CN108875710A - Elevator door speed of service estimation method based on energy threshold algorithm - Google Patents
Elevator door speed of service estimation method based on energy threshold algorithm Download PDFInfo
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Abstract
The invention discloses a kind of elevator door real time execution speed estimation method based on energy threshold algorithm.The present invention includes the following steps：Step 1, the acceleration signal that original elevator door operation is obtained by acceleration transducer；Step 2 carries out trend term to the acceleration signal of acquisition and handles；Step 3 is converted to frequency domain, filtering and noise reduction by Fast Fourier Transform (FFT)；Step 4 carries out inverse Fourier transform to filtered frequency-region signal, calculates the energy of acceleration signal；Step 5, the operation phase given threshold to elevator remove jittering noise signal；Step 6 carries out Integral Processing to final acceleration signal q (t), obtains corresponding real-time speed signal.The present invention removes shaking interference signal existing for elevator switch door moment and switch gate gap, while also ensuring the loss for reducing useful signal, and integral obtains speed signal.
Description
Technical field
The invention belongs to electrical controls and digital processing field, are related to a kind of elevator door based on energy threshold algorithm
Real time execution speed estimation method.
Background technique
Elevator is that can guarantee that a people or cargo steadily pacify as a kind of public transport, most basic most essential function
It is sent to entirely to purpose floor, but for a long time, the case where China causes casualties because of elevator accident occurs repeatedly how
It can guarantee elevator safety operation, reduce loss to greatest extent, become government regulator, elevator manufacturer, elevator supplier etc.
Department needs cardinal task urgently to be resolved.
In the safe operation of elevator, the switch and the speed of service of door are to measure the important indicator of elevator whether normal operation
One of.The speed of service when acceleration signal of elevator door can be used to calculate and estimate in fact well, but acceleration transducer exists
Along with various noises and interference during collection in worksite, lead to speed during acceleration signal processing, after integral
Signal has the result after very big error and quadratic integral, and there are more serious distortions.After using frequency domain filtering simultaneously, electricity
Stationary state and switch gate moment of the terraced door in switch gap door, all there is the dither signals of small magnitude.Therefore, elevator door
Acceleration signal accurate Processing Algorithm be velocity amplitude and operating status when estimating in fact core.
This patent proposes that a kind of elevator door combined based on energy threshold algorithm with time-domain and frequency-domain filtering algorithm is transported in real time
Row speed estimation method, the algorithm can accurately estimate acceleration value, velocity amplitude and operation in elevator door real time execution
State.These parameters will all operate normally as elevator, the important indicator in safety monitoring and maintenance.
Summary of the invention
The present invention is handled collected elevator door acceleration signal by a series of signal Processing Algorithm, quasi- to obtain
More accurate elevator door operation acceleration and velocity amplitude, in order to understand whether elevator is safely operated, for the standard of elevator operation
The efficiency of probability, raising monitoring and O&M that really judgement, reduction accident occur reduces human cost offer guarantee.
The main implementation process of this patent algorithm is as follows：Firstly, the elevator door switch acceleration signal data to actual measurement carry out
Trend term is gone to handle；And time-domain signal is transformed by frequency domain by Fast Fourier Transform (FFT), carry out frequency domain filtering；Then, pass through
Inverse Fourier transform uses energy threshold algorithm process to transformed time-domain signal, filters out elevator switch door moment and open
Shaking interference signal existing for shutdown gap；Finally, to treated, acceleration signal is integrated, and obtains the real-time of elevator door
Speed signal.
The invention mainly comprises following steps：
Step 1, the acceleration signal that original elevator door operation is obtained by acceleration transducer；
1-1, the side that acceleration transducer is lain in a horizontal plane in elevator door；
The switch of 1-2, artificial control elevator door, by acceleration transducer record elevator door opens the door each time at closing time
Acceleration signal data.
