CN113184651A - Method for preprocessing elevator running state signal and extracting characteristic quantity - Google Patents
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Abstract
The invention discloses a method for preprocessing an elevator running state signal and extracting a characteristic quantity, which comprises the following steps: the method comprises the steps that a capacitance type three-axis acceleration sensor and a piezoelectric type three-axis acceleration sensor respectively acquire a first vibration acceleration signal and a second vibration acceleration signal of an elevator; performing signal preprocessing on the first/second vibration acceleration signals to obtain first/second acceleration signals; obtaining a time-velocity curve based on the first acceleration signal, and obtaining a peak-to-peak value and a root-mean-square based on the second acceleration signal; segmenting the time-speed curve and the time-speed standard curve, respectively selecting a plurality of points as a measurement characteristic value point and a standard characteristic value point, and measuring the characteristic value point and the standard characteristic value point to obtain the speed curve variation degree; and performing signal preprocessing and analysis on the low-frequency signal of the elevator starting and stopping stage and the high-frequency signal of the elevator running whole process so as to obtain characteristic quantities reflecting the starting and braking performance of the elevator, the fault in the elevator running process and the fault degree.
Description
Technical Field
The invention relates to the technical field of elevator safety prevention and control, in particular to a method for preprocessing an elevator running state signal and extracting a characteristic quantity.
Background
Passenger elevators have become irreplaceable vertical transport means in daily life in cities today, playing an increasing role. As a special equipment related to life safety of people, elevator accidents are gradually increasing with the increasing total amount. According to GB/T24474-.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for preprocessing a signal of an elevator running state and extracting a characteristic quantity, which is used for preprocessing and analyzing a vibration acceleration low-frequency signal at the starting and stopping stage of the elevator and a vibration acceleration high-frequency signal which possibly represents fault hidden danger in the whole running process of the elevator so as to obtain the characteristic quantity reflecting the starting and braking performance of the elevator, the fault in the running process of the elevator and the fault degree.
In order to achieve the purpose of the invention, the method for preprocessing the elevator running state signal and extracting the characteristic value comprises the following steps: s1, respectively acquiring a first vibration acceleration signal and a second vibration acceleration signal of the elevator based on a first acceleration sensor and a second acceleration sensor, wherein the first acceleration sensor is a capacitance type three-axis acceleration sensor, and the second acceleration sensor is a piezoelectric type three-axis acceleration sensor; s2, respectively carrying out signal preprocessing on the first vibration acceleration signal and the second vibration acceleration signal to respectively obtain a first acceleration signal and a second acceleration signal; s3, obtaining a time-velocity curve based on the first acceleration signal, and obtaining a peak-to-peak value and a root-mean-square based on the second acceleration signal; and S4, segmenting the time-speed curve and the time-speed standard curve respectively, selecting a plurality of points as a measurement characteristic value point and a standard characteristic value point respectively, and obtaining the speed curve variation degree based on the measurement characteristic value point and the standard characteristic value point.
The capacitive acceleration sensor is beneficial to obtaining low-frequency signals of the start and stop stages of the elevator. The piezoelectric acceleration sensor is beneficial to acquiring a high-frequency signal which possibly represents a fault in the whole running process of the elevator.
Furthermore, the first vibration acceleration signal is a vibration acceleration signal in the starting and braking processes of the elevator, and at least comprises a car vertical vibration acceleration signal detected by a capacitance type three-axis acceleration sensor, and the second vibration acceleration signal at least comprises a Z-axis vibration acceleration signal detected by a piezoelectric type three-axis acceleration sensor.
The capacitive triaxial acceleration sensor and the piezoelectric triaxial acceleration sensor are both arranged in the center of the ground of the car, the plane formed between the X axis of the two sensors and the guide rail of the car is vertically directed to the car door, and the measured car vibration signal is a car horizontal vibration signal; the Y axis of the sensor is parallel to the car door, and the measured car vibration signal is also a car horizontal vibration signal; the Z axis of the sensor is vertical to the floor of the car, and the measured car vibration signal is a car vertical vibration signal.
Further, in step S2, the signal preprocessing on the first vibration acceleration signal at least includes low-pass filtering, eliminating the influence of gravity, eliminating signal noise and removing a trend term, and the signal preprocessing on the second vibration acceleration signal at least includes eliminating the signal noise and removing the trend term.
