconstructing tactile information within the sensing area based on measurements made only at the boundary. However, spatial performance of EIT-based tactile sensors has demonstrated location dependency in previous reports, which severely affects correct interpretation of tactile stimuli.

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The objective of this dataset is the fault diagnosis in diesel engines to assist the predictive maintenance, through the analysis of the variation of the pressure curves inside the cylinders and the torsional vibration response of the crankshaft. Hence a fault simulation model based on a zero-dimensional thermodynamic model was developed. The adopted feature vectors were chosen from the thermodynamic model and obtained from processing signals as pressure and temperature inside the cylinder, as well as, torsional vibration of the engine’s flywheel.

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Instructions: 

This dataset is used for i) analyzing the influence of process information on monitoring signals through signal processing methods; ii) training and testing models of tool monitoring and tool wear prediction especially for cutting conditions with large variations including cutting parameters, material and geometry of cutting tools, and workpiece materials, and also cutting conditions with continuous changes. This data set includes monitoring signals collected from machining process of sidewalls and closed pockets. The sidewall machining belongs to the cutting process with fixed cutting conditions; the closed pocket machining belongs to the cutting process of continuously varying cutting conditions for the reason that the tool path of closed pocket includes line, arc, full cutting and non-full cutting. Although cutting parameters are given fixed in the arc tool path area, the actual cutting parameters (such as feed, cutting width) are constantly changing due to the change of cutting geometry.

NUAA_Ideahouse data set is tool wear data under variable cutting conditions, the copyright is reserved by NUAA Ideahouse, when you use this data, please also refer the following paper, where the dataset is firstly published: "Liu C, Li Y, Li J, Hua J. A meta-invariant feature space method for accurate tool wear prediction under cross-conditions[J]. IEEE Transactions on Industrial Informatics, 2021. "

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