WO2022100084A1 - Appareil de détection de vibrations anormales dans un dispositif industriel - Google Patents

Appareil de détection de vibrations anormales dans un dispositif industriel Download PDF

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Publication number
WO2022100084A1
WO2022100084A1 PCT/CN2021/099699 CN2021099699W WO2022100084A1 WO 2022100084 A1 WO2022100084 A1 WO 2022100084A1 CN 2021099699 W CN2021099699 W CN 2021099699W WO 2022100084 A1 WO2022100084 A1 WO 2022100084A1
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WIPO (PCT)
Prior art keywords
industrial equipment
neural network
vibration
soft core
embedded soft
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PCT/CN2021/099699
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English (en)
Chinese (zh)
Inventor
孙广宇
罗国杰
韩平
孙康睿
张波
李亚军
曹玉龙
李加敏
Original Assignee
杭州未名信科科技有限公司
浙江省北大信息技术高等研究院
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Publication of WO2022100084A1 publication Critical patent/WO2022100084A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information

Definitions

  • the invention relates to the technical field of measurement, in particular to an abnormal vibration detection device of industrial equipment, which can be used for operation monitoring of industrial equipment.
  • Embodiments of the present invention provide an abnormal vibration detection device for industrial equipment.
  • a brief summary is given below. This summary is not intended to be an extensive review, nor is it intended to identify key/critical elements or delineate the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the detailed description that follows.
  • an embodiment of the present invention provides a device for detecting abnormal vibration of industrial equipment.
  • the device includes:
  • the industrial equipment is connected with the IoT node module, and the intelligent edge computing platform is communicated with the IoT node module, wherein,
  • the IoT node module detects and analyzes the vibration data of the industrial equipment to determine whether the vibration of the industrial equipment is abnormal.
  • the IoT node module includes an embedded soft core, a neural network and a self-learning algorithm are built in the embedded soft core, and the self-learning algorithm is used to realize unsupervised online training of the neural network.
  • the IoT node module includes a vibration sensor, a digital-to-analog converter, an acceleration unit, and a wireless communication unit; wherein, the vibration sensor, the digital-to-analog converter, the acceleration unit, and the wireless communication unit are electrically connected in sequence; wherein,
  • the wireless communication unit is communicatively connected to the intelligent edge computing platform.
  • the acceleration unit includes an FPGA subunit
  • the wireless communication unit includes a microcontroller with integrated wireless functionality; wherein,
  • the FPGA subunit includes an embedded soft core and a neural network; wherein,
  • the digital-to-analog converter is connected to the FPGA subunit through an integrated circuit bus; the FPGA subunit is connected to the microcontroller with integrated wireless function through a queue serial peripheral interface.
  • the device further includes:
  • Alarm terminal wherein, the alarm terminal and the intelligent edge computing platform are connected in communication.
  • the vibration sensor is used to collect the vibration information of the industrial equipment to be detected in real time and send it to the digital-to-analog converter;
  • the digital-to-analog converter is used to convert the analog information of the vibration information into a digital signal and send it to the FPGA subunit in the acceleration unit through the integrated circuit bus;
  • the embedded soft core in the FPGA sub-unit is used to generate calculation results after calculation based on the pre-trained neural network, and send the calculation results to the microcontroller with integrated wireless function in the wireless communication unit through the queue serial peripheral interface;
  • the microcontroller with integrated wireless function is used for processing the calculation result through the mean square error method, generating a processing result, and determining whether the industrial equipment to be detected is abnormal based on the processing result.
  • the microcontroller with integrated wireless function determines that the industrial equipment to be detected is abnormal; and when the processing result is less than the preset value, the microcontroller with integrated wireless function determines that the industrial equipment to be detected is abnormal. The device is not abnormal.
  • the microcontroller with integrated wireless function when an abnormality occurs, the microcontroller with integrated wireless function generates early warning information, and sends the early warning information to the intelligent edge computing platform through the wireless communication unit;
  • the intelligent edge computing platform performs data processing and statistics on the early warning information, generates alarm information, and sends the alarm information to the alarm terminal.
  • the embedded soft core generates a pre-trained neural network according to the following method, including:
  • Collect training data samples of multiple industrial equipment running normally and input the training data samples into the neural network for unsupervised online training based on the model training component, and generate a pre-trained neural network after the training.
