WO2018158910A1 - Dispositif et procédé de diagnostic - Google Patents

Dispositif et procédé de diagnostic Download PDF

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Publication number
WO2018158910A1
WO2018158910A1 PCT/JP2017/008303 JP2017008303W WO2018158910A1 WO 2018158910 A1 WO2018158910 A1 WO 2018158910A1 JP 2017008303 W JP2017008303 W JP 2017008303W WO 2018158910 A1 WO2018158910 A1 WO 2018158910A1
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WO
WIPO (PCT)
Prior art keywords
rotating machine
diagnostic
current
lissajous
machine system
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PCT/JP2017/008303
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English (en)
Japanese (ja)
Inventor
哲司 加藤
牧　晃司
永田　稔
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株式会社日立製作所
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Application filed by 株式会社日立製作所 filed Critical 株式会社日立製作所
Priority to PCT/JP2017/008303 priority Critical patent/WO2018158910A1/fr
Priority to TW107106931A priority patent/TWI665458B/zh
Publication of WO2018158910A1 publication Critical patent/WO2018158910A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M99/00—Subject matter not provided for in other groups of this subclass
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K11/00—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection
    • H02K11/20—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection for measuring, monitoring, testing, protecting or switching
    • H02K11/27—Devices for sensing current, or actuated thereby

