CN106989923A - Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis - Google Patents
Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis Download PDFInfo
- Publication number
- CN106989923A CN106989923A CN201710191434.8A CN201710191434A CN106989923A CN 106989923 A CN106989923 A CN 106989923A CN 201710191434 A CN201710191434 A CN 201710191434A CN 106989923 A CN106989923 A CN 106989923A
- Authority
- CN
- China
- Prior art keywords
- stator current
- fault
- wavelet packet
- permanent magnetic
- detection method
- 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.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
Landscapes
- Physics & Mathematics (AREA)
- Acoustics & Sound (AREA)
- General Physics & Mathematics (AREA)
- Motor Or Generator Frames (AREA)
Abstract
The invention discloses the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis, fault diagnosis is carried out using the stator current of permanent magnetic motor, signal analysis is carried out to stator current by analysis method of wavelet packet, wavelet packet analysis is a kind of multiresolution algorithm, signal can be peeled off by frequency range, when it is determined that after electrical fault frequency, calculating corresponding small echo packet node, root mean square is asked to small echo packet node coefficient, according to root mean square failure judgement situation.Permanent magnetic motor bearing spot corrosion fault detection method of the present invention, fault message is able to detect that the initial stage of breaking down in bearing, and safety, maintenance for motor etc. suffer from important meaning.
Description
Technical field
The present invention relates to the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis, belong to permanent magnetism
Motor bearings fault detection technique field.
Background technology
As gearing, magneto is due to the advantages of its power density is big, loss is low, power factor is high, in industry
In occupation of consequence in, therefore, its security is just most important.In all electrical faults, bearing fault is accounted for
50% or so, it is one of major failure form of motor, so the fault detect of bearing is the important content of electrical fault detection.
Fault message is able to detect that in bearing the initial stage of breaking down, and safety, maintenance for motor etc. suffer from important meaning.
Traditional detection method is that the fault detect of motor is carried out by the way of periodic inspection maintenance, and this method lacks
Point is it is obvious that not quick enough and very inconvenient, it is impossible to reach the purpose monitored in real time.When motor bearings breaks down, motor
Rotating shaft can be shaken, and so as to detect the vibration signal of rotating shaft with acceleration transducer, vibration signal is extracted, can
To obtain bearing fault information.But this method needs to increase extra sensor, and vibration signal is influenceed very big by operating mode.
The content of the invention
The technical problems to be solved by the invention are：Permanent magnetic motor bearing spot corrosion based on stator current wavelet packet analysis is provided
Fault detection method, fault diagnosis is carried out using the stator current of magneto.
The present invention uses following technical scheme to solve above-mentioned technical problem：
Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis, comprises the following steps：
Step 1, the stator current of permagnetic synchronous motor is sampled, obtains sampled current signals；
Step 2, according to the rotating speed of permagnetic synchronous motor, calculate motor bearings and occur the spot corrosion event of Internal and external cycle during pitting fault
Hinder frequency；
Step 3, it is corresponding small according to the sample frequency of stator current and the pitting fault frequency of Internal and external cycle calculating Internal and external cycle
Ripple packet node；
Step 4, wavelet packet analysis is carried out to sampled current signals, obtains the corresponding small echo packet node coefficient of Internal and external cycle, it is right
The corresponding small echo packet node coefficient of Internal and external cycle seeks root mean square, and the failure situation of Internal and external cycle is judged according to root mean square.
As a preferred embodiment of the present invention, the pitting fault frequency calculation formula of inner ring described in step 2 is：
Wherein, fIRFThe pitting fault frequency of inner ring is represented, N represents rolling element number, dbRepresent rolling element diameter, dcRepresent
Retainer diameter, α represents rolling element and the contact angle of outer ring, frRepresent permanent-magnetic synchronous motor rotor rotational frequency.
As a preferred embodiment of the present invention, the pitting fault frequency calculation formula of outer ring described in step 2 is：
Wherein, fORFThe pitting fault frequency of outer ring is represented, N represents rolling element number, dbRepresent rolling element diameter, dcRepresent
Retainer diameter, α represents rolling element and the contact angle of outer ring, frRepresent permanent-magnetic synchronous motor rotor rotational frequency.
