CN107607321B - A kind of equipment fault accurate positioning method - Google Patents
A kind of equipment fault accurate positioning method Download PDFInfo
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- CN107607321B CN107607321B CN201710798271.XA CN201710798271A CN107607321B CN 107607321 B CN107607321 B CN 107607321B CN 201710798271 A CN201710798271 A CN 201710798271A CN 107607321 B CN107607321 B CN 107607321B
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Abstract
The present invention relates to a kind of equipment fault accurate positioning methods, the described method includes: obtaining equipment detection data, Supervision measured value is calculated according to the equipment detection data, the Supervision measured value is operated normally into data with equipment and is compared, judge equipment running status, and recording exceptional data；It identifies abnormal data associated components, the Mishap Database of abnormal data and associated components is compared, identify failure；Identification failure is pushed to monitoring personnel, corresponding maintenance is extracted from Maintenance plan and suggests being pushed to monitoring personnel together；Judgement identifies whether correct after maintenance, and by fault data typing Mishap Database and to be labeled as identification correct if correct, and the reference data as machine learning, positioning terminate；Otherwise in fault data typing Mishap Database and it will be labeled as identification mistake, and the reference data as machine learning, positioning terminate.
Description
Technical field
The present invention relates to equipment detection fields, and in particular to a kind of equipment fault accurate positioning method.
Background technique
It is PHM (prognostic and health management technology) that existing machinery equipment fault, which detects main way, and PHM is mainly sharp
With the failure predication technology 1) based on model；2) the failure predication technology based on data-driven；3) event based on reliability of statistics
Hinder Predicting Technique.In the field of power station, it is mainly used in wind-power electricity generation, using ultrasound examination generator set vibration state,
Again by abnormal vibration value, equipment fault is inferred.But in hydroelectric field, since hydro-generating Unit complexity is remote
Higher than wind power generating set, along with water flow noise is excessive, so that hydro-generating Unit is not available same technique and carries out equipment
Detection, it is even more impossible to position to equipment fault.
Existing hydro-generating Unit trouble hunting, or only by dismantling whole equipment, manual inspection is repaired, not only
Can take a significant amount of time with maintenance expense, can also shut down because of long-time cause electricity supply to reduce due to the recessive huge warp of bring
Ji loss.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of equipment fault accurate positioning method, to set state into
Row monitoring, and positioning and the analysis of causes are carried out to equipment fault.
The technical scheme to solve the above technical problems is that a kind of equipment fault accurate positioning method, including with
Lower step:
S1, equipment detection data is obtained, Supervision measured value is calculated according to the equipment detection data, is set described
Standby monitor value operates normally data with equipment and compares, and judges equipment running status, and recording exceptional data；
S2, identification abnormal data associated components, the Mishap Database of abnormal data and associated components is compared, identification event
Barrier；
S3, identification failure is pushed to monitoring personnel, corresponding maintenance is extracted from Maintenance plan and suggests pushing together
To monitoring personnel；
Judgement identifies whether correct after S4, maintenance, enters step S5 if correct, otherwise enters step S6；
S5, by fault data typing Mishap Database and to be labeled as identification correct, and the reference number as machine learning
According to positioning terminates；
S6, in fault data typing Mishap Database and identification mistake, and the reference number as machine learning will be labeled as
According to positioning terminates.
The beneficial effects of the present invention are: the data such as vibration, temperature, water level when the machine unit that utilizes water for producing electric power is run, to whole
A operating states of the units is monitored, and according to historical failure data, carries out positioning and the analysis of causes, this hair to equipment fault
It is bright to shift to an earlier date prediction data, while quickly identification abort situation and reason before failure occurs.Reduce service personnel's inspection
The time repaired and the shutdown as caused by failure have saved a large amount of economic loss for power station.
Detailed description of the invention
Fig. 1 is the method for the present invention flow chart；
Fig. 2 is the flow chart step by step of step S1 of the present invention.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
As shown in Figure 1, a kind of equipment fault accurate positioning method, includes the following steps S1-S4:
S1, equipment detection data is obtained, Supervision measured value is calculated according to the equipment detection data, is set described
Standby monitor value operates normally data with equipment and compares, and judges equipment running status, and recording exceptional data.
The detection data includes vibration detection data, temperature detection data and pressure detection data, the monitor value packet
Include vibration monitoring value, temperature monitoring value and pressure monitoring value.
