CN107576488A - A kind of method that diagnosis is monitored to equipment running status using vibration algorithm - Google Patents
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Abstract
The present invention is a kind of method that equipment running status are monitored with diagnosis using vibration algorithm, the running status of equipment can be monitored, time domain is carried out to the vibration signal of equipment, frequency-domain analysis, the operation trend of equipment can be analyzed simultaneously, the residual life of equipment can also be predicted using intelligence learning algorithm, according to actual conditions appropriate signal acquisition strategy can be selected to be acquired vibration signal, the situation of change of monitoring vibration signal, the monitoring to some associated technical parameters can be realized, alarm, the time domain waveform monitoring to vibration signal can be realized, pass through the analysis to signal, the running status of equipment can finally be diagnosed by the correlated characteristic of signal, determine the abort situation of equipment, fault type.The present invention for reduce maintenance cost, reduce production cost, improve the economic and social benefits and cut much ice, the generation important in inhibiting for avoiding great economic loss and catastrophic failure.
Description
Technical field
The present invention relates to field of diagnosis about equipment fault, more particularly to a kind of use to vibrate algorithm to equipment running status progress
The method of monitoring, diagnosing.
Background technology
In modern enterprise production equipment maximize increasingly, serialization, high speed and automation, this also turns into modern large-scale enterprise
The principal character of industry production.The structure of equipment is sufficiently complex with forming, and production scale is very huge, the contact between each several part
It is especially close.Although productivity ratio is so favorably improved, production cost is reduced and improves product quality, from another
From the point of view of aspect, lose caused by stopping work and greatly increase because plant equipment breaks down.The large-scale production industry of modernization,
Such as oil, petrochemical industry, chemical industry, electric power, steel all using unit, at full capacity, successional production operation mode, some large scale computers
Tool is into the key equipment in modernization large-scale production device, once there is machine halt trouble, it will trigger chain reaction, cause
The shutdown of large area, the economic loss thereby resulted in are very huge.Except when production accident can be caused to draw during equipment fault
Outside the massive losses risen, the maintenance cost that device fails cause the damage of equipment and needed is also an enormous expenditure.
At the same time, modern industry equipments are often complicated, if machinery is torn open according to common method for maintaining
The substantial amounts of time can not only be wasted by holding inspection, equally can also produce high maintenance cost.Therefore either slave unit failure is drawn
Loss caused by production accident see or from massive losses caused by maintenance of equipment with maintenance complexity from the point of view of, equipment
The change of maintenance mode is all necessary.Modern comfort fault diagnosis technology is due to the needs of Aero-Space earliest, from
Last century early sixties grow up in the U.S..Hereafter, Britain, Germany, Sweden, Japan and other countries also start failure in succession
The research of diagnostic techniques, and achieve significant effect.By the development in more than 30 years, mechanical fault diagnosis is via initial boat
The military enterprises such as sky, space flight expand to civil area, and the multidisciplinary side to be intersected with technology is developed into from simple detection means
Edge subject, computer technology and network technology are also widely applied in fault diagnosis and have greatly facilitated fault diagnosis
Development.
The species of rotating machinery is various, as reductor, steam turbine, gas turbine, the hydraulic turbine, generator, aero-engine,
The equipment such as centrifugal compressor, typically electric power, oil, petrochemical industry, metallurgy, machinery, aviation and some war industry departments
Key equipment.Rotary machinery fault diagnosis has formd many fault diagnosis technologies, typically commonly used by constantly development
Fault diagnosis technology has following several：
1st, vibration diagnostic method
Vibration diagnostic method is detection mesh with the acceleration signal in the running status of equipment, rate signal, displacement signal etc.
Mark, carry out characteristic quantity analysis, spectrum analysis and Time-Frequency Analysis.Wherein Time-Frequency Analysis is vibration analysis method the most ripe,
The fault message of most rotors can be found in the time domain of vibration signal, frequency-domain analysis, therefore analysis of vibration signal
It is the Main Means of rotary machinery fault diagnosis.
