WO2018020563A1 - 電動機の診断装置 - Google Patents
電動機の診断装置 Download PDFInfo
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- WO2018020563A1 WO2018020563A1 PCT/JP2016/071800 JP2016071800W WO2018020563A1 WO 2018020563 A1 WO2018020563 A1 WO 2018020563A1 JP 2016071800 W JP2016071800 W JP 2016071800W WO 2018020563 A1 WO2018020563 A1 WO 2018020563A1
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- WIPO (PCT)
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- unit
- current
- power spectrum
- electric motor
- sideband
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02K—DYNAMO-ELECTRIC MACHINES
- H02K15/00—Methods or apparatus specially adapted for manufacturing, assembling, maintaining or repairing of dynamo-electric machines
- H02K15/02—Methods or apparatus specially adapted for manufacturing, assembling, maintaining or repairing of dynamo-electric machines of stator or rotor bodies
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- 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
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- 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
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
- G01R19/25—Arrangements for measuring currents or voltages or for indicating presence or sign thereof using digital measurement techniques
- G01R19/2506—Arrangements for conditioning or analysing measured signals, e.g. for indicating peak values ; Details concerning sampling, digitizing or waveform capturing
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/16—Spectrum analysis; Fourier analysis
Definitions
- the present invention relates to a motor diagnosis apparatus that is used in a control center such as a closed switchboard and diagnoses whether there is an abnormality in an induction motor.
- Patent Document 1 Japanese Patent Document 1
- An object of the present invention is to provide an electric motor diagnosis device capable of diagnosing the presence or absence of the motor.
- An electric motor diagnosis apparatus includes a current input unit that detects and inputs an electric current of an electric motor, an FFT analysis unit that analyzes a power spectrum of the current when the current from the current input unit is in a stable state, A peak detection calculation unit for detecting a peak portion of the power spectrum obtained by the FFT analysis unit, an averaging calculation unit for averaging a plurality of times of the power spectrum analyzed by the FFT analysis unit, and the averaging calculation unit A sideband wave extraction unit that extracts the sideband wave of the power spectrum averaged in step B, and a warning output unit that outputs a warning when a sideband wave having a signal strength equal to or higher than a set value is extracted by the sideband wave extraction unit. It is characterized by this.
- a current input unit that detects and inputs a current of an electric motor
- an FFT analysis unit that analyzes a power spectrum of the current when the current from the current input unit is in a stable state
- the FFT analysis unit The peak detection calculation unit for detecting the peak portion of the power spectrum obtained in step 1, the averaging calculation unit for averaging a plurality of times of the power spectrum analyzed by the FFT analysis unit, and the average calculation unit
- a sideband extraction unit for extracting sidebands of the power spectrum, and an alarm output unit for outputting an alarm when a sideband wave having a signal intensity equal to or higher than a set value is extracted by the sideband extraction unit.
- FIG. 2 It is a block diagram which shows the structure of the logic calculating part of the diagnostic apparatus of the electric motor in Embodiment 2 of this invention. It is explanatory drawing explaining the trend analysis of the diagnostic apparatus of the electric motor in Embodiment 2 of this invention. It is explanatory drawing explaining the setting of the threshold value of the diagnostic apparatus of the electric motor in Embodiment 1 of this invention.
- FIG. 1 is a schematic configuration diagram showing an installation state of a motor diagnostic device according to Embodiment 1 of the present invention
- FIG. 2 is a block diagram showing a configuration of a logical operation unit of the motor diagnostic device according to Embodiment 1 of the present invention
- FIG. 3 is an explanatory diagram for explaining the frequency analysis result when the load variation of the electric motor of the electric motor diagnosis apparatus according to Embodiment 1 of the present invention is large
- FIG. 4 shows the frequency of the electric motor diagnosis apparatus according to Embodiment 1 of the present invention.
- FIG. 5 is a flowchart for explaining the operation of the motor diagnostic apparatus according to Embodiment 1 of the present invention
- FIG. 9 is a threshold for the motor diagnostic apparatus according to Embodiment 1 of the present invention. It is explanatory drawing explaining the setting of a value.
- the main circuit 1 drawn from the power system includes a circuit breaker 2, an electromagnetic contactor 3, and a current detector such as an instrument transformer that detects a one-phase load current of the three-phase main circuit 1.
- a vessel 4 is provided.
