CN109211233B - Elevator motion detection and abnormal position parking judgment method based on acceleration sensor - Google Patents

Elevator motion detection and abnormal position parking judgment method based on acceleration sensor Download PDF

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CN109211233B
CN109211233B CN201811112016.6A CN201811112016A CN109211233B CN 109211233 B CN109211233 B CN 109211233B CN 201811112016 A CN201811112016 A CN 201811112016A CN 109211233 B CN109211233 B CN 109211233B
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elevator
elevator car
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car
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CN109211233A (en
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朱培逸
孙振
吕岗
徐本连
施健
鲁明丽
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Changshu Institute of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

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Abstract

The invention discloses an elevator motion detection and abnormal position parking judgment based on an acceleration sensor, which is carried out according to the following steps: step 1, estimating the running distance of an elevator car, acquiring motion parameters of the elevator by a three-axis acceleration sensor positioned on the elevator car, estimating the running distance of the elevator between two times of stopping in each trip by an extended Kalman filtering algorithm, and returning a displacement estimation value and a variance of the elevator in the running process of one trip; step 2, judging whether the elevator car stops at a floor or not, and estimating the position of the elevator car and the height of the floor on the basis of setting a reference point by combining an SLAM algorithm based on the displacement estimation value and the variance result in the step 1; and 3, judging whether the elevator has an abnormal parking fault or not through position estimation. The invention realizes the detection of the motion state of the elevator by using the three-axis accelerometer, estimates the state of the elevator by using an extended Kalman filtering method, and can quickly obtain the displacement information of the elevator car.

Description

Elevator motion detection and abnormal position parking judgment method based on acceleration sensor
Technical Field
The invention belongs to the field of elevator motion detection, and particularly relates to an elevator motion detection and abnormal position parking judgment method based on an acceleration sensor.
Background
Accurate estimation of the position of the elevator car is of great importance to the normal operation of the elevator. When the elevator stops abnormally, the elevator is opened under the condition that the elevator is not aligned with the floor, and the danger that people fall down from the elevator can occur. The existing elevator car accurate floor stopping detection method is based on the principle that elevator and floor signals correspond to each other. The stability and accuracy of signals are greatly depended on, a large number of detection signal sending and receiving devices are required to be installed (each floor is required to be installed), the maintenance cost is high, and the applicability is poor. When the elevator is lost due to the situations of sudden stop, system power failure or slippage between a traction rope and a traction sheave, the elevator cannot work normally if the position of the elevator car cannot be confirmed.
An important aspect of elevator abnormal position stop detection is to determine at which height the elevator is and to detect if the elevator is on uneven floors, crouching to the bottom or rushing to the top. Height (altitude) information can be provided for users through a satellite positioning system (GPS), but the height judgment has a large error in civil products, is only suitable for height judgment with large span, and cannot be used for height judgment with small indoor span. In addition, due to the shielding effect of the outer wall of the building on satellite signals, the use of an indoor satellite positioning system is extremely difficult.
The present invention seeks to provide a car positioning system within a room, particularly a hoistway. The system collects parameters such as the motion state of the elevator car through an elevator motion detection system to judge the position of the elevator car, and determines whether the elevator has abnormal position parking fault according to the motion result. The accelerometer in the motion detection system can sense the motion state of an object in an all-around manner. By detecting the stress of the accelerometer in each direction, the motion direction of the elevator car, the change amplitude of the acceleration and the moving distance (from the beginning of the standstill) can be judged, and finally, the accurate estimation of the position of the elevator car is obtained. At present, no solution for abnormal floor-stopping faults of the elevator based on the accelerometer exists.
Disclosure of Invention
1. The invention aims to provide a novel method.
The method comprises the steps of acquiring the motion state of the elevator by using an accelerometer, taking the acceleration of the elevator in the vertical direction as input, estimating the running distance of the elevator in each trip (between two stops) by using extended Kalman filtering, constructing the running track of the elevator by using a Simultaneous Localization and Mapping (SLAM) algorithm, estimating the floor where the elevator is located by combining data association, and judging whether the elevator stops abnormally.
