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CN108875710B - Elevator door running speed estimation method based on energy threshold algorithm - Google Patents

Elevator door running speed estimation method based on energy threshold algorithm Download PDF

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CN108875710B
CN108875710B CN201810817721.XA CN201810817721A CN108875710B CN 108875710 B CN108875710 B CN 108875710B CN 201810817721 A CN201810817721 A CN 201810817721A CN 108875710 B CN108875710 B CN 108875710B
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曹九稳
戴浩桢
张未来
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Hangzhou Safer Information Technology Co ltd
Hangzhou Dianzi University
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Abstract

The invention discloses a real-time running speed estimation method of an elevator door based on an energy threshold algorithm. The invention comprises the following steps: step 1, acquiring an acceleration signal of the operation of an original elevator door through an acceleration sensor; step 2, trend item removing processing is carried out on the obtained acceleration signals; step 3, converting the signal into a frequency domain through fast Fourier transform, and filtering and denoising the signal; step 4, performing inverse Fourier transform on the filtered frequency domain signal, and calculating the energy of the acceleration signal; step 5, setting a threshold value for the running stage of the elevator, and removing a jitter noise signal; and 6, carrying out integration processing on the final acceleration signal q (t) to obtain a corresponding real-time speed signal. The invention removes the jitter interference signals existing at the moment of opening and closing the door of the elevator and in the gap between the opening and closing the door, and simultaneously can ensure that the loss of useful signals is reduced, and the speed signals are obtained by integration.

Description

Elevator door running speed estimation method based on energy threshold algorithm
Technical Field
The invention belongs to the field of electrical control and digital signal processing, and relates to a real-time running speed estimation method of an elevator door based on an energy threshold algorithm.
Background
As a public transport means, the most essential function of an elevator is to ensure that people or goods can be stably and safely delivered to a target floor, but for a long time, the situation of casualties caused by elevator accidents frequently occurs in China, how to ensure the safe operation of the elevator can be ensured, the loss is reduced to the maximum extent, and the elevator becomes the first major affairs which needs to be solved urgently by government supervision departments, elevator manufacturers, elevator suppliers and other departments.
In the safe operation of the elevator, the opening and closing of the door and the operation speed are one of the important indexes for judging whether the elevator operates normally or not. The acceleration signal of the elevator door can be well used for calculating and estimating the real-time running speed of the elevator door, but the acceleration sensor is accompanied by various noises and interferences in the field acquisition process, so that the speed signal after integration has a large error in the acceleration signal processing process, and the result after secondary integration has serious distortion. Meanwhile, after frequency domain filtering is adopted, small-amplitude jitter signals exist in the elevator door in the static state of opening and closing the gap door and at the moment of opening and closing the door. Therefore, the accurate processing algorithm of the acceleration signal of the elevator door is a core part of estimating its real-time speed value and running state.
The patent provides an elevator door real-time running speed estimation method based on the combination of an energy threshold algorithm and a time domain frequency domain filtering algorithm, and the algorithm can accurately estimate the acceleration value, the speed value and the running state of the elevator door in real-time running. These parameters will be important indicators in normal elevator operation, safety monitoring and maintenance.
Disclosure of Invention
According to the elevator door acceleration signal processing method, the acquired elevator door acceleration signal is processed through a series of signal processing algorithms, so that accurate elevator door operation acceleration and speed values are obtained, whether the elevator is operated safely or not can be known conveniently, and guarantee is provided for accurate judgment of elevator operation, reduction of accident probability, improvement of monitoring and operation and maintenance efficiency and reduction of labor cost.
The algorithm of the patent mainly comprises the following implementation processes: firstly, carrying out trend removing item processing on actually measured elevator door switch acceleration signal data; converting the time domain signal into a frequency domain through fast Fourier transform, and carrying out frequency domain filtering; then, after Fourier inverse transformation, the transformed time domain signal is processed by adopting an energy threshold algorithm, and jitter interference signals existing at the moment of opening and closing the door of the elevator and at the gap of opening and closing the door are filtered; and finally, integrating the processed acceleration signal to obtain a real-time speed signal of the elevator door.
The invention mainly comprises the following steps:
step 1, acquiring an acceleration signal of the operation of an original elevator door through an acceleration sensor;
1-1, horizontally placing an acceleration sensor at one side of an elevator door;
1-2, manually controlling the opening and closing of the elevator door, and recording acceleration signal data of the elevator door when the elevator door is opened and closed every time through an acceleration sensor.
