WO2019035279A1 - Algorithme d'intelligence artificielle - Google Patents

Algorithme d'intelligence artificielle Download PDF

Info

Publication number
WO2019035279A1
WO2019035279A1 PCT/JP2018/023934 JP2018023934W WO2019035279A1 WO 2019035279 A1 WO2019035279 A1 WO 2019035279A1 JP 2018023934 W JP2018023934 W JP 2018023934W WO 2019035279 A1 WO2019035279 A1 WO 2019035279A1
Authority
WO
WIPO (PCT)
Prior art keywords
artificial intelligence
sensor node
sensor
kernel
input data
Prior art date
Application number
PCT/JP2018/023934
Other languages
English (en)
Japanese (ja)
Inventor
義範 宮前
光治 谷内
Original Assignee
ローム株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ローム株式会社 filed Critical ローム株式会社
Priority to JP2019536436A priority Critical patent/JP7012086B2/ja
Publication of WO2019035279A1 publication Critical patent/WO2019035279A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Definitions

  • the invention disclosed herein relates to artificial intelligence algorithms.
  • a nonpatent literature 1 and a nonpatent literature 2 can be mentioned, for example.
  • abnormality detection of the monitoring target device is performed by collecting and analyzing measurement data of the sensor node by the server. Therefore, the amount of communication exchanged between the sensor node and the server is very large, which has been a factor that hinders the introduction of the system.
  • the invention disclosed herein has an easy-to-install machine health monitoring system, a sensor node used therefor, an artificial intelligence chip, And, it aims at providing an artificial intelligence algorithm.
  • the artificial intelligence algorithm disclosed in the present specification generates a feature vector by extracting feature quantities for each frequency band from input data using a plurality of band pass filters connected in parallel; Determining a value of a kernel function using the feature vector and the support vector (first configuration).
  • the artificial intelligence algorithm according to the first configuration may be configured (second configuration) to further include a step of detecting abnormality of the input data from the value of the kernel function.
  • the input data may be vibration data (third configuration).
  • the kernel function is a linear kernel, a Gaussian kernel, or a RBF [radial base function] kernel (fourth configuration) Good.
  • the artificial intelligence chip disclosed in the present specification generates a feature vector by extracting feature quantities for each frequency band from input data using a plurality of band pass filters connected in parallel.
  • a configuration includes a processing unit and a classifier for obtaining a value of a kernel function using the feature vector and the support vector.
  • the artificial intelligence chip having the fifth configuration further includes a post-processing unit (a sixth configuration) for detecting an abnormality of the input data from the value of the kernel function.
  • a post-processing unit for detecting an abnormality of the input data from the value of the kernel function.
  • the classifier may be a configuration (seventh configuration) that is an OCSVM [one class support vector machine] configured by hardware.
  • a sensor node disclosed in the present specification includes: a sensor; an artificial intelligence chip having any of the fifth to seventh configurations for receiving the input data from the sensor; the artificial intelligence chip and the server And a communication unit that performs wireless communication among the above (eighth configuration).
  • the sensor may be a vibration sensor (ninth configuration).
  • the sensor node having the eighth or ninth configuration includes an environmental power generation unit, a storage unit for storing power generated by the environmental power generation unit, and each unit of the sensor node using the generated power or stored power of the storage unit. And a power management unit for supplying power to the circuit (10th configuration).
  • the machine health monitoring system disclosed in the present specification includes a sensor node having the above-described tenth configuration attached to a monitoring target device, and a server that receives an abnormality flag from the sensor node (a 11)).
  • the server may be configured (12th configuration) to receive an abnormal flag from the sensor node and report an abnormal state.
  • a diagram showing an exemplary configuration of an artificial intelligence chip A diagram showing an example of the configuration of a kernel arithmetic processing unit
  • AI-SNP artificial intelligence-sensor node processor
  • FIG. 1 and FIG. 2 are diagrams each showing an example of a facility maintenance method in a factory.
  • TBM method repair or replacement is performed when the operation time of the device meets a predetermined standard.
  • CBM condition-based-maintenance
  • FIG. 3 is a view showing problems when adopting a Wi-SUN [wireless smart utility network] as a wireless module of the sensor node.
  • Wi-SUN wireless smart utility network
  • IIoT infrared thermometer
