CN110775758A - Elevator running health degree evaluation method based on car acceleration signal analysis - Google Patents

Elevator running health degree evaluation method based on car acceleration signal analysis Download PDF

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CN110775758A
CN110775758A CN201910954100.0A CN201910954100A CN110775758A CN 110775758 A CN110775758 A CN 110775758A CN 201910954100 A CN201910954100 A CN 201910954100A CN 110775758 A CN110775758 A CN 110775758A
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elevator
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running
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health degree
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赵春晖
李泽华
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Zhejiang University ZJU
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks

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Abstract

The invention discloses an elevator running health degree evaluation method based on car acceleration signal analysis. The invention aims at the problem that the existing elevator lacks a real-time and accurate evaluation standard of the running health degree of the elevator. The elevator car acceleration signal reflecting the elevator vibration condition is fully utilized, the elevator car acceleration in the actual running process is collected in real time, the vibration signal closely related to the elevator running performance is obtained by a wavelet modulus maximum denoising and trend removing method, then a plurality of characteristic variables are selected as evaluation indexes, and finally a scoring function is determined and a plurality of evaluation indexes are combined to give quantitative evaluation results in a plurality of aspects of the elevator running health degree. The method realizes timely and quantitative evaluation of the comprehensive operation health degree of the elevator, can effectively master the change condition of the operation performance of the elevator, is beneficial to improving the comfort level of passengers, provides valuable reference basis for maintenance of the elevator, can prevent major safety accidents, and ensures safe and reliable operation of the elevator.

Description

Elevator running health degree evaluation method based on car acceleration signal analysis
Technical Field
The invention belongs to the technical field of elevator running health degree evaluation, and particularly relates to an elevator running health degree evaluation method based on car acceleration signal analysis.
Background
With the continuous improvement of the urbanization level of China, house buildings are continuously and longitudinally developed under the condition of limited land resources, and the increase of the aging degree stimulates the matching requirements of elevators of various buildings, so that the total amount of elevators in China is rapidly increased. The survey shows that the inventory of the Chinese elevators increases year by year in 2011-plus 2018, the growth rates are all kept above 10%, and the total registration amount of the domestic elevators reaches 627.83 ten thousands of elevators at the end of 2018. It is expected that the elevator inventory in the middle of 2023 will exceed ten million. Meanwhile, the daily use of a plurality of elevators is very frequent, and the braking times of the elevators in markets and the like reach 4500 times per day. The safety management and maintenance problems of the elevators are increasingly prominent due to the huge total amount and frequent use times, for example, in 2016, the national quality control administration totally repairs nearly 4 thousands of elevators with hidden trouble, but 48 elevator safety accidents still happen and 41 people die. Therefore, in order to better understand the real-time running state of the elevator, make maintenance and repair and remove fault hidden dangers in advance, the research has important practical significance for the real-time effective evaluation of the running health degree of the elevator.
The elevator is used as a mechanical device, the acceleration signal reflecting the vibration condition of the elevator contains a large amount of device state information, can reflect multiple contents such as the running quality of the elevator, the comfort degree of passengers and the like, also contains the premonitory sign of multiple faults, and the acquisition method is simple and practical, so the method is very important for the research of the elevator acceleration signal. Currently, acceleration signals are processed in many ways, and can be roughly classified into time domain analysis, frequency domain analysis, and time-frequency domain analysis. Generally, in consideration of the non-stationarity of the elevator acceleration signal, a time-frequency domain analysis method such as a wavelet decomposition method (WT) or an Empirical Mode Decomposition (EMD) is used more often. For the general overview, the time-frequency domain characteristics are extracted by various acceleration signal analysis processing methods to form a characteristic vector, and the characteristic vector is input into various classifier models to perform fault diagnosis. However, the previous work of evaluating the health of the elevator operation by analyzing the acceleration signal is relatively lacking.
The invention provides an elevator operation health degree evaluation method based on car acceleration signal analysis for a traction type elevator. The method comprises the steps of firstly, carrying out noise reduction processing on collected running acceleration signals by using a noise reduction method based on a wavelet mode maximum value, then carrying out trend removal on the noise-reduced signals to extract effective vibration signals, then carrying out time window division on the vibration signals to respectively calculate each evaluation index, and finally, bringing the evaluation indexes into a preset evaluation function to obtain comprehensive evaluation of the running health degree of the elevator. The invention can comprehensively evaluate the running health degree of the elevator on line in real time, is beneficial to knowing the equipment state and provides accurate basis for elevator maintenance. The research report related to the invention is not seen yet.
Disclosure of Invention
The invention aims to provide an elevator running health degree evaluation method based on car acceleration signal analysis for special equipment of a traction type elevator.
The purpose of the invention is realized by the following technical scheme: an elevator running health degree evaluation method based on car acceleration signal analysis specifically comprises the following steps:
(1) selecting normal operation data of the elevator: selecting an acceleration signal corresponding to the running process without any fault label from a history database of the elevator, and performing pretreatment operations such as abnormal value cleaning on the selected acceleration signal to obtain a pretreated acceleration signal a (t); wherein t is a time parameter.
(2) And (3) reducing noise of an acceleration signal: the preprocessed acceleration signal a (t) is denoised by a denoising method based on wavelet modulus maxima, and the method comprises the following substeps:
(2.1) selecting a wavelet for performing the wavelet transform, the selected wavelet to have a first order vanishing moment.
And (2.2) determining the number of the resolution scales required by the signal, wherein the resolution scales are required to ensure that the maximum scale can ensure that the number of the maximum value points of the modulus of the effective signal is more than that of the noise signal. Typically 3 to 5 decomposition scales.
(2.3) performing discrete wavelet transform shown in the formula (1) on the preprocessed acceleration signals a (t) obtained in the step (1) according to the wavelets selected in the step (2.1) and the decomposition scale number determined in the step (2.2), wherein the discrete wavelet transform is obtained by the step (1)
Figure BDA0002226687100000021
Is twoInto a wavelet function, W a(2 nM) is a discrete wavelet coefficient, and n and m are discretization parameters of expansion and translation factors respectively.
Figure BDA0002226687100000022
(2.4) determining a modulus maximum point corresponding to the discrete wavelet coefficient on each decomposition scale: and comparing the modulus maximum value on each decomposition scale with a given threshold TS, and removing the modulus maximum value points with the amplitude smaller than TS. TS can be selected according to equation (2), where P is the signal power, K is the amplitude of the maximum modulo maximum, and J is the selected decomposition scale.
Figure BDA0002226687100000023
(2.5) reconstructing a signal by the discrete wavelet coefficient corresponding to the modulus maximum value reserved on each decomposition scale according to a formula (3) to obtain the noise-reduced elevator running process acceleration signal a *(t)。
Figure BDA0002226687100000024
(3