Step 2 carries out trend term to the acceleration signal of acquisition and handles；
2-1, the acceleration signal data to acquisition, calculate its trend term；
The sampled data for surveying the elevator door acceleration signal obtained is { xk(k=1,2,3 ..., n), n is hits
According to length sampled data length is extended to N, and make N=2 for ease of calculationL, L is the smallest positive integral for making N >=n, is enabled
Sampling time interval Δ t=1 a, if polynomial function：
Determine polynomial functionEach undetermined coefficient aj(j=0,1 ..., m), so that polynomial functionWith hits
According to xkError sum of squares E it is minimum, i.e.,
The condition that meeting E has extreme value is：
Successively take E to aiLocal derviation is sought, a m+1 member system of linear equations is generated：
Solving equations find out m+1 undetermined coefficient aj(j=0,1 ..., m).It is above it is various in, j be the multinomial set
Order, value range are 0≤j≤m.
2-2, the acceleration signal after eliminating linear trend item is obtained.
Eliminate linear trend item calculation formula be：
It is curvilinear trend item when m >=2.In the processing of elevator running door acceleration signal, m=1 is usually taken, 2,3.To sampling
Y is obtained after the processing of data progress polynomial trend item eliminationk。
Step 3 is converted to frequency domain, filtering and noise reduction by Fast Fourier Transform (FFT)；
3-1, discrete Fourier transform (DFT)；
Due to actual samples signal be discrete and time T in the sample length N of sampled signal be limited, will go
Gesture item treated { yk(k=1,2,3 ..., n) as N point sequence y (r) (r=0,1,2 ... N-1), add to elevator door
Speed signal carries out needing the discrete logarithm using Fourier transformation, the table of discrete Fourier transform (DFT) when Fourier transformation
It is up to formula：
In formula：Y (k1) is equivalent to Y (k1 Δ f), sample frequency
3-2, Fast Fourier Transform (FFT) (FFT)；
3-2-1. is due to the length by sampled signal and calculates cost and is limited, using Fast Fourier Transform (FFT) to going
The signal y (r) of gesture item is handled：
Leaf transformation can be expressed as in the N point discrete Fourier of N point sequence y (r)：
Wherein, W=e-j2π/N
Utilize Fourier's variation coefficient W(k1)rPeriodicity, i.e.,
W(k1)r=Wk1(r+N)=W(k1+N)r
Using its symmetry, i.e.,
W(k1)r+N/2=-W(k1)r
The discrete Fourier transform of long sequence can be decomposed into the discrete fourier of short sequence according to its periodicity by 3-2-2.
Transformation.
By sample length N=2LThe sequences y (r) (r=0,1,2 ..., N-1) handled through past trend term, first by r
Odd even is divided into two groups：
Ask it respectivelyThe discrete fourier variation of point, obtaining first half is：
Latter half is：
3-2-3. repeat step 3-2-2, can be obtained y (r) FFT transform result d (r) (r=0,1,2 ... N-1).
3-3, frequency domain filtering is carried out to amplitude-frequency signal.
Using finite impulse response Finite Impulse Response filter, the difference equation form of FIR filter is represented by：
In formula：D (n1) and p (n1) is respectively input time-domain signal sequence Jing Guo Fast Fourier Transform (FFT) and by frequency domain
The output frequency-region signal sequence of filtering；bk3For filter factor, n1 >=0, k3=0,1,2 ... N-1.
The z-transform of the impulse response function h (n) of FIR filter is ssystem transfer function, is represented by：
Then its impulse response function is：
Step 4 carries out inverse Fourier transform to filtered frequency-region signal, calculates the energy of acceleration signal；
The inverse Fourier transform of 4-1, frequency-region signal；
In formula：F (r) is equivalent to f (r Δ t), sampling time interval Δ t=1, r=0,1,2...N-1.
4-2, acceleration signal is normalized；
Acceleration signal first to amplitude less than given threshold A is normalized, by filtering out again after normalized
Shaking interference noise；Under normal circumstances, the amplitude of shaking interference noise is respectively less than normalization coefficient C, and other useful signals are big
In C.Therefore, to acceleration signal, interference letter is distinguished divided by can effectively be for further processing after preset normalization coefficient
Number and useful signal.
Wherein, f (t)=f (r Δ t).
Its energy waveform is obtained to acceleration signal g (t) squares after normalized, this makes dither signal and useful
The difference of signal is bigger, is more advantageous to given threshold B and removes most of dither signal.
The energy balane of 4-3, acceleration signal.