- a motor 5 such as a three-phase induction motor as a load is connected to the main circuit 1, and mechanical equipment 6 is driven to operate by the motor 5.
- the motor diagnosis device 7 is input from the rating information input unit 8 and the rating information input unit 8 in which the power frequency, the rated output of the motor 5, the rated voltage, the rated current, the number of poles, the rated number of revolutions, etc.
- a rating information storage unit 9 for storing the rating information is provided.
- the rating information is information that can be easily obtained by looking at the catalog of the manufacturer of the motor 5 or the nameplate attached to the motor 5.
- rating information of all the motors 5 to be diagnosed is input in advance, but in the following description, one motor 5 will be described.
- the motor diagnosis device 7 diagnoses whether there is an abnormality in the motor 5 by using the current input unit 10 for inputting the current detected by the current detector 4 and the current input from the current input unit 10.
- a logic operation unit 11 and an alarm output unit 21 that outputs an alarm by turning on an alarm or an abnormal lamp when an abnormality is found in the logic operation unit 11 are provided.
- the configuration of the logical operation unit 11 uses the current fluctuation calculation unit 12 to obtain the current fluctuation input from the current input unit 10 and the result obtained by the current fluctuation calculation unit 12 to extract a stable current section.
- An FFT analysis section determination unit 13 that determines a power spectrum analysis section, an FFT analysis section 14 that performs power spectrum analysis using the current of the section determined by the FFT analysis section determination section 13, and an analysis by the FFT analysis section 14
- a peak detection calculation unit 15 for detecting a peak location included in the power spectrum
- a rotation frequency band determination unit 16 for obtaining a peak location due to the rotation frequency from the peak location detected by the peak detection calculation unit 15, and a plurality of times
- Frequency axis conversion calculation unit 17 that matches the frequency of the rotation frequency band of the power spectrum, and the frequency axis converted by frequency axis conversion calculation unit 17
- Averaging calculation unit 18 that averages the power spectrum for several times and the power spectrum averaged by averaging calculation unit 18 are used to extract whether there are peak points on both
- the current fluctuation calculation unit 12 calculates a statistical variation in the current value based on the current from the current input unit 10.
- the calculation of the variation includes techniques such as standard deviation and Mahalanobis distance.
- the FFT analysis section determination unit 13 extracts only the current section in which the current value whose variation is equal to or less than the threshold value is stable, from the statistical variation of the current value obtained by the current fluctuation calculation unit 12, and the power spectrum analysis section To decide.
- the load torque of the motor 5 fluctuates, the current value varies, and when a power spectrum analysis of a current waveform having a large variation is performed, the signal intensity on both sides near the power supply frequency increases as shown in FIG. As a result, peak points such as sidebands do not appear.
- a threshold value of the FFT analysis section determination unit 13 is provided.
- the FFT analysis unit 14 calculates the current power spectrum intensity by performing frequency analysis using the current waveform input in the section determined by the FFT analysis section determination unit 13. By performing the power spectrum analysis with a current waveform in a state where the current value is stable, the power spectrum intensity does not increase on both sides in the vicinity of the power supply frequency, and the peak portion appears surely.
- the peak detection calculation unit 15 detects the peak location due to the power supply frequency, the peak location due to the rotation frequency, the peak location due to the sideband, and other peak locations from the analysis result of the current power spectrum intensity. The peak location can be detected by extracting a portion where the steep slope of the result calculated by the first-order, second-order and third-order differential calculations is inverted.
- the differential calculation up to the third order it is possible to detect a peak portion having a smaller signal intensity.
- the peak portion due to the power supply frequency can be easily confirmed because it occurs at the position of the power supply frequency (generally 50 Hz or 60 Hz) stored in the rating information storage unit 9.
- the rotation frequency band determination unit 16 obtains a rotation frequency from the rated rotation number stored in the rating information storage unit 9, and the signal intensity near the position shifted by the rotation frequency on both sides with respect to the power supply frequency is a similar peak point. To extract. Generally, since the motor 5 slips according to the state of the load torque and the rotational speed is deviated, the peak portion due to the rotational frequency also appears deviated accordingly. The rotational frequency band determination unit 16 extracts a peak portion in the frequency band considering this shift and determines it as a rotational frequency band.
- the frequency axis conversion calculation unit 17 is necessary for correctly performing the averaging calculation performed by the averaging calculation unit 18.