Definitions

  • the present invention relates to a diagnostic apparatus and a diagnostic method.
  • the rotating machine system In order to prevent sudden failure of the rotating machine system (rotating machine and its associated devices (cables, power converters)), the rotating machine system is appropriately stopped and diagnosed offline, so that the deterioration condition can be grasped and It can be prevented to some extent.
  • Some types of deterioration become apparent only when voltage is applied. Therefore, there is a need for diagnosing the state of the rotating machine based on the information on the current of the rotating machine system.
  • Non-patent document 1 is known as a conventional technique related to diagnosis based on current information of a rotating machine system.
  • a method called Motor Current Signature Analysis (MCSA) is used to determine damage to rotor bars, rotor eccentricity, stator core damage, winding shorts, bearing deterioration, etc.
  • the diagnosis can be made by detecting a specific frequency spectrum.
  • Patent Document 1 especially in bearing diagnosis, a method of acquiring vibration sensor data at two locations and judging an abnormality from a change in the trajectory inclination or radius of a Lissajous figure drawn with the instantaneous value of each sensor's data as an axis. Is disclosed.
  • Non-Patent Document 1 and Patent Document 1 have the following problems.
  • it is necessary to detect a specific frequency spectrum.
  • To detect a specific frequency spectrum with high accuracy it is expensive for diagnosis corresponding to long-time measurement at a high sampling rate.
  • a new data logger is necessary, and the increase in diagnostic costs has been a problem.
  • the specific frequency spectrum may appear unintentionally, and there has been a problem of false alarms.
  • the fundamental frequency changes and a spectrum appears at a frequency different from the expected frequency, causing a problem of misreporting.
  • Patent Document 1 is a diagnosis based on vibration sensor information, and it is necessary to attach a vibration sensor to a position sensitive to a motor failure, and there is a problem that an installation place is limited. .
  • a diagnostic sensor for diagnosis and an expensive data logger having a sampling rate sufficient to obtain a trajectory of a Lissajous figure are required, and an increase in diagnostic cost is a problem.
  • the object of the present invention is to provide a diagnostic apparatus that performs highly accurate diagnosis even when data measurement is performed in a short time, with a low sampling rate and with a general-purpose device.
  • the diagnostic device of the present invention includes a current measurement unit that measures current flowing in at least two locations of the rotating machine, and a classification unit that classifies current data measured by the current measurement unit for each command value information of the power converter.
  • a distribution of Lissajous figures obtained by superimposing a plurality of periods of current data of at least two phases classified by the classification unit (defined here as a distribution in which each point is not connected by a line in time series but plotted as a collection of points.
  • the status of the rotating machine or power converter is diagnosed from the change in the Lissajous figure's distribution A diagnostic unit is provided.
  • the diagnostic method of the present invention measures the current flowing in at least two places of the rotating machine, classifies the measured current data for each command value information of the power converter, and classifies at least two phases and a plurality of cycles. Create a Lissajous figure distribution by superimposing current data, compare the created distribution with the Lissajous figure distribution set in advance as a normal state, and change the surroundings of the rotating machine or the power converter connected to the rotating machine Diagnose device status. Further details of the diagnostic method apparatus configuration and diagnostic method will be described in the detailed description.
  • an expensive data logger is unnecessary, and the state of the rotating machine system can be diagnosed even when the driving condition of the motor changes.
  • Configuration diagram of diagnosis apparatus The block diagram of the diagnostic apparatus of a conventional method.
  • FIG. 6 is a configuration diagram according to a third embodiment.
  • FIG. FIG. 6 is a configuration diagram according to a fifth embodiment.
  • a rotating machine system including a rotating machine such as an electric motor (motor) or a generator, a cable attached to the rotating machine, and a power conversion device, occurs in various parts, and a variety of failure factors. For example, insulation deterioration, bearing deterioration, short circuit, disconnection, water immersion, etc. can be considered. Moreover, the electric motor is often installed for a long time in a harsh environment, and a diagnostic technique according to the installation condition is required.
  • FIG. 14 An example of a conventional diagnostic device 14 is shown in FIG.
  • a conventional diagnostic apparatus one-phase current sensor information is acquired by the current measurement unit 11, and the diagnosis unit 12 performs diagnosis based on the value of the specific frequency spectrum of the spectrum obtained by Fourier transform. Since the change in the specific frequency spectrum is measured by Fourier transform, it is necessary to perform continuous measurement at a constant sampling rate. Therefore, it is necessary to increase the capacity of the memory for temporarily storing the measured data or increase the communication speed with the device for storing the data, and an expensive device is required. Moreover, since the change of the control signal is not assumed, there is a problem that the frequency of misreporting and misreporting is high.
  • the present inventors plot the intermittent sensor values for two phases among the load current values of the rotating machine on one plane, and visualize the state of the rotating machine as a data distribution map similar to one Lissajous figure. I examined that.
