[go: up one dir, main page]

WO2008116966A2 - Procédé et appareil pour surveiller l'état de machines électriques - Google Patents

Procédé et appareil pour surveiller l'état de machines électriques Download PDF

Info

Publication number
WO2008116966A2
WO2008116966A2 PCT/FI2008/000047 FI2008000047W WO2008116966A2 WO 2008116966 A2 WO2008116966 A2 WO 2008116966A2 FI 2008000047 W FI2008000047 W FI 2008000047W WO 2008116966 A2 WO2008116966 A2 WO 2008116966A2
Authority
WO
WIPO (PCT)
Prior art keywords
data
sensors
sensor
electric machine
data storage
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/FI2008/000047
Other languages
English (en)
Other versions
WO2008116966A3 (fr
Inventor
Jukka Toukonen
Pasi Paloheimo
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ABB Oy
Original Assignee
ABB Oy
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 ABB Oy filed Critical ABB Oy
Publication of WO2008116966A2 publication Critical patent/WO2008116966A2/fr
Publication of WO2008116966A3 publication Critical patent/WO2008116966A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors

Definitions

  • the present invention relates generally to condition monitoring for electric machines, especially rotating electric machines, i.e. electric motors or generators.
  • U.S. Patent 5,917,428 discloses a motor provided with a diagnostic apparatus forming a man machine interface for the local operator of the motor.
  • the diagnostic apparatus is further connected through a communication port and via a telecommunications network to a remote supervisory computer.
  • the diagnostic apparatus alternatively executes first operations which sample data received from a plurality of sensors and determine the health of the motor and second operations which display collected and computed data on the display of the man machine interface and process the data supplied by the local operator through the said interface.
  • the supervisory computer periodically calls the diagnostic apparatus to execute algorithms for computing various parameter values and for displaying motor data on the supervisory computer in graphical form.
  • U.S. Patent 6,529,135 discloses an integrated electric motor monitor in which three types of information is stored in a memory incorporated into the motor. Prognostic information is stored upon occurrence of a first circumstance, such as when the motor is operating within its normal load profile. Diagnostic information, which indicates the motor's operational condition, is stored upon occurrence of a second circumstance, such as when the motor's load factor is within a load factor range that is most common for the motor. Hazardous event information is stored upon occurrence of a third circumstance, such as an indication that the motor is operating abnormally.
  • a drawback related to the current monitoring systems is that the efforts to accurately and efficiently perform machine diagnostics and prognostics detract from the overall cost-effectiveness of the system, especially when a plurality of machines is involved.
  • the present invention seeks to alleviate or eliminate the above-mentioned drawbacks.
  • the present invention seeks to provide a novel condition monitoring system for electric machines, which allows cost-effective condition monitoring of one or more machines and versatile possibilities for not only machine diagnostics and prognostics but also for retrospective analyses of the machine operation.
  • an electric machine that fails to have a local user interface for the condition monitoring is provided with a data storage which is operatively connected to a communication interface for transferring stored data from the storage to a remote supervisory unit provided with analysis tools for analyzing the data.
  • the machine is further provided with two parallel processes, which store and process data received from a plurality of sensors measuring various characteristics indicative of the operational condition of the electric machine.
  • One process receives data from the sensors and stores the data into the data storage so that the data storage always contains history data representing past values of the sensor signals.
  • the signals of two or more sensors are mutually relevant, i.e. if a change in one signal may reflect in the signal values received from another sensor, the mutually relevant sensor signals are stored substantially simultaneously in the data storage.
  • the other process examines whether the current state of the machine indicates a need for an analysis of the condition of the machine. If such a need is detected, the process compiles a data set to be transferred to the remote supervisory unit for an analysis.
  • the data set contains history data representing a selected set of sensor signals and current values of the said signals.
  • the selected set preferably depends on the current state of the machine, i.e. on the type of the sensor data that indicated the need for the analysis.
  • the method includes acquiring sensor signals from a plurality sensors, wherein the sensor signals are indicative of the operational condition of the electric machine, and storing time- stamped values of the sensor signals into a data storage integrated into the electric machine, thereby to maintain the time-stamped values as sensor history data in the data storage.