2nd, temperature analysis method
The running status of many plant equipment is relevant with temperature, therefore according to the change of plant equipment and ambient temperature
Change, can be with the change of the running status of identifying system.Temperature diagnostic method is also a kind of method that fault diagnosis uses earliest.Now
The measurement of industrial medium temperature degree mainly uses thermal resistance, thermocouple temperature measurement sensor, and infrared temperature-test technology gradually grows up in recent years,
More and more extensive application will be had in future.
3rd, oil analyzing technology
Oil analyzing technology utilizes various routines, simple, precision or neutralization using spectrum analysis and analyzing iron spectrum as representative
Lubricating oil analysis instrument and method, mechanical wear chip contained in it is particularly to the physicochemical property of lubricating oil and other are micro-
Grain carries out the measurement of qualitative, quantitative, so as to obtain the state of wear about parts, machine operation situation and systemic contamination journey
The important information of degree etc..
4th, acoustics diagnosis
Acoustics diagnosis carries out sound level, the sound intensity, sound source, sound field, sound using noise, acoustic resistance, ultrasonic sound emission as detection target
Spectrum analysis.Ultrasonic diagnosis, sound emission diagnosis are using relatively broad.In recent years, mechanical noise blind source separate technology
(Blind Source Separation) gradually application development gets up, and effect of the acoustics with diagnosis in fault diagnosis will not
It is disconnected to strengthen.
But these existing equipment fault diagnosis methods are typically all pre- without the signal using correlation when gathering signal
Processing method, directly the signal collected is analyzed, wherein the serious interference such as noise included influences signal characteristic abstraction
Precision；And the characteristic analysis method limited precision of signal, because current signature analysis method is mainly time-domain analysis
Method and the signal frequency domain analysis method based on Fourier transformation, the frequency-domain analysis method based on Fourier transformation is only
Suitable for stationary signal, and the vibration signal at scene for non-stationary and the signal containing much noise, this allows for signal
Feature extraction precision hardly results in raising；Prior art can only be monitored to the operating mode of equipment, realize simple warning function,
Analysis can not be made to the operation trend of equipment, can not be transported by the calculating of relevant parameter to carry out the higher equipment of precision
Row trend analysis.
The content of the invention
Present invention seek to address that the deficiencies in the prior art, and a kind of use is provided and vibrates algorithm to equipment running status progress
The method of monitoring, diagnosing.
The present invention to achieve the above object, using following technical scheme：One kind is using vibration algorithm to equipment running status
It is monitored the method for diagnosis, it is characterised in that including below scheme：
1. the equipment vibrating signal collected is subjected to Signal Pretreatment, due to can not in the vibration signal that collects now
What is avoided contains noise, carries out noise reduction pretreatment to the vibration signal collected first, reduces interference of the noise to signal, improve
Signal to noise ratio, suitable iir filter is selected to be filtered according to the practical operation situation of equipment；
2. carrying out the calculating of amplitude field parameter to pretreated signal, to realize the early warning of equipment working condition, and then judge
Whether the running status of equipment is normal, and described amplitude field parameter includes：Root-mean-square value, average value, waveform index, pulse refer to
Mark, margin index, peak index, peak swing and kurtosis index；
3. being made a concrete analysis of to the equipment of work condition abnormality, pass through FFT amplitude spectrums, the power of computing device vibration signal
Spectrum, cepstrum, envelope spectrum, resonance and demodulation are composed to obtain the fault characteristic frequency of equipment, judge faulty equipment abort situation and
Fault type；
4. calculating kurtosis, earthquake intensity and the peak value of vibration signal, trend analysis, analysis are carried out to the relevant parameter being calculated
The overall operation state of equipment, confirm the fault severity level of equipment；
5. by the way that the model of equipment relevant parameter is calculated, and it is pre- using its progress intelligence of operation trend to equipment
Survey, the residual life of equipment is accurately predicted using intelligent prediction algorithms, so as to be carried for related technical staff
For reference, maintenance, the maintenance plan of equipment are formulated in help.
Particularly, the flow 1. in the form of wave filter have：It is Butterworth filter, Chebyshev filter, anti-
Chebyshev filter, elliptic filter and Bessel filter.