Step 2, trend item removing processing is carried out on the obtained acceleration signals;
2-1, calculating a trend item of the acquired acceleration signal data;
the sampling data of the elevator door acceleration signal obtained by actual measurement is { xkN is the length of the sample data, and for convenience of calculation, the sample data length is extended to N, and N is 2LL is the minimum integer for N ≧ N, let the sampling interval Δ t equal to 1, set a polynomial function:
Figure BDA0001740706040000031
determining polynomial functions
Figure BDA0001740706040000032
Each undetermined coefficient a ofj(j ═ 0, 1.. times, m), such that the polynomial function
Figure BDA0001740706040000033
And the sampled data xkHas the smallest sum of squared errors E, i.e.
Figure BDA0001740706040000034
The condition that E has an extreme value is as follows:
Figure BDA0001740706040000035
taking E pairs of a in sequenceiThe partial derivatives are calculated to generate an m +1 element linear equation set:
Figure BDA0001740706040000036
solving the equation set to obtain m +1 undetermined coefficients aj(j ═ 0, 1.. times, m). In the above formulas, j is a set polynomial order, and the value range of j is more than or equal to 0 and less than or equal to m.
And 2-2, obtaining the acceleration signal without the linear trend term.
The formula for eliminating the linear trend term is as follows:
Figure BDA0001740706040000037
when m is more than or equal to 2, the curve trend item is shown. In the actual elevator door acceleration signal processing, m is usually 1,2, and 3. The sampled data is processed by polynomial trend item elimination to obtain yk。
Step 3, converting the signal into a frequency domain through fast Fourier transform, and filtering and denoising the signal;
3-1, Discrete Fourier Transform (DFT);
since the actual sampled signal is discrete and the sample length N of the sampled signal over time T is finite, { y after processing the detrending termkAs an N-point sequence y (r) (0, 1,2.. gtoren-1), a discrete algorithm using a fourier transform is required to perform fourier transform on the elevator door acceleration signal, and the expression of the Discrete Fourier Transform (DFT) is:
Figure BDA0001740706040000041
in the formula: y (k1) is equivalent to Y (k 1. DELTA.f), the sampling frequency
Figure BDA0001740706040000042
3-2, Fast Fourier Transform (FFT);
3-2-1. due to the limitation of the length of the sampling signal and the calculation cost, the signal y (r) of the detrending term is processed by adopting fast Fourier transform:
the N-point discrete fourier transform of the N-point sequence y (r) can be expressed as:
Figure BDA0001740706040000043
wherein W is e-j2π/N
Using Fourier coefficient of variation W(k1)rIs periodic, i.e.
W(k1)r＝Wk1(r+N)＝W(k1+N)r
By virtue of its symmetry, i.e.
W(k1)r+N/2＝-W(k1)r
3-2-2. the discrete fourier transform of a long sequence can be decomposed into a discrete fourier transform of a short sequence according to its periodicity.
Sample length N2LThe detrended sequence y (r) (0, 1,2.., N-1) is first divided into two groups according to the parity of r:
Figure BDA0001740706040000044
respectively calculate it
Figure BDA0001740706040000045
Discrete fourier transform of the points, the first half is obtained as:
Figure BDA0001740706040000046
the rear half part is:
Figure BDA0001740706040000051
3-2-3, repeating the step 3-2-2 to obtain the FFT result d (r) (r is 0,1,2.. N-1) of y (r).
And 3-3, carrying out frequency domain filtering on the amplitude-frequency signal.
With a finite impulse response FIR digital filter, the difference equation form of the FIR filter can be expressed as:
Figure BDA0001740706040000052
in the formula: d (n1) and p (n1) are respectively an input time domain signal sequence subjected to fast Fourier transform and an output frequency domain signal sequence subjected to frequency domain filtering; bk3N1 is equal to or greater than 0, and k3 is equal to 0,1,2.
The z-transform of the impulse response function h (n) of the FIR filter is the system transfer function, which can be expressed as:
Figure BDA0001740706040000053
then its impulse response function is:
Figure BDA0001740706040000054
step 4, performing inverse Fourier transform on the filtered frequency domain signal, and calculating the energy of the acceleration signal;
4-1, inverse Fourier transform of the frequency domain signal;
Figure BDA0001740706040000055
in the formula: f (r) is equivalent to f (r Δ t), and the sampling time interval Δ t is 1, r is 0,1,2.