The ENERGY E 1 of acceleration signal f (t) is defined as：
A square processing is made to the amplitude of the signal after inverse Fourier transform.
Step 5, the operation phase given threshold to elevator remove jittering noise signal；
5-1, the useful signal target phase of the shake of low amplitude value and amplitude is distinguished, determines the target phase of signal,
The loss of useful signal is reduced to the full extent.To making that square treated, accurate threshold value B is arranged in waveform, and is sentenced
Disconnected, the waveform greater than threshold value retains, and the waveform less than threshold value is zeroed：
And the waveform after judgement is named as energy signal amplitude u (t)；
5-2, energy signal amplitude is multiplied with normalization coefficient, it is specific as follows obtains final acceleration signal q (t)：
Q (t)=u (t) C
Step 6 carries out Integral Processing to final acceleration signal q (t), obtains corresponding real-time speed signal r (k5)：
The sampled data of final acceleration signal q (t) is { qk5(k5=1 2,3 ..., n), in numerical integration takes sampling
Time step Δ t is integration step, and trapezoidal numerical quadrature formula is：
The present invention has the beneficial effect that：
The present invention is handled collected elevator door acceleration signal by a series of signal Processing Algorithm, quasi- to obtain
More accurate elevator door operation acceleration and velocity amplitude, in order to understand whether elevator is safely operated, for the standard of elevator operation
The efficiency of probability, raising monitoring and O&M that really judgement, reduction accident occur reduces human cost offer guarantee.
The present invention carries out trend term to obtained complicated original signal and pre-processes, and Fast Fourier Transform (FFT) is frequency domain and filters
Wave, then inverse Fourier transform are time domain, determine target phase according to the energy of signal, given threshold removes difficulties --- electricity
Shaking interference signal existing for terraced switch gate moment and switch gate gap, while also ensuring the loss for reducing useful signal, product
Get speed signal.
The present invention is the judgement of the processing to field measurement signal, has stronger anti-interference ability under complex situations, then
To have better adaptability and accuracy rate for acceleration signal processing ideally, accurate speed signal will be to electricity
Terraced door normal operation, maintenance, safety monitoring provide huge help.
Detailed description of the invention
Fig. 1 is elevator door acceleration original signal figure；
Fig. 2 is that elevator door acceleration original signal removes the figure after trend term；
Fig. 3 is frequency domain figure after elevator door acceleration signal goes trend term to handle；
Fig. 4 is that elevator door acceleration signal removes the figure after trend term after frequency domain filtering；
Fig. 5 is that elevator door acceleration signal frequency domain filtering switchs to time-domain diagram later；
Fig. 6 is figure of the elevator door acceleration signal time domain after energy threshold algorithm；
Fig. 7 is the hodograph that elevator door acceleration signal time-domain integration obtains.
Specific embodiment
The invention will be further described with example with reference to the accompanying drawing.
As shown in figs. 1-7, the present invention is by going trend using a kind of time domain to actual measurement elevator door acceleration signal processing
Item, Fast Fourier Transform (FFT) are frequency domain filtering, inverse Fourier transform is time domain and energy threshold algorithm is taken to denoise, and integral obtains
The algorithm of accurate speed signal, proposes a kind of elevator door real time execution speed estimation method based on energy threshold algorithm.
The present invention passes through field measurement first and obtains the mixed and disorderly unordered elevator door acceleration original signal of Fig. 1.Fig. 2 is to adding
Speed original signal carries out trend term treated time-domain diagram, and through past trend term, treated that water has been arrived in image integral translation
On horizontal line, it is meant that eliminate a large amount of DC component noise；Fig. 3 is that Fig. 2 passes through Fast Fourier Transform (FFT) for frequency domain, analyzes it
Filter parameter is arranged in spectrum signature；Fig. 4 is to Fig. 3 using the ripples filter methods such as FIR setting passband 8HZ, stopband 35HZ frequency domain
Filtered figure；Fig. 5 is time-domain diagram of the Fig. 4 Jing Guo inverse Fourier transform；Fig. 6 uses energy threshold algorithm to Fig. 5, to elevator
The dither signal of door existing small magnitude when the stationary state and door of switch gap door are shut, obtains after carrying out return-to-zero
Accurate acceleration signal figure；Fig. 7 is to the speed signal figure obtained after acceleration signal figure time-domain integration.