  • the transmission data amount is It becomes 60 kB / s in X axis, Y axis, Z axis). This is a considerably large value compared to the communication bandwidth of Wi-SUN (100 kbps ⁇ 12.5 kB / s).
  • Wi-SUN 100 kbps ⁇ 12.5 kB / s.
  • the power consumption of the wireless module must be dramatically reduced.
  • the AI-SNP disclosed herein it is possible to provide a very useful machine health monitoring system deployment method that does not require additional wiring and batteries, and thus, It will be possible to dramatically accelerate the new and old devices IIoT around the world.
  • AI-SNPs for machine health monitoring systems and artificial intelligence algorithms (hereinafter referred to as AI algorithms) implemented therein will be described.
  • AI algorithms artificial intelligence algorithms
  • the applicant continues to study AI algorithms for machine health monitoring systems using open data and real data from their own factories.
  • the applicant of the present application has obtained not only a knowledge of the AI algorithm but also a lot of knowledge of data collection know-how.
  • OCSVM one class support vector machine
  • spiking neural network or convolutional neural network
  • convolutional neural network As the AI algorithm, one class support vector machine (hereinafter referred to as OCSVM [one class support vector machine]), spiking neural network, or convolutional neural network are known, and the options are not limited at all. It is not a thing.
  • OCSVM one class support vector machine
  • spiking neural network or convolutional neural network
  • convolutional neural network convolutional neural network
  • the artificial intelligence chip 10 (hereinafter referred to as an AI chip 10) of this configuration example is a semiconductor chip capable of executing an AI algorithm based on input data (such as vibration data and temperature data) from a sensor. And a pre-processing unit 11, a classifier 12, and a post-processing unit 13.
  • the AI chip 10 is required to have low power consumption and a small area to the extent that it can be mounted on a sensor node.
  • the above-mentioned AI algorithm is designed to realize functions such as abnormality diagnosis, toolware evaluation, or life expectancy of a monitoring target device.
  • a preprocessing unit (pre-processor) 11 extracts feature quantities for each frequency band from input data (for example, raw vibration data obtained by a vibration sensor) using a plurality of band pass filters connected in parallel. By doing this, a feature vector is generated.
  • a feature vector is generated.
  • the above-mentioned feature quantity for example, after acquiring FFT [fast Fourier transform] amplitude of a frequency spectrum every 50 Hz from 1 Hz to 20 kHz (200 dim), respective root mean square (RMS [root mean square] ) Should be calculated.
  • a classifier 12 uses a feature vector input from the pre-processing unit 11 and a support vector stored in advance, and uses a kernel function (eg, linear kernel, Gaussian kernel, or RBF [radial base function] Find the kernel) value.
  • a kernel function eg, linear kernel, Gaussian kernel, or RBF [radial base function] Find the kernel
  • OCSVM configured by hardware.
  • the support vector for example, input data less than 33 hours after the monitoring target device starts operation may be used for learning, and a hyperplane for OK / NG determination may be created.
  • the AI algorithm described above is implemented using the preprocessing unit 11, the classifier 12, and the post-processing unit 13.
  • OCSVM used as the classifier 12 is a kind of support vector machine, and is a lightweight and practical AI algorithm by unsupervised learning. OCSVM detects one class problem on one piece of software and is not similar to the machine health monitoring system. The reason is that OCSVM itself is poor in affinity with complex and high-speed time-series data.
  • an AI chip 10 in which the application of OCSVM is possible by applying a simple pre-processing as compared to the conventional method.
  • raw input data is simply processed using only FFT calculation in order to obtain feature quantities (RMS of the frequency spectrum obtained from 1 Hz to 20 kHz every 50 Hz from 1 Hz to 20 kHz).
  • RMS feature quantities
  • the classifier 12 is a simple OCSVM using the kernel method.
  • the kernel function as described above, it is possible to select a linear kernel, a Gaussian kernel, an RBF kernel or the like. For example, if an RBF kernel can be selected, the AI algorithm by OCSVM becomes powerful. However, in order to do so, an additional function circuit (logarithmic calculation circuit) is required, so that point needs to be noted.
  • the classifier 12 (especially its kernel operation processing unit) is configured using an adder and a multiplier will be described.
  • many small-scale analog PEs are used to reduce AD / DA [analog-to-digital / digital-to-analog] and eliminate high-speed clock. It is desirable to arrange [processing engine] so that computing units and memories have an analog structure.
  • FIG. 5 is a diagram showing an example of the monitoring target device (here, a milling machine).