  • the method also includes examining, based on the sensor signals, whether an analysis of the operational condition of the electric machine is required and retrieving selected sensor history data from the data storage when the examining indicates that the analysis is required, the selected sensor history data comprising stored time-stamped values of a selected set of sensors.
  • the method further includes forming, in response to the retrieving, at least one data set that includes the selected sensor history data and current sensor signal values of the selected set of sensors and transferring the at least one data set through a communications network to a remote supervisory unit.
  • the diagnostic module includes a plurality of sensors integrated into the electric machine, the plurality of sensors being configured to produce sensor signals indicative of the operational condition of the electric machine, and a data storage integrated into the electric machine.
  • the diagnostic module also includes a first control module integrated into the electric machine and configured to store time- stamped values of the sensor signals into the data storage, thereby to maintain the time-stamped values as sensor history data in the data storage.
  • the diagnostic module further includes a second control module configured to examine whether an analysis of the operational condition of the electric machine is required and a data transfer module responsive to the second control module, the data transfer module being configured to retrieve selected sensor history data from the data storage, form at least one data set that includes the selected sensor history data, and transfer the at least one data set through a network interface to a remote supervisory unit, wherein the selected history data comprises stored time-stamped values of a selected set of sensors and the data transfer module is further configured to add current sensor signal values of the selected set of sensors to the data set.
  • a further aspect of the invention is that of providing a system for monitoring an operational condition of an electric machine.
  • the system includes a diagnostic module in conjunction with the electric machine and a remote supervisory unit configured to communicate with the diagnostic module through a communications system and to analyze the condition of the machine based on the data set(s) transmitted by the diagnostic module.
  • the invention enables a cost-effective implementation of a system that enables machine diagnostics and prognostics together with a possibility for a detailed analysis of the causal relations that may have led to a particular malfunction situation.
  • the invention provides an information- preserving tool for the analysis of the machine operation.
  • the monitoring mechanism of the invention may be employed to optimize the maintenance operations during the life span of the machine. Since the monitoring provides information on the effect of the actual operating environment on the condition of the machine, the monitoring mechanism may be employed to adjust the manufacturer's recommended preventive maintenance program based on the effect of the actual operation environment on a particular machine.
  • FIG. 1 illustrates one embodiment of the system of the invention
  • FIG. 2 is a flow diagram illustrating one embodiment of the control unit integrated into the electric machine;
  • FIG. 3 illustrates one embodiment of the operation of the calculation unit shown in FIG. 2;
  • FIG. 4 illustrates one embodiment of the operation of the configuration unit shown in FIG. 2;
  • FIGs. 5 and 6 illustrate the retrieval of history data from the data storage when a need for the analysis of the condition of the electric machine has been detected
  • FIG. 7 illustrates one embodiment of the operation of a supervisory computer of the system of the invention.
  • FIG. 1 illustrates one embodiment of the system of the present invention.
  • DM integrated diagnostic module
  • control unit 13 that receives the signals generated by the sensors
  • a (non-volatile) data storage 14 for storing the motor data supplied by the control unit
  • a network interface 15 through which the motor data may be transmitted to a communications network 16.
  • the communications network may comprise a local area network 17 which may further be connected to other networks, such as the Internet 19, through gateway servers 18.
  • the analysis of the motor data is carried out in one or more supervisory computers 19a, 19b, which may be connected to various points of the communications network.
  • the integrated diagnostic module fails to have a local user interface at the motor. Instead, each supervisory computer is provided with a user interface for controlling the operation of the diagnostic module. Furthermore, the integrated diagnostic module fails to have tools for analyzing the motor data. Instead, the motor data is transferred through the network interface 15 to a supervisory computer provided with a user interface for controlling the control unit and with algorithms for analyzing the data.