2. The technical scheme adopted by the invention is disclosed.
The invention discloses an elevator motion detection and abnormal position parking judgment method based on an acceleration sensor, which comprises the following steps of:
step 1. estimation of travel distance of elevator car
A three-axis acceleration sensor positioned on an elevator car collects motion parameters of the elevator, the running distance of the elevator between two times of stopping in each trip is estimated through an extended Kalman filtering algorithm, and a displacement estimation value and a variance of the elevator in the running process of one trip are returned;
step 2, elevator car stops layer and fault judgment
Based on the displacement estimation value and the variance result in the step 1, estimating the position of the elevator car and the height of the floor on the basis of setting a reference point by combining an SLAM algorithm;
step 3, judging whether the elevator has abnormal parking fault or not through position estimation; the estimation of the running distance of the elevator car is carried out according to the following steps:
11) in the estimation of the running distance of the elevator, the moving distance p (l) and the moving speed of the elevator car between two continuous stops (l-1, l) can be estimated by assisting inertial navigation under the constraint condition that the transverse speed and the longitudinal speed are zero, and firstly, at the moment t, the state vector of the elevator is assumed to be xd(t), which is defined as
Figure GDA0002600768420000021
Wherein d (t), s (t), uz(t) respectively representing the distance moved after the elevator is stopped for the last time, the speed of the elevator car and the measurement deviation of the vertical acceleration, including the deviation caused by the gravity of the earth, and s (t-1) is an auxiliary state variable;
12) in one movement, according to the zero-speed auxiliary inertial navigation system, the motion state vector of the elevator car at the time t can be defined as:
Figure GDA0002600768420000022
wherein
Figure GDA0002600768420000023
xd(t-1) is the distance traveled since the last stop,
Figure GDA0002600768420000031
is the acceleration measured by the elevator car, Δ t is the sampling interval, w (t) process noise, white gaussian noise, its covariance
Figure GDA0002600768420000032
Wherein
Figure GDA0002600768420000033
The variance of the accelerometer noise is represented,
Figure GDA0002600768420000034
representing a noise variance of the accelerometer bias model;
13) because the position and the motion state of the elevator car are measured and estimated by only an accelerometer, a traditional measurement updating equation cannot be formulated for the state space model; however, the elevator car is generally stationary or moving at a constant speed, both of which can be detected by the accelerometer; assuming that the time at which the elevator car is stationary or moving at a constant speed is known, the pseudo-metric model of the state space is as follows:
Figure GDA0002600768420000035
wherein
Figure GDA0002600768420000036
Figure GDA0002600768420000037
And
Figure GDA0002600768420000038
the pseudo measurement model provides two different update models for the motion estimation of the elevator, namely the observation noise in uniform motion and static motion respectively: a zero-speed updating model and a constant-speed updating model;
14) the elevator car only moves in the vertical direction within a limited range, the simplified model regards the movement of the elevator car as uniform linear movement, the static state is special uniform linear movement, the acceleration measured by the three-axis acceleration sensor also takes the mean value in the travel time, and the problem that the elevator car starts to accelerate or decelerate is converted into the problem that the mean value of the acceleration of the elevator car changes locally;
15) establishing car running distance estimation and car motion state estimation based on an extended Kalman filter according to a state space vector, and measuring average acceleration of a three-axis accelerometer
Figure GDA0002600768420000039
As input to the algorithm;
16) when the elevator is in uniform linear motion, zero speed hypothesis is fitted to observation data through calculation to obtain normalized prediction error xidJudging whether the elevator car is static or moves at a constant speed; setting a threshold value gammadAnd determining the state of the elevator car, and updating the observation model: if xid＞γdSelecting constant-speed measurement updating; if xid＜γdSelecting zero velocity measurement update;
17) when the algorithm detects that the elevator car stops moving and the distance traveled exceeds a threshold value gammapWill output the estimated value of the running distance of the elevator car
Figure GDA00026007684200000310
And its variance estimate
Figure GDA00026007684200000311
And updates the system state vector and returns the updated value, repeating the processes 15) -17) on the next movement.
Further, in step 15)
Firstly, estimating the state vector by an extended Kalman filter algorithm
Figure GDA0002600768420000041
And its covariance matrix
Figure GDA0002600768420000042
Updating iteration is carried out, and the specific iteration steps are prediction and updating:
(ii) prediction
Figure GDA0002600768420000043
Figure GDA0002600768420000044
Update of
Figure GDA0002600768420000045
Figure GDA0002600768420000046
Figure GDA0002600768420000047
Then, whether the elevator car moves uniformly and linearly is judged through a uniform linear motion detector, if so, an iterative updating algorithm is continuously executed, and if not, the next travel is calculated.