The beneficial effects of the invention are as follows：The present invention can be monitored to the running status of equipment, the vibration letter to equipment
Number time domain, frequency-domain analysis are carried out, while the operation trend of equipment can be analyzed, intelligence learning algorithm pair can also be applied
The residual life of equipment is predicted.The present invention reduces production cost, increased economic efficiency and social for reducing maintenance cost
Benefit cuts much ice, the generation important in inhibiting for avoiding great economic loss and catastrophic failure.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the present invention；
It is described in detail referring to the drawings below with reference to embodiments of the invention.
Embodiment
The invention will be further described with reference to the accompanying drawings and examples：
As shown in figure 1, a kind of method that equipment running status are monitored with diagnosis using vibration algorithm, its feature exist
In, including below scheme：
1. the equipment vibrating signal collected is subjected to Signal Pretreatment, due to can not in the vibration signal that collects now
What is avoided contains noise, carries out noise reduction pretreatment to the vibration signal collected first, reduces interference of the noise to signal, improve
Signal to noise ratio, suitable iir filter is selected to be filtered according to the practical operation situation of equipment；The form of wave filter has：Bart
Butterworth wave filter, Chebyshev filter, chebyshev filters inverted filter, elliptic filter and Bessel filter；
2. carrying out the calculating of amplitude field parameter to pretreated signal, to realize the early warning of equipment working condition, and then judge
Whether the running status of equipment is normal, and described amplitude field parameter includes：Root-mean-square value, average value, waveform index, pulse refer to
Mark, margin index, peak index, peak swing and kurtosis index；
3. being made a concrete analysis of to the equipment of work condition abnormality, pass through FFT amplitude spectrums, the power of computing device vibration signal
Spectrum, cepstrum, envelope spectrum, resonance and demodulation are composed to obtain the fault characteristic frequency of equipment, judge faulty equipment abort situation and
Fault type；
4. calculating kurtosis, earthquake intensity and the peak value of vibration signal, trend analysis, analysis are carried out to the relevant parameter being calculated
The overall operation state of equipment, confirm the fault severity level of equipment；
5. by the way that the model of equipment relevant parameter is calculated, and it is pre- using its progress intelligence of operation trend to equipment
Survey, the residual life of equipment is accurately predicted using intelligent prediction algorithms, so as to be carried for related technical staff
For reference, maintenance, the maintenance plan of equipment are formulated in help.
The present invention is exemplarily described above in conjunction with accompanying drawing, it is clear that present invention specific implementation is not by aforesaid way
Limitation, as long as employ the inventive concept and technical scheme of the present invention progress various improvement, or it is not improved directly application
In other occasions, within protection scope of the present invention.
Claims (2)
- A kind of 1. method that diagnosis is monitored to equipment running status using vibration algorithm, it is characterised in that including with dirty Journey：1. the equipment vibrating signal collected is subjected to Signal Pretreatment, due to inevitable in the vibration signal that collects now Contain noise, noise reduction pretreatment is carried out to the vibration signal collected first, interference of the noise to signal is reduced, improves noise Than selecting suitable iir filter to be filtered according to the practical operation situation of equipment；2. carrying out the calculating of amplitude field parameter to pretreated signal, to realize the early warning of equipment working condition, and then equipment is judged Running status it is whether normal, described amplitude field parameter includes：It is root-mean-square value, average value, waveform index, pulse index, abundant Spend index, peak index, peak swing and kurtosis index；3. being made a concrete analysis of to the equipment of work condition abnormality, by the FFT amplitude spectrums of computing device vibration signal, power spectrum, fall Frequency spectrum, envelope spectrum, resonance and demodulation are composed to obtain the fault characteristic frequency of equipment, judge the abort situation and failure classes of faulty equipment Type；4. calculating kurtosis, earthquake intensity and the peak value of vibration signal, trend analysis, analytical equipment are carried out to the relevant parameter being calculated Overall operation state, confirm the fault severity level of equipment；5. carrying out intelligent predicting by the way that the model of equipment relevant parameter is calculated, and using its operation trend to equipment, adopt The residual life of equipment is accurately predicted with intelligent prediction algorithms, so as to provide ginseng for the technical staff of correlation Examine, help to formulate maintenance, the maintenance plan of equipment.