As shown in Fig. 2, step S1 specifically includes following S11-S16 step by step:
S11, according to formula (1), vibration monitoring value f (x) is calculated using vibration detection data；
A in formula0To vibrate nominal parameter, anFor the amplitude of vibration detection data low frequency component, bnFor vibration detection data
The amplitude of high fdrequency component, n are periodicity, and t is each cycle time, and T is total time, and x is vibration detection data；
S12, according to formula (2), temperature monitoring value g (y) is calculated using temperature detection data；
W in formulafFor the every pole the number of turns of coil, 2P is hydraulic turbine number of poles, αfFor copper wire thickness, LαfFor copper wire coiling length, If
For exciting current, RfFor excitation winding D.C. resistance, y is temperature detection data；
S13, according to formula (3), pressure monitoring value w (z) is calculated using pressure detection data；
α is resistance coefficient in formula, and n is pulsation period, ρ0For fluid density, l is duct length, and C is fluid flow, and z is
Pressure detection data；
S14, will be in vibration monitoring value, temperature monitoring value and pressure monitoring value typing monitor database；
S15, equipment is operated normally in data inputting historical data base；
S16, monitor value and historical data base are compared, whether normal judges operating status, and obtain abnormal data.
S2, identification abnormal data associated components, the Mishap Database of abnormal data and associated components is compared, identification event
Barrier.
S3, identification failure is pushed to monitoring personnel, corresponding maintenance is extracted from Maintenance plan and suggests pushing together
To monitoring personnel.
Judgement identifies whether correct after S4, maintenance, enters step S5 if correct, otherwise enters step S6；
Judgement identifies whether correct formula (4) in the step S4 are as follows:
W in formulaijFor random weight, θiFor random parameter, PiFor design variables, xiFor for trained monitor value, xi=
{ f (x), g (y), w (z) }.
S5, by fault data typing Mishap Database and to be labeled as identification correct, and the reference number as machine learning
According to positioning terminates.
S6, in fault data typing Mishap Database and identification mistake, and the reference number as machine learning will be labeled as
According to positioning terminates.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (2)
1. a kind of equipment fault accurate positioning method, which comprises the following steps:
S1, equipment detection data is obtained, Supervision measured value is calculated according to the equipment detection data, by the equipment monitoring
Value operates normally data with equipment and compares, and judges equipment running status, and recording exceptional data；
S2, identification abnormal data associated components, the Mishap Database of abnormal data and associated components is compared, identifies failure；
S3, identification failure is pushed to monitoring personnel, corresponding maintenance is extracted from Maintenance plan and suggests being pushed to prison together
Survey personnel；
Judgement identifies whether correct after S4, maintenance, enters step S5 if correct, otherwise enters step S6；
S5, by fault data typing Mishap Database and to be labeled as identification correct, and the reference data as machine learning is fixed
Position terminates；
S6, in fault data typing Mishap Database and it will be labeled as identification mistake, and the reference data as machine learning is fixed
Position terminates；The detection data includes vibration detection data, temperature detection data and pressure detection data, and the monitor value includes
Vibration monitoring value, temperature monitoring value and pressure monitoring value；The step S1 specifically include it is following step by step:
S11, according to formula (1), vibration monitoring value f (x) is calculated using vibration detection data；
A in formula0To vibrate nominal parameter, anFor the amplitude of vibration detection data low frequency component, bnFor the high frequency division of vibration detection data
The amplitude of amount, n are periodicity, and t is each cycle time, and T is total time, and x is vibration detection data；
S12, according to formula (2), temperature monitoring value g (y) is calculated using temperature detection data；
W in formulafFor the every pole the number of turns of coil, 2P is hydraulic turbine number of poles, αfFor copper wire thickness, LαfFor copper wire coiling length, IfFor excitation
Electric current, RfFor excitation winding D.C. resistance, y is temperature detection data；
S13, according to formula (3), pressure monitoring value w (z) is calculated using pressure detection data；
α is resistance coefficient in formula, and n is pulsation period, ρ0For fluid density, l is duct length, and C is fluid flow, and z is pressure inspection
Measured data；
S14, will be in vibration monitoring value, temperature monitoring value and pressure monitoring value typing monitor database；
S15, equipment is operated normally in data inputting historical data base；
S16, monitor value and historical data base are compared, whether normal judges operating status, and obtain abnormal data.
2. equipment fault accurate positioning method according to claim 1, which is characterized in that judge identification in the step S4
Whether correct formula (4) are as follows:
W in formulaijFor random weight, θiFor random parameter, PiFor design variables, xiFor for trained monitor value, xi=f (x),
G (y), w (z) }.
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| CN108595006A (en) * | 2018-04-24 | 2018-09-28 | 许昌学院 | A kind of interactive system of the experimental facilities Automatic Control based on remote control |
| CN108981796B (en) * | 2018-06-06 | 2020-11-03 | 江苏大学 | Five-in-one hydraulic mechanical fault diagnosis method |
| CN110553789A (en) * | 2019-09-16 | 2019-12-10 | 中车株洲电力机车有限公司 | state detection method and device of piezoresistive pressure sensor and brake system |
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