Furthermore, the step 2 of elevator car floor stopping and fault judgment is carried out according to the following steps:
21) obtaining an estimated value of a car running distance through the step 1
Figure GDA0002600768420000048
Sum variance estimate
Figure GDA0002600768420000049
On the basis, the estimation of the position of the elevator car and the stop floor can be realized through an SLAM algorithm, and whether the elevator has faults or not is detected through the estimated car position;
22) introducing new state vectors
Figure GDA00026007684200000410
Where p (l) is the position of the elevator car after the first trip,p (l) represents the distance of movement of the elevator car between two consecutive stops (l-1, l); m is(i)The height of the ith floor is taken as M, and the total number of the floors is taken as M; since the height of each floor is constant, the state vector is described as:
Figure GDA00026007684200000411
wherein e1Is a unit matrix which is formed by the following steps,
Figure GDA00026007684200000412
is the error of the estimated value of the running distance of the elevator car, and the assumed error omegas(l) Is zero mean, uncorrelated, and has a variance of
Figure GDA00026007684200000413
23) Assuming that the floor i where the elevator car stops for the ith run is known, the corresponding pseudo-observation model is:
Figure GDA00026007684200000414
wherein
Figure GDA0002600768420000051
vs(l) Is a pseudo-measurement error which is a small deviation between the stopping point position of the elevator car and the floor height due to the elevator control system being imperfect, is assumed to be zero-mean, uncorrelated, and is vs(l) Has a variance of
Figure GDA0002600768420000052
If the floor and the stopping point of the elevator car are known, the position of the elevator car is tracked and the height of the floor is estimated through SLAM based on extended Kalman filtering;
24) in the actual working environment, the total number of floors M and the travel sequence of the elevator car are unknown; suppose an elevator car initial state estimation
Figure GDA0002600768420000053
And corresponding covariance matrix
Figure GDA0002600768420000054
If the elevator car position is valid, the association between the elevator car position and the estimated floor height is realized through data association based on the maximum likelihood number, and the data association selects an observation model which minimizes the normalized prediction error, so the floor height where the elevator car is located can be obtained through a minimization function:
Figure GDA0002600768420000055
25) to complete the step described in 24) above, a desired initial value is necessary; the method for obtaining the ideal initial state comprises the steps that an elevator car moves from a bottom layer to a highest layer in sequence, and each layer stops at least once;
26) based on SLAM algorithm of extended Kalman filter, estimated value of running distance of elevator car obtained in stage 1
Figure GDA0002600768420000056
Variance estimation of sum distance estimation error
Figure GDA0002600768420000057
As input to the state vector
Figure GDA0002600768420000058
Sum state covariance
Figure GDA0002600768420000059
Carrying out updating iteration;
27) calculating normalized prediction error
Figure GDA00026007684200000510
And solving the floor i where the elevator car is located through a minimization function;
28) comparing the normalized error xisAnd an assumed threshold value gammasIf xi is large or smalls＞γsIf the state vector is abnormal, the state vector is not measured and updated, and an alarm is given to prompt the abnormal stop fault of the elevator car; if the error xis＜γsAnd then the elevator car is proved to run normally, the state vector is measured and updated, the updated value is returned, the next round of running process is entered, and the steps 26) -28) are repeated.
Further, step 26), the specific iteration steps are divided into prediction and update:
(ii) prediction
Figure GDA0002600768420000061
Figure GDA0002600768420000062
Update of
Figure GDA0002600768420000063
Figure GDA0002600768420000064
Figure GDA0002600768420000065
Further, it is detected whether the elevator has a fault, which includes uneven floor, squat bottom or rushing top.
3. The technical effect produced by the invention.
(1) The invention realizes the detection of the motion state of the elevator by using the three-axis accelerometer, estimates the state of the elevator by using an extended Kalman filtering method, and can quickly obtain the displacement information of the elevator car.
(2) The elevator floor positioning system realizes elevator floor positioning by using the three-axis accelerometer, does not need information interaction between the elevator and the floor, and has low cost and high applicability.
