CN110456194A - Frequency conversion fan service life monitoring method and device for air disinfecting and purifying device - Google Patents
Frequency conversion fan service life monitoring method and device for air disinfecting and purifying device Download PDFInfo
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- CN110456194A CN110456194A CN201910698055.7A CN201910698055A CN110456194A CN 110456194 A CN110456194 A CN 110456194A CN 201910698055 A CN201910698055 A CN 201910698055A CN 110456194 A CN110456194 A CN 110456194A
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- Prior art keywords
- frequency conversion
- conversion fan
- service life
- current value
- monitoring method
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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
- G01R19/0092—Arrangements for measuring currents or voltages or for indicating presence or sign thereof measuring current only
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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
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Disinfection, Sterilisation Or Deodorisation Of Air (AREA)
- Apparatus For Disinfection Or Sterilisation (AREA)
Abstract
The present invention relates to a kind of frequency conversion fan service life monitoring methods and device for air disinfecting and purifying device, comprising: step S1: when frequency conversion fan is powered, acquiring the timing information of electric current and is sent to server；Step S2: the timing information of electric current of the server based on acquisition extracts feature vector, and the feature vector of extraction is inputted trained first model to obtain remaining life percentage.Compared with prior art, the present invention determines the remaining life of frequency conversion fan by networking mode and cloud, alleviates the pressure of terminal, to be conducive to the cost of reduction device, and more accurately obtains result.
Description
Technical field
The present invention relates to a kind of fan life detection techniques, more particularly, to a kind of change for air disinfecting and purifying device
Frequency fan life monitoring method and device.
Background technique
The people of concern with to(for) health, air cleaning class product have obtained significant progress, purify class at present and all take
The life tests for filter core are loaded with, but often all lack the detection for blower, the reason is that, existing purification class product
All be using strainer combination blower by the way of, the requirement to blower is lower, be not related to for blower precision control.
In this regard, some novel purification class products are without filter core or need to carry out frequency conversion fan accurate control
System, or it is normally opened due to needing, and the loss of blower is big, needs to safeguard blower.Therefore exist for the life tests of blower
It is necessary on these products, and may be implemented accurately to detect the service life of blower without product at present.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be used for air sterillization
The frequency conversion fan service life monitoring method and device of purification device.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device, comprising:
Step S1: it when frequency conversion fan is powered, acquires the timing information of electric current and is sent to server；
Step S2: the timing information of electric current of the server based on acquisition extracts feature vector, and by the feature vector of extraction
Trained first model is inputted to obtain remaining life percentage.
The training process of first model includes:
Step A1: obtaining multiple groups test data, wherein every group of test data includes that a frequency conversion fan is energized to for the first time certainly
The timing information of electrical current when end-of-life；
Step A2: for each group of test data, multiple feature vectors, and all spies based on this group of test data are extracted
Sign vector obtains the remaining life percentage of each feature vector；
Step A3: using all feature vectors and corresponding remaining life percentage as training set data the first mould of training
Type.
Described eigenvector includes accumulative energization electricity and on-off times.
Switching frequency in each group test data is not mutually equal, and for same group of test data, removes last two secondly, appointing
The energization electricity anticipated between switching twice in succession is equal, wherein the switching frequency is logical between switching twice in succession twice
Power consumption.
The timing information of the electric current specifically:
{(i1,t1),(i2,t2),…,(ik,tk),…,(iN,tN)}
Wherein: i1For first current value, t1At the beginning of first current value, i2For for the second current value, t2For
At the beginning of second current value, ikFor k-th of current value, tkAt the beginning of k-th of current value, iNFor the last one
Current value, tNAt the beginning of the last one current value.
Remaining life percentage is determined by accumulative energization electricity accounting in the step A2.
A kind of device for realizing the above method, including controller and main power source further include current detection module and energy storage electricity
Pond, the energy-storage battery are welded on mainboard, are connect with the current detection module and controller and are powered for it, the main electricity
Source is connect with controller, current detection module and frequency conversion fan respectively.
The frequency conversion fan includes motor and blade assembly.
Compared with prior art, the invention has the following advantages:
1) remaining life that frequency conversion fan is determined by networking mode and cloud, alleviates the pressure of terminal, to have
Conducive to the cost of reduction device, and more accurately obtain result.