  • the milling machine 210 of this figure has a motor 211 and bearings 212 to 215.
  • An accelerometer 216 and a thermocouple 217 are attached to the bearings 212 to 215, respectively.
  • a sensor node mounted with the above-described AI chip 10 constantly measures vibration generated during operation of the milling machine 210 to determine whether or not an abnormality occurs in the bearing.
  • FIG. 6 is a diagram showing a contrast example of the anomaly detection operation by new and old AI algorithms (each represented as “light” algorithm and “heavy” algorithm in the figure).
  • waveforms one hour and 88 hours after
  • the new AI algorithm is depicted.
  • the vertical axis indicates the distance to the hyperplane
  • the horizontal axis indicates the operation time.
  • a time chart showing an abnormality detection operation by the old AI algorithm is depicted.
  • the waveforms of the vibration data obtained at the sensor node are almost identical in appearance both after one hour and after 88 hours.
  • an abnormality determination operation is performed on such vibration data by the new AI algorithm, an abnormality can be detected when the operating time of the milling machine 210 reaches 88 hours.
  • the above-mentioned abnormality determination operation is extremely useful in knowing the sign of failure.
  • an abnormality is first detected after 88 hours from the start of operation, and an abnormality is completely detected after 91 hours.
  • the adoption of the new AI algorithm reduces the operation load compared to the old AI algorithm, and at the same or higher accuracy than this, the abnormality of the monitoring target device It is possible to detect Therefore, it can be said that the new AI algorithm is suitable for implementation in a sensor node requiring low power consumption and area saving.
  • one class (u) -SVM (OCSVM) using a band pass filter (BPF) can be adopted as an AI algorithm for performing an abnormality detection operation.
  • OCSVM is known as an unsupervised anomaly detection method.
  • (u) is introduced to clarify the upper limit value of the margin error and the lower limit value of the ratio of the support vector to all the vectors.
  • the BPF is a means for simply obtaining frequency information without performing a high load FFT operation for the AI chip 10.
  • FIG. 7 is a view showing an example of the configuration of the AI chip 10 (a configuration without the post-processing unit 13).
  • the preprocessing unit 11 includes a plurality of band pass filters 11 a connected in parallel.
  • the classifier 12 includes a support vector storage unit 12 a and a kernel operation processing unit 12 b.
  • the band pass filter 11a is operated. These band pass filters 11a output feature values x 0 to x k , respectively.
  • the preprocessing unit 11 generates a feature vector X (x 0 , x 1 ,..., X k ) having the feature values x 0 to x k as elements, and outputs the feature vector to the classifier 12.
  • the vector X n is stored.
  • the above function value f (X) is checked to determine whether the abnormal state should be transmitted to the server.
  • FIG. 8 is a diagram showing an example of the configuration of the kernel arithmetic processing unit 12b.
  • the kernel arithmetic processing unit 12b of this configuration example includes a plurality of vector operators b10 and an adder b20.
  • the adder b20 generates a function value f (X) by adding together a plurality of function values ⁇ i K (X, X i ).
  • FIG. 9 is a diagram showing an exemplary configuration of the vector computing unit b10.
  • the vector computing unit b10 of this configuration example includes a kernel computing unit b11 and a multiplier b12.
  • the kernel computing unit b11 receives the input of the feature vector X and the support vector X i , and generates a function value K (X, X i ) using a predetermined kernel function K.
  • kernel function K such as linear kernel, Gaussian kernel, or RBF kernel.
  • the linear kernel is a simple multiplication of the feature vector X and the support vector Xi, and can be implemented on the AI chip 10 with a very simple hardware configuration.
  • the RBF kernel can freely define the boundary of the OK / NG determination.
  • the following equations (3a) to (3c) are arithmetic equations of linear kernel, Gaussian kernel, and RBF kernel, respectively.
  • the multiplier b12 generates a function value ⁇ i K (X, X i ) by multiplying the function value K (X, X i ) by the coefficient ⁇ i .
  • FIG. 10 is a view showing an example of the configuration of a sensor node on which the AI chip 10 is mounted.
  • the sensor node 1 of this configuration example functions as one component of the machine health monitoring system 300 together with the server 2, and in addition to the AI chip 10 described above, the sensor 20, the communication unit 30, An environmental power generation unit 40, a storage unit 50, and a power management unit 60 are included.
  • the sensor node 1 may be understood as a sensor itself attached to a monitored device (not shown) or may be understood as a gateway connected to the sensor. That is, the sensor 20 may be externally attached to the sensor node 1.