  • the physical variables measured by the sensors of the integrated diagnostic module DM include variables that are commonly measured in diagnostic systems analyzing motor condition.
  • the sensors may thus include, for example, various temperature sensors, one or more vibration sensors, and at least one sensor for measuring the rotation speed of the motor.
  • the analog signals measured are sampled and digitized either in the sensors or in the control unit 13.
  • FIG. 2 illustrates one embodiment of the control unit 13.
  • the digitized time series obtained from the sensors are processed by two parallel units; a configuration unit 21 and a calculation unit 22, which operate independently of each other.
  • the configuration unit 21 is responsible for storing time-stamped sensor data in the data storage 14, while the calculation unit 22 is responsible for deriving measurement data from the sensor data and for detecting, based on the measurement data, the need for an analysis of the condition of the motor.
  • the measurement data which may also be stored in the data storage, may include various statistical variables derived from the sensor data and events detected based on the calculated variables.
  • the calculation unit may also utilize a combination of calculated variables to create an event to be stored in the data storage.
  • event information indicative of motor stall may be stored in the data storage. Such an event may trigger the analysis of the condition of the motor.
  • the storing of the sensor data is carried out substantially continuously for each sensor.
  • the data storage thus contains time-stamped history data that represents the sensor signals obtained during a preceding time period whose length depends on the capacity of the storage and on the storing technique utilized.
  • the calculation unit may employ a compression technique in the storing of the data.
  • FIG. 3 illustrates an embodiment for storing the sensor data generated by a particular sensor or sensor group.
  • sensor data from a particular sensor is stored periodically when a timer expires (steps 32 and 33). If the data received from the said particular sensor and the data received from one or more other sensors are mutually relevant, the sensor data from all said sensors are stored simultaneously at step 33.
  • the configuration unit sets the timer (step 35) and performs the next storing operation when the timer expires again.
  • the configuration unit may is preferably configurable so that the operator of the system may define from the supervisory computer, which sensor signals are to be stored and how often.
  • the calculation unit 22 derives measurement data from the sensor data and detects the need for the analysis based on the measurement data derived.
  • the measurement data may include various motor parameters and event data indicative of the occurrence of predetermined events.
  • FIG. 4 illustrates one embodiment of the operation of the calculation unit. Based on the current data obtained from the sensors, the calculation unit determines measurement data that includes at least one motor parameter and/or event information at step 42. The calculation unit then examines the measurement data derived in order to define whether an analysis of the condition of the motor is needed at the supervisory computer (step 43). If this is not the case, the calculation unit attaches time stamps to the derived measurement data and stores the time-stamped data (step 44).
  • the calculation unit compiles a data set and transmits it to the supervisory computer through the communications network (step 45 and 46).
  • the data set may be formed by retrieving history data from the data storage and inserting the history data and the current sensor data into a data message to be transferred to the supervisory computer.
  • the history data comprises past values of the sensor data received from the sensors that are associated with the motor parameter or event that indicated the need for an analysis.
  • the message may also include an indication of the reason why an analysis is required, and measurement data derived from the sensor data.
  • the supervisory computer may be provided with the same algorithms as the calculation unit for deriving the measurement data based on the sensor data contained in the message. In this case the storing of the measurement data may be omitted unless the calculation unit needs the past values of the measurement data for determining the need for the analysis.
  • FIG. 5 illustrates one embodiment of the compilation of the data set to be transmitted to the supervisory computer. The figure shows a plurality of horizontal time lines extending backwards from current time instant Tc, each time line illustrating the time period P from which the data storage holds sensor data from a particular sensor.
  • the figure further illustrates the detection of the need to analyze the condition of the motor (circled number one), the retrieval of history data from the data storage (circled number two), and the insertion of the current sensor data in the data set (circled number three). It is assumed here that a parameter derived from sensor data obtained from sensor number three indicates a need for an analysis of the condition of the motor. It is further assumed that sensors number four and six include relevant information with respect to sensor number three. Therefore, the calculation unit retrieves the stored history data of sensors number three, four, and six, and adds this data, together with the current sensor data of the said sensors, to the data set to be transmitted to the supervisory computer.