  • the method further includes:
  • the embedded soft core downloads the weight file of the pre-trained neural network and writes the weight file into the embedded soft core.
  • the device for detecting abnormal vibration of industrial equipment first collects the vibration information of the industrial equipment to be detected in real time through a vibration sensor and sends it to a digital-to-analog converter, and then converts the analog information of the vibration information into a digital signal through the digital-to-analog converter for transmission.
  • the microcontroller with integrated wireless function in the unit is finally used to process the calculation result by the mean square error method through the microcontroller with integrated wireless function, generate the processing result, and determine whether the industrial equipment to be detected is abnormal based on the processing result. Because the present invention has built-in neural network and self-learning algorithm in the embedded soft core, it can realize online collection of normal operation data for unsupervised online training of the neural network, so that the detection result of the neural network after training is more accurate, so that the maintenance requirements of industrial equipment can be predicted. To avoid emergency maintenance events, maximize the uptime of industrial equipment, thereby improving industrial production efficiency.
  • FIG. 1 is a schematic diagram of a device for detecting abnormal vibration of industrial equipment provided by an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of a process for detecting abnormal vibration of industrial equipment according to an embodiment of the present invention
  • FIG. 3 is a schematic process diagram of an abnormal vibration detection process of an industrial equipment provided by an embodiment of the present invention.
  • the present invention provides a device, method, storage medium and electronic device for detecting abnormal vibration of industrial equipment, so as to solve the problems existing in the above-mentioned related technical problems.
  • the present invention since the present invention has built-in neural network and self-learning algorithm in the embedded soft core, it can realize online collection of normal operation data for unsupervised online training of the neural network, so that the neural network detection result after training is more accurate , so that the maintenance requirements of industrial equipment can be predicted to avoid emergency maintenance events, so as to maximize the normal operation time of industrial equipment, thereby improving industrial production efficiency.
  • the following uses exemplary embodiments to describe in detail.
  • FIG. 1 is a schematic diagram of a device for detecting abnormal vibration of industrial equipment according to an embodiment of the present invention.
  • the device includes industrial equipment, an IoT node module, and an intelligent edge computing platform; wherein, the industrial equipment and the IoT node The module is connected, and the intelligent edge computing platform is connected to the IoT node module in communication, wherein the IoT node module detects and analyzes the vibration data of the industrial equipment to determine whether the vibration of the industrial equipment is abnormal.
  • the IoT node module includes an embedded soft core, a neural network and a self-learning algorithm are built in the embedded soft core, and the self-learning algorithm is used to realize unsupervised online training of the neural network.
  • the IoT node module includes an embedded soft core, a neural network and a self-learning algorithm are built in the embedded soft core, and the self-learning algorithm is used to realize unsupervised online training of the neural network.
  • the intelligent edge computing platform is used to receive data from IoT nodes for data processing and statistics.
  • the alarm terminal includes mobile phone alarm and big data platform alarm, and real-time alarm when the vibration is abnormal.
  • the IoT node module includes a vibration sensor, a digital-to-analog converter, an acceleration unit, and a wireless communication unit; wherein, the vibration sensor, the digital-to-analog converter, the acceleration unit, and the wireless communication unit are electrically connected in sequence; wherein, the vibration sensor and the industrial equipment connection; the wireless communication unit communicates with the intelligent edge computing platform.
  • vibration sensors can detect a variety of industrial equipment, and are used to collect operational data information generated by the industrial equipment.
  • digital-to-analog converter also known as D/A converter, referred to as DAC
  • DAC digital-to-analog converter
  • the acceleration unit includes an FPGA subunit; wherein, the wireless communication unit includes a microcontroller integrating wireless functions; wherein, the FPGA subunit includes an embedded soft core and a neural network; The FPGA subunit is connected; the FPGA subunit is connected with the microcontroller with integrated wireless function through a queue serial peripheral interface.
  • the device further includes: an alarm terminal; wherein, the alarm terminal is connected in communication with the intelligent edge computing platform.