- 2. a kind of method that diagnosis is monitored to equipment running status using vibration algorithm according to claim 1, its Be characterised by, the flow 1. in the form of wave filter have：Butterworth filter, Chebyshev filter, the traditional method of indicating the pronunciation of a Chinese character are than snow Husband's wave filter, elliptic filter and Bessel filter.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108387309A (en) * | 2018-04-25 | 2018-08-10 | 哈尔滨电气股份有限公司 | A kind of turbine shafting vibrating data collection, pretreatment and remotely send system |
| CN108593084A (en) * | 2018-03-30 | 2018-09-28 | 刘文飞 | A kind of determination method of communications equipment room equipment health status |
| CN108827632A (en) * | 2018-03-30 | 2018-11-16 | 刘文飞 | A kind of determination method of communications equipment room equipment health status |
| CN108843611A (en) * | 2018-06-15 | 2018-11-20 | 浙江大学 | A kind of axial flow blower state identification method based on vibration signal combined spectrum correlation analysis |
| CN109211548A (en) * | 2018-08-31 | 2019-01-15 | 沃德传动（天津）股份有限公司 | A kind of mechanical failure diagnostic method |
| CN109446482A (en) * | 2018-09-21 | 2019-03-08 | 天津长芦汉沽盐场有限责任公司 | Application of the condition monitoring and fault diagnosis technology in refrigeration unit operational management |
| CN109901537A (en) * | 2019-03-18 | 2019-06-18 | 北京大通惠德科技有限公司 | Mechanical equipment method for monitoring operation states and system for edge calculations side |
| CN110044586A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Ship machine equipment failure judgment method, device, system and storage medium |
| CN110987396A (en) * | 2019-12-13 | 2020-04-10 | 三一重型装备有限公司 | Intelligent fault diagnosis and service life prediction method for coal mining machine rocker arm |
| CN111693411A (en) * | 2020-07-24 | 2020-09-22 | 中国石油化工股份有限公司 | Method and device for state detection and fault diagnosis of radial reactor of moving bed |
| CN112014048A (en) * | 2020-09-03 | 2020-12-01 | 盾构及掘进技术国家重点实验室 | Shield tunneling machine cutter head detection and repair method |
| CN112014094A (en) * | 2020-09-03 | 2020-12-01 | 盾构及掘进技术国家重点实验室 | Shield tunneling machine main driving performance monitoring and repairing method |
| CN113280910A (en) * | 2021-04-27 | 2021-08-20 | 圣名科技(广州)有限责任公司 | Real-time monitoring method and system for long product production line equipment |
| CN113454363A (en) * | 2019-02-22 | 2021-09-28 | 株式会社日本制钢所 | Abnormality detection system and abnormality detection method |
| CN114265010A (en) * | 2021-11-10 | 2022-04-01 | 国网浙江省电力有限公司绍兴供电公司 | Transformer fault sound source positioning method and system based on microphone array |
| CN114577328A (en) * | 2020-11-30 | 2022-06-03 | 核工业理化工程研究院 | Anti-interference method and device for vibration measurement |
| CN116448404A (en) * | 2023-06-15 | 2023-07-18 | 安徽容知日新科技股份有限公司 | Abnormality monitoring method, abnormality monitoring device, electronic device, and computer-readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203117391U (en) * | 2013-01-14 | 2013-08-07 | 天津震东润科智能科技有限公司 | Wind driven generator condition monitoring and intelligent analysis system |
| CN103487749A (en) * | 2013-09-18 | 2014-01-01 | 国家电网公司 | On-line monitoring and diagnosing system and method for mechanical state of high-voltage circuit breaker |
| CN103940597A (en) * | 2014-04-08 | 2014-07-23 | 昆明理工大学 | Method for detecting mechanical faults based on generalized extremum morphological filtering |
| CN108805059A (en) * | 2018-05-29 | 2018-11-13 | 东华大学 | The Fault Diagnosis of Gear Case method that sparse regularization filtering is decomposed with adaptive sparse |
-
2017