The present invention is handled collected elevator door acceleration signal by a series of signal Processing Algorithm, quasi- to obtain
More accurate elevator door operation acceleration and velocity amplitude, operate normally as elevator, in safety monitoring and maintenance
Important indicator.
Concrete methods of realizing is as follows：
Step 1 obtains original elevator door operation acceleration signal by acceleration transducer
1-1, the side that acceleration transducer is lain in a horizontal plane in elevator door；
The switch of 1-2, artificial control elevator door, by acceleration transducer record elevator door opens the door each time at closing time
The data of acceleration signal, as shown in figure.
Step 2 carries out trend term to the acceleration signal of acquisition and handles
2-1, original acceleration signal data are obtained, calculates trend term；
The sampled data for surveying elevator door acceleration signal is { xk(k=1,2,3 ..., n), n is the length of sampled data
Degree, for ease of calculation, sampled data length, which is extended to N, (makes N=2L, L is integer and N >=n), enable sampling time interval Δ
T=1 a, if polynomial function：
Determine functionEach undetermined coefficient aj(j=0,1 ..., m), so that functionWith discrete data xkError it is flat
Side and E are minimum, i.e.,
The condition that meeting E has extreme value is：
Successively take E to aiLocal derviation is sought, can produce a m+1 member system of linear equations：
Solving equations find out m+1 undetermined coefficient aj(j=0,1 ..., m).It is above it is various in, j be the multinomial set
Order, value range are 0≤j≤m.
2-2, the acceleration signal for eliminating linear trend item is obtained.
Eliminate linear trend item calculation formula be：
It is curvilinear trend item when m >=2, usually takes m=1,2,3.The place of polynomial trend item elimination is carried out to sampled data
Y is obtained after reasonk。
Step 3 is converted to frequency domain, filtering and noise reduction by Fast Fourier Transform (FFT)
3-1, discrete Fourier transform (DFT)；
It will remove trend term treated { yk(k=1,2,3 ..., n) as N point sequence y (r) (r=0,1,2 ... N-
1) discrete logarithm using Fourier transformation, discrete fourier are needed when, carrying out Fourier transformation to elevator door acceleration signal
Transformation (DFT) expression formula be：
In formula：Y (k1) is equivalent to Y (k1 Δ f), sample frequency
3-2, Fast Fourier Transform (FFT) (FFT)；
3-2-1. this patent is using Fast Fourier Transform (FFT) to going trend term signal to handle：
Leaf transformation can be expressed as in the N point discrete Fourier of N point sequence y (r)：
Wherein, W=e-j2π/N
Utilize Fourier's variation coefficient W(k1)rPeriodicity, i.e.,
W(k1)r=Wk1(r+N)=W(k1+N)r
Using its symmetry, i.e.,
W(k1)r+N/2=-W(k1)r
The discrete Fourier transform of long sequence can be decomposed into the discrete fourier of short sequence according to its periodicity by 3-2-2.
Transformation.
By sample length N=2LThe sequences y (r) (r=0,1,2 ..., N-1) handled through past trend term, first by r
Odd even is divided into two groups：
Ask it respectivelyThe discrete fourier variation of point, obtaining first half is：
Latter half is：
3-2-3. repeat step 3-2-2, can be obtained y (r) FFT transform result d (r) (r=0,1,2 ... N-1).
3-3, frequency domain filtering is carried out to amplitude-frequency signal.
Using finite impulse response Finite Impulse Response filter, the difference equation of FIR filter is：
In formula：D (n1) and p (n1) is respectively input time-domain signal sequence Jing Guo Fast Fourier Transform (FFT) and by frequency domain
The output frequency-region signal sequence of filtering；bk3For filter factor, n1 >=0, k3=0,1,2 ... N-1.
The z-transform of the impulse response function h (n) of FIR filter is ssystem transfer function：
Then its impulse response function is：
Step 4 carries out inverse Fourier transform to filtered frequency-region signal, calculates the energy of acceleration signal
The inverse Fourier transform of 4-1, frequency-region signal；
In formula：F (r) is equivalent to f (r Δ t), sampling time interval Δ t=1, r=0,1,2...N-1.