- the position of the sideband generated by the abnormality of the electric motor 5 is closely related to the rotational frequency band, and the frequency band of the sideband is often a multiple of the rotational frequency band. Further, as described above, the rotation frequency band appears with a shift depending on the load torque of the electric motor 5. For this reason, it is necessary to match the frequency axis of the power spectrum analysis results for a plurality of times of averaging by the peak location tracking method. Specifically, as shown in FIG.
- the frequency of the rotation frequency band is a position away from the power supply frequency by fr
- the sideband frequency is a position away from the power supply frequency by fb
- the electric motor 5 is in an unloaded state.
- the conversion of the frequency axis of all the peak locations is performed by multiplying the conversion rate ⁇ with the rotational frequency band as a reference.
- the averaging calculation unit 18 averages the power spectrum analysis results for a plurality of times in which the frequency axis is matched by the frequency axis conversion calculation unit 17.
- the averaging process reduces the base noise to reduce the S of the peak portion. / N ratio can be improved. Specifically, when the power spectrum analysis results for 10 times are averaged, the peak portion due to noise or the like that has occurred only once is reduced to 1/10 of the signal intensity. On the other hand, if the frequency band is a rotational frequency band or sideband, the peak location occurs 10 times, and the frequency is matched by converting the frequency axis using the peak tracking method. It does not change. In the above description, the case where the power spectrum analysis results for 10 times are averaged has been described. However, the present invention is not limited to 10 times, and a plurality of times may be averaged.
- the sideband wave extraction unit 19 extracts, as sideband waves, peak portions that are generated at positions shifted by the same frequency on both sides around the power supply frequency from the power spectrum analysis result averaged by the averaging calculation unit 18.
- the peak position obtained by the peak detection calculation unit 15 is selected as a candidate.
- the sideband wave determination unit 20 determines whether or not the motor 5 is abnormal from the number of sideband waves extracted by the sideband wave extraction unit 19 and the signal intensity. When it is determined that the electric motor 5 is abnormal, an alarm is output from the alarm output unit 21.
- the electric motor diagnosis device 7 is activated at predetermined time intervals to execute the following processing.
- step 101 the current of the electric motor 5 detected by the current detector 4 is input by the current input unit 10.
- step 102 the fluctuation of the effective value (hereinafter referred to as current value) of the current input from the current input unit 10 is calculated by the current fluctuation calculation unit 12, and the FFT analysis interval determination unit 13 is used by using the calculation result.
- the process determines whether the current is in a stable state. If the determination result shows that the variation in the current value is an unstable state (NO) equal to or greater than a preset threshold value, the process returns to step 101 and is repeated until the current becomes stable.
- NO unstable state
- the process proceeds to step 103.
- the threshold value for example, field data of a plurality of motors are acquired in advance, and the current variation value (standard deviation) of the data is selected within a small variation value range, and the selected value is set as a threshold. Value.
- the variation value is calculated 50 times, and 0.8, which is the fifth smallest variation value in the rearranged order, is determined as the threshold value.
- a fixed learning period may be provided in the electric motor 5, and the same calculation may be performed from the current variation value (standard deviation) acquired during the learning period.
- Step 103 the FFT analysis unit 14 performs frequency analysis between 0 Hz and a frequency 120 Hz that is twice the power supply frequency 60 Hz using the current waveform in the section where the input current value is in a stable state, and the result of the power spectrum analysis To the peak detection calculation unit 15.
- step 104 the peak detection calculation unit 15 detects all peak portions included in the power spectrum analysis result.
- step 105 the rotation frequency band determination unit 16 extracts a peak position in the rotation frequency band from the detected peak positions and determines a rotation frequency band.
- step 106 the frequency axis conversion calculation unit 17 converts the frequency axes of all peak portions so that the detected rotation frequency band becomes the rotation frequency band at the time of no load.
- Step 107 the operation from Step 101 to Step 106 is repeated 10 times, and 10 power spectrum analysis results whose frequency axes are converted are collected.
- step 108 the averaging calculation unit 18 averages the collected ten power spectrum analysis results.
- step 109 the sideband wave extraction unit 19 extracts sideband waves by paying attention to the peak portion of the averaged power spectrum analysis result.
