公开(公告)号：US20240361276A1

公开(公告)日：2024-10-31

申请号：US18556357

申请日：2022-12-07

Applicant: ZHEJIANG UNIVERSITY

Inventor： Di CUI ,  Chengqiao DING ,  Dachen WANG

IPC: G01N29/06 ,  G01N29/24 ,  G01N29/44 ,  G01N33/02 ,  G06F17/14

CPC classification number: G01N29/069 ,  G01N29/2418 ,  G01N29/4418 ,  G01N29/4481 ,  G01N33/025 ,  G06F17/148 ,  G06F2218/06

Abstract: Disclosed are a nondestructive detection system and a method for an internal defect of a fruit. The system comprises an aluminum profile frame, a conveyor belt, a tray, a pulse type gas spray device, and a laser Doppler vibrometer; when a piece of fruit passes a detection station, the pulse type gas spray device excites the fruit to vibrate, and the laser Doppler vibrometer evaluates a vibration response signal of the fruit; a time-domain vibration characteristic parameter and a frequency-domain vibration characteristic parameter are acquired by means of a wavelet transform and a fast Fourier transform; and a prediction model for an internal defect of the fruit is established on the basis of the acquired time-domain and frequency-domain vibration characteristic parameters.

公开(公告)号：US20230408451A1

公开(公告)日：2023-12-21

申请号：US18331253

申请日：2023-06-08

Applicant: DOOSAN ENERBILITY CO., LTD

Inventor： June Sung SEO ,  Jun Woo Yoo ,  Jung Min Lee

IPC: G01N29/06 ,  G01N29/44

CPC classification number: G01N29/0645 ,  G01N29/4481 ,  G01N2291/023

Abstract: The embodiments of the present disclosure relate to an ultrasonic flaw-detection system and an ultrasonic flaw-detection method. The ultrasonic flaw-detection system may include: an ultrasonic flaw-detection device configured to transmit an ultrasonic wave to a detection target, collect an ultrasonic echo wave reflected from the detection target, and then generate a signal data; a signal data preprocessor configured to preprocesses the signal data; a defect candidate group selection unit configured to select a defect candidate group based on the preprocessed signal data and generate defect candidate signal data based on the selection; an image data generator configured to generate image data based on the defect candidate signal data included in the defect candidate group; and a defect determination unit configured to determine whether there is a defect in the defect candidate group based on the image data.

公开(公告)号：US20230258611A1

公开(公告)日：2023-08-17

申请号：US18106669

申请日：2023-02-07

Applicant: Schlage Lock Company LLC

Inventor： Daniel Langenberg ,  Nathanael L. Thomas

IPC: G01N29/44 ,  G01C19/5776 ,  G01P15/08 ,  G01P13/00 ,  G01N29/14

CPC classification number: G01N29/4454 ,  G01C19/5776 ,  G01P15/0802 ,  G01N29/4481 ,  G01P13/00 ,  G01N29/14 ,  G01N2291/0258

Abstract: A method according to one embodiment includes receiving sensor data from a plurality of sensors of a door device associated with a door, analyzing the sensor data to determine behavior data indicative of a behavior of the door device, and comparing the behavior data to a plurality of representative data associated with a plurality of door faults to determine a corresponding likelihood that the sensor data corresponds with each of the door faults.

公开(公告)号：US20180088084A1

公开(公告)日：2018-03-29

申请号：US15278964

申请日：2016-09-28

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Amos Cahan ,  Theodore G. van Kessel

IPC: G01N29/04 ,  G01N29/44 ,  G01N29/46 ,  G01N33/02

CPC classification number: G01N29/043 ,  G01N29/226 ,  G01N29/4436 ,  G01N29/4472 ,  G01N29/4481 ,  G01N29/46 ,  G01N33/02 ,  G01N2291/0235 ,  G01N2291/0255 ,  G01N2291/02827

Abstract: Measuring and/or monitoring food doneness using ultrasound. The present invention provides a method, system, computer program product and sensor for measuring and/or monitoring the degree of food doneness. The present invention provides means for non-invasively and continuously determining the degree of food doneness using ultrasound technology. In general, the present invention works by applying ultrasound signals to a food item, receiving the ultrasound signals emitted back from the food item, and analyzing the input and output signals to determine the degree of food doneness. The food doneness sensor can be a stand-alone device, embedded into a cooking tool, embedded into a cooking apparatus, and/or embedded into a food production assembly line. The present invention can be used personally/commercially. User feedback regarding the performance of the present invention can be provided to a cloud database and used to modify/adjust the measuring/monitoring process. Finally, multiple ultrasound sensors/transponders can be coupled together.