4-2, acceleration signal is normalized；
Acceleration signal first to amplitude less than given threshold A is normalized, by filtering out again after normalized
Shaking interference noise；Under normal circumstances, the amplitude of shaking interference noise is respectively less than normalization coefficient C, and other useful signals are big
In C.Therefore, to acceleration signal, interference letter is distinguished divided by can effectively be for further processing after preset normalization coefficient
Number and useful signal.
Wherein, f (t)=f (r Δ t).
Its energy waveform is obtained to acceleration signal g (t) squares after normalized, this makes dither signal and useful
The difference of signal is bigger, is more advantageous to given threshold B and removes most of dither signal.
The energy balane of 4-3, acceleration signal.
The ENERGY E 1 of acceleration signal f (t) is defined as：
A square processing is made to the amplitude of the signal after inverse Fourier transform.
Step 5, the operation phase given threshold for determining elevator remove jittering noise signal
5-1, the useful signal target phase of the shake of low amplitude value and amplitude is distinguished, determines the target phase of signal,
The loss of useful signal is reduced to the full extent.Accurate threshold value B is set according to waveform, the waveform greater than threshold value retains, small
It is zeroed in the waveform of threshold value：
And the waveform after judgement is named as energy signal amplitude u (t)；
5-2, energy signal amplitude is multiplied with normalization coefficient, it is specific as follows obtains final acceleration signal q (t)：
Q (t)=u (t) C
Step 6 carries out Integral Processing to final acceleration signal q (t), obtains corresponding real-time speed signal r (k5).
6-1, final acceleration signal q (t) sampled data be { qk5(k5=1,2,3 ..., n), take in numerical integration
Sampling time step delta t is integration step, and trapezoidal numerical quadrature formula is：
Step 4, in 5 it should be noted that as follows：
The setting of normalization coefficient C and threshold value B is needed to be judged according to the amplitude of shake waveform, be carried out according to the actual situation
Selection, and be adjusted with the staining effect of filtering.
Claims (5)
1. the elevator door real time execution speed estimation method based on energy threshold algorithm, it is characterised in that include the following steps：
Step 1, the acceleration signal that original elevator door operation is obtained by acceleration transducer；
1-1, the side that acceleration transducer is lain in a horizontal plane in elevator door；
The switch of 1-2, artificial control elevator door are opened the door each time by acceleration transducer record elevator door and are accelerated at closing time
Spend signal data；
Step 2 carries out trend term to the acceleration signal of acquisition and handles；
Step 3 is converted to frequency domain, filtering and noise reduction by Fast Fourier Transform (FFT)；
Step 4 carries out inverse Fourier transform to filtered frequency-region signal, calculates the energy of acceleration signal；
Step 5, the operation phase given threshold to elevator remove jittering noise signal；
Step 6 carries out Integral Processing to final acceleration signal q (t), obtains corresponding real-time speed signal.