As a preferred embodiment of the present invention, judge that the failure situation of Internal and external cycle is specific according to root mean square described in step 4
For：Setting a period of time, if the root mean square of the corresponding small echo packet node coefficient of inner ring constantly increases within this time, judge
Pitting fault occurs for inner ring, and sends alarm；If the root mean square of the corresponding small echo packet node coefficient in outer ring within this time not
Disconnected increase, then judge that pitting fault occurs for outer ring, and send alarm.
As a preferred embodiment of the present invention, the permagnetic synchronous motor is the permagnetic synchronous motor that square wave is controlled or just
The permagnetic synchronous motor of string ripple control.
The present invention uses above technical scheme compared with prior art, with following technique effect：
1st, permanent magnetic motor bearing spot corrosion fault detection method of the present invention, fault diagnosis is carried out using the stator current of permanent magnetic motor,
Signal analysis is carried out to stator current by analysis method of wavelet packet, with good time frequency analysis characteristic, suitable for electrical fault
The extraction of signal.
2nd, permanent magnetic motor bearing spot corrosion fault detection method of the present invention, is able to detect that failure is believed the initial stage of breaking down in bearing
Breath, has great significance for the safe of motor, maintenance etc..
Brief description of the drawings
Fig. 1 is the flow of the permanent magnetic motor bearing spot corrosion fault detection method of the invention based on stator current wavelet packet analysis
Figure.
Fig. 2 is motor bearings schematic cross-section.
Fig. 3 is magneto equivalent-circuit model.
Node coefficient oscillogram of the bus current under wavelet packet analysis when Fig. 4 is brshless DC motor fault-free.
Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9 be respectively fault degree from low to high when, the node coefficient under wavelet packet analysis
Oscillogram.
Figure 10 is the root mean square line chart of Fig. 4-Fig. 9 waveforms.
The node coefficient oscillogram under wavelet packet analysis when Figure 11 is the permagnetic synchronous motor fault-free of sine wave control.
Figure 12, Figure 13, Figure 14, Figure 15, Figure 16 be respectively sine wave control permagnetic synchronous motor in fault degree from low
To it is high when, the node coefficient oscillogram under wavelet packet analysis.
Figure 17 is the root mean square line chart of Figure 11-Figure 16 waveforms.
Embodiment
Embodiments of the present invention are described below in detail, the example of the embodiment is shown in the drawings.Below by
The embodiment being described with reference to the drawings is exemplary, is only used for explaining the present invention, and is not construed as limiting the claims.
Comprising abundant information in the stator current of motor, that has been succeeded at present in many electrical faults should
With.When pitting fault occurs for bearing, rotating shaft shakes, and causes the air gap of motor script very little to change, so that
Corresponding change occurs for stator current.Bearing occurs after pitting fault, and the fault-signal included in stator current is smaller, so needing
Suitable signal analysis method is used to extract bearing fault signal.Wavelet packet analysis is current popular signal
Analysis method, it has good time frequency analysis characteristic, the extraction suitable for motor failure signal.
When pitting fault occurs for motor bearings, the rotating shaft of motor can be with a frequency related with abort situation to rotating speed
Shake, and the air gap of script very little changes between the vibrations meeting drive motor rotor of rotating shaft, so that can be in stator electricity
Corresponding harmonic content is induced on stream.The overall vibrations of motor do not influence the change of stator current, so operating mode influences on it
It is smaller.Magneto is divided to sine wave control and square wave to control two kinds, and square wave control turns into brshless DC motor, brshless DC motor
Using the step mode of 22 conductings, so a phase only turns on 2/3 cycle in each electricity cycle, and bus current is then always maintained at
Continuously, so selection bus current is used as detection target；And the phase current of the permagnetic synchronous motor of sine wave control keeps continuous,
So it is detection target to select phase current.Because the fault-signal included in electric current is very faint, so needing selection advanced
Signal analysis algorithm carries out signal extraction.Wavelet packet analysis is a kind of multiresolution algorithm, signal can be carried out by frequency range
Peel off, when it is determined that after electrical fault frequency, then can determine corresponding small echo packet node, can be determined according to node coefficient
Failure situation.