公开(公告)号：US20170293862A1

公开(公告)日：2017-10-12

申请号：US15481575

申请日：2017-04-07

Applicant: FANUC CORPORATION

Inventor： Yohei KAMIYA ,  Yasuo YAMADA

IPC: G06N99/00 ,  G06N5/04 ,  H04L29/08 ,  G01M15/02 ,  G01N29/44

CPC classification number: G06N20/00 ,  G01M15/02 ,  G01M15/14 ,  G01N29/14 ,  G01N29/4436 ,  G01N29/4445 ,  G01N29/4454 ,  G01N29/4481 ,  G01N2291/0258 ,  G01N2291/2696 ,  G06N5/04 ,  H04L67/10

Abstract: A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.

公开(公告)号：US20170168024A1

公开(公告)日：2017-06-15

申请号：US14969893

申请日：2015-12-15

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Ehsan DEHGHAN NIRI ,  Curtis Wayne ROSE ,  Andrew Batton WITNEY

IPC: G01N29/14 ,  G06N5/04 ,  G06N99/00 ,  G01N29/44 ,  G01R31/34

CPC classification number: G01N29/14 ,  G01N27/92 ,  G01N29/2418 ,  G01N29/4445 ,  G01N29/4481 ,  G01N2291/0258 ,  G01N2291/0289 ,  G01N2291/2638 ,  G01R31/1227 ,  G01R31/343 ,  G06N5/047 ,  G06N99/005

Abstract: A monitoring system includes an acoustic emission monitoring system including acoustic emission sensors, a partial discharge monitoring system including partial discharge sensors and synchronized with the acoustic emission monitoring system, and a computer receiving acoustic emission data from the acoustic emission sensors and electrical data from the partial discharge sensors. The computer is configured to classify a first statistical event as a fatigue cracking event by pattern recognition of the acoustic emission data and determine a first location and a first damage condition resulting from the fatigue cracking event, classify a second statistical event as a partial discharge event by pattern recognition of the acoustic emission data or the electrical data, and fuse the acoustic emission data and the electrical data for the second statistical event and determine a second location and a second damage condition resulting from the partial discharge event. Methods of monitoring are also disclosed.

公开(公告)号：US08781632B2

公开(公告)日：2014-07-15

申请号：US13002312

申请日：2009-07-09

Applicant: Bahman Tohidi ,  Jinhai Yang ,  Antonin Chapoy

Inventor： Bahman Tohidi ,  Jinhai Yang ,  Antonin Chapoy

IPC: G05B21/02

CPC classification number: G01N29/024 ,  G01N27/06 ,  G01N29/326 ,  G01N29/4481 ,  G01N2291/0215 ,  G01N2291/0224

Abstract: A method for analyzing a fluid containing one or more analytes of interest includes; measuring a plurality of properties of a sample fluid with unknown concentrations of the one or more analytes of interest; and using the measurements and a model of the relationship between the plurality of properties and concentrations of the one or more analytes to calculate the concentration of at least one of the analytes of interest. The model may be an artificial neural network. The method may be used to monitor the concentration of inhibitors of gas hydrate formation in a fluid. Apparatus for use in the method is also provided.

Abstract translation: 用于分析含有一种或多种感兴趣的分析物的流体的方法包括： 测量具有未知浓度的一种或多种感兴趣分析物的样品液体的多种性质; 以及使用所述测量和所述一种或多种分析物的所述多种性质和浓度之间的关系的模型来计算所述目的物中至少一种分析物的浓度。 该模型可能是人造神经网络。 该方法可用于监测流体中天然气水合物形成抑制剂的浓度。 还提供了用于该方法的装置。

公开(公告)号：US20140165729A1

公开(公告)日：2014-06-19

申请号：US13728410

申请日：2012-12-27

Applicant: Korea Institute of Machinery & Materials

Inventor： Hyun Sup JI ,  Jong O Lee ,  No Hoe JU

IPC: G01N29/14

CPC classification number: G01N29/14 ,  G01N29/4481 ,  G01N2291/0231 ,  G01N2291/0258

Abstract: An acoustic emission diagnosis device is provided for a gas vessel using a probabilistic neural network, and a method of diagnosing a defect of the gas vessel using the same, in which acoustic emission signal sensors are attached to multiple portions of the gas vessel. Acoustic emission signals are detected when filling the inside of the gas vessel with gas, when holding the pressure after filling, and when decreasing the pressure. Features in which the detected acoustic emission signals are varied are extracted, and a damaged degree of the gas vessel is determined using the probabilistic neural network that has been trained through a classification learning algorithm for the extracted features.