2. the elevator door real time execution speed estimation method according to claim 1 based on energy threshold algorithm, feature
It is that step 2 is implemented as follows：
2-1, the acceleration signal data to acquisition, calculate its trend term；
The sampled data for surveying the elevator door acceleration signal obtained is { xk(k=1,2,3 ..., n), n is the length of sampled data
Degree, for ease of calculation, is extended to N for sampled data length, and make N=2L, L is the smallest positive integral for making N >=n, when enabling sampling
Between interval of delta t=1, if a polynomial function：
Determine polynomial functionEach undetermined coefficient aj(j=0,1 ..., m), so that polynomial functionWith sampled data xk
Error sum of squares E it is minimum, i.e.,
The condition that meeting E has extreme value is：
Successively take E to aiLocal derviation is sought, a m+1 member system of linear equations is generated：
Solving equations find out m+1 undetermined coefficient aj(j=0,1 ..., m)；It is above it is various in, j be the multinomial order set,
Its value range is 0≤j≤m；
2-2, the acceleration signal after eliminating linear trend item is obtained；
Eliminate linear trend item calculation formula be：
It is curvilinear trend item when m >=2；In the processing of elevator running door acceleration signal, m=1 is taken, 2,3, sampled data is carried out
Y is obtained after the processing that polynomial trend item is eliminatedk。
3. the elevator door real time execution speed estimation method according to claim 2 based on energy threshold algorithm, feature
It is that step 3 is implemented as follows：
3-1, discrete Fourier transform；
Due to actual samples signal be discrete and time T in the sample length N of sampled signal be limited, trend term will be removed
Treated { yk(k=1,2,3 ..., n) as N point sequence y (r) (r=0,1,2 ... N-1), to elevator door acceleration
Signal carries out needing the discrete logarithm using Fourier transformation, the expression formula of discrete Fourier transform (DFT) when Fourier transformation
For：
In formula：Y (k1) is equivalent to Y (k1 Δ f), sample frequency(k1, r=0,1,2 ... N-1)；
3-2, Fast Fourier Transform (FFT) (FFT)；
3-2-1. is due to the length by sampled signal and calculates cost and is limited, using Fast Fourier Transform (FFT) to removing trend term
Signal y (r) handled：
Leaf transformation can be expressed as in the N point discrete Fourier of N point sequence y (r)：
Wherein, W=e-j2π/N
Utilize Fourier's variation coefficient W(k1)rPeriodicity, i.e.,
W(k1)r=Wk1(r+N)=W(k1+N)r
Using its symmetry, i.e.,
W(k1)r+N/2=-W(k1)r
The discrete Fourier transform of long sequence can be decomposed into the discrete Fourier transform of short sequence according to its periodicity by 3-2-2.；
By sample length N=2LThe sequences y (r) (r=0,1,2 ..., N-1) handled through past trend term, first press r odd even
It is divided into two groups：
Ask it respectivelyThe discrete fourier variation of point, obtaining first half is：
Latter half is：
3-2-3. repeat step 3-2-2, can be obtained y (r) FFT transform result d (r) (r=0,1,2 ... N-1)；
3-3, frequency domain filtering is carried out to amplitude-frequency signal；
Using finite impulse response Finite Impulse Response filter, the difference equation form of FIR filter is represented by：
In formula：D (n1) and p (n1) is respectively input time-domain signal sequence Jing Guo Fast Fourier Transform (FFT) and by frequency domain filtering
Output frequency-region signal sequence；bk3For filter factor, n1 >=0, k3=0,1,2 ... N-1；
The z-transform of the impulse response function h (n) of FIR filter is ssystem transfer function, is represented by：
Then its impulse response function is：
4. the elevator door real time execution speed estimation method according to claim 3 based on energy threshold algorithm, feature
It is that step 4 is implemented as follows：
Step 4 carries out inverse Fourier transform to filtered frequency-region signal, calculates the energy of acceleration signal；
The inverse Fourier transform of 4-1, frequency-region signal；
In formula：F (r) is equivalent to f (r Δ t), sampling time interval Δ t=1, r=0,1,2...N-1；
4-2, acceleration signal is normalized；
Acceleration signal first to amplitude less than given threshold A is normalized, by filtering out shake after normalized again
Interference noise；Under normal circumstances, the amplitude of shaking interference noise is respectively less than normalization coefficient C, and other useful signals are greater than C；
Therefore, to acceleration signal, divided by interference signal and useful is effectively distinguished after preset normalization coefficient for further processing
Signal；
Wherein, f (t)=f (r Δ t)；
Its energy waveform is obtained to acceleration signal g (t) squares after normalized, is conducive to given threshold B and removes big portion
Divide dither signal；
The energy balane of 4-3, acceleration signal；
The ENERGY E 1 of acceleration signal f (t) is defined as：
A square processing is made to the amplitude of the signal after inverse Fourier transform.
