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



Delta 3D printers have the potential to significantly increase throughput in additive manufacturing because they enable faster and more precise motion when 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 due to their coupled nonlinear dynamics which vary as a function of position.


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.


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.


<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.


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.




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.


The datasets are related to the findings and results of our investigations of the minimal force thresholds perception in robotic surgical applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system displays grasping tasks by a surgical grasper. The experiments included the display of a set of presentations in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). Sixty participants took part in these experiments and were asked to distinguish between consecutive presentations.


This folder contains the onboard sensor measurements for the EksoGT robotic exoskeleton during experiments with three able-bodied individuals and three non-able-bodied individuals with a spinal cord injury. The data is divided into .mat files by the trial. All able-bodied subjects completed three repetitions of each commanded intent change (Speed Up, Slow Down, and No Change) for each trial set.


Please see ReadMe.pdf for details about the dataset.


Acoustic measurement data from Multilayer Ceramic Capacitors (MLCCs). Contains preprocessed data from intact and damaged MLCCs for damage detection (classification) purposes.


Contains acoustic measurement data from 180 multilayer ceramic capacitors (2220 case size, 22 uF, 24V), soldered onto two test circuit boards. The measurements were performed by placing a piezoelectric point contact sensor on top of each capacitor, and subjecting the MLCC to a voltage frequency sweep from 100 Hz to 2 MHz over a duration of 100 ms. The resulting acoustic waveforms have been denoised, bandpass filtered, and downsampled. Furthermore, instantaneous phase response was calculated for each MLCC.

The dataset contains measurements from both intact and mechanically damaged components for quality assurance purposes (classification task). The acoustic signature of each MLCC is represented by an eight-dimensional feature vectror in the file inputs.mat:

  1. Acoustic emission amplitude at the highest resonace peak
  2. Frequency of the highest resonance peak
  3. Amplitude of the second-highest resonance peak
  4. Frequency of the second-highest resonance peak
  5. Total phase shift during frequency sweep
  6. Median amplitude of 10 of the highest resonance peaks
  7. Median frequency of 10 of the highest resonance peaks
  8. Mean group delay ripple calculated from the phase response of each component

The labels (0=no damage; 1=damage) for each component are found in targets.mat. Note that the labelling process was done by cross-sectioning each component and inspecting the sample visually under a microscope. Therefore, the labels may not be completely accurate, as the signs of damage can be difficult to observe.