  • the six-axis acceleration sensor first collects the digital signal corresponding to the vibration information of the industrial equipment and sends it to the embedded soft core (the microprocessor IP of the FPGA) through the IIC (Inter-Integrated Circuit, integrated circuit bus).
  • Core in which the microprocessor IP core of FPGA runs fast, occupies less resources, and has strong configurability.
  • the microprocessor IP core of the FPGA there is a model of the industrial equipment to be tested created by the Long Short-Term Memory Neural Network (LSTM, Long Short-Term Memory) algorithm.
  • the neural network is implemented using 16-bit fixed-point numbers, and the trained model is compressed by sparse matrix.
  • the algorithm performs parallel computing to realize the deployment of a two-layer 64-point LSTM network, which meets the requirements of real-time computing monitoring.
  • the computing results are sent to the microcontroller with integrated wireless function through the queue serial peripheral interface.
  • the mean square error is used in this module to judge whether it is abnormal, if the alarm information is higher than the threshold, the alarm information will be transmitted to the intelligent edge computing server through the wireless network, and if it is lower than the threshold, it will be silent.
  • the device for detecting abnormal vibration of industrial equipment first collects the vibration information of the industrial equipment to be detected in real time through a vibration sensor and sends it to a digital-to-analog converter, and then converts the analog information of the vibration information into a digital signal through the digital-to-analog converter for transmission.
  • the microcontroller with integrated wireless function in the unit is finally used to process the calculation result by the mean square error method through the microcontroller with integrated wireless function, generate the processing result, and determine whether the industrial equipment to be detected is abnormal based on the processing result. Because the present invention has built-in neural network and self-learning algorithm in the embedded soft core, it can realize online collection of normal operation data for unsupervised online training of the neural network, so that the detection result of the neural network after training is more accurate, so that the maintenance requirements of industrial equipment can be predicted. To avoid emergency maintenance events, maximize the uptime of industrial equipment, thereby improving industrial production efficiency.
  • an embodiment of the present invention provides a schematic flowchart of an industrial equipment abnormal vibration detection process applied to an industrial equipment abnormal vibration detection apparatus.
  • the detection process of the embodiment of the present invention may include the following steps:
  • the vibration sensor is used to collect the vibration information of the industrial equipment to be detected in real time and send it to the digital-to-analog converter;
  • the vibration sensor is a sensor component that detects the vibration of the equipment. Vibration information is the data information collected when industrial equipment vibrates during operation. It should be noted that the vibration sensor is preferably a six-axis acceleration sensor.
  • the vibration sensor (six-axis acceleration sensor) transmits the data to the digital-to-analog conversion unit, and then transmits it to the FPGA through the integrated line bus, and the FPGA transmits it to the wireless communication unit through the queue serial peripheral interface (the integrated wireless function is the controller).
  • the abnormal vibration detection device of industrial equipment detects the abnormality of industrial equipment
  • the user first connects the industrial equipment to be detected with the abnormal vibration detection device of industrial equipment, and starts the abnormal vibration detection of industrial equipment after the connection is successful.
  • the device performs abnormality detection on the industrial equipment to be detected, and collects the vibration information of the industrial equipment to be detected in real time through the vibration sensor during detection.
  • the digital-to-analog converter is used to convert the analog information of the vibration information into a digital signal and send it to the FPGA subunit in the acceleration unit;
  • digital-to-analog converter also known as D/A converter, referred to as DAC
  • DAC digital-to-analog converter
  • the vibration data is collected by the vibration sensor and input to the digital-to-analog converter, and the digital-to-analog converter converts the analog signal into a digital signal.
  • the embedded soft core in the FPGA sub-unit is used to perform computation based on the pre-trained neural network to generate a computation result and send the computation result to the micro-controller with integrated wireless function in the wireless communication unit through the queue serial peripheral interface device;
  • the pre-trained model of the industrial equipment to be inspected in the embedded soft core is generated after collecting normal operation data online for unsupervised online training.