(3) Compared with other methods, the method provided by the invention can be used for estimating the motion state of the elevator and detecting the abnormal stopping of the elevator at the later stage only by enabling the elevator to traverse once to acquire data such as floors, floor heights and the like, and the working efficiency is greatly improved.
Drawings
Fig. 1 is a flow chart of the present invention for estimating the travel distance of an elevator car.
Fig. 2 is a flow chart of the elevator car floor stopping and fault judgment of the invention.
Fig. 3 is a diagram of elevator floor location estimation in a particular embodiment.
Detailed Description
Examples
The invention provides an elevator motion detection method based on a three-axis acceleration sensor, which comprises the three-axis acceleration sensor, a data line and an upper computer, wherein the three-axis acceleration sensor is placed on an elevator car, motion parameters of an elevator, such as the motion direction, the speed and the acceleration of the elevator, are acquired when the elevator runs, the parameters acquired by the accelerometer are used as input, and the running distance of the elevator in each trip (between two stops) is estimated through an extended Kalman filter algorithm. And returning the estimated displacement value and variance of the elevator in one-time running process. And then combined with the SLAM algorithm, the position of the elevator car and the height of the floor are estimated (based on the set reference point). And finally, judging whether the elevator has abnormal parking faults or not through position estimation.
The invention provides an elevator motion state estimation and floor-stopping abnormity detection method based on a three-axis acceleration sensor, which comprises the following steps:
1. elevator car travel distance estimation
11) In the estimation of the running distance of the elevator, the moving distance p (l) and the moving speed of the elevator car between two continuous stops (l-1, l) can be estimated by assisting inertial navigation under the constraint condition that the transverse speed and the longitudinal speed are zero. First, assume that at time t, the state vector of the elevator is xd(t), which is defined as
Figure GDA0002600768420000071
Wherein d (t), s (t), uzAnd (t) respectively represents the moving distance of the elevator after the last stop, the speed of the elevator car and the measurement deviation of the vertical acceleration (including the deviation caused by the gravity of the earth), and s (t-1) is an auxiliary state variable.
12) In one movement, according to the zero-speed auxiliary inertial navigation system, the motion state vector of the elevator car at the time t can be defined as:
Figure GDA0002600768420000072
wherein
Figure GDA0002600768420000073
xd(t-1) is the distance traveled since the last stop,
Figure GDA0002600768420000074
is the acceleration measured by the elevator car, Δ t is the sampling interval, w (t) process noise (white gaussian noise, its covariance
Figure GDA0002600768420000075
Wherein
Figure GDA0002600768420000076
The variance of the accelerometer noise is represented,
Figure GDA0002600768420000077
representing the noise variance of the accelerometer bias model.
13) Since the position and the movement state of the elevator car are only measured and estimated by an accelerometer, a traditional measurement update equation cannot be formulated for the state space model. However, the elevator car is typically stationary or moving at a constant speed, both of which are detectable by the accelerometer. Assuming that the time at which the elevator car is stationary or moving at a constant speed is known, a pseudo-metric model of the state space can be established:
Figure GDA0002600768420000078
wherein
Figure GDA0002600768420000081
Figure GDA0002600768420000082
And
Figure GDA0002600768420000083
the pseudo measurement model provides two different update models for the motion estimation of the elevator, namely the observation noise in uniform motion and static motion respectively: a zero-speed updating model and a constant-speed updating model.
14) It is known that the elevator car moves only in a limited range in the vertical direction. In order to simplify the model and facilitate calculation, the invention regards the movement of the elevator car as uniform linear movement (the static state is a special uniform linear movement), and the acceleration measured by the three-axis acceleration sensor also takes the mean value in the travel time, and converts the problem that the elevator car starts to accelerate or decelerate into the problem that the mean value of the acceleration of the elevator car is locally changed.
15) Establishing car running distance estimation and car motion state estimation based on an extended Kalman filter according to a state space vector, and measuring average acceleration (or negative) of a three-axis accelerometer
Figure GDA0002600768420000084
As input to the algorithm. Firstly, estimating the state vector by an extended Kalman filter algorithm
Figure GDA0002600768420000085
And its covariance matrix
Figure GDA0002600768420000086
Updating iteration is carried out, and the specific iteration steps are prediction and updating:
(ii) prediction
Figure GDA0002600768420000087
Figure GDA0002600768420000088
Update of
Figure GDA0002600768420000089
Figure GDA00026007684200000810
Figure GDA00026007684200000811
Then, whether the elevator car moves uniformly and linearly is judged through a uniform linear motion detector, if so, an iterative updating algorithm is continuously executed, and if not, the next travel is calculated.