5. the elevator door real time execution speed estimation method according to claim 4 based on energy threshold algorithm, feature
It is that step 5 is implemented as follows：
Step 5, the operation phase given threshold to elevator remove jittering noise signal；
5-1, the useful signal target phase of the shake of low amplitude value and amplitude is distinguished, determines the target phase of signal, it is maximum
The loss of reduction useful signal in degree；To making that square treated, accurate threshold value B is arranged in waveform, and is judged, greatly
Retain in the waveform of threshold value, the waveform less than threshold value is zeroed：
And the waveform after judgement is named as energy signal amplitude u (t)；
5-2, energy signal amplitude is multiplied with normalization coefficient, it is specific as follows obtains final acceleration signal q (t)：
Q (t)=u (t) C
Step 6 carries out Integral Processing to final acceleration signal q (t), obtains corresponding real-time speed signal r (k5)：
The sampled data of final acceleration signal q (t) is { qk5(k5=1,2,3 ..., n), take the sampling time in numerical integration
Step delta t is integration step, and trapezoidal numerical quadrature formula is：
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109684937A (en) * | 2018-12-06 | 2019-04-26 | 国电南瑞科技股份有限公司 | A kind of signal antinoise method and device based on FFT and Mathematical Morphology method |
| CN110642110A (en) * | 2019-09-23 | 2020-01-03 | 猫岐智能科技（上海）有限公司 | Method for accurately acquiring door opening and closing time of elevator |
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| CN112466322A (en) * | 2020-11-27 | 2021-03-09 | 华侨大学 | Electromechanical device noise signal feature extraction method |
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| CN109684937A (en) * | 2018-12-06 | 2019-04-26 | 国电南瑞科技股份有限公司 | A kind of signal antinoise method and device based on FFT and Mathematical Morphology method |
| CN109684937B (en) * | 2018-12-06 | 2022-08-26 | 国电南瑞科技股份有限公司 | Signal denoising method and device based on FFT and mathematical morphology method |
| WO2020147711A1 (en) * | 2019-01-18 | 2020-07-23 | 西人马帝言(北京)科技有限公司 | Elevator operation status monitoring method and device |
| CN111611832A (en) * | 2019-02-26 | 2020-09-01 | 上汽通用汽车有限公司 | Method and system for obtaining vehicle response displacement based on acceleration signal |
| CN111611832B (en) * | 2019-02-26 | 2023-11-17 | 上汽通用汽车有限公司 | Method and system for acquiring vehicle response displacement based on acceleration signal |
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| CN112146678A (en) * | 2019-06-27 | 2020-12-29 | 华为技术有限公司 | Method for determining calibration parameters and electronic equipment |
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| CN110642110A (en) * | 2019-09-23 | 2020-01-03 | 猫岐智能科技（上海）有限公司 | Method for accurately acquiring door opening and closing time of elevator |
| CN112357713A (en) * | 2020-11-27 | 2021-02-12 | 杭州电子科技大学 | Multifunctional elevator safety protection system and method |
| CN112472074A (en) * | 2020-11-27 | 2021-03-12 | 吉林农业科技学院 | Sitting gait data acquisition and analysis system based on acceleration sensor |
| CN112466322A (en) * | 2020-11-27 | 2021-03-09 | 华侨大学 | Electromechanical device noise signal feature extraction method |
| CN112466322B (en) * | 2020-11-27 | 2023-06-20 | 华侨大学 | Noise signal feature extraction method for electromechanical equipment |
| CN113184651A (en) * | 2021-04-08 | 2021-07-30 | 浙江理工大学 | Method for preprocessing elevator running state signal and extracting characteristic quantity |
| CN113213297A (en) * | 2021-05-08 | 2021-08-06 | 浙江工业大学 | Displacement sensor data processing method applied to elevator safety detection system |
| CN113392511A (en) * | 2021-05-28 | 2021-09-14 | 广西电网有限责任公司电力科学研究院 | On-load tap-changer mechanical state monitoring method based on frequency spectrum envelope symbol entropy |
| CN116662937A (en) * | 2023-07-31 | 2023-08-29 | 西安交通大学城市学院 | Method for monitoring and evaluating air data safety of aircraft |
| CN116662937B (en) * | 2023-07-31 | 2023-10-20 | 西安交通大学城市学院 | Method for monitoring and evaluating air data safety of aircraft |
| CN118582662A (en) * | 2024-08-07 | 2024-09-03 | 山东鑫海矿业技术装备股份有限公司 | Air storage tank intelligent management method and system based on air pressure monitoring |
| CN118582662B (en) * | 2024-08-07 | 2024-10-29 | 山东鑫海矿业技术装备股份有限公司 | Air storage tank intelligent management method and system based on air pressure monitoring |
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