When inner ring, outer ring pitting fault occur for bearing, corresponding failure vibrations feature frequency can be obtained by following formula
Rate：
Wherein, fORFRepresent outer ring pitting fault frequency, fIRFRepresent inner ring pitting fault frequency, frRepresent that rotor turns
Dynamic frequency, N represents rolling element number, dbRepresent rolling element diameter, dcRetainer diameter is represented, α represents connecing for rolling element and outer ring
Feeler.
Motor bearings schematic cross-section is as shown in Figure 2.
1st, brshless DC motor
In brshless DC motor, after bearing breaks down, motor speed is shaken by one with failure-frequency with frequency
Dynamic, then rotating speed can be expressed as：
N=n0+Δn·cos(2πfc·t) (3)
Wherein, n represents motor speed, n0The constant part of rotating speed is represented, Δ n represents fluctuation of speed amplitude, fcRepresent motor
Bearing fault frequency, t represents the time.The proportional relation of back-emf and rotating speed of permanent magnetic synchronous electrical motor, so motor can be obtained
Back-emf is as follows：
E=e0+Δe·cos(2πfc·t) (4)
Wherein, e represents winding back emf, e0The constant part of back-emf is represented, Δ e represents the fluctuation amplitude of back-emf.
According to circuit theory, can obtain bus current is：
Wherein, IdcRepresent bus current, UdcMotor busbar voltage is represented, p represents differential operator, LsRepresent motor mutually electricity
Sense, R represents motor phase resistance, I0The constant part of electric current is represented, Δ I represents the amplitude of current fluctuation.It follows that stator is electric
Included and harmonic content of the failure-frequency with frequency in stream.
2nd, sine wave control permagnetic synchronous motor
After magneto failure, electromagnetic power, rotating speed, torque of motor etc. can be caused to change, so setting after failure
Electromagnetic power, mechanical angular speed and electromagnetic torque be respectively Pe′、ωr′、Te′.According to synchronous motor under dq coordinate systems etc.
Effect circuit model is (as shown in Figure 3) to be understood, the electromagnetic power of motor can be expressed as in bearing fault：
Wherein, PeThe electromagnetic power of ' expression motor, P represents the number of pole-pairs of motor, ωrRepresent that the motor under health status turns
Sub- angular speed, Δ ωrRepresent motor angular velocity fluctuation, Id、IqElectric current of the motor dq axles under bearing health condition is represented respectively
Value, Δ Iq、ΔIdThe fluctuation of ac-dc axis electric current during motor bearings failure, λ are represented respectivelyafRepresent permanent magnet flux linkage.Wherein, Δ Iq、Δ
IdEqual very little, be so as to the variable quantity that obtains electromagnetic power：
Wherein, Δ PeRepresent electromagnetic power fluctuation.Then electromagnetic torque increment is represented by：
Wherein, Δ TeRepresent electromagnetic power fluctuation.On the other hand, q shaft currents are obtained by der Geschwindigkeitkreis by PI regulations, institute
So that q shaft currents have identical variation tendency with rotating speed, according to the equation of motion：
Wherein, J represents the rotary inertia of motor, Δ TLRepresent the fluctuation of load torque.Formula (8) is brought into, can be obtained：
Due to Δ ωrWith Δ IqThere is identical changing rule, so when can be seen that stable state from the differential equation, Δ ωr
Changing rule with Δ TLUnanimously, so as to deduce, Δ Iq、Δωr、ΔTe、ΔTLCharacteristic function it is identical.