  • the AI chip 10 is a semiconductor device that receives power supply from the power management unit 60 and operates at the edge of the sensor 20, receives input data from the sensor 20, performs abnormality detection processing, and transmits the detection result via the communication unit 30. Report to server 2 at. Communication between the AI chip 10 and the communication unit 30 may be performed, for example, via a UART (universal asynchronous receiver / transmitter) interface.
  • UART universal asynchronous receiver / transmitter
  • the sensor 20 is a unit that receives power supply from the power management unit 60 and measures a predetermined measurement target (such as vibration or current).
  • a vibration sensor can be suitably used.
  • a vibration sensor In order to eliminate the high speed clock of the AI chip 10, it is desirable to use the sensor 20 as an analog output type.
  • the most advanced vibration sensors are those of logic output type with a logic interface. The reason is that the analog output type is susceptible to noise and fatal to a high precision sensor. Therefore, when using the analog output type sensor 20, it is important to dispose the AI chip 10 in the vicinity of the sensor 20 so as not to be affected by noise.
  • the communication unit 30 is a module for receiving power supply from the power management unit 60 and performing wireless communication with the server 2.
  • the AI chip 10 normally communicates with the server 2 only when error data is detected, but when sending learning data to the server 2, it is necessary to communicate with a larger capacity than normal. In view of this, it can be said that, for example, it is desirable to adopt a Wi-SUN module capable of high-speed wireless communication as the communication unit 30.
  • a piezoelectric element such as a piezoelectric element may be used as the power generation element.
  • sunlight or illumination light it is preferable to use a silicon-based, compound-based, or organic-based photoelectric element as the power generation element.
  • a thermoelectric element such as a Peltier element may be used as the power generation element.
  • the measurement target of the sensor 20 and the energy source of the environmental power generation unit 40 be common.
  • vibration is to be measured by the sensor 20 and the above vibration is used as an energy source by the environmental power generation unit 40.
  • the energy is generated by the environmental power generation unit 40 in response to the vibration, and therefore, the power to the sensor 20 can be more reliably than in the case where the energy source It becomes possible to supply.
  • a large capacity (about 1 F) supercapacitor is required as the storage unit 50.
  • the power management unit 60 supplies power to each unit (the AI chip 10, the sensor 20, and the communication unit 30) of the sensor node 1 using the generated power of the environmental power generation unit 40 or the stored power of the storage unit 50. It is an internal power supply circuit (for example, a DC / DC converter with a DC 3.3 V output).
  • the environmental power generation unit 40 can not stably supply the generated power. Therefore, in order to realize stable operation of the sensor node 1, the operation of the power management unit 60 is very important. That is, in the power management unit 60, not only the storage control of the storage unit 50, but also the appropriate impedance matching control needs to be performed so that the maximum power can be obtained from the environmental power generation unit 40.
  • the laying of the power supply wiring and the replacement of the battery become unnecessary. Further, since wireless communication is performed between the sensor node 1 and the server 2, signal wiring connecting the two is also unnecessary. Therefore, the sensor node 1 can be disposed at an arbitrary position.
  • the server 2 When the server 2 receives an abnormality flag from the sensor node 1, the server 2 notifies the staff in the head office of an abnormal state.
  • facility maintenance can be performed by the CBM method (FIG. 2).
  • the machine health monitoring system is described as an example, but the application target of the artificial intelligence algorithm (or the artificial intelligence chip mounted with the same) is not limited to this. It is possible to apply to a living body health monitoring system for managing physical condition of
  • the invention disclosed herein can be used, for example, in a machine health monitoring system for smart factories.
  • sensor node 10 artificial intelligence chip (AI chip) 11 pre-processing unit 11a band pass filter 12 classifier (OCSVM) 12a support vector storage unit 12b kernel operation processing unit b10 vector operation unit b11 kernel operation unit b12 multiplier b20 adder 13 post-processing unit 20 sensor 30 communication unit (Wi-SUN) 40 Environmental Power Generation Unit 50 Power Storage Unit (Super Capacitor) 60 Power Management Unit 100 Headquarters 200 Factory 210 Milling Machine 211 Motor 212-215 Bearing 216 Accelerometer 217 Thermocouple 300 Machine Health Monitoring System