4-2, normalizing the acceleration signal;
firstly, normalizing the acceleration signal with the amplitude smaller than a set threshold A, and filtering out the jitter interference noise after normalization processing; in general, the amplitude of the jitter interference noise is smaller than the normalized coefficient C, and the other useful signals are larger than C. Therefore, the acceleration signal can be further processed and distinguished from the interference signal and the useful signal after being divided by the preset normalization coefficient.
Figure BDA0001740706040000061
Where f (t) is f (r · Δ t).
The energy waveform of the normalized acceleration signal g (t) is obtained by squaring the normalized acceleration signal g (t), so that the difference between the jitter signal and the useful signal is larger, and the setting of the threshold B is more favorable for removing most of the jitter signal.
4-3, calculating the energy of the acceleration signal.
The energy E1 of the acceleration signal f (t) is defined as:
Figure BDA0001740706040000062
and squaring the amplitude of the signal after the Fourier transform.
Step 5, setting a threshold value for the running stage of the elevator, and removing a jitter noise signal;
and 5-1, distinguishing the jitter with low amplitude from the useful signal running section with high amplitude, determining the signal running section and reducing the loss of the useful signal to the maximum extent. Setting an accurate threshold B for the waveform subjected to the square processing, judging, reserving the waveform larger than the threshold, and enabling the waveform smaller than the threshold to return to zero:
Figure BDA0001740706040000071
the judged waveform is named as an energy signal amplitude u (t);
5-2, multiplying the amplitude of the energy signal by a normalization coefficient to obtain a final acceleration signal q (t), wherein the final acceleration signal q (t) is as follows:
q(t)＝u(t)·C
step 6, integrating the final acceleration signal q (t) to obtain a corresponding real-time speed signal r (k 5):
the sampling data of the final acceleration signal q (t) is { q }k5And (k5 ═ 1,2, 3.., n), sampling time step Δ t in numerical integration is taken as an integration step, and trapezoidal numerical integration formula is:
Figure BDA0001740706040000072
the invention has the following beneficial effects:
according to the elevator door acceleration signal processing method, the acquired elevator door acceleration signal is processed through a series of signal processing algorithms, so that accurate elevator door operation acceleration and speed values are obtained, whether the elevator is operated safely or not can be known conveniently, and guarantee is provided for accurate judgment of elevator operation, reduction of accident probability, improvement of monitoring and operation and maintenance efficiency and reduction of labor cost.
The invention carries out trend item removing pretreatment on the obtained complex original signal, fast Fourier transform is carried out to the complex original signal, the complex original signal is filtered, then Fourier inversion is carried out to the complex original signal, the complex original signal is converted into a frequency domain and is filtered, then the complex original signal is converted into a time domain, an operation section is determined according to the energy of the complex original signal, a threshold value is set, and difficult problems, namely jitter interference signals existing at the moment of opening and closing a door of an elevator and in the gap of opening and closing the door, are removed, meanwhile, the loss of useful signals can be reduced, and the velocity signal is obtained by integration.
The invention processes and judges the on-site measured signals, has stronger anti-jamming capability under complex conditions, has better adaptability and accuracy rate aiming at the acceleration signal processing under an ideal state, and provides great help for normal operation, maintenance and safety monitoring of the elevator door by accurate speed signals.
Drawings
FIG. 1 is a graph of an elevator door acceleration raw signal;
FIG. 2 is a graph of an elevator door acceleration raw signal after detrending;
FIG. 3 is a frequency domain diagram of elevator door acceleration signal after detrending item processing;
FIG. 4 is a graph of the elevator door acceleration signal after being filtered in the frequency domain after being detrended;
FIG. 5 is a time domain diagram of the elevator door acceleration signal after frequency domain filtering;
FIG. 6 is a graph of the elevator door acceleration signal time domain after passing through an energy threshold algorithm;
fig. 7 is a velocity diagram obtained by time domain integration of the elevator door acceleration signal.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1-7, the invention provides an algorithm for obtaining an accurate speed signal by processing an actually measured elevator door acceleration signal, adopting a time domain de-trending term, fast fourier transform to frequency domain filtering, fourier inversion to time domain, and adopting an energy threshold algorithm for de-noising and integrating, and provides an elevator door real-time operation speed estimation method based on the energy threshold algorithm.