16) When the elevator is in uniform linear motion, zero speed hypothesis is fitted to observation data through calculation to obtain normalized prediction error xidWhether the elevator car is static or moves at a constant speed is judged. Setting a threshold value gammadFor comparison. Determining the state of the elevator car, and updating the observation model: if xid＞γdSelecting constant-speed measurement updating; if xid＜γdThe zero velocity measurement update is selected.
17) When the algorithm detects that the elevator car stops moving and the distance traveled exceeds a threshold value gammapWill output the estimated value of the running distance of the elevator car
Figure GDA0002600768420000091
And its variance estimate
Figure GDA0002600768420000092
And are paired with each otherThe system state vector is updated and returns to the updated value, and the process of 15) -17) is repeated at the next movement.
2. Elevator car stops layer and fault judgment
21) Through the 1 st stage, a series of estimated values of the running distance of the car can be obtained
Figure GDA0002600768420000093
Sum variance estimate
Figure GDA0002600768420000094
On the basis, the invention can realize the estimation of the position of the elevator car and the stopping floor through the SLAM algorithm, and simultaneously detect whether the elevator has faults of uneven floors, squat bottom or top rushing and the like through the estimated car position.
22) Introducing new state vectors
Figure GDA0002600768420000095
Where p (l) is the position of the elevator car after the first trip, m(i)Is the height of the ith floor, and M is the total number of floors. Since the height of each floor is constant, the state vector can be described as:
Figure GDA0002600768420000096
wherein e1Is a unit matrix which is formed by the following steps,
Figure GDA0002600768420000097
is the error of the estimated travel distance of the elevator car, which is assumed to be zero-mean, uncorrelated and with a variance of
Figure GDA0002600768420000098
23) Assuming that the floor i where the elevator car stops for the ith run is known, the corresponding pseudo-observation model is:
Figure GDA0002600768420000099
wherein
Figure GDA00026007684200000910
vs(l) Is a false measurement error, which is a small deviation between the stopping point position of the elevator car and the floor height, mainly due to an imperfect elevator control system. The error is assumed to be zero mean, uncorrelated and with a variance of
Figure GDA00026007684200000911
The floor height can be estimated by tracking the elevator car position by means of SLAM based on extended kalman filtering, provided that the floor and the stopping point of the elevator car are known.
24) In the actual operating environment, the total number of floors M and the travel sequence of the elevator cars are unknown. Suppose an elevator car initial state estimation
Figure GDA00026007684200000912
And corresponding covariance matrix
Figure GDA00026007684200000913
If the data association is valid, the association between the elevator car position and the estimated floor height can be realized through the data association based on the maximum likelihood number, and the data association selects an observation model which minimizes the normalized prediction error, so the floor height where the elevator car is located can be obtained through a minimization function:
Figure GDA0002600768420000101
25) the desired initial value is necessary to complete the procedure described in 24. The method of obtaining the ideal initial state may be by moving the elevator car from the bottom floor to the highest floor in sequence, each floor stopping at least once.
26) Based on SLAM algorithm of extended Kalman filter, estimated value of running distance of elevator car obtained in stage 1
Figure GDA0002600768420000102
Sum distanceVariance estimation from estimation error
Figure GDA0002600768420000103
As input to the state vector
Figure GDA0002600768420000104
Sum state covariance
Figure GDA0002600768420000105
Updating iteration is carried out, and the specific iteration steps are prediction and updating:
(ii) prediction
Figure GDA0002600768420000106
Figure GDA0002600768420000107
Update of
Figure GDA0002600768420000108
Figure GDA0002600768420000109
Figure GDA00026007684200001010
27) Calculating normalized prediction error
Figure GDA00026007684200001011
And the floor i where the elevator car is located is solved through a minimization function.
28) Comparing the normalized error xisAnd an assumed threshold value gammasThe size of (d); if xis＞γsIf the state vector is abnormal, the state vector is not measured and updated, and an alarm is given to prompt the abnormal stop fault of the elevator car; if the error xis＜γsAnd then the elevator car is proved to run normally, the state vector is measured and updated, the updated value is returned, the next round of running process is entered, and the steps 26) -28) are repeated.