  • the Lissajous figure is a plane figure obtained by combining two waves. Since the current sensor data of the three-phase motor is shifted by 120 degrees from each other, when the two phases are combined, it becomes an inclined elliptical shape. Normally, the data is created by continuous data, but the inventors superimpose data for a plurality of frequencies obtained at predetermined times obtained intermittently, and use the resulting data distribution for evaluation. As a result, it is possible to diagnose the state of the rotating machine system with higher accuracy than intermittent data and to omit expensive equipment such as a data logger.
  • the diagnostic device of the present embodiment includes a current measuring unit that measures current flowing in at least two locations of the rotating machine, and a rotating machine and a rotating machine based on current data output from the current measuring unit.
  • the current flowing in at least two places of the rotating machine is measured, and the measured current data is classified for each command value information of the power conversion device.
  • Create a Lissajous figure distribution map by overlaying the classified current data for multiple periods for each information, evaluate the created Lissajous figure distribution map, and diagnose the state of the rotating machine system based on the result Can do.
  • the above diagnostic device can be incorporated into a rotating machine system.
  • a plurality of rotating machines may be connected to the power conversion device.
  • a rotating machine system having a current measuring unit that measures a flowing current and a control unit that outputs a command value for controlling the rotating machine, and diagnoses the state of a device electrically or mechanically connected to the rotating machine And a classification unit that classifies the current data output from the current measurement unit and input to the diagnostic unit for each of the command values, and the diagnostic unit stores the current data of the rotating machine in at least two phases and Create a Lissajous figure distribution obtained by overlapping multiple periods.
  • a current measurement unit that measures current flowing in at least two places of the rotating machine
  • a classification unit that classifies the current data measured by the current measurement unit for each command value information of the power converter
  • Distribution of Lissajous figures obtained by superimposing a plurality of periods of current data of at least two phases classified by the classification unit (here, points are defined as distributions in which points are not connected by a line in time series but plotted as a collection of points.
  • FIG. 1 shows a configuration diagram of the diagnostic apparatus of Example 1, and the diagnostic apparatus and the diagnostic method will be described. A common part with the description of FIG. 2 is omitted.
  • Example 1 the power source 1, the cable 2, and the power conversion device 7 are electrically connected, and the power conversion device 7 outputs a three-phase AC voltage.
  • the output of the three-phase AC voltage is controlled by adjusting the timing for operating the switching element of the inverter so that the rotation speed and torque of the motor have desired values.
  • the control is determined based on control information set in advance and information on the current output from the inverter.
  • the current information is acquired by the current sensors 4a and 4b and the current measuring unit 9 and fed back to the control unit 8.
  • the current information of the current sensors 4a and 4b acquired by the current measuring unit does not necessarily have to be a constant sampling interval, and does not necessarily need to be a continuous measurement.
  • the interval between the data acquisition of the current sensor 4a and the data acquisition of the current sensor 4b is preferably constant allowing a certain variation.
  • a measurement device that excels in real-time processing a certain data acquisition interval that allows a certain variation can be obtained.
  • a measuring device using a microcomputer is an example.
  • the current measurement unit 9 can be designed to store a certain amount of data in the memory of the microcomputer, insert a data communication process to the storage device, and then clear the memory to resume data storage. It is possible to make a system using a current sensor or a memory.
  • FIG. 3a is a diagram showing a case where a frequency spectrum separated by 1 Hz from the fundamental frequency 50 Hz of the U-phase current appears.
  • a frequency spectrum separated by 1 Hz from the fundamental frequency 50 Hz of the U-phase current appears.
  • sideband waves of the fundamental frequency are generated due to the deterioration.
  • the U-phase current appears as a waveform having a beat of 1 Hz period.
  • diagnosis can be made by using any two types of current values, without increasing the sampling speed and the data length to be measured.
  • FIG. 4 shows normal U-phase and W-phase current waveforms, that is, a 50 Hz sine wave without undulations.
  • the U-phase and W-phase currents are 120 degrees out of phase.
  • FIG. 5 shows a conceptual diagram of U-phase and W-phase current waveforms when sidebands are generated, as in FIG.
  • the sampling rate is 100 Hz and the data measurement time is 1 second.
  • FIGS. 4 and 5 based on the respective current waveforms obtained as a result of sampling at high speed, the U-phase current is plotted on the horizontal axis and the W-phase current is plotted on the vertical axis.
  • the distribution is as follows. 6A is an example of a normal state based on FIG. 4, and FIG. 6B is a conceptual diagram when a sideband wave is generated due to deterioration of a rotating machine or the like based on FIG. 6A and 6B, it can be seen that in the deteriorated state, the distribution of the Lissajous figure is thicker than that in the normal state, and the thickness of the Lissajous figure distribution changes as the tendency of deterioration progresses.
  • FIG. 7 shows an example (FIG. 7a: normal, FIG. 7b: deterioration) in which Lissajous figure distributions are created for normal and deteriorated states using U-phase and W-phase currents obtained at a sampling rate of 4.975 Hz.
  • the data length to be measured is 1000 points, and the data length is the same as in Figure 6. 6 and 7 show almost the same Lissajous figure distribution.