- step 110 when the sideband wave is not extracted by the sideband wave extraction unit 19 or when the sideband wave is extracted but the signal intensity is smaller than the set value, the sideband wave determination unit 20 detects that the motor 5 is abnormal. Is not generated (NO), and the diagnosis process is terminated. On the other hand, if the signal strength of the sideband extracted by the sideband extraction unit 19 is greater than the set value (YES), a signal is sent to the alarm output unit 21 as an abnormality has occurred in the electric motor 5, and step 111 is performed.
- the diagnosis process is terminated.
- the set value of the sideband determination unit 20 the signal intensity A of the normal sideband is learned, the standard deviation ⁇ is calculated, and the detected sideband peak value is 99.7%, which is A + 3 ⁇ .
- the range in which data exists is set as the setting value.
- a + 3 ⁇ ⁇ ⁇ may be obtained by multiplying by a safety coefficient ⁇ (for example, 2 or more).
- Another method for determining the set value is determined from past data at the time of the failure of the same motor. As the number of failure cases increases, an accurate failure location and failure degree can be determined by sidebands. It becomes like this.
- FIG. FIG. 6 is a schematic configuration diagram showing the installation status of the motor diagnostic device according to Embodiment 2 of the present invention
- FIG. 7 is a block diagram showing the configuration of the logical operation unit of the motor diagnostic device according to Embodiment 2 of the present invention
- FIG. 8 is an explanatory diagram for explaining the trend analysis of the electric motor diagnosis apparatus according to the second embodiment of the present invention.
- the first embodiment the case where the abnormality of the electric motor 5 is diagnosed using the averaged power spectrum analysis result has been described.
- the averaged power spectrum analysis result is time-series. A case will be described in which trend monitoring is performed after saving the file.
- the motor diagnosis apparatus 7 is provided with an FFT information storage unit 22 for storing the averaged power spectrum analysis results in time series. As shown in FIG. An analysis unit 23 is provided.
- the trend analysis unit 23 pays attention to the sideband wave of a specific frequency of the power spectrum analysis result stored in the FFT information storage unit 22 in time series, and the signal intensity of the sideband wave is time-series as shown in FIG. It is what you want to display.
- the set values in FIG. 8 are set values used by the sideband determination unit 20.
- the embodiments can be freely combined within the scope of the invention, or the embodiments can be appropriately modified and omitted.
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- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Manufacturing & Machinery (AREA)
- Power Engineering (AREA)
- Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
Description
実施の形態1.
図1はこの発明の実施の形態1における電動機の診断装置の設置状況を示す概略構成図、図2はこの発明の実施の形態1における電動機の診断装置の論理演算部の構成を示すブロック図、図3はこの発明の実施の形態1における電動機の診断装置の電動機の負荷変動が大きい場合の周波数解析結果を説明する説明図、図4はこの発明の実施の形態1における電動機の診断装置の周波数軸の変換を説明する説明図、図5はこの発明の実施の形態1における電動機の診断装置の動作を説明するフロー図、図9はこの発明の実施の形態1における電動機の診断装置のしきい値の設定を説明する説明図である。