Abstract translation: 为使用概率神经网络的气体容器提供声发射诊断装置，以及使用该声发射诊断装置的气体容器的缺陷的诊断方法，其中声发射信号传感器附着在气体容器的多个部分。 当气体充满气体时，在充填后保持压力时，以及当压力降低时，检测出声发射信号。 提取检测到的声发射信号变化的特征，并且使用已经通过用于提取的特征的分类学习算法训练的概率神经网络来确定气体容器的损坏程度。

公开(公告)号：US08146429B2

公开(公告)日：2012-04-03

申请号：US12534581

申请日：2009-08-03

Applicant: Ifeanyi Charles Ume ,  Renfu Li ,  Matthew Rogge ,  Tsun-Yen Wu

Inventor： Ifeanyi Charles Ume ,  Renfu Li ,  Matthew Rogge ,  Tsun-Yen Wu

IPC: G01N29/04

CPC classification number: G01N29/4445 ,  G01N29/11 ,  G01N29/4481 ,  G01N2291/0258 ,  G01N2291/0427 ,  G01N2291/2675

Abstract: A method for determining the type of a defect in a weld may include determining a defect location and a corresponding defect signal by analyzing ultrasonic response signals collected from a plurality of measurement locations along the weld. The defect signal and the plurality of defect proximity signals corresponding to ultrasonic response signals from measurement locations on each side of the defect location may then be input into a trained artificial neural network. The trained artificial neural network may be operable to identify the type of the defect located at the defect location based on the defect signal and the plurality of defect proximity signals and output the type of the defect located at the defect location. The trained artificial neural network may also be operable to determine a defect severity classification based on the defect signal and the plurality of defect proximity signals and output the severity classification.

Abstract translation: 用于确定焊缝中的缺陷类型的方法可以包括通过分析从沿着焊缝的多个测量位置收集的超声响应信号来确定缺陷位置和对应的缺陷信号。 然后可以将缺陷信号和对应于来自缺陷位置每侧的测量位置的超声波响应信号的多个缺陷接近信号输入到经过训练的人造神经网络中。 经训练的人造神经网络可以用于基于缺陷信号和多个缺陷接近信号来识别位于缺陷位置处的缺陷的类型，并输出位于缺陷位置处的缺陷的类型。 经训练的人造神经网络还可以用于基于缺陷信号和多个缺陷接近信号来确定缺陷严重性分类，并输出严重性分类。

公开(公告)号：US20090183552A1

公开(公告)日：2009-07-23

申请号：US12300952

申请日：2007-05-12

Applicant: Judit Angster ,  Andreas Schmohl ,  Andras Miklos

Inventor： Judit Angster ,  Andreas Schmohl ,  Andras Miklos

IPC: G01N21/17

CPC classification number: G01N29/348 ,  G01N1/405 ,  G01N21/1702 ,  G01N29/2425 ,  G01N29/4481 ,  G01N2021/1704 ,  G01N2291/021 ,  G01N2291/0423

Abstract: A gas detector with a selectively adsorbing surface (3) and an acoustic measuring cell (5) is presented. The detector is characterized in that the selectively adsorbing surface (3) and the acoustic measuring cell (5) can be arranged with respect to one another such that gases desorbed by means of thermal desorption from the adsorbing surface (3) reach the acoustic measuring cell (5) and there trigger a pressure wave that can be measured by one or more acoustic pick-ups (13, 14), in particular microphones, which are arranged in the acoustic measuring cell (5). Furthermore, a corresponding method is provided. The detector is particularly suitable for measuring contaminants in interior spaces and ventilation systems.

Abstract translation: 提出了具有选择性吸附表面（3）和声测量单元（5）的气体检测器。 检测器的特征在于，选择性吸附表面（3）和声学测量单元（5）可以相对于彼此布置，使得通过从吸附表面（3）的热解吸解吸的气体到达声学测量单元 （5），并且触发可以由布置在声测量单元（5）中的一个或多个声拾取器（13,14）特别是麦克风测量的压力波。 此外，提供了相应的方法。 检测器特别适用于测量内部空间和通风系统中的污染物。