  • the distribution of the Lissajous figure may be confirmed by human eyes, or the difference in the distribution of the Lissajous figure may be digitized and compared by a method such as machine learning. That is, according to the present embodiment, it is possible to easily detect the occurrence of undulation not only in the case of performing high-speed sampling but also in any case of a slow sampling frequency.
  • the sampling speed is slow, it is desirable to measure asynchronously with the fundamental frequency. Specifically, at a sampling rate having a period that is an integral multiple of the fundamental wave, a value of only a certain phase is always obtained, and there is a risk of false alarm in the case of deterioration in which a change appears only in a specific phase. Therefore, it is desirable that the sampling rate is a frequency different from an integral multiple of the fundamental wave period. As a result, a distribution map for diagnosis similar to the case where data is acquired at a high sampling rate can be acquired.
  • Lissajous figure points are filled in time-sequential order with lines (Lissajous figure trajectory)
  • the inside of the Lissajous figure distribution will be filled with lines, and the difference between the normal state and the deteriorated state may not be visible. It is desirable not to display the trajectory by connecting the lines in series order.
  • the diagnosis unit 10 outputs the above result.
  • Means for transmitting the diagnosis result to the user can be selected as appropriate. Examples of the method for transmitting to the user include display of a display, lighting of a lamp, notification by e-mail, and the like.
  • the contents are also (1) a method for displaying the distribution of Lissajous figures on the screen and allowing the user to determine whether or not it is compatible, (2) a method for quantifying the difference in the distribution of Lissajous figures in some way and telling the user (3) in advance A method of notifying the user when a predetermined threshold is exceeded can be considered.
  • machine learning can be applied.
  • an algorithm for machine learning an algorithm that makes the difference between Lissajous figures clear should be selected.
  • a local subspace method can be cited. In the local subspace method, for all the points in the distribution of the Lissajous figure to be diagnosed, two closest points are selected from the distribution of the Lissajous figure defined as normal, and the straight line connecting the two points is diagnosed. In this method, the degree of deterioration is defined by the distance between target points.
  • the average value of the distances of all the points to be diagnosed In addition to the method of calculating the distance for all points to be diagnosed and digitizing the change in the distribution of the Lissajous figure, the average value of the distances of all the points to be diagnosed, the number of only the points of the specific phase, etc.
  • An arbitrary evaluation method can be selected according to the variation error of the current waveform.
  • a clustering method such as vector quantization clustering or K-means clustering can be used.
  • a technique called a deep neural network which is a method for automatically finding feature quantities based on a large amount of data, can be applied.
  • the control pattern changes When the control pattern changes, the fundamental frequency, torque, etc. of the motor change. Therefore, the conventional method sometimes diagnoses this as an abnormality. Further, when learning is performed as a normal state including a state in which the control pattern is changed, a change due to deterioration may be overlooked and reported. Therefore, it is desirable to diagnose normality and abnormality for each control pattern.
  • the control command of the control unit and the current information were combined to improve the diagnostic accuracy. Since the diagnosis unit 10 diagnoses the state of the motor system based on the distribution of Lissajous figures drawn with the currents of the U phase and the W phase classified as the same state as the control command, the classification unit as shown in FIG. 6 was provided. Hereinafter, the function combining the classification unit 6 and the diagnosis unit 10 will be described.
  • a voltage command value, a current command value, an excitation current command value, a torque current command value, a speed command value, a frequency command value, and the like can be arbitrarily selected from command values that can be output by the power converter 7 Can do.
  • the classification unit 10 does not necessarily need to use all command values that can be output by the power conversion device 7, and can use only command values that are highly sensitive to the deterioration of the detection target.
  • the command value with high sensitivity may be selected so that the difference between the deteriorated state and the normal state can be easily seen by comparing the distribution of the Lissajous figure in the deteriorated state with the distribution of the Lissajous figure in the normal state.
  • the classification unit 6 distributes the current information obtained from the current measurement unit 9 according to control command values (control command A and control command B) obtained from the control unit 8. Based on the distribution of the classification unit 6, the diagnosis unit 10 stores current information and command value information at intervals of about 1 minute, and creates a Lissajous figure distribution map for each control command value.
  • Figure 8 shows the distribution of normal and deteriorated Lissajous figures classified by each control command.
  • 8a and 8c show the result of drawing the distribution of the Lissajous figure in the control command A, and FIGS. 8b and 8d in the control command B.
  • FIG. 8a and FIG. 8b show the result of drawing the distribution of the Lissajous figure in the control command A, and FIGS. 8b and 8d in the control command B.
  • the current information acquired from the current measurement unit is divided into one associated with the control command A and one associated with the control command B.
  • a normal state and a deteriorated state are assigned for each of the control command A and the control command B.
  • a normal state and a deteriorated state are assigned.
  • Fig. 9 shows learning data Y (control command A normal) in the vicinity of the data X to be diagnosed (control command A normal or degraded) in the Lissajous figure.
  • Search for data X control command A normal or degraded