FFT解析区間判定部13は、電流変動演算部12で求めた電流値の統計的なばらつきから、ばらつきがしきい値以下の電流値が安定した状態の電流区間のみを抽出してパワースペクトル解析区間を決定する。一般に電動機5の負荷トルクが変動していると電流値にばらつきが生じて、ばらつきの大きい電流波形のパワースペクトル解析を実施すると、図3に示すように電源周波数の近傍両側の信号強度が増大して、側帯波などのピーク箇所が出現しなくなる。これを防止するためにFFT解析区間判定部13のしきい値が設けられている。
ピーク検出演算部15は、電流パワースペクトル強度の解析結果から電源周波数によるピーク箇所と回転周波数によるピーク箇所と側帯波によるピーク箇所およびその他のピーク箇所を検出する。ピーク箇所の検出は1次と2次と3次の微分計算によって算出した結果の急峻な傾きが反転する部分を抽出することで検出可能である。微分計算を3次まで実施することによって、より小さい信号強度のピーク箇所の検出が可能となる。電源周波数によるピーク箇所は、定格情報記憶部9に保存されている電源周波数(一般に50Hzまたは60Hz)の位置に生じるため簡単に確認できる。
側帯波判定部20は、側帯波抽出部19で抽出された側帯波の個数と信号強度から電動機5が異常か否かを判定する。電動機5が異常であると判定した場合には、警報出力部21から警報を出力する。
図6はこの発明の実施の形態2における電動機の診断装置の設置状況を示す概略構成図、図7はこの発明の実施の形態2における電動機の診断装置の論理演算部の構成を示すブロック図、図8はこの発明の実施の形態2における電動機の診断装置のトレンド解析を説明する説明図である。上記実施の形態1では、平均化処理されたパワースペクトル解析結果を使用して電動機5の異常を診断する場合について説明したが、実施の形態2では平均化処理されたパワースペクトル解析結果を時系列に保存しておいてトレンド監視を行う場合について説明する。
Claims (3)
- 電動機の電流を検出して入力する電流入力部と、前記電流入力部からの電流が安定状態のときに前記電流のパワースペクトルを解析するFFT解析部と、前記FFT解析部で求められたパワースペクトルのピーク箇所を検出するピーク検出演算部と、前記FFT解析部で解析されたパワースペクトルの複数回分を平均化する平均化演算部と、前記平均化演算部で平均化されたパワースペクトルの側帯波を抽出する側帯波抽出部と、前記側帯波抽出部で設定値以上の信号強度の側帯波が抽出されたとき警報出力を行う警報出力部を備えていることを特徴とする電動機の診断装置。
- 前記平均化演算部は複数回分のパワースペクトルの周波数軸を変換して回転周波数帯によるピーク箇所を合わした状態で平均化することを特徴とする請求項1に記載の電動機の診断装置。
- 前記平均化演算部で平均化されたパワースペクトルの情報を保存しておいて側帯波をトレンド監視することを特徴とする請求項1または2に記載の電動機の診断装置。
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KR1020197001614A KR102139164B1 (ko) | 2016-07-26 | 2016-07-26 | 전동기의 진단 장치 |
CN201680087645.0A CN109478832B (zh) | 2016-07-26 | 2016-07-26 | 电动机的诊断装置 |
EP16910466.8A EP3492938B1 (en) | 2016-07-26 | 2016-07-26 | Electric motor diagnosis device |
PCT/JP2016/071800 WO2018020563A1 (ja) | 2016-07-26 | 2016-07-26 | 電動機の診断装置 |
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CN112313492A (zh) * | 2018-06-19 | 2021-02-02 | 松下知识产权经营株式会社 | 诊断系统、诊断方法和程序 |
CN113316889A (zh) * | 2019-01-10 | 2021-08-27 | 株式会社日立产机系统 | 电力转换装置、旋转机系统以及诊断方法 |
CN113647013A (zh) * | 2019-04-10 | 2021-11-12 | 三菱电机株式会社 | 电动机设备的异常诊断装置、电动机设备的异常诊断方法和电动机设备的异常诊断系统 |
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JP7306968B2 (ja) * | 2019-11-06 | 2023-07-11 | 株式会社日本製鋼所 | 異常検知装置、異常検知方法及びコンピュータプログラム |
WO2021166168A1 (ja) * | 2020-02-20 | 2021-08-26 | 三菱電機株式会社 | 電動機の診断装置 |
CN117501619A (zh) * | 2021-06-21 | 2024-02-02 | 三菱电机株式会社 | 带电动机的设备的故障征兆检测装置以及带电动机的设备的故障征兆检测方法 |
CN116699402B (zh) * | 2023-08-02 | 2023-12-08 | 江苏一东航空机械有限公司 | 直流无刷电机传感器数据处理方法 |
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- 2016-07-26 CN CN201680087645.0A patent/CN109478832B/zh active Active
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CN112313492A (zh) * | 2018-06-19 | 2021-02-02 | 松下知识产权经营株式会社 | 诊断系统、诊断方法和程序 |
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CN113647013A (zh) * | 2019-04-10 | 2021-11-12 | 三菱电机株式会社 | 电动机设备的异常诊断装置、电动机设备的异常诊断方法和电动机设备的异常诊断系统 |
CN113647013B (zh) * | 2019-04-10 | 2024-05-10 | 三菱电机株式会社 | 电动机设备的异常诊断装置、电动机设备的异常诊断方法和电动机设备的异常诊断系统 |
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EP3492938B1 (en) | 2021-09-01 |
KR20190020757A (ko) | 2019-03-04 |
CN109478832B (zh) | 2021-06-18 |
CN109478832A (zh) | 2019-03-15 |
EP3492938A4 (en) | 2019-06-05 |
KR102139164B1 (ko) | 2020-07-29 |
EP3492938A1 (en) | 2019-06-05 |
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