  • the long-short-term memory neural network is first used to create the industrial equipment model to be tested, and then the model training component is created by the self-learning algorithm, and finally a plurality of training data samples of the normal operation of the industrial equipment are collected, and based on the The model training component inputs the training data samples into the model of the industrial equipment to be tested for training and performs unsupervised online training, and generates a pre-trained model of the industrial equipment to be tested after the training.
  • the weight file of the pre-trained industrial equipment model to be detected is downloaded, and the weight file is written into the embedded soft core.
  • the embedded soft core has the detection capability.
  • the digital signal of the industrial equipment to be detected is sent to the embedded soft core through the integrated circuit bus, and based on the pre-trained to-be-detected soft core Detect the weight file in the industrial equipment model and perform the calculation to generate the calculation result.
  • vibration sensor digital-to-analog converter
  • acceleration unit and wireless communication are all completed in the IoT node.
  • neural network and self-learning algorithm are added, which does not require manual calibration of training data, and realizes self-learning with the cooperation of edge computing and IOT nodes.
  • passive wireless easy to deploy.
  • the microcontroller with integrated wireless function is used to process the calculation result through the mean square error method, generate the processing result, and determine whether the industrial equipment to be detected is abnormal based on the processing result.
  • mean-square error is a measure that reflects the degree of difference between the estimator and the estimator.
  • the calculation result is used as the parameter of the mean square error algorithm, and the processing result is obtained after the second calculation. According to the processing result, it can be determined whether the industrial equipment to be detected appears abnormal.
  • the processing result is greater than or equal to the preset threshold, it is determined that the industrial equipment to be detected is abnormal; and when the processing result is less than the preset value, it is determined that the industrial equipment to be detected is not abnormal.
  • early warning information is generated, and the early warning information is sent to the intelligent edge computing platform through the microcontroller with integrated wireless function.
  • the alarm information is sent to the alarm terminal.
  • FIG. 3 is an architecture diagram of an abnormal vibration detection of industrial equipment provided by an embodiment of the present invention.
  • the architecture includes a vibration source (ie, the industrial equipment to be detected), an IoT node (including a vibration sensor, a digital-to-analog converter, and a digital-to-analog converter). (A/D unit), acceleration unit and wireless communication), intelligent edge computing platform and alarm platform (alarm platform includes abnormal vibration mobile phone APP alarm and abnormal vibration big data platform alarm).
  • the acceleration unit includes an FPGA subunit, and the FPGA subunit includes an embedded soft core and an industrial equipment model to be detected.
  • FIG. 4 An example diagram of a scene in an embodiment of the present invention is shown in FIG. 4 .
  • the device for detecting abnormal vibration of industrial equipment first collects the vibration information of the industrial equipment to be detected in real time through a vibration sensor and sends it to a digital-to-analog converter, and then converts the analog information of the vibration information into a digital signal through the digital-to-analog converter for transmission.
  • the microcontroller with integrated wireless function in the unit is finally used to process the calculation result by the mean square error method through the microcontroller with integrated wireless function, generate the processing result, and determine whether the industrial equipment to be detected is abnormal based on the processing result. Because the present invention has built-in neural network and self-learning algorithm in the embedded soft core, it can realize online collection of normal operation data for unsupervised online training of the neural network, so that the detection result of the neural network after training is more accurate, so that the maintenance requirements of industrial equipment can be predicted. To avoid emergency maintenance events, maximize the uptime of industrial equipment, thereby improving industrial production efficiency.
  • the present invention further provides a computer-readable medium on which program instructions are stored, and when the program instructions are executed by a processor, implement the methods for detecting abnormal vibration of industrial equipment provided by the above method embodiments.
  • the present invention also provides a computer program product containing instructions, which, when running on a computer, enables the computer to execute the method for detecting abnormal vibration of industrial equipment in each of the above method embodiments.
  • the realization of all or part of the processes in the methods of the above embodiments can be accomplished by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed , which may include the processes of the above-mentioned method embodiments.
  • the storage medium can be a magnetic disk, an optical disk, a read-only storage memory or a random storage memory, and the like.