  • diagnosis data Y1 control command A normal
  • learning data Y2 control command A normal
  • the degree of abnormality was defined by the distance between the straight line connecting the data of Y1, Y2 and the data of the diagnosis target data X (control command A normal or deteriorated).
  • the degree of abnormality of the diagnosis target data group X1 to n (control command A normal or degraded) with respect to the learning data group Y can be quantified.
  • Fig. 10 shows the determination and evaluation results of the diagnosis results for the normal state and the abnormal state.
  • An average value of the degree of abnormality of normal current data by the control command A and an average value of the degree of abnormality of current data when the device is deteriorated by the control command A are shown.
  • the average value of the degree of abnormality increases due to deterioration. If a threshold value is set in advance by prior examination, an increase in the degree of abnormality, that is, the progress of deterioration can be displayed to the user.
  • the case where a spectrum is generated at a specific frequency has been described.
  • the type of deterioration does not generate a spectrum at a specific frequency
  • some change in current occurs due to the load or impedance change of the motor due to the deterioration. Therefore, it can be detected by the diagnostic apparatus and diagnostic method of the first embodiment.
  • Deterioration that does not appear as a change in the specific frequency spectrum is specifically deterioration other than the deterioration that appears as a peak at the specific frequency that can be detected by MCSA, that is, grease deterioration, thermal deterioration of insulation, and moisture absorption. Is assumed.
  • the diagnostic apparatus of the first embodiment it is possible to diagnose a motor system including a motor, a cable, a power converter, a load, and other devices electrically or mechanically connected to the motor.
  • a motor system including a motor, a cable, a power converter, a load, and other devices electrically or mechanically connected to the motor.
  • the current flowing through the motor changes due to changes in the impedance and load of those devices. Deterioration can be detected by a technique.
  • FIG. 11 is an example of a diagnostic system including a command unit 13.
  • the command unit 13 sets a control command in the control unit 8 and changes it.
  • the information of the control unit 8 is input to the classification unit and diagnosed.
  • the information of the command unit 13 is input to the classification unit together with the control command value obtained from the control unit 8 or instead of the control command value. And may be diagnosed.
  • FIG. 11 when information such as the presence of multiple types of loads to be rotated, changes in control command values according to time, etc. are stored in the command unit 13, the information of the command unit 13 is input to the classification unit, You may make a diagnosis. Thereby, diagnostic accuracy can be improved.
  • Example 1 In Example 1 and Example 2, an example in which one motor 3 is connected to one power conversion device 7 has been described. In Example 3, as shown in FIG. 12, an example in which a plurality of motors 3 are connected to one power conversion device 7 will be described.
  • diagnosis is performed using the result of measuring the entire load current by the current sensor in the power converter.
  • the diagnosis method is the same as when one rotating machine is connected, and the diagnosis data is diagnosed as a Lissajous figure distribution along with classification using control command values and reflection of command value information.
  • the current sensor and the current measurement unit in the power converter are used.
  • the current sensors 4a and 4b and the current measurement unit 9 prepared separately from the power converter are used. Even so, the effect of the present invention can be obtained. Since it is not necessary to support long-term data measurement at a high sampling rate, an inexpensive general-purpose measuring device can be used.
  • the current sensor and the current measurement unit in the power conversion device are used.
  • a current sensor may be provided. Moreover, it is good also as providing the current sensor independent of a power converter device in the position where a motor and a power converter device are connected, and measuring the whole load current.
  • a classification unit / diagnostic unit can be provided for each rotating machine, or multiple sensor information can be diagnosed with a single classification unit / diagnostic unit. It is also possible to process.
  • Example 5 describes an example in which three current sensors are installed for each of a zero-phase current and a two-phase load current.
  • the state of the motor system is diagnosed based on the information of the two current sensors, but as shown in FIG. 14, the distribution of the Lissajous figure is based on the information of three or more current sensors. You may get. In this case, when comparing the distributions, it is possible to obtain and evaluate a Lissajous figure distribution by a plurality of combinations of two selected from three or more current sensors.
  • the current value measured by the current sensor in the case of a three-phase AC rotating machine, in addition to the U-phase, V-phase, and W-phase load currents, as well as the zero-phase current measured by clamping three phases
  • Two or more currents can be set, such as two-phase current measured by clamping the selected two phases, leakage current measured by clamping both the winding start and end of the motor winding, and current flowing from the motor to the ground.
  • the position of a highly sensitive current sensor can be selected for each degradation mechanism. For example, bearing deterioration can be detected with high sensitivity at load current, and insulation deterioration can be detected with zero-phase current. What is necessary is just to select arbitrarily the electric current which raises diagnostic sensitivity with respect to the cause of deterioration, and to install a current sensor in the position.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Power Engineering (AREA)
  • Control Of Electric Motors In General (AREA)
  • Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)