1) The following description is made with reference to fig. 1 and 2. Taking a seven-storey elevator as an example, one side of the elevator is provided with a detection module of 10 MPU-9150 three-axis acceleration sensors for acquiring the acceleration of the elevator in three directions. The collected acceleration in the vertical direction of the elevator is used as input to construct a system state vector x of the elevatord. And performing prediction updating on a state vector matrix and a covariance matrix according to the extended Kalman filtering.
2) And after the extended Kalman filtering is updated, judging whether the elevator car is in stable linear motion. The invention simplifies the acceleration process of the elevator, determines the acceleration magnitude of the elevator car during acceleration or deceleration by means of averaging, and expresses the detection of when the elevator car accelerates or decelerates as a problem of detecting local variations in the average of the accelerometer measurements. The stationary linear motion judger judges that it is an effective motion if the elevator is in a uniform linear motion, and otherwise, ends the process.
3) Dividing the stationary linear motion into stationary and uniform motion, and calculating the normalized prediction error obtained when fitting the zero-velocity hypothesis to the observed data
Figure GDA0002600768420000111
If the speed of the elevator car exceeds the set value, the elevator car is judged to be not zero and to be in uniform motion, and a uniform observation model is adopted
Figure GDA0002600768420000112
Otherwise, judging that the elevator speed is zero and is in a static state, and adopting a zero-speed observation model
Figure GDA0002600768420000113
4) And performing extended Kalman filtering updating on the elevator state vector matrix, the gain matrix and the covariance matrix under a new observation model.
5) When the elevator car stops moving, the running distance is checked
Figure GDA0002600768420000114
Whether or not it is greater than threshold value gammasIf both of them satisfy the condition, the estimated value of the travel distance of the elevator car is output
Figure GDA0002600768420000115
And its estimated variance
Figure GDA0002600768420000116
And updating the elevator system state vector matrix and the covariance matrix thereof and returning the updated values thereof.
6) Obtaining elevator travel parameters
Figure GDA0002600768420000117
And
Figure GDA0002600768420000118
and then, the learning of the normal floor stopping position of the elevator is completed by combining with the SLAM algorithm. First a new elevator state vector x is defineds(l) In that respect Estimating the running distance of the elevator car obtained in the 1 st stage
Figure GDA0002600768420000119
Variance estimation of sum distance estimation error
Figure GDA00026007684200001110
As input, the state vector is processed according to the SLAM algorithm based on the extended Kalman filter
Figure GDA00026007684200001111
Sum state covariance
Figure GDA00026007684200001112
Performing update iteration to obtain
Figure GDA00026007684200001113
And their cooperationVariance matrix Ps。
7) The invention adopts data correlation based on maximum likelihood number, and can obtain an observation model which minimizes the normalized prediction error:
Figure GDA00026007684200001114
and calculating the estimated elevator floor height and the floor i by using a minimization function.
8) If the magnitude of the prediction error is below the outlier threshold, then the new state vector estimate is evaluated
Figure GDA0002600768420000121
And updating the gain matrix and the covariance matrix, otherwise calling an abnormal stopping program, and displaying the parking of the elevator at an abnormal position. By adopting the SLAM algorithm, the elevator only needs to finish one-time normal operation (run all floors), and the system can automatically learn all correct floor positions. The whole learning process realizes independence of the floor height, the floor number and the elevator brand of the elevator building.