2) actual test data training pattern is utilized, the accuracy of model can be improved.
3) feature vector includes accumulative energization electricity and on-off times, has taken into account on-off times, improves monitoring precision.
4) same group of test data, except last two secondly, arbitrary continuation switch twice between energization electricity it is equal, can be with
So that the remaining life percentage in training set data is more accurate.
5) in timing information, only the data at variation moment are stored, have greatly reduced data volume, reduce flow and
The consumption of memory space.
6) energy-storage battery is welded on mainboard, can not be disassembled, and can be current detecting mould in air disinfecting and purifying device
Block carries out afterflow power supply, to obtain the timing information of more accurate electric current.
Detailed description of the invention
Fig. 1 is the key step flow diagram of the method for the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
Embodiment 1
A kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device, as shown in Figure 1, comprising:
Step S1: it when frequency conversion fan is powered, acquires the timing information of electric current and is sent to server；
Step S2: the timing information of electric current of the server based on acquisition extracts feature vector, and by the feature vector of extraction
Trained first model is inputted to obtain remaining life percentage, wherein the first model can be using convolutional neural networks etc..
Wherein, the training process of the first model includes:
Step A1: obtaining multiple groups test data, wherein every group of test data includes that a frequency conversion fan is energized to for the first time certainly
The timing information of electrical current when end-of-life；
Step A2: for each group of test data, multiple feature vectors, and all spies based on this group of test data are extracted
Sign vector obtains the remaining life percentage of each feature vector, wherein remaining life percentage is by adding up energization electricity accounting
It determines；
Step A3: using all feature vectors and corresponding remaining life percentage as training set data the first mould of training
Type.
Feature vector includes accumulative energization electricity and on-off times, has taken into account on-off times, improves monitoring precision, each group
Switching frequency in test data is not mutually equal, and for same group of test data, except last two secondly, arbitrary continuation is opened twice
Energization electricity between pass is equal, wherein switching frequency is the energization electricity between switching twice in succession twice.
The timing information of electric current specifically:
{(i1,t1),(i2,t2),…,(ik,tk),…,(iN,tN)}
Wherein: i1For first current value, t1At the beginning of first current value, i2For for the second current value, t2For
At the beginning of second current value, ikFor k-th of current value, tkAt the beginning of k-th of current value, iNFor the last one
Current value, tNAt the beginning of the last one current value.
A kind of device for realizing the above method, including controller and main power source further include current detection module and energy storage electricity
Pond, energy-storage battery are welded on mainboard, are connect with current detection module and controller and are powered for it, main power source respectively with control
Device, current detection module are connected with frequency conversion fan, and frequency conversion fan includes motor and blade assembly.Energy-storage battery is welded in mainboard
On, it can not be disassembled, can be that current detection module carries out afterflow power supply in air disinfecting and purifying device, to obtain more quasi-
The timing information of true electric current.
In the present embodiment, for obtaining for step A2 training set, each time from ik-1Become ikAs kth time energization section,
From t1To tkCurrent value is integrated and can obtain accumulative energization electricity, in test data, by obtained accumulative energization electricity
The ratio of amount and total energization electricity is as used service life percentage, and 1 to subtract used service life percentage be remaining life
Percentage.
The testing experiment of test set is carried out using the application, when the prediction accuracy of remaining life is relative to only considering to be powered
Between mode, improve 30% or more.
Claims (8)
1. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device characterized by comprising
Step S1: it when frequency conversion fan is powered, acquires the timing information of electric current and is sent to server；
Step S2: the timing information of electric current of the server based on acquisition extracts feature vector, and the feature vector of extraction is inputted
Trained first model is to obtain remaining life percentage.
2. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device according to claim 1, special
Sign is that the training process of first model includes:
Step A1: obtaining multiple groups test data, wherein every group of test data includes that a frequency conversion fan is energized to the service life for the first time certainly
At the end of electrical current timing information；
Step A2: for each group of test data, extracting multiple feature vectors, and all features based on this group of test data to
Measure the remaining life percentage of each feature vector；
Step A3: using all feature vectors and corresponding remaining life percentage as training set data the first model of training.
3. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device according to claim 1 or 2,
It is characterized in that, described eigenvector includes accumulative energization electricity and on-off times.
4. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device according to claim 3, special
Sign is that the switching frequency in each group test data is not mutually equal, and for same group of test data, removes last two secondly, appointing
The energization electricity anticipated between switching twice in succession is equal, wherein the switching frequency is logical between switching twice in succession twice
Power consumption.
5. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device according to claim 1, special
Sign is, the timing information of the electric current specifically:
{(i1,t1),(i2,t2),…,(ik,tk),…,(iN,tN)}
Wherein: i1For first current value, t1At the beginning of first current value, i2For for the second current value, t2It is second
At the beginning of a current value, ikFor k-th of current value, tkAt the beginning of k-th of current value, iNFor the last one electric current
Value, tNAt the beginning of the last one current value.
6. a kind of frequency conversion fan service life monitoring method for air disinfecting and purifying device according to claim 4, special
Sign is that remaining life percentage is determined by accumulative energization electricity accounting in the step A2.
7. a kind of device for realizing any the method in claim 1~6, including controller and main power source, which is characterized in that
It further include current detection module and energy-storage battery, the energy-storage battery is welded on mainboard, with the current detection module and control
Device processed is connected and is powered for it, and the main power source is connect with controller, current detection module and frequency conversion fan respectively.
8. device according to claim 7, which is characterized in that the frequency conversion fan includes motor and blade assembly.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910698055.7A CN110456194B (en) | 2019-07-31 | 2019-07-31 | Method and device for monitoring service life of variable frequency fan for air disinfection and purification device |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910698055.7A CN110456194B (en) | 2019-07-31 | 2019-07-31 | Method and device for monitoring service life of variable frequency fan for air disinfection and purification device |
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| CN110456194A true CN110456194A (en) | 2019-11-15 |
| CN110456194B CN110456194B (en) | 2021-07-16 |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1862278A (en) * | 2005-05-12 | 2006-11-15 | 通用电气公司 | Method and system for predicting remaining life for motors featuring on-line insulation condition monitor |
| CN102749584A (en) * | 2012-07-17 | 2012-10-24 | 哈尔滨工业大学 | Prediction method for residual service life of turbine generator based on ESN (echo state network) of Kalman filtering |
| CN103019135A (en) * | 2012-11-28 | 2013-04-03 | 北京金风科创风电设备有限公司 | Method and system for predicting service life of fan component |
| CN103470521B (en) * | 2013-09-26 | 2016-03-23 | 海南金盘电气有限公司 | Fan life prediction device |
| CN106612094A (en) * | 2015-10-26 | 2017-05-03 | 发那科株式会社 | Machine learning apparatus and method, life prediction apparatus and motor driving apparatus |
-
2019
- 2019-07-31 CN CN201910698055.7A patent/CN110456194B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1862278A (en) * | 2005-05-12 | 2006-11-15 | 通用电气公司 | Method and system for predicting remaining life for motors featuring on-line insulation condition monitor |
| CN102749584A (en) * | 2012-07-17 | 2012-10-24 | 哈尔滨工业大学 | Prediction method for residual service life of turbine generator based on ESN (echo state network) of Kalman filtering |
| CN103019135A (en) * | 2012-11-28 | 2013-04-03 | 北京金风科创风电设备有限公司 | Method and system for predicting service life of fan component |
| CN103470521B (en) * | 2013-09-26 | 2016-03-23 | 海南金盘电气有限公司 | Fan life prediction device |
| CN106612094A (en) * | 2015-10-26 | 2017-05-03 | 发那科株式会社 | Machine learning apparatus and method, life prediction apparatus and motor driving apparatus |
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| CN110456194B (en) | 2021-07-16 |
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Address after: 1188 Jiuye Road, Qingpu District, Shanghai, 201712 Patentee after: Billion Oxygen Environmental Technology (Shanghai) Co.,Ltd. Address before: 201619 No. 1586 Changxing East Road, Dongjing Town, Songjiang District, Shanghai Patentee before: Yiyi Environmental Technology (Shanghai) Co.,Ltd. |