Further, low-pass filtering is carried out on the first vibration acceleration signal based on a preset high-frequency cut-off frequency, the Z axis of the first acceleration sensor is kept parallel to the gravity direction, and g is subtracted from the vertical vibration acceleration signal of the elevator car, so that the gravity influence is eliminated.
And realizing low-pass filtering and gravity influence elimination of the first vibration acceleration signal based on the step.
Further, in step S2, the signal noise elimination preprocessing is performed on the first/second vibration acceleration signal, and the method specifically includes: s211, carrying out wavelet decomposition on the first/second vibration acceleration signals to obtain a wavelet coefficient omegaf(j,k)＝ωs(j,k)+ωe(j, k). Wherein (j, k) is not less than 0 and is a positive integer; j is the number of levels of wavelet transform, k is the length of the signal sequence, ωf(j, k) is the wavelet coefficient, omega, of the wavelet decomposition of the detection signals(j, k) is the wavelet coefficient, ω, of the original signal after wavelet decompositione(j, k) is the wavelet coefficient of the noise signal; s212, performing threshold processing on the wavelet coefficient, and improving a threshold function into
Wherein λ is2＝kλ1,k＞1；
S213, on the basis of meeting the continuity, when omega isj,kWavelet coefficients of a function at infinite increase
The problem of singly adopting a hard threshold or a soft threshold is solved by improving the threshold method, the problem of reconstructed signal oscillation is eliminated, the integrity of information is kept, and the denoising effect is improved.
Further, in step S2, the detrending item for the first/second vibration acceleration signals specifically includes: s221, dividing the first/second vibration acceleration signals into a linear trend term and a high-order trend term; s222, setting the time from the start of the elevator to the brake standstill as T, presetting the linear trend of the acceleration sensor as bt + c, and performing integral calculation on the linear trend to obtain a difference value between a speed correction value and a true value which can be calculated by a formula (1), a difference value between a speed measurement value and the true value which can be calculated by a formula (2), a difference value between a displacement correction value and a true displacement which can be calculated by a formula (3), and a difference value between a displacement measurement value and the true displacement which can be calculated by a formula (4), wherein the value of T is 0-T;
s223, when the elevator is stationary, i.e., T is T, the corrected and measured displacement errors are respectively equation (5) and equation (6),
considering the particularity of the acceleration curve of the elevator, the least square method cannot be used for eliminating the trend term in the vibration signal of the elevator, so the step is adopted for eliminating the trend term. The trend eliminating terms need to be carried out on the first vibration acceleration signal and the second vibration acceleration signal.
Further, in step S3, the obtaining a time-velocity curve based on the first acceleration signal specifically includes: and integrating the vertical vibration acceleration signals of the elevator car in the preprocessed first acceleration signals to obtain a time-speed curve.
Further, step S4 specifically includes: s41, based on a preset speed threshold value, segmenting a time speed curve and a time speed standard curve respectively into a starting acceleration section, a braking deceleration section and a uniform motion section; s42, selecting a plurality of points as measurement characteristic value points for the segmented time-speed curve, and selecting the same plurality of points as standard characteristic value points for the segmented time-speed standard curve; s43, degree of curve variation
Wherein,
for time velocity standard curve to reach ithThe time taken to measure the characteristic value points,
for the time taken for the time-velocity measurement curve to reach the ith standard feature point, t5％The time taken for the speed profile to reach 5% of the preset nominal speed is measured for the time speed profile and the time speed standard profile.
Further, the peak-to-peak value is max (x (N) -min (x (N)), where x (N) is the second acceleration signal, N is the number of samples, and the root-mean-square value is
The peak value and the root mean square reflect whether the elevator is in fault or not and the fault degree in the running process.
The invention has the beneficial effects that:
1. analyzing a low-frequency signal considering the starting and stopping stages of the elevator and a high-frequency signal possibly representing fault hidden danger in the whole running process of the elevator by comprehensively utilizing the characteristics of a capacitive acceleration sensor and a piezoelectric acceleration sensor;
2. the wavelet transformation with improved threshold is adopted to perform denoising processing on the original signal, the oscillation of the reconstructed signal is improved, noise interference can be better eliminated, and the signal characteristics are reserved;
3. the method for carrying out segmentation processing on the speed curve and extracting the characteristic points to calculate the curve variation degree is visual and clear;
4. the curve variation degree, the peak-to-peak value and the root-mean-square are selected as characteristic quantities, the characteristic quantities of the starting performance and the braking performance of the elevator are reflected, and whether the elevator breaks down or not and the degree of the failure in the running process of the elevator are also reflected, so that the running state of the elevator car is visually reflected.