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un système de suivi d'intégrité de machine qui peut facilement être mis en place. Une puce d'intelligence artificielle (10), dans laquelle est installé un nouvel algorithme d'intelligence artificielle, comprend : une unité de prétraitement (11) qui génère un vecteur de caractéristiques en extrayant une quantité de caractéristiques de chaque bande de fréquences à partir des données d'entrée à l'aide d'une pluralité de filtres passe-bande connectés en parallèle ; un classificateur (12) qui obtient une valeur d'une fonction de noyau à l'aide du vecteur de caractéristiques et d'un vecteur de support ; et une unité de post-traitement (13) qui détecte une anomalie des données d'entrée à partir de la valeur de la fonction de noyau. Cette puce d'intelligence artificielle (10) fonctionne comme un élément du système de suivi d'intégrité de machine qui est mis en place, par exemple, dans une usine intelligente en étant monté sur un nœud de capteur conjointement avec un capteur ou une unité de communication.
PCT/JP2018/023934 2017-08-18 2018-06-25 Algorithme d'intelligence artificielle WO2019035279A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2019536436A JP7012086B2 (ja) 2017-08-18 2018-06-25 人工知能アルゴリズム

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2017157852 2017-08-18
JP2017-157852 2017-08-18

Publications (1)

Publication Number Publication Date
WO2019035279A1 true WO2019035279A1 (fr) 2019-02-21

Family

ID=65362832

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/023934 WO2019035279A1 (fr) 2017-08-18 2018-06-25 Algorithme d'intelligence artificielle

Country Status (2)

Country Link
JP (1) JP7012086B2 (fr)
WO (1) WO2019035279A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021149614A1 (fr) * 2020-01-24 2021-07-29 三菱電機株式会社 Dispositif de détection de défaut dans un câble métallique
JP2022168313A (ja) * 2021-04-25 2022-11-07 広海 大谷 Ｓｑｌデータベース―グラフハミルトン閉路接合モデルａｉチップ、自動精度補正機構によるジャイロスコープ他ａｉチップ、ｒａｍのアクセス制限フラグロックチップ、シリアル登録ｒａｍアクセス制限フラグロックチップ、ａｉ実行結果解析及び保存カスタムチップ、自動解析データ整列整理チップ、ゲームエンジンチップ、４ｄエンジンチップ、物理シミュレーターチップ、ａｉチップボード、４ｄエンジンチップボード、物理シミュレーターチップボード、連鎖フィードバックポイントシステムチップとロールフォワードシステムチップ、ロールバックシステムチップ、エマージェンシー制御装置直通回路及びチップ

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006105943A (ja) * 2004-10-08 2006-04-20 Omron Corp 知識作成装置及びパラメータ探索方法並びにプログラム製品
JP2014035279A (ja) * 2012-08-09 2014-02-24 Bridgestone Corp 路面状態判別方法とその装置
WO2015072023A1 (fr) * 2013-11-15 2015-05-21 富士通株式会社 Système, nœud de communication et procédé de commutation
US20160098037A1 (en) * 2014-10-06 2016-04-07 Fisher-Rosemount Systems, Inc. Data pipeline for process control system anaytics
WO2017126236A1 (fr) * 2016-01-20 2017-07-27 三菱電機株式会社 Dispositif de détection d'anomalie et procédé de détection d'anomalie

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6244996B2 (ja) * 2014-03-10 2017-12-13 富士通株式会社 識別関数特定装置、識別関数特定プログラム、識別関数特定方法および生体認証装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006105943A (ja) * 2004-10-08 2006-04-20 Omron Corp 知識作成装置及びパラメータ探索方法並びにプログラム製品
JP2014035279A (ja) * 2012-08-09 2014-02-24 Bridgestone Corp 路面状態判別方法とその装置
WO2015072023A1 (fr) * 2013-11-15 2015-05-21 富士通株式会社 Système, nœud de communication et procédé de commutation
US20160098037A1 (en) * 2014-10-06 2016-04-07 Fisher-Rosemount Systems, Inc. Data pipeline for process control system anaytics
WO2017126236A1 (fr) * 2016-01-20 2017-07-27 三菱電機株式会社 Dispositif de détection d'anomalie et procédé de détection d'anomalie