  • the past and current measurement data derived from the sensor data of sensors number three, four, and six may also be inserted into the data set, as is shown by dashed arrows in the figure.
  • the data set 50 may be transmitted as one or more messages to the supervisory computer.
  • the history data covers a preceding time period P, i.e. the storing of the sensor data is carried out substantially continuously.
  • the history data retrieved from the data storage represents time periods P2, P5, and P8.
  • FIG. 6 assumes that the sensors of the motor are divided into three sets, each set comprising the sensors that generate relevant information for analyzing the sensor data of that set.
  • FIG. 7 illustrates an example of an analysis that may be carried out in the supervisory computer to find out the reason for a rise in bearing temperature of a wind generator.
  • the sensor data forming the data set sent to a supervisory computer includes in this example stored and current values for bearing and ambient temperatures as well as those for the vibration and the rotation speed of the generator.
  • the calculation unit thus detects a need for an analysis when the temperature of the bearings rises above a predetermined threshold value.
  • a data set including the above-mentioned data is compiled and sent through the communications network to a supervisory computer.
  • the analysis carried out by the supervisory computer may be divided into three portions.
  • the supervisory computer may determine, based on the bearing and ambient temperatures, a measure M1 indicative of the effect of ambient temperature on the detected temperature rise (step 71).
  • the effect of the ambient temperature may be estimated by examining the absolute values of ambient temperature and the duration of increased values of ambient temperature.
  • the supervisory computer may estimate, based on vibration history data, a measure M2 indicative of the effect of a possible generator misalignment on the temperature rise of the bearings (step 72). This may be carried out by determining the magnitude of axial vibration during the period of increased bearing temperatures. Axial vibration is indicative of misalignment, which may be caused by loosening of the mounting of the generator. This may in turn be caused by aging of the flexible damping elements of the generator, for example.
  • the supervisory computer may determine a measure indicative of the effect of a possible bearing fault on the temperature rise (step 73). This may be carried out by determining, based on the vibration and rotation speed data received in the data set, the amplitude of the vibration at the bearing pass frequency. A significant increase in the amplitude causes an increase in the bearing temperature and serves as an indication of an incipient bearing fault.
  • the source of the temperature rise of the bearings may be detected at a comparison step 74.
  • the maintenance operations required may then be determined according to the source detected.
  • the supervisory computer may give a recommendation for the maintenance operations (step 75), i.e. the supervisory computer may perform all the above steps.
  • the logic of steps 74 and 75 may utilize pre- stored look-up tables, for example, for finding out the source of malfunction and the corresponding maintenance recommendation.
  • the supervisory computer performs only steps 71-73 or steps 71-74, while the operator of the supervisory computer decideson the source based on the results obtained from steps 71-73 or determines the maintenance operations based on the source indicated by the supervisory computer.
  • the malfunction situation involved bearing temperature rise. Similar algorithms may be defined for detecting the sources of other types of malfunctions possible in the machine.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention porte sur la surveillance de l'état de machines électriques (10). Afin d'obtenir un mécanisme rentable permettant des analyses rétrospectives efficaces, des données reçues à partir d'une pluralité de capteurs de machines (12) sont horodatées et sont stockées dans la mémoire (14) de la machine (10). Si un besoin d'analyse de l'état de la machine (10) est détecté, les données stockées originaires d'un ensemble particulier de capteurs (12) sont transmises par l'intermédiaire d'un réseau de communication (16) à une unité de supervision à distance (19a, 19b) qui effectue l'analyse. L'ensemble particulier dépend de manière typique de l'évènement qui indique le besoin d'une analyse.
PCT/FI2008/000047 2007-03-23 2008-03-25 Procédé et appareil pour surveiller l'état de machines électriques Ceased WO2008116966A2 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
FI20070243A FI20070243A7 (fi) 2007-03-23 2007-03-23 Menetelmä ja laitteisto sähkökoneiden kunnon seuraamiseksi
FI20070243 2007-03-23