The invention firstly obtains the disordered original acceleration signal of the elevator door shown in figure 1 through field actual measurement. FIG. 2 is a time domain diagram of the acceleration raw signal after being processed by the detrending term, and the image after being processed by the detrending term is wholly translated to a horizontal line, which means that a large amount of direct current component noise is eliminated; FIG. 3 is the frequency domain of FIG. 2 after fast Fourier transform, which is analyzed for spectral characteristics and filter parameters are set; FIG. 4 is a diagram of the band pass 8HZ and the stop band 35HZ of FIG. 3 after frequency filtering by FIR equiripple filtering; FIG. 5 is a time domain plot of FIG. 4 after an inverse Fourier transform; fig. 6 is an accurate acceleration signal diagram obtained by performing zeroing processing on a small-amplitude jitter signal existing when the elevator door is in a static state of the opening and closing gap door and the door is closed by adopting an energy threshold algorithm in fig. 5; fig. 7 is a velocity signal diagram obtained by time-domain integration of an acceleration signal diagram.
The invention processes the collected elevator door acceleration signals through a series of signal processing algorithms, and plans to obtain more accurate elevator door running acceleration and speed values as important indexes in normal running, safety monitoring and maintenance of the elevator.
The specific implementation method is as follows:
step 1, obtaining an original elevator door running acceleration signal through an acceleration sensor
1-1, horizontally placing an acceleration sensor at one side of an elevator door;
1-2, manually controlling the opening and closing of the elevator door, and recording the data of the acceleration signal when the elevator door is opened and closed every time through an acceleration sensor, as shown in the figure I.
Step 2, trend item removing processing is carried out on the obtained acceleration signals
2-1, acquiring original acceleration signal data and calculating a trend item;
the sampling data of the actually measured elevator door acceleration signal is { xkN is the length of the sample data, and the sample data length is extended to N (N is 2) for ease of calculationLL is an integer and N ≧ N), let the sampling interval Δ t equal to 1, set a polynomial function:
Figure BDA0001740706040000091
determining a function
Figure BDA0001740706040000092
Each undetermined coefficient a ofj(j ═ 0, 1.. times, m), such that the function is
Figure BDA0001740706040000093
And discrete data xkHas the smallest sum of squared errors E, i.e.
Figure BDA0001740706040000101
The condition that E has an extreme value is as follows:
Figure BDA0001740706040000102
taking E pairs of a in sequenceiThe partial derivatives can generate an m +1 element linear equation set:
Figure BDA0001740706040000103
solving the equation set to obtain m +1 undetermined coefficients aj(j ═ 0, 1.. times, m). In the above formulas, j is a set polynomial order, and the value range of j is more than or equal to 0 and less than or equal to m.
And 2-2, obtaining an acceleration signal eliminating the linear trend term.
The formula for eliminating the linear trend term is as follows:
Figure BDA0001740706040000104
when m is more than or equal to 2, the curve trend term is usually taken as m is 1,2 and 3. The sampled data is processed by polynomial trend item elimination to obtain yk。
Step 3, converting the frequency domain into a frequency domain through fast Fourier transform, and filtering and denoising
3-1, Discrete Fourier Transform (DFT);
processed by detrending term { ykAs an N-point sequence y (r) (0, 1,2.. gtoren-1), performing fourier transform on the elevator door acceleration signal by using a discrete algorithm of fourier transform, where the expression of Discrete Fourier Transform (DFT) is:
Figure BDA0001740706040000105
in the formula: y (k1) is equivalent to Y (k 1. DELTA.f), the sampling frequency
Figure BDA0001740706040000106
3-2, Fast Fourier Transform (FFT);
3-2-1, the patent adopts fast Fourier transform to process the trend removing item signal:
the N-point discrete fourier transform of the N-point sequence y (r) can be expressed as:
Figure BDA0001740706040000111
wherein W is e-j2π/N
Using Fourier coefficient of variation W(k1)rIs periodic, i.e.
W(k1)r＝Wk1(r+N)＝W(k1+N)r
By virtue of its symmetry, i.e.
W(k1)r+N/2＝-W(k1)r
3-2-2. the discrete fourier transform of a long sequence can be decomposed into a discrete fourier transform of a short sequence according to its periodicity.