Drawings
Fig. 1 is a flow chart of a method for preprocessing an elevator running state signal and extracting a characteristic value according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an acceleration sensor installation method according to an embodiment of the present invention;
FIG. 3 illustrates a velocity profile feature selection according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating curve variation calculation according to an embodiment of the present invention.
Detailed Description
In order to facilitate a better understanding of the invention for those skilled in the art, the invention will be described in further detail with reference to the accompanying drawings and specific examples, which are given by way of illustration only and do not limit the scope of the invention.
Example 1
As shown in fig. 1, a flow chart of a method for preprocessing an elevator running state signal and extracting a feature value according to an embodiment of the present invention specifically includes:
and S1, respectively acquiring a first vibration acceleration signal and a second vibration acceleration signal of the elevator based on the first acceleration sensor and the second acceleration sensor.
The first acceleration sensor is a capacitance type three-axis acceleration sensor, in one embodiment of the invention, the capacitance type three-axis acceleration sensor is V3012-THDP2, wherein the measuring range is +/-2 g, the sensitivity is 1802mV/g, and the bandwidth is 0-100 Hz. The second acceleration sensor is a piezoelectric type three-axis acceleration sensor, the model is PCB-356B21, the measuring range is +/-500 g, the sensitivity is 10mV/g, and the bandwidth is 2-7000 Hz. As shown in fig. 2, in the method for installing an acceleration sensor in an embodiment of the present invention, a three-axis acceleration sensor is placed in the center of the car floor, a plane formed between an X-axis of the sensor and a car guide rail is vertically directed to a car door, and a measured car vibration signal is a car horizontal vibration signal. The Y axis of the sensor is parallel to the car door, and the measured car vibration signal is also a car horizontal vibration signal; the Z axis of the sensor is vertical to the floor of the car, and the measured car vibration signal is a car vertical vibration signal.
The capacitance type triaxial acceleration sensor has the characteristic of low-frequency-band sensitivity, so that the collected first vibration acceleration signal is a vibration acceleration signal in the starting and braking processes of the elevator, namely the absolute acceleration.
The piezoelectric type triaxial acceleration sensor obtains complete signals due to large frequency bandwidth, and the second vibration acceleration signal obtained by collection is a vibration acceleration signal of the whole motion stage of the elevator, namely the relative acceleration.
And S2, respectively performing signal preprocessing on the first vibration acceleration signal and the second vibration acceleration signal to respectively obtain a first acceleration signal and a second acceleration signal.
In one embodiment of the invention, the first vibration acceleration signal is subjected to a series of signal preprocessing such as low-pass filtering, gravity influence elimination, signal noise elimination and de-trend term, wherein the first vibration acceleration signal at least comprises a car vertical vibration signal. And performing signal preprocessing for eliminating signal noise and removing trend terms on the second vibration acceleration signal, wherein the second vibration acceleration signal at least comprises a Z-axis vibration acceleration signal in the uniform motion stage of the elevator operation.
Because the capacitive sensor has a low-frequency response characteristic, the capacitive sensor is mainly used for measuring acceleration signals of the elevator in the starting and braking stages, and a low-pass filtering mode is needed to remove high-frequency random interference in signals collected by the sensor. Because the acceleration of the elevator in the starting and braking stages is close to zero frequency, in the embodiment, the preset high-frequency cutoff frequency is 2Hz, and the low-pass filtering processing is carried out on the vertical vibration signal of the elevator car.
Because in the Z axle direction, capacitanc acceleration sensor can receive the gravity influence, and the interference of gravity has not directly to be eliminated, only can compensate gravity through the calculation, so with the Z axle parallel of gravity and sensor during the measurement, conveniently get rid of the gravity influence during the calculation, only need subtract gravity acceleration g, just obtain the change situation of motion car actual acceleration value.
And respectively eliminating signal noise of the vertical vibration signal of the elevator car and the vibration acceleration signal of the Z axis.