Publications (2)

Publication Number Publication Date
WO2008116966A2 true WO2008116966A2 (fr) 2008-10-02
WO2008116966A3 WO2008116966A3 (fr) 2009-03-26

Family

ID=37930045

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/FI2008/000047 Ceased WO2008116966A2 (fr) 2007-03-23 2008-03-25 Procédé et appareil pour surveiller l'état de machines électriques

Country Status (2)

Country Link
FI (1) FI20070243A7 (fr)
WO (1) WO2008116966A2 (fr)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2490583A (en) * 2011-04-29 2012-11-07 Gen Electric System and Method for Condition Monitoring of Electrical Machines
WO2018037348A1 (fr) * 2016-08-23 2018-03-01 Abb Schweiz Ag Système de surveillance de l'état d'une pluralité de moteurs
US10063124B2 (en) 2015-12-10 2018-08-28 Schweitzer Engineering Laboratories, Inc. Shaft mounted monitor for rotating machinery
US10077810B2 (en) 2014-04-14 2018-09-18 Dynapar Corporation Sensor hub comprising a rotation encoder
US10298168B2 (en) 2016-07-07 2019-05-21 Schweitzer Engineering Laboratories, Inc. Slip-dependent motor model
US10403116B2 (en) 2017-06-20 2019-09-03 General Electric Company Electrical signature analysis of electrical rotating machines
CN111781496A (zh) * 2020-06-04 2020-10-16 扬州工业职业技术学院 一种电机控制装置及运行数据采集存储方法
US10928814B2 (en) 2017-02-24 2021-02-23 General Electric Technology Gmbh Autonomous procedure for monitoring and diagnostics of machine based on electrical signature analysis
US11218103B2 (en) 2019-10-12 2022-01-04 Schweitzer Engineering Laboratories, Inc. Induction motor slip calculation
EP3540547B1 (fr) 2018-03-13 2022-07-20 Gebhardt Fördertechnik GmbH Procédé de surveillance d'un système de transport automatisé et système de transport correspondant
US11411474B1 (en) 2021-11-17 2022-08-09 Beta Air, Llc Systems and methods for monitoring health of a motor
US11588432B2 (en) 2017-11-17 2023-02-21 Schweitzer Engineering Laboratories, Inc. Motor monitoring and protection using residual voltage
US12203990B2 (en) 2021-11-17 2025-01-21 Beta Air Llc Systems and methods for monitoring health of a motor

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0810555A3 (fr) * 1996-05-31 1999-11-17 Eskom Surveillance d'un système
US6640196B1 (en) * 2001-08-16 2003-10-28 Reliance Electric Technologies, Llc System and method for motor fault detection by space vector angular fluctuation
US6834256B2 (en) * 2002-08-30 2004-12-21 General Electric Company Method and system for determining motor reliability