Sample length N2LThe detrended sequence y (r) (0, 1,2.., N-1) is first divided into two groups according to the parity of r:
Figure BDA0001740706040000112
respectively calculate it
Figure BDA0001740706040000113
Discrete fourier transform of the points, the first half is obtained as:
Figure BDA0001740706040000114
the rear half part is:
Figure BDA0001740706040000115
3-2-3, repeating the step 3-2-2 to obtain the FFT result d (r) (r is 0,1,2.. N-1) of y (r).
And 3-3, carrying out frequency domain filtering on the amplitude-frequency signal.
A finite impulse response FIR digital filter is adopted, and the difference equation of the FIR filter is as follows:
Figure BDA0001740706040000121
in the formula: d (n1) and p (n1) are respectively an input time domain signal sequence subjected to fast Fourier transform and an output frequency domain signal sequence subjected to frequency domain filtering; bk3N1 is equal to or greater than 0, and k3 is equal to 0,1,2.
The z-transform of the impulse response function h (n) of the FIR filter is the system transfer function:
Figure BDA0001740706040000122
then its impulse response function is:
Figure BDA0001740706040000123
step 4, carrying out inverse Fourier transform on the filtered frequency domain signal, and calculating the energy of the acceleration signal
4-1, inverse Fourier transform of the frequency domain signal;
Figure BDA0001740706040000124
in the formula: f (r) is equivalent to f (r Δ t), and the sampling time interval Δ t is 1, r is 0,1,2.
4-2, normalizing the acceleration signal;
firstly, normalizing the acceleration signal with the amplitude smaller than a set threshold A, and filtering out the jitter interference noise after normalization processing; in general, the amplitude of the jitter interference noise is smaller than the normalized coefficient C, and the other useful signals are larger than C. Therefore, the acceleration signal can be further processed and distinguished from the interference signal and the useful signal after being divided by the preset normalization coefficient.
Figure BDA0001740706040000131
Where f (t) is f (r · Δ t).
The energy waveform of the normalized acceleration signal g (t) is obtained by squaring the normalized acceleration signal g (t), so that the difference between the jitter signal and the useful signal is larger, and the setting of the threshold B is more favorable for removing most of the jitter signal.
4-3, calculating the energy of the acceleration signal.
The energy E1 of the acceleration signal f (t) is defined as:
Figure BDA0001740706040000132
and squaring the amplitude of the signal after the Fourier transform.
Step 5, determining the set threshold value of the running stage of the elevator, and removing the dithering noise signal
And 5-1, distinguishing the jitter with low amplitude from the useful signal running section with high amplitude, determining the signal running section and reducing the loss of the useful signal to the maximum extent. Setting an accurate threshold B according to the waveform, reserving the waveform larger than the threshold, and enabling the waveform smaller than the threshold to return to zero:
Figure BDA0001740706040000133
the judged waveform is named as an energy signal amplitude u (t);
5-2, multiplying the amplitude of the energy signal by a normalization coefficient to obtain a final acceleration signal q (t), wherein the final acceleration signal q (t) is as follows:
q(t)＝u(t)·C
and 6, integrating the final acceleration signal q (t) to obtain a corresponding real-time speed signal r (k 5).
6-1, and the sampling data of the final acceleration signal q (t) is qk5And (k5 ═ 1,2, 3.., n), sampling time step Δ t in numerical integration is taken as an integration step, and trapezoidal numerical integration formula is:
Figure BDA0001740706040000141
the following needs to be noted in steps 4 and 5:
the setting of the normalization coefficient C and the threshold B needs to be judged according to the amplitude of the jitter waveform, is selected according to the actual situation, and is adjusted by the feedback of the filtering effect.

Claims (5)

1. The method for estimating the real-time running speed of the elevator door based on the energy threshold algorithm is characterized by comprising the following steps of:
step 1, acquiring an acceleration signal of the operation of an original elevator door through an acceleration sensor;
1-1, horizontally placing an acceleration sensor at one side of an elevator door;
1-2, manually controlling the opening and closing of the elevator door, and recording acceleration signal data of the elevator door when the elevator door is opened and closed each time through an acceleration sensor;
step 2, trend item removing processing is carried out on the obtained acceleration signals;
step 3, converting the signal into a frequency domain through fast Fourier transform, and filtering and denoising the signal;
step 4, performing inverse Fourier transform on the filtered frequency domain signal, and calculating the energy of the acceleration signal;
step 5, setting a threshold value for the running stage of the elevator, and removing a jitter noise signal;
and 6, carrying out integration processing on the final acceleration signal q (t) to obtain a corresponding real-time speed signal.