Performing wavelet transform with omegaf(j,k)＝ωs(j,k)+ωe(j, k), wherein (j, k) is not less than 0 and is a positive integer; j is the layering number of wavelet transformation, and k is the length of the signal sequence; omegaf(j, k) is the wavelet coefficient, omega, of the wavelet decomposition of the detection signals(j, k) is the wavelet coefficient, ω, of the original signal after wavelet decompositione(j, k) are wavelet coefficients of the noise signal.
And performing threshold processing on the wavelet coefficients of the decomposed signals. In the conventional wavelet transform, the conventional hard and soft threshold method is widely used, wherein the hard threshold is used
And when the absolute value of the wavelet coefficient is larger than the threshold, keeping the size of the threshold, otherwise, taking zero as the threshold. From the above formula, the continuity of the hard threshold is poor, and the reconstructed signal may have oscillation.
Soft threshold
And when the wavelet coefficient is larger than the fixed threshold value, taking the absolute value of the wavelet coefficient as the wavelet coefficient, and otherwise, taking zero. The soft threshold function is known by the formula to have continuity, but the fixed deviation of the soft threshold function is obvious along with the increase of the wavelet coefficient, the sudden change position of the signal is too smooth, the characteristic data of a sudden change point is lost, and the accuracy of the reconstructed signal is influenced. A function is proposed that improves the threshold value,
wherein λ is2＝kλ1And k is more than 1. On the basis of satisfying the continuity, and when ω isj,kWavelet coefficients of a function at infinite increase
The problem of reconstructed signal oscillation before improvement is solved, the integrity of information is kept, and the denoising effect is improved.
And carrying out a detrending item on the first/second vibration acceleration signals. In the signal sampled by the sensor, there may be low frequency components with a period length longer than the sampling length, which is a low frequency trend term, and the zero drift phenomenon of the semiconductor device may cause the whole deviation of the reference line in the measurement signal, so the trend term of the vibration signal is generally divided into two types, i.e. linear and high-order polynomial. When eliminating the linear trend term and the higher-order trend term, the least square method is often adopted because it is suitable for the random vibration signal to contain the larger-order trend term. In the actual operation of the elevator, the starting phase is accompanied by a large acceleration, then a smooth random vibration phase of small magnitude, and finally a braking phase of large deceleration. The particularity of the elevator acceleration curve determines that the least square method cannot be used to eliminate the trend term in the elevator vibration signal. If such deviations are not handled, they are integrated over time, and the resulting car travel speed, and thus the car displacement resulting from this integration, tend to contain large errors. In practice, it has been found experimentally that the deviation is orders of magnitude smaller than the acceleration signal, and no deviation is seen. However, after the acceleration signals are subjected to integration processing, the deviation results are superposed, the car speed in the constant speed stage is found to have a linear trend of being inclined obviously, and the displacement deviation obtained after twice integration is often very large and is an error which cannot be ignored.
In one embodiment of the invention, the time from the start of the elevator to the brake standstill is set as T by combining the actual running characteristics of the elevator, and the mean value of the running acceleration of the elevator is theoretically zero in the T time. However, the average value of the acceleration in the actual T time is often not zero due to the fact that the actual signal of the car contains the trend item.
The linear trend of the acceleration sensor is preset to be bt + c, the difference value between the speed correction value and the true value obtained through integral calculation can be calculated by an expression (1), the difference value between the speed measurement value and the true value can be calculated by an expression (2), the difference value between the displacement correction value and the true displacement can be calculated by an expression (3), and the difference value between the displacement measurement value and the true displacement can be calculated by an expression (4), wherein the value of T is 0-T.
S3, obtaining a time-velocity curve based on the first acceleration signal, and obtaining a peak-to-peak value and a root-mean-square based on the second acceleration signal;
and integrating the first acceleration signal obtained by signal preprocessing to obtain a time-speed curve of the running of the elevator.
Classical characteristic quantity extraction is carried out on the second acceleration signal, and the time domain indexes of the peak value and the root-mean-square 2 elevator vibration acceleration signals are taken as characteristic quantities of the signals and used for representing the elevator state, wherein: x (N) is the second acceleration signal, and N is the number of samples. From a statistical point of view: peak to Peak: max (x (n)) -min (x (n))), from the perspective of signal energy: root mean square RMS:
the peak value and the root mean square reflect whether the elevator is in fault or not and the fault degree in the running process.