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2021149614A1 (fr) * 2020-01-24 2021-07-29 三菱電機株式会社 Dispositif de détection de défaut dans un câble métallique
JPWO2021149614A1 (fr) * 2020-01-24 2021-07-29
JP7275324B2 (ja) 2020-01-24 2023-05-17 三菱電機株式会社 ワイヤロープ探傷装置
JP2022168313A (ja) * 2021-04-25 2022-11-07 広海 大谷 Ｓｑｌデータベース―グラフハミルトン閉路接合モデルａｉチップ、自動精度補正機構によるジャイロスコープ他ａｉチップ、ｒａｍのアクセス制限フラグロックチップ、シリアル登録ｒａｍアクセス制限フラグロックチップ、ａｉ実行結果解析及び保存カスタムチップ、自動解析データ整列整理チップ、ゲームエンジンチップ、４ｄエンジンチップ、物理シミュレーターチップ、ａｉチップボード、４ｄエンジンチップボード、物理シミュレーターチップボード、連鎖フィードバックポイントシステムチップとロールフォワードシステムチップ、ロールバックシステムチップ、エマージェンシー制御装置直通回路及びチップ

Also Published As

Publication number Publication date
JPWO2019035279A1 (ja) 2020-07-27
JP7012086B2 (ja) 2022-01-27

Similar Documents

Publication Publication Date Title
Jin et al. A data-driven approach for bearing fault prognostics
Xu et al. Data-driven fault diagnostics and prognostics for predictive maintenance: A brief overview
US10539932B2 (en) Machine diagnostics based on overall system energy state
US10481195B2 (en) Distributed IoT based sensor analytics for power line diagnosis
Hu et al. Dynamic degradation observer for bearing fault by MTS–SOM system
CA2875071A1 (fr) Procede et systeme de test de l'integrite operationnelle d'un equipement de forage
JP7012086B2 (ja) 人工知能アルゴリズム
Peco Chacón et al. State of the art of artificial intelligence applied for false alarms in wind turbines
Chen et al. An integrated framework for statistical change detection in running status of industrial machinery under transient conditions
Kannan et al. Nominal features-based class specific learning model for fault diagnosis in industrial applications
Durbhaka Convergence of artificial intelligence and internet of things in predictive maintenance systems–a review
Maurya et al. Intelligent fault diagnostic system for rotating machinery based on IoT with cloud computing and artificial intelligence techniques: a review
Albano et al. Advanced sensor-based maintenance in real-world exemplary cases
Elforjani et al. Monitoring gearbox using a wireless temperature node powered by thermal energy harvesting module
Wang Key techniques in intelligent predictive maintenance (IPdM)–a framework of intelligent faults diagnosis and prognosis system (IFDaPS)
JP6192413B2 (ja) 軸受装置の振動解析方法、軸受装置の振動解析装置、および転がり軸受の状態監視装置
Albano et al. Sensors: the enablers for proactive maintenance in the real world
Yi et al. On a Prediction Method for Remaining Useful Life of Rolling Bearings via VMD-Based Dispersion Entropy and GAN
Wang et al. Wavelet Packet Transform‐Assisted Least Squares Support Vector Machine for Gear Wear Degree Diagnosis
Kirbaş et al. A new vibration-based hybrid anomaly detection model for preventing high-power generator failures in power plants
KR20230100999A (ko) 회전 기계 설비의 탁월 주파수 결정 방법, 및 이를 수행하는 컴퓨팅 시스템
Yu et al. Local preserving projections-based feature selection and Gaussian mixture model for machine health assessment
Contreras et al. Wireless sensor network-based infrastructure damage detection constrained by energy consumption
Jacome et al. Real-Time Fault Identification of Photovoltaic Systems Based on Remote Monitoring with IoT
Koukoura et al. An insight into wind turbine planet bearing fault prediction using SCADA data

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18845560

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2019536436

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18845560

Country of ref document: EP

Kind code of ref document: A1