2. The method for estimating the real-time running speed of the elevator door based on the energy threshold algorithm as claimed in claim 1, wherein the step 2 is implemented as follows:
2-1, calculating a trend item of the acquired acceleration signal data;
the sampling data of the elevator door acceleration signal obtained by actual measurement is { xkN is the length of the sample data, and for convenience of calculation, the sample data length is extended to N, and N is 2LL is the minimum integer for N ≧ N, let the sampling interval Δ t equal to 1, set a polynomial function:
Figure FDA0001740706030000011
determining polynomial functions
Figure FDA0001740706030000021
Each undetermined coefficient a ofj(j ═ 0, 1.. times, m), such that the polynomial function
Figure FDA0001740706030000022
And the sampled data xkHas the smallest sum of squared errors E, i.e.
Figure FDA0001740706030000023
The condition that E has an extreme value is as follows:
Figure FDA0001740706030000024
taking E pairs of a in sequenceiThe partial derivatives are calculated to generate an m +1 element linear equation set:
Figure FDA0001740706030000025
solving the equation set to obtain m +1 undetermined coefficients aj(j ═ 0,1,. ·, m); in the above formulas, j is a set polynomial order, and the value range of j is more than or equal to 0 and less than or equal to m;
2-2, obtaining an acceleration signal after the linear trend term is eliminated;
the formula for eliminating the linear trend term is as follows:
Figure FDA0001740706030000026
when m is more than or equal to 2, the curve trend item is defined; in the actual elevator door acceleration signal processing, m is 1,2 and 3, and the sampled data is subjected to polynomial trend term elimination to obtain yk。
3. The method for estimating the real-time running speed of the elevator door based on the energy threshold algorithm as claimed in claim 2, wherein the step 3 is implemented as follows:
3-1, discrete Fourier transform;
since the actual sampled signal is discrete and the sample length N of the sampled signal over time T is finite, { y after processing the detrending termkAs an N-point sequence y (r) (0, 1,2.. gtoren-1), a discrete algorithm using a fourier transform is required to perform fourier transform on the elevator door acceleration signal, and the expression of the Discrete Fourier Transform (DFT) is:
Figure FDA0001740706030000031
in the formula: y (k1) is equivalent to Y (k 1. DELTA.f), the sampling frequency
Figure FDA0001740706030000032
(k1,r＝0,1,2,...N-1)；
3-2, Fast Fourier Transform (FFT);
3-2-1. due to the limitation of the length of the sampling signal and the calculation cost, the signal y (r) of the detrending term is processed by adopting fast Fourier transform:
the N-point discrete fourier transform of the N-point sequence y (r) can be expressed as:
Figure FDA0001740706030000033
wherein W is e-j2π/N
Using Fourier coefficient of variation W(k1)rIs periodic, i.e.
W(k1)r＝Wk1(r+N)＝W(k1+N)r
By virtue of its symmetry, i.e.
W(k1)r+N/2＝-W(k1)r
3-2-2. according to the periodicity, the discrete Fourier transform of the long sequence can be decomposed into the discrete Fourier transform of the short sequence;
sample length N2LThe detrended sequence y (r) (0, 1,2.., N-1) is first divided into two groups according to the parity of r:
Figure FDA0001740706030000034
respectively calculate it
Figure FDA0001740706030000035
Discrete fourier transform of the points, the first half is obtained as:
Figure FDA0001740706030000036
the rear half part is:
Figure FDA0001740706030000041
3-2-3, repeating the step 3-2-2 to obtain an FFT result d (r) (r is 0,1,2.. N-1) of y (r);
3-3, carrying out frequency domain filtering on the amplitude-frequency signal;
with a finite impulse response FIR digital filter, the difference equation form of the FIR filter can be expressed as:
Figure FDA0001740706030000042
in the formula: d (n1) and p (n1) are respectively an input time domain signal sequence subjected to fast Fourier transform and an output frequency domain signal sequence subjected to frequency domain filtering; bk3N1 is not less than 0, k3 is 0,1,2.. N-1;
the z-transform of the impulse response function h (n) of the FIR filter is the system transfer function, which can be expressed as:
Figure FDA0001740706030000043
then its impulse response function is:
Figure FDA0001740706030000044
4. the method for estimating the real-time running speed of the elevator door based on the energy threshold algorithm as claimed in claim 3, wherein the step 4 is implemented as follows:
step 4, performing inverse Fourier transform on the filtered frequency domain signal, and calculating the energy of the acceleration signal;
4-1, inverse Fourier transform of the frequency domain signal;
Figure FDA0001740706030000051
in the formula: f (r) is equivalent to f (r Δ t), the sampling time interval Δ t is 1, r is 0,1,2.. N-1;
4-2, normalizing the acceleration signal;
firstly, normalizing the acceleration signal with the amplitude smaller than a set threshold A, and filtering out the jitter interference noise after normalization processing; in general, the amplitude of the jitter interference noise is smaller than the normalized coefficient C, and other useful signals are larger than C; therefore, the acceleration signal is effectively further processed and distinguished from the interference signal and the useful signal after being divided by the preset normalization coefficient;
Figure FDA0001740706030000052
wherein f (t) is f (r · Δ t);
the energy waveform of the acceleration signal g (t) after the normalization processing is obtained by squaring the acceleration signal g (t), which is beneficial to setting a threshold B to remove most of jitter signals;
4-3, calculating the energy of the acceleration signal;
the energy E1 of the acceleration signal f (t) is defined as:
Figure FDA0001740706030000053
and squaring the amplitude of the signal after the Fourier transform.