9) As shown in fig. 3, after the three-axis acceleration sensor is installed in the elevator car, the elevator position estimation values obtained by the extended kalman filter estimation after all sensors acquire data are all within a 3 σ confidence interval, which indicates that the filter estimation is reasonable and consistent. In addition, after the starting training sequence, the position error increase is effectively limited based on the estimation of the SLAM, the specific floor where the elevator runs is easily obtained from the filtering estimation, and when the elevator car is detected to stay at the direct position of two floors based on the estimation of the SLAM, a signal of abnormal floor stop of the elevator car is sent out, which shows that the method provided by the invention can not construct the running track of the elevator, and can realize the estimation of the floor where the elevator is located through data association and judge whether the elevator stops abnormally or not.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (5)

1. An elevator motion detection and abnormal position parking judgment method based on an acceleration sensor is characterized by comprising the following steps:
step 1. estimation of travel distance of elevator car
A three-axis acceleration sensor positioned on an elevator car collects motion parameters of the elevator, the running distance of the elevator between two times of stopping in each trip is estimated through an extended Kalman filtering algorithm, and a displacement estimation value and a variance of the elevator in the running process of one trip are returned;
step 2, elevator car stops layer and fault judgment
Based on the displacement estimation value and the variance result in the step 1, estimating the position of the elevator car and the height of the floor on the basis of setting a reference point by combining an SLAM algorithm;
step 3, judging whether the elevator has abnormal parking fault or not through position estimation; the estimation of the running distance of the elevator car is carried out according to the following steps:
11) in the estimation of the running distance of the elevator, the moving distance p (l) and the moving speed of the elevator car between two continuous stops (l-1, l) can be estimated by assisting inertial navigation under the constraint condition that the transverse speed and the longitudinal speed are zero, and firstly, at the moment t, the state vector of the elevator is assumed to be xd(t), which is defined as
Figure FDA0002600768410000011
Wherein d (t), s (t), uz(t) respectively representing the distance moved after the elevator is stopped for the last time, the speed of the elevator car and the measurement deviation of the vertical acceleration, including the deviation caused by the gravity of the earth, and s (t-1) is an auxiliary state variable;
12) in one movement, according to a zero-speed auxiliary inertial navigation system, defining a motion state vector of an elevator car at a time t as follows:
Figure FDA0002600768410000012
wherein
Figure FDA0002600768410000013
xd(t-1) is the distance traveled since the last stop,
Figure FDA0002600768410000014
is the acceleration measured by the elevator car, Δ t is the sampling interval, w (t) process noise, white gaussian noise, its covariance
Figure FDA0002600768410000015
Wherein
Figure FDA0002600768410000016
The variance of the accelerometer noise is represented,
Figure FDA0002600768410000021
representing a noise variance of the accelerometer bias model;
13) because the position and the motion state of the elevator car are measured and estimated by only an accelerometer, a traditional measurement updating equation cannot be formulated for the state space model; however, the elevator car is generally stationary or moving at a constant speed, both states being detected by the accelerometer; assuming that the time at which the elevator car is stationary or moving at a constant speed is known, the pseudo-metric model of the state space is as follows:
Figure FDA0002600768410000022
wherein
Figure FDA0002600768410000023
Figure FDA0002600768410000024
And
Figure FDA0002600768410000025
the pseudo measurement model provides two different update models for the motion estimation of the elevator, namely the observation noise in uniform motion and static motion respectively: a zero-speed updating model and a constant-speed updating model;
14) the elevator car only moves in the vertical direction within a limited range, the simplified model regards the movement of the elevator car as uniform linear movement, the static state is special uniform linear movement, the acceleration measured by the three-axis acceleration sensor also takes the mean value in the travel time, and the problem that the elevator car starts to accelerate or decelerate is converted into the problem that the mean value of the acceleration of the elevator car changes locally;
15) establishing car running distance estimation and car motion state estimation based on an extended Kalman filter according to a state space vector, and measuring average acceleration of a three-axis accelerometer
Figure FDA0002600768410000026
As input to the algorithm;
16) when the elevator is in uniform linear motion, zero speed hypothesis is fitted to observation data through calculation to obtain normalized prediction error xidJudging whether the elevator car is static or moves at a constant speed; setting a threshold value gammadAnd determining the state of the elevator car, and updating the observation model: if xid＞γdSelecting constant-speed measurement updating; if xid＜γdSelecting zero velocity measurement update;
17) when the algorithm detects that the elevator car stops moving and the distance traveled exceeds a threshold value gammapWill output the estimated value of the running distance of the elevator car
Figure FDA0002600768410000027
And its variance estimate
Figure FDA0002600768410000028
And updates the system state vector and returns the updated value, repeating the processes 15) -17) on the next movement.