If Δ TLFor Δ ГLcos(2πfcT), by above-mentioned analysis it is known that Δ IqIt can be set toIts
InRepresent the first phase of current fluctuation.Motor A phase currents are represented by：
i′A=cos θ (Iq+ΔIq)+sinθ·ΔId (11)
Wherein, θ represents motor electrical angle, can be tried to achieve by formula below：
θ=∫ P ωrdt (12)
WillThe π f of ω=2 bring formula (11) into, can obtain：
There it can be seen that fault-signal is in electric current iAOn the harmonic frequency that shows be Pfr±fc。
Wavelet packet analysis is a kind of multi-resolution signals analysis method, selects suitable wavelet basis function, can be by signal
Peeled off according to frequency range.Wavelet packet analysis is carried out to current signal, determines that calculating obtains corresponding small node after failure-frequency
Point.Signal reconstruction is carried out to small nodal point, then can obtain fault message.
As shown in figure 1, present invention wavelet packet analysis detection motor bearings failure can be divided into following several steps：
Step 1, stator current is sampled；
Step 2, Internal and external cycle failure-frequency is calculated by motor speed；
Step 3, corresponding small echo packet node is calculated according to sample frequency and failure-frequency；
Step 4, wavelet packet analysis is carried out to signal, and asks respective nodes coefficient root mean square, failure judgement situation.
As shown in figure 4, for brshless DC motor in fault-free node coefficient ripple of the bus current under wavelet packet analysis
Shape, as shown in Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, respectively brshless DC motor fault degree from low to high when, wavelet packet point
Node coefficient waveform under analysis, is the root mean square line chart of Fig. 4-Fig. 8 waveforms as shown in Figure 10.
As shown in figure 11, be sine wave control permagnetic synchronous motor fault-free when wavelet packet analysis under node coefficient
Waveform, as shown in Figure 12, Figure 13, Figure 14, Figure 15, Figure 16, respectively sine wave control permagnetic synchronous motor fault degree from
When low to high, the node coefficient waveform under wavelet packet analysis is the root mean square line chart of Figure 11-Figure 16 waveforms as shown in figure 17.
It can be seen that Δ TLIn continuous increase, permanent magnetism machine bearing occurs in that pitting fault, and Δ TLWith time t
It is relevant.
The technological thought of above example only to illustrate the invention, it is impossible to which protection scope of the present invention is limited with this, it is every
According to technological thought proposed by the present invention, any change done on the basis of technical scheme each falls within the scope of the present invention
Within.
Claims (5)
1. the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis, it is characterised in that including as follows
Step：
Step 1, the stator current of permagnetic synchronous motor is sampled, obtains sampled current signals；
Step 2, according to the rotating speed of permagnetic synchronous motor, calculate motor bearings and occur the pitting fault frequency of Internal and external cycle during pitting fault
Rate；
Step 3, the corresponding wavelet packet of Internal and external cycle is calculated according to the sample frequency of stator current and the pitting fault frequency of Internal and external cycle
Node；
Step 4, wavelet packet analysis is carried out to sampled current signals, the corresponding small echo packet node coefficient of Internal and external cycle is obtained, to inside and outside
Enclose corresponding small echo packet node coefficient and seek root mean square, the failure situation of Internal and external cycle is judged according to root mean square.
2. the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis according to claim 1, its
It is characterised by, the pitting fault frequency calculation formula of inner ring described in step 2 is：
Wherein, fIRFThe pitting fault frequency of inner ring is represented, N represents rolling element number, dbRepresent rolling element diameter, dcRepresent to keep
Frame diameter, α represents rolling element and the contact angle of outer ring, frRepresent permanent-magnetic synchronous motor rotor rotational frequency.
3. the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis according to claim 1, its
It is characterised by, the pitting fault frequency calculation formula of outer ring described in step 2 is：
Wherein, fORFThe pitting fault frequency of outer ring is represented, N represents rolling element number, dbRepresent rolling element diameter, dcRepresent to keep
Frame diameter, α represents rolling element and the contact angle of outer ring, frRepresent permanent-magnetic synchronous motor rotor rotational frequency.
4. the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis according to claim 1, its
It is characterised by, the failure situation for judging Internal and external cycle according to root mean square described in step 4 is specially：Setting a period of time, if inner ring phase
The root mean square for the small echo packet node coefficient answered constantly increases within this time, then judges that pitting fault occurs for inner ring, and send
Alarm；If the root mean square of the corresponding small echo packet node coefficient in outer ring constantly increases within this time, judge that point occurs for outer ring
Failure is lost, and sends alarm.
5. the permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis according to claim 1, its
It is characterised by, the permagnetic synchronous motor is the permagnetic synchronous motor of the permagnetic synchronous motor that square wave is controlled or sine wave control.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710191434.8A CN106989923A (en) | 2017-03-28 | 2017-03-28 | Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710191434.8A CN106989923A (en) | 2017-03-28 | 2017-03-28 | Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106989923A true CN106989923A (en) | 2017-07-28 |
Family
ID=59413267
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710191434.8A Pending CN106989923A (en) | 2017-03-28 | 2017-03-28 | Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106989923A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109238698A (en) * | 2018-10-15 | 2019-01-18 | 株洲中车时代电气股份有限公司 | A kind of motor bearings method for diagnosing faults based on current signal |
| WO2020078132A1 (en) | 2018-10-15 | 2020-04-23 | 株洲中车时代电气股份有限公司 | Motor bearing failure diagnosis device |
| CN111879514A (en) * | 2020-07-31 | 2020-11-03 | 南京机电职业技术学院 | Brushless direct current motor bearing fault diagnosis method based on ELM model |
| CN116106742A (en) * | 2023-04-07 | 2023-05-12 | 国家石油天然气管网集团有限公司 | Motor bearing fault diagnosis method and system based on DQ transformation |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1811377A (en) * | 2006-03-03 | 2006-08-02 | 西安交通大学 | Method for quantitatively identifying rolling bearing damage |
| CN101943632A (en) * | 2009-07-06 | 2011-01-12 | 包米勒公司 | Electric drive system, discern this system and/or survey the method and the device of damage |
| CN103884506A (en) * | 2014-03-25 | 2014-06-25 | 福建省工业设备安装有限公司 | Production equipment on-line monitor and fault diagnosis system and method based on wireless network |
| CN103914617A (en) * | 2014-03-25 | 2014-07-09 | 北京交通大学 | Fault diagnosis method for subway vehicle bogie bearings |
| CN104792523A (en) * | 2015-04-22 | 2015-07-22 | 电子科技大学 | Vibration spectrum feature analysis method based on planetary gear structure equivalent bearing model |
| CN106323635A (en) * | 2016-08-04 | 2017-01-11 | 中国航空综合技术研究所 | Rolling bearing fault on-line detection and state assessment method |
| CN106404399A (en) * | 2016-11-03 | 2017-02-15 | 北京印刷学院 | Bearing fault diagnosis method based on adaptive redundancy lifting wavelet packet decomposition tree |
-
2017
- 2017-03-28 CN CN201710191434.8A patent/CN106989923A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1811377A (en) * | 2006-03-03 | 2006-08-02 | 西安交通大学 | Method for quantitatively identifying rolling bearing damage |
| CN101943632A (en) * | 2009-07-06 | 2011-01-12 | 包米勒公司 | Electric drive system, discern this system and/or survey the method and the device of damage |
| CN103884506A (en) * | 2014-03-25 | 2014-06-25 | 福建省工业设备安装有限公司 | Production equipment on-line monitor and fault diagnosis system and method based on wireless network |
| CN103914617A (en) * | 2014-03-25 | 2014-07-09 | 北京交通大学 | Fault diagnosis method for subway vehicle bogie bearings |
| CN104792523A (en) * | 2015-04-22 | 2015-07-22 | 电子科技大学 | Vibration spectrum feature analysis method based on planetary gear structure equivalent bearing model |
| CN106323635A (en) * | 2016-08-04 | 2017-01-11 | 中国航空综合技术研究所 | Rolling bearing fault on-line detection and state assessment method |
| CN106404399A (en) * | 2016-11-03 | 2017-02-15 | 北京印刷学院 | Bearing fault diagnosis method based on adaptive redundancy lifting wavelet packet decomposition tree |
Non-Patent Citations (1)
| Title |
|---|
| 李青松: "基于定子电流分析的机车牵引电机轴承故障诊断", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109238698A (en) * | 2018-10-15 | 2019-01-18 | 株洲中车时代电气股份有限公司 | A kind of motor bearings method for diagnosing faults based on current signal |
| WO2020078132A1 (en) | 2018-10-15 | 2020-04-23 | 株洲中车时代电气股份有限公司 | Motor bearing failure diagnosis device |
| CN111879514A (en) * | 2020-07-31 | 2020-11-03 | 南京机电职业技术学院 | Brushless direct current motor bearing fault diagnosis method based on ELM model |
| CN116106742A (en) * | 2023-04-07 | 2023-05-12 | 国家石油天然气管网集团有限公司 | Motor bearing fault diagnosis method and system based on DQ transformation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106989923A (en) | Permanent magnetic motor bearing spot corrosion fault detection method based on stator current wavelet packet analysis | |
| CN106771835B (en) | Rotor of steam turbo generator turn-to-turn short circuit diagnostic device and method based on detection coil | |
| JP5546545B2 (en) | Method for monitoring and / or analyzing an electric machine during operation of the rotor of the electric machine and apparatus for monitoring and / or analyzing the electric machine during operation | |
| Le Roux et al. | Detecting rotor faults in low power permanent magnet synchronous machines | |
| Yang et al. | Cost-effective condition monitoring for wind turbines | |
| Ibrahim et al. | A new bearing fault detection method in induction machines based on instantaneous power factor | |
| US10317467B2 (en) | Synchronous machine monitoring and determination of a loss-of-field event using time stamped electrical and mechanical data | |
| EP3480610B1 (en) | Diagnosing a winding set of a stator | |
| CN104569733B (en) | A kind of method for determining motor excitation winding interturn short-circuit abort situation | |
| Wang et al. | Bearing fault diagnosis of switched reluctance motor in electric vehicle powertrain via multisensor data fusion | |
| US20240183649A1 (en) | Linear hall-based eccentricity diagnosis method and detection system for permanent magnet motor | |
| CN103823150A (en) | Turbo generator rotor interturn short circuit fault diagnosis method based on multi sensor joint | |
| Gong et al. | Bearing fault detection for direct-drive wind turbines via stator current spectrum analysis | |
| EP3444585B1 (en) | Method for determining a state of a bearing, module for determining a state of a bearing, railway vehicle and system | |
| CN106772037A (en) | Synchronous generator rotor winding interturn short-circuit diagnostic method based on twin coil | |
| CN107192947A (en) | The permagnetic synchronous motor event diagnostic method monitored based on magnetic field | |
| Wang et al. | Detection and evaluation of the interturn short circuit fault in a BLDC-based hub motor | |
| CN106443318B (en) | Hydraulic generator rotor winding interturn short-circuit diagnostic method based on core through screw rod | |
| CN110907864A (en) | Fault detection method, device and equipment for motor stator winding and storage medium | |
| KR20100045718A (en) | Apparatus and method for monitoring permanent magnet demagnetization of permanent magnet synchronous motors, and a medium having computer readable program for executing the method | |
| CN113311307B (en) | Diode open-circuit fault detection method for three-phase brushless exciter rotating rectifier | |
| CN106501665B (en) | Hydraulic generator rotor winding interturn short-circuit diagnostic method based on detection coil | |
| Pietrzak et al. | Stator phase current STFT analysis for the PMSM stator winding fault diagnosis | |
| Boudinar et al. | Induction motor’s bearing fault diagnosis using an improved short time Fourier transform | |
| US11959978B2 (en) | Method of detecting a rotor bar fault and a method of estimating an additional operating expenditure due to one or more mechanical anomalies in an electrical machine |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170728 |
|
| RJ01 | Rejection of invention patent application after publication |