5. The method for estimating the real-time running speed of the elevator door based on the energy threshold algorithm as claimed in claim 4, wherein the step 5 is implemented as follows:
step 5, setting a threshold value for the running stage of the elevator, and removing a jitter noise signal;
5-1, distinguishing the jitter with low amplitude from the useful signal operation section with high amplitude, determining the operation section of the signal, and reducing the loss of the useful signal to the maximum extent; setting an accurate threshold B for the waveform subjected to the square processing, judging, reserving the waveform larger than the threshold, and enabling the waveform smaller than the threshold to return to zero:
Figure FDA0001740706030000061
the judged waveform is named as an energy signal amplitude u (t);
5-2, multiplying the amplitude of the energy signal by a normalization coefficient to obtain a final acceleration signal q (t), wherein the final acceleration signal q (t) is as follows:
q(t)＝u(t)·C
step 6, integrating the final acceleration signal q (t) to obtain a corresponding real-time speed signal r (k 5):
the sampling data of the final acceleration signal q (t) is { q }k5And (k5 ═ 1,2, 3.., n), sampling time step Δ t in numerical integration is taken as an integration step, and trapezoidal numerical integration formula is:
Figure FDA0001740706030000062
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN113213297B (en) * 2021-05-08 2022-09-23 浙江工业大学 Displacement sensor data processing method applied to elevator safety detection system
CN113392511B (en) * 2021-05-28 2022-11-22 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
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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

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6029930A (en) * 1997-07-04 2000-02-29 Finmeccanica S.P.A. Method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter
CN101113936A (en) * 2007-07-20 2008-01-30 广州市计量检测技术研究院 Virtual oscillating table detection signal processing method and equipment thereof
CN102346809A (en) * 2011-06-30 2012-02-08 中国人民解放军理工大学工程兵工程学院 Method for converting blasting-vibration acceleration into velocity
CN106323451A (en) * 2015-06-26 2017-01-11 陕西重型汽车有限公司 Method and apparatus for acquiring displacement signal by acceleration signal

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104344882B (en) * 2013-07-24 2017-08-04 中国国际航空股份有限公司 A kind of aircraft shaking detection system and method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6029930A (en) * 1997-07-04 2000-02-29 Finmeccanica S.P.A. Method of monitoring a transmission assembly of a vehicle equipped with acceleration sensors, in particular a helicopter
CN101113936A (en) * 2007-07-20 2008-01-30 广州市计量检测技术研究院 Virtual oscillating table detection signal processing method and equipment thereof
CN102346809A (en) * 2011-06-30 2012-02-08 中国人民解放军理工大学工程兵工程学院 Method for converting blasting-vibration acceleration into velocity
CN106323451A (en) * 2015-06-26 2017-01-11 陕西重型汽车有限公司 Method and apparatus for acquiring displacement signal by acceleration signal

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DSP控制的电梯门机系统;翟朝朝;《中国优秀硕士学位论文全文数据库 信息科技辑》;20151015;正文全文 *
加速度信号随机噪声及趋势项实时消除方法研究;何鹏举, 冯亮;《电子设计工程》;20130731;第21卷(第14期);第18-22页 *

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