2. The acceleration sensor-based elevator motion detection and abnormal position parking determination method according to claim 1, wherein: in step 15)
Firstly, estimating the state vector by an extended Kalman filter algorithm
Figure FDA0002600768410000029
And its covariance matrix
Figure FDA0002600768410000031
Updating iteration is carried out, and the specific iteration steps are prediction and updating:
(ii) prediction
Figure FDA0002600768410000032
Figure FDA0002600768410000033
Update of
Figure FDA0002600768410000034
Figure FDA0002600768410000035
Figure FDA0002600768410000036
Then, whether the elevator car moves uniformly and linearly is judged through a uniform linear motion detector, if so, an iterative updating algorithm is continuously executed, and if not, the next travel is calculated.
3. The acceleration sensor-based elevator motion detection and abnormal position stop judgment method according to claim 1, wherein the step 2 of elevator car floor stop and fault judgment is performed according to the following steps:
21) obtaining an estimated value of a car running distance through the step 1
Figure FDA0002600768410000037
Sum variance estimate
Figure FDA0002600768410000038
On the basis, the estimation of the position of the elevator car and the stopping floor is realized through an SLAM algorithm, and whether the elevator has faults or not is detected through the estimated car position;
22) introducing new state vectors
Figure FDA0002600768410000039
Wherein p (l) is the position of the elevator car after the first trip, p (l) represents the distance traveled by the elevator car between two consecutive stops (l-1, l); m is(i)The height of the ith floor is taken as M, and the total number of the floors is taken as M; since the height of each floor is constant, the state vector is described as:
Figure FDA00026007684100000312
wherein e1Is a unit matrix which is formed by the following steps,
Figure FDA00026007684100000310
is the error of the estimated value of the running distance of the elevator car, and the assumed error omegas(l) Is zero mean, uncorrelated, and has a variance of
Figure FDA00026007684100000311
23) Assuming that the floor i where the elevator car stops for the ith run is known, the corresponding pseudo-observation model is:
Figure FDA0002600768410000041
wherein
Figure FDA0002600768410000042
vs(l) Is a pseudo-measurement error which is a small deviation between the stopping point position of the elevator car and the floor height due to the elevator control system being imperfect, is assumed to be zero-mean, uncorrelated, and is vs(l) Has a variance of
Figure FDA0002600768410000043
If the floor and the stopping point of the elevator car are known, the position of the elevator car is tracked and the height of the floor is estimated through SLAM based on extended Kalman filtering;
24) in the actual working environment, the total number of floors M and the travel sequence of the elevator car are unknown; suppose an elevator car initial state estimation
Figure FDA0002600768410000044
And corresponding covariance matrix
Figure FDA0002600768410000045
If the elevator car position is valid, the association between the elevator car position and the estimated floor height is realized through data association based on the maximum likelihood number, and the data association selects an observation model which minimizes the normalized prediction error, so the floor height where the elevator car is located can be obtained through a minimization function:
Figure FDA0002600768410000046
25) to complete the step described in 24) above, a desired initial value is necessary; the method for obtaining the ideal initial state comprises the steps that an elevator car moves from a bottom layer to a highest layer in sequence, and each layer stops at least once;
26) based on SLAM algorithm of extended Kalman filter, estimated value of running distance of elevator car obtained in stage 1
Figure FDA0002600768410000047
Variance estimation of sum distance estimation error
Figure FDA0002600768410000048
As input to the state vector
Figure FDA00026007684100000411
Sum state covariance
Figure FDA0002600768410000049
Carrying out updating iteration;
27) calculating normalized prediction error
Figure FDA00026007684100000410
And solving the floor i where the elevator car is located through a minimization function;
28) comparing the normalized error xisAnd an assumed threshold value gammasIf xi is large or smalls＞γsIf the state vector is abnormal, the state vector is not measured and updated, and an alarm is given to prompt the abnormal stop fault of the elevator car; if the error xis＜γsAnd then the elevator car is proved to run normally, the state vector is measured and updated, the updated value is returned, the next round of running process is entered, and the steps 26) -28) are repeated.
4. The method for detecting motion and determining abnormal position of elevator based on acceleration sensor as claimed in claim 3, wherein said step 26), specific iteration steps are divided into prediction and update:
(ii) prediction
Figure FDA0002600768410000051
Figure FDA0002600768410000052
Update of
Figure FDA0002600768410000053
Figure FDA0002600768410000054
Figure FDA0002600768410000055
5. The acceleration sensor-based elevator motion detection and abnormal position parking determination method according to claim 3, wherein: and detecting whether the elevator has faults, wherein the faults comprise uneven floors, squat bottoms or rushing tops.
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