And S4, segmenting the time-speed curve and the time-speed standard curve respectively, selecting a plurality of points as a measurement characteristic value point and a standard characteristic value point respectively, and comparing the measurement characteristic value point and the standard characteristic value point to obtain the speed curve variation degree.
As shown in fig. 3, the time-speed curve and the time-speed standard curve are segmented into a start acceleration segment S1, a brake deceleration segment S3 and a uniform motion segment S2. In one embodiment of the invention, a start of the start-up acceleration section is defined as 5% above the preset rated speed and a stop of the start-up acceleration section is defined as the speed reaching 95% of the preset rated speed. The speed is less than 95% of the preset rated speed as the beginning of the deceleration section of the brake, and the speed is reduced to 5% of the preset rated speed as the end of the deceleration section. And outside the starting acceleration section and the braking deceleration section, the part with the speed more than 95 percent of the preset rated speed is a uniform motion section.
The characteristic value points are extracted after the time speed curve and the time speed standard curve are segmented, and in one embodiment of the invention, 5 points of 5%, 25%, 50%, 75% and 95% of rated speeds on the time speed curve are sequentially selected as the characteristic value points in the starting acceleration section S1, which is shown in a1-a5 of fig. 3. In the braking deceleration section S3, 5 points of 5%, 25%, 50%, 75%, and 95% of the rated speed are sequentially selected as characteristic value points, see B1-B5 of fig. 3, and a1-a5 and B1-B5 are measured characteristic value points of the time-speed curve.
In the same way, the standard characteristic value point of the time speed standard curve is obtained.
In one embodiment of the invention, the curve variability is determined on the basis of the measured characteristic points and the standard characteristic points.
Taking the starting acceleration as an example, the curve variation is defined as: starting at 5% of the rated speed, the ratio of the difference in time taken for the time speed profile and the time speed standard profile to reach the same speed to the time taken for the standard speed profile to reach that speed from 5% of the rated speed. For convenience of calculation, the time-velocity standard curve and the 5% rated velocity point of the time-velocity curve are translated to coincide, as shown in fig. 4, which is the curve variation degree SiSchematic diagram of the calculation of (1).
Wherein,
the time taken for the time-speed standard curve and the time-speed curve to reach a certain proportion of the rated speed, t5％For the time speed standard curve and the time speed curve reaching the rated valueThe speed is 5% and the time is 25%, 50%, 75% and 95%. The curve variation reflects the starting and braking performance of the elevator. For example i in figure 4 is 25%,
and respectively reaching the time of A1 and a1, wherein the curve of A1 is a time speed standard curve, and the curve of a1 is a time speed curve.
In conclusion, the above description is only for the preferred embodiment of the present invention and should not be construed as limiting the present invention, and any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A method for preprocessing an elevator running state signal and extracting a characteristic quantity is characterized by specifically comprising the following steps of:
s1, respectively acquiring a first vibration acceleration signal and a second vibration acceleration signal of the elevator based on a first acceleration sensor and a second acceleration sensor, wherein the first acceleration sensor is a capacitance type three-axis acceleration sensor, and the second acceleration sensor is a piezoelectric type three-axis acceleration sensor;
s2, respectively carrying out signal preprocessing on the first vibration acceleration signal and the second vibration acceleration signal to respectively obtain a first acceleration signal and a second acceleration signal;
s3, obtaining a time-velocity curve based on the first acceleration signal, and obtaining a peak-to-peak value and a root-mean-square based on the second acceleration signal;
and S4, segmenting the time-speed curve and the time-speed standard curve respectively, selecting a plurality of points as a measurement characteristic value point and a standard characteristic value point respectively, and obtaining the speed curve variation degree based on the measurement characteristic value point and the standard characteristic value point.
2. The method for preprocessing the elevator running state signal and extracting the characteristic quantity according to claim 1, wherein the first vibration acceleration signal is a vibration acceleration signal during the starting and braking of the elevator, and at least comprises a car vertical vibration acceleration signal detected by a capacitive three-axis acceleration sensor, and the second vibration acceleration signal at least comprises a Z-axis vibration acceleration signal detected by a piezoelectric three-axis acceleration sensor.