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8829840B2 (en) 2011-04-29 2014-09-09 General Electric Company Auto-compensating system and method for condition monitoring of electrical machines
GB2490583A (en) * 2011-04-29 2012-11-07 Gen Electric System and Method for Condition Monitoring of Electrical Machines
US10077810B2 (en) 2014-04-14 2018-09-18 Dynapar Corporation Sensor hub comprising a rotation encoder
US10063124B2 (en) 2015-12-10 2018-08-28 Schweitzer Engineering Laboratories, Inc. Shaft mounted monitor for rotating machinery
US10298168B2 (en) 2016-07-07 2019-05-21 Schweitzer Engineering Laboratories, Inc. Slip-dependent motor model
WO2018037348A1 (fr) * 2016-08-23 2018-03-01 Abb Schweiz Ag Système de surveillance de l'état d'une pluralité de moteurs
CN107764316A (zh) * 2016-08-23 2018-03-06 Abb 瑞士有限公司 用于监测多个马达的状况的系统
CN109792227A (zh) * 2016-08-23 2019-05-21 Abb瑞士股份有限公司 用于监测多个马达的状况的系统
CN109792227B (zh) * 2016-08-23 2024-03-29 Abb瑞士股份有限公司 用于监测多个马达的状况的系统
US10928814B2 (en) 2017-02-24 2021-02-23 General Electric Technology Gmbh Autonomous procedure for monitoring and diagnostics of machine based on electrical signature analysis
US10403116B2 (en) 2017-06-20 2019-09-03 General Electric Company Electrical signature analysis of electrical rotating machines
US11588432B2 (en) 2017-11-17 2023-02-21 Schweitzer Engineering Laboratories, Inc. Motor monitoring and protection using residual voltage
EP3540547B1 (fr) 2018-03-13 2022-07-20 Gebhardt Fördertechnik GmbH Procédé de surveillance d'un système de transport automatisé et système de transport correspondant
US11218103B2 (en) 2019-10-12 2022-01-04 Schweitzer Engineering Laboratories, Inc. Induction motor slip calculation
CN111781496A (zh) * 2020-06-04 2020-10-16 扬州工业职业技术学院 一种电机控制装置及运行数据采集存储方法
US11411474B1 (en) 2021-11-17 2022-08-09 Beta Air, Llc Systems and methods for monitoring health of a motor
US12203990B2 (en) 2021-11-17 2025-01-21 Beta Air Llc Systems and methods for monitoring health of a motor

Also Published As

Publication number Publication date
FI20070243L (fi) 2009-01-12
FI20070243A0 (fi) 2007-03-23
WO2008116966A3 (fr) 2009-03-26
FI20070243A7 (fi) 2009-01-12

Similar Documents

Publication Publication Date Title
WO2008116966A2 (fr) Procédé et appareil pour surveiller l'état de machines électriques
JP3780508B2 (ja) 工作機械の異常診断装置、異常診断方法及び異常診断プログラム
KR102407484B1 (ko) 정보 처리 방법, 정보 처리 시스템, 및 정보 처리 장치
US7200520B2 (en) Device and method for monitoring an electric power station
Zaher et al. Online wind turbine fault detection through automated SCADA data analysis
US6208953B1 (en) Method for monitoring plants with mechanical components
CN110023862B (zh) 诊断装置、诊断方法及计算机可读取的记录介质
EP1538503A1 (fr) Procédé d'évaluation des événements dans une centrale électrique répartie et procédé pour déterminer un plan de réduction
JP2017188030A (ja) 主軸または主軸を駆動するモータの故障予知を学習する機械学習装置および機械学習方法、並びに、機械学習装置を備えた故障予知装置および故障予知システム
CN102758655B (zh) 用于保护旋转机械的方法
JP2002330558A (ja) 電力送配システム監視方法および電力送配システム
US20230131828A1 (en) Predictive maintenance system and method for intelligent manufacturing equipment
US20040199361A1 (en) Method and apparatus for equipment diagnostics and recovery with self-learning
KR102561675B1 (ko) 생산현장 가동 설비 상태 모니터링을 통한 고장진단 및 예측 시스템
US20240247659A1 (en) Method for monitoring the operation of a fan, apparatus and fan
US7996337B2 (en) Method and system for remotely determining the reliability of a DC motor system
WO2020253950A1 (fr) Procédé de surveillance, procédé de prédiction, système de surveillance et programme informatique
US6904388B2 (en) Method for preventing or minimizing operational failures in a technical installation
US10444729B2 (en) Monitoring device, machine tool, and monitoring system
KR20110075332A (ko) 설비 고장 예측 방법
US20230359191A1 (en) System and method for determining a cause of an operating anomaly of a machine, computer program and electronically readable data storage device
US20240248150A1 (en) Monitoring an event in a power converter
CN119784359A (zh) 基于工业物联网的设备运维管理方法、系统、设备及介质
CN118468169A (zh) 基于物联网的设备故障监测方法、装置、设备及存储介质
KR102797798B1 (ko) 진단 장치 및 진단 방법 그리고 플라스마 처리 장치 및 반도체 장치 제조 시스템

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: 08718506

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 08718506

Country of ref document: EP

Kind code of ref document: A2