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Abstract

Est divulgué un appareil permettant de détecter des vibrations anormales dans un dispositif industriel. L'appareil comprend un dispositif industriel, un module de nœud IdO et une plateforme informatique de bord intelligente. Le dispositif industriel est connecté au module de nœud IdO, et la plateforme informatique de bord intelligente est en connexion de façon à communiquer avec le module de nœud IdO. Le module de nœud IdO effectue une détection sur des données de vibration du dispositif industriel, effectue une analyse, et détermine si des vibrations dans le dispositif industriel sont anormales ou non. Le module de nœud IdO comprend un cœur logiciel intégré. Le cœur logiciel intégré est doté d'un réseau neuronal incorporé et d'un algorithme d'auto-apprentissage incorporé. L'algorithme d'auto-apprentissage est utilisé pour réaliser un apprentissage en ligne non supervisé du réseau neuronal. Dans la présente invention, le cœur logiciel intégré est doté du réseau neuronal incorporé et de l'algorithme d'auto-apprentissage incorporé, de telle sorte que des données opérationnelles peuvent être collectées en ligne et utilisées pour effectuer un apprentissage en ligne non supervisé du réseau neuronal, ce qui permet d'améliorer la précision des résultats de détection du réseau neuronal entraîné. De cette manière, l'invention permet de prédire des besoins de maintenance de dispositifs industriels, d'éviter l'apparition d'événements de réparation d'urgence, et d'augmenter le temps de fonctionnement des dispositifs industriels, ce qui permet d'améliorer l'efficacité d'une production industrielle.
PCT/CN2021/099699 2020-11-11 2021-06-11 Appareil de détection de vibrations anormales dans un dispositif industriel WO2022100084A1 (fr)

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CN112697267A (zh) * 2020-11-11 2021-04-23 杭州未名信科科技有限公司 一种工业设备异常振动检测装置
CN114167270B (zh) * 2021-11-30 2023-09-19 广东电网有限责任公司 一种基于边缘计算的刀闸状态识别系统
CN115203292B (zh) * 2022-09-15 2022-11-25 昆仑智汇数据科技(北京)有限公司 一种工业设备的数据处理方法、装置及设备
CN116009502B (zh) * 2023-03-22 2023-06-06 深圳华龙讯达信息技术股份有限公司 工业自动化平台智能数据采集系统及方法

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060238912A1 (en) * 2005-04-26 2006-10-26 Hitachi Global Storage Technologies Netherlands B.V. Magnetic disk drive and recording method
CN104929864A (zh) * 2015-02-06 2015-09-23 青岛科技大学 基于fpga的嵌入式风力发电机组运行状态监测及故障诊断系统
US20180144621A1 (en) * 2016-11-21 2018-05-24 Nec Corporation Measurement data processing method
CN110119333A (zh) * 2019-02-21 2019-08-13 北京天泽智云科技有限公司 一种异常检测边缘计算系统
CN110830943A (zh) * 2019-11-06 2020-02-21 湖南银河电气有限公司 一种基于边缘计算和大数据分析的设备状态监测系统
CN110958299A (zh) * 2019-10-30 2020-04-03 浙江省北大信息技术高等研究院 一种集成多组网协议多边缘计算框架的边缘计算处理平台
CN111031069A (zh) * 2019-12-26 2020-04-17 广东省智能制造研究所 一种具有边缘计算功能的振动采集分析终端及方法
CN112697267A (zh) * 2020-11-11 2021-04-23 杭州未名信科科技有限公司 一种工业设备异常振动检测装置

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110542474A (zh) * 2019-09-04 2019-12-06 中国科学院上海高等研究院 器件的振动信号检测方法、系统、介质及装置
CN111174905B (zh) * 2020-02-13 2023-10-31 欧朗电子科技有限公司 低功耗物联网振动异常检测装置及其检测方法
CN111412977A (zh) * 2020-03-09 2020-07-14 华南理工大学 一种机械设备振动感知数据的预处理方法

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060238912A1 (en) * 2005-04-26 2006-10-26 Hitachi Global Storage Technologies Netherlands B.V. Magnetic disk drive and recording method
CN104929864A (zh) * 2015-02-06 2015-09-23 青岛科技大学 基于fpga的嵌入式风力发电机组运行状态监测及故障诊断系统
US20180144621A1 (en) * 2016-11-21 2018-05-24 Nec Corporation Measurement data processing method
CN110119333A (zh) * 2019-02-21 2019-08-13 北京天泽智云科技有限公司 一种异常检测边缘计算系统
CN110958299A (zh) * 2019-10-30 2020-04-03 浙江省北大信息技术高等研究院 一种集成多组网协议多边缘计算框架的边缘计算处理平台
CN110830943A (zh) * 2019-11-06 2020-02-21 湖南银河电气有限公司 一种基于边缘计算和大数据分析的设备状态监测系统
CN111031069A (zh) * 2019-12-26 2020-04-17 广东省智能制造研究所 一种具有边缘计算功能的振动采集分析终端及方法
CN112697267A (zh) * 2020-11-11 2021-04-23 杭州未名信科科技有限公司 一种工业设备异常振动检测装置

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