Abstract

L'invention concerne un dispositif de diagnostic permettant de diagnostiquer avec une grande précision une machine tournante même lorsqu'un diagnostic est basé sur une petite quantité de données de courant. Ledit dispositif de diagnostic est pourvu d'une unité de mesure de courant servant à mesurer le courant circulant à travers au moins deux emplacements dans une machine tournante et d'une unité de diagnostic servant à diagnostiquer, sur la base de données de courant délivrées par l'unité de mesure de courant, l'état de la machine tournante et d'un dispositif périphérique connecté électriquement ou mécaniquement à la machine tournante. L'unité de diagnostic crée une carte de distribution de courbe de Lissajous par superposition de multiples périodes des deux types de données de courant obtenues par l'unité de mesure de courant et diagnostique l'état du système de machine tournante à partir des résultats d'évaluation de la carte de distribution.
PCT/JP2017/008303 2017-03-02 2017-03-02 Dispositif et procédé de diagnostic WO2018158910A1 (fr)

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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113227751A (zh) * 2019-01-11 2021-08-06 松下知识产权经营株式会社 诊断系统、诊断方法、程序以及记录介质
US11372048B2 (en) * 2019-11-29 2022-06-28 Hitachi, Ltd. Diagnostic device and diagnostic method
US11708816B2 (en) * 2020-04-28 2023-07-25 Mitsubishi Heavy Industries, Ltd. Condition monitoring device and condition monitoring method for wind turbine power generating apparatus
WO2023176039A1 (fr) * 2022-03-17 2023-09-21 三菱電機株式会社 Dispositif de diagnostic d'équipement et système de diagnostic d'équipement

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7198089B2 (ja) * 2019-01-10 2022-12-28 株式会社日立産機システム 電力変換装置、回転機システム、及び診断方法
JP7218239B2 (ja) * 2019-04-26 2023-02-06 株式会社日立製作所 電気機械の診断方法および診断装置、並びに回転電機

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0974794A (ja) * 1995-09-05 1997-03-18 Toyota Motor Corp 交流電動機制御回路の異常検出装置
JP2003274691A (ja) * 2002-03-14 2003-09-26 Jfe Steel Kk 交流電動機の回転子異常検出方法及び回転子異常検出装置
JP2005251185A (ja) * 2004-02-05 2005-09-15 Toenec Corp 電気設備診断システム
JP2010288352A (ja) * 2009-06-10 2010-12-24 Nippon Steel Corp 設備の異常診断方法
JP2011075522A (ja) * 2009-10-02 2011-04-14 Jfe Mechanical Co Ltd 設備機器の診断装置
EP2851698A1 (fr) * 2013-09-18 2015-03-25 ABB Technology Ltd Procédé de détection d'une panne d'une machine électrique
JP2015227889A (ja) * 2015-08-10 2015-12-17 株式会社高田工業所 回転機械系の異常診断方法

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4184827A (en) * 1975-12-16 1980-01-22 General Electric Company Apparatus for monitoring and controlling the operation of a dual platen press
US6986649B2 (en) * 2003-04-09 2006-01-17 Motorola, Inc. Micropump with integrated pressure sensor

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0974794A (ja) * 1995-09-05 1997-03-18 Toyota Motor Corp 交流電動機制御回路の異常検出装置
JP2003274691A (ja) * 2002-03-14 2003-09-26 Jfe Steel Kk 交流電動機の回転子異常検出方法及び回転子異常検出装置
JP2005251185A (ja) * 2004-02-05 2005-09-15 Toenec Corp 電気設備診断システム
JP2010288352A (ja) * 2009-06-10 2010-12-24 Nippon Steel Corp 設備の異常診断方法
JP2011075522A (ja) * 2009-10-02 2011-04-14 Jfe Mechanical Co Ltd 設備機器の診断装置
EP2851698A1 (fr) * 2013-09-18 2015-03-25 ABB Technology Ltd Procédé de détection d'une panne d'une machine électrique
JP2015227889A (ja) * 2015-08-10 2015-12-17 株式会社高田工業所 回転機械系の異常診断方法

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113227751A (zh) * 2019-01-11 2021-08-06 松下知识产权经营株式会社 诊断系统、诊断方法、程序以及记录介质
CN113227751B (zh) * 2019-01-11 2024-06-04 松下知识产权经营株式会社 诊断系统、诊断方法、程序以及记录介质
US11372048B2 (en) * 2019-11-29 2022-06-28 Hitachi, Ltd. Diagnostic device and diagnostic method
US11708816B2 (en) * 2020-04-28 2023-07-25 Mitsubishi Heavy Industries, Ltd. Condition monitoring device and condition monitoring method for wind turbine power generating apparatus
WO2023176039A1 (fr) * 2022-03-17 2023-09-21 三菱電機株式会社 Dispositif de diagnostic d'équipement et système de diagnostic d'équipement

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