3. The method for preprocessing the signals of the running state of the elevator and extracting the characteristic quantities according to claim 2, wherein the signal preprocessing of the first vibration acceleration signal in step S2 at least comprises low-pass filtering, gravity influence elimination, signal noise elimination and a de-trend term, and the signal preprocessing of the second vibration acceleration signal at least comprises signal noise elimination and a de-trend term.
4. The method for preprocessing the elevator running state signal and extracting the characteristic quantity according to claim 3, characterized in that the first vibration acceleration signal is low-pass filtered based on a preset high-frequency cut-off frequency, the Z axis of the first acceleration sensor is kept parallel to the gravity direction, and g is subtracted from the car vertical vibration acceleration signal, so that the gravity influence is eliminated.
5. The method for preprocessing the elevator running state signal and extracting the characteristic quantity according to claim 4, wherein in the step S2, the preprocessing of eliminating the signal noise is performed on the first/second vibration acceleration signals, which specifically comprises:
s211, performing wavelet decomposition on the first/second vibration acceleration signals, and performing wavelet transformation omegaf(j,k)＝ωs(j,k)+ωe(j, k). Wherein (j, k) is not less than 0 and is a positive integer; j is the number of levels of wavelet transform, k is the length of the signal sequence, ωf(j, k) is the wavelet coefficient, omega, of the wavelet decomposition of the detection signals(j, k) is the wavelet coefficient, ω, of the original signal after wavelet decompositione(j, k) is the wavelet coefficient of the noise signal;
s212, threshold processing is carried out on the decomposed wavelet coefficient, and a threshold function is improved to
Wherein λ is2＝kλ1,k＞1；
S213, on the basis of meeting the continuity, when omega isj,kWavelet coefficients of a function at infinite increase
6. The method for preprocessing and extracting the characteristic values of the elevator running state signals according to claim 3, wherein the step S2 of detrending the first/second vibration acceleration signals specifically comprises:
s221, dividing the first/second vibration acceleration signals into a linear trend term and a high-order trend term;
s222, setting the time from the start of the elevator to the brake standstill as T, presetting the linear trend of the acceleration sensor as bt + c, and performing integral calculation on the linear trend to obtain a difference value between a speed correction value and a true value which can be calculated by a formula (1), a difference value between a speed measurement value and the true value which can be calculated by a formula (2), a difference value between a displacement correction value and a true displacement which can be calculated by a formula (3), and a difference value between a displacement measurement value and the true displacement which can be calculated by a formula (4), wherein the value of T is 0-T;
s223, when the elevator is stationary, i.e., T is T, the corrected and measured displacement errors are respectively equation (5) and equation (6),
7. the method for preprocessing the elevator running state signal and extracting the characteristic quantity according to claim 3, wherein in the step S3, the obtaining the time-velocity curve based on the first acceleration signal specifically comprises: and integrating the vertical vibration acceleration signals of the elevator car in the preprocessed first acceleration signals to obtain a time-speed curve.
8. The method for preprocessing and extracting the characteristic values of the elevator running state signals according to claim 3, wherein the step S4 specifically comprises:
s41, based on a preset speed threshold value, segmenting a time speed curve and a time speed standard curve respectively into a starting acceleration section, a braking deceleration section and a uniform motion section;
s42, selecting a plurality of points as measurement characteristic value points for the segmented time-speed curve, and selecting the same plurality of points as standard characteristic value points for the segmented time-speed standard curve;
s43, degree of curve variation
Wherein,
for the time taken for the time velocity standard curve to reach the ith measured characteristic value pointIn the middle of the furnace, the gas-liquid separation chamber,
for the time taken for the time-velocity measurement curve to reach the ith standard feature point, t5％The time taken for the speed profile to reach 5% of the preset nominal speed is measured for the time speed profile and the time speed standard profile.
9. The method for preprocessing and extracting the characteristic value of the elevator running state signal according to claim 1, wherein the peak-peak value is max (x (N) -min (x (N)), where x (N) is the second acceleration signal, N is the sampling number, and the root mean square value is
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| US11542124B2 (en) * | 2019-05-13 | 2023-01-03 | Otis Elevator Company | Sensor fusion door status detection |
| CN116782352A (en) * | 2023-08-25 | 2023-09-19 | 深圳国人无线通信有限公司 | Energy saving method and system for elevator communication system |
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