- 2017-08-30 CN CN201710761454.4A patent/CN107576488A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN203117391U (en) * | 2013-01-14 | 2013-08-07 | 天津震东润科智能科技有限公司 | Wind driven generator condition monitoring and intelligent analysis system |
| CN103487749A (en) * | 2013-09-18 | 2014-01-01 | 国家电网公司 | On-line monitoring and diagnosing system and method for mechanical state of high-voltage circuit breaker |
| CN103940597A (en) * | 2014-04-08 | 2014-07-23 | 昆明理工大学 | Method for detecting mechanical faults based on generalized extremum morphological filtering |
| CN108805059A (en) * | 2018-05-29 | 2018-11-13 | 东华大学 | The Fault Diagnosis of Gear Case method that sparse regularization filtering is decomposed with adaptive sparse |
Non-Patent Citations (1)
| Title |
|---|
| 孙建: ""滚动轴承振动故障特征提取与寿命预测研究"", 《中国优秀硕士学位论文全文数据库》 * |
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| CN108593084A (en) * | 2018-03-30 | 2018-09-28 | 刘文飞 | A kind of determination method of communications equipment room equipment health status |
| CN108827632A (en) * | 2018-03-30 | 2018-11-16 | 刘文飞 | A kind of determination method of communications equipment room equipment health status |
| CN108387309A (en) * | 2018-04-25 | 2018-08-10 | 哈尔滨电气股份有限公司 | A kind of turbine shafting vibrating data collection, pretreatment and remotely send system |
| CN108843611A (en) * | 2018-06-15 | 2018-11-20 | 浙江大学 | A kind of axial flow blower state identification method based on vibration signal combined spectrum correlation analysis |
| CN108843611B (en) * | 2018-06-15 | 2019-08-27 | 浙江大学 | A state identification method for axial fans based on vibration signal combined with spectral correlation coefficient analysis |
| CN109211548A (en) * | 2018-08-31 | 2019-01-15 | 沃德传动（天津）股份有限公司 | A kind of mechanical failure diagnostic method |
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| US12174084B2 (en) | 2019-02-22 | 2024-12-24 | The Japan Steel Works, Ltd. | Anomaly detection system and anomaly detection method |
| CN113454363A (en) * | 2019-02-22 | 2021-09-28 | 株式会社日本制钢所 | Abnormality detection system and abnormality detection method |
| CN113454363B (en) * | 2019-02-22 | 2024-07-26 | 株式会社日本制钢所 | Abnormality detection system and abnormality detection method |
| CN110044586A (en) * | 2019-03-13 | 2019-07-23 | 中交广州航道局有限公司 | Ship machine equipment failure judgment method, device, system and storage medium |
| CN109901537A (en) * | 2019-03-18 | 2019-06-18 | 北京大通惠德科技有限公司 | Mechanical equipment method for monitoring operation states and system for edge calculations side |
| CN110987396A (en) * | 2019-12-13 | 2020-04-10 | 三一重型装备有限公司 | Intelligent fault diagnosis and service life prediction method for coal mining machine rocker arm |
| CN110987396B (en) * | 2019-12-13 | 2021-07-30 | 三一重型装备有限公司 | An intelligent fault diagnosis and life prediction method for shearer rocker arm |
| CN111693411A (en) * | 2020-07-24 | 2020-09-22 | 中国石油化工股份有限公司 | Method and device for state detection and fault diagnosis of radial reactor of moving bed |
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| CN112014048A (en) * | 2020-09-03 | 2020-12-01 | 盾构及掘进技术国家重点实验室 | Shield tunneling machine cutter head detection and repair method |
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| CN114265010A (en) * | 2021-11-10 | 2022-04-01 | 国网浙江省电力有限公司绍兴供电公司 | Transformer fault sound source positioning method and system based on microphone array |
| CN116448404A (en) * | 2023-06-15 | 2023-07-18 | 安徽容知日新科技股份有限公司 | Abnormality monitoring method, abnormality monitoring device, electronic device, and computer-readable storage medium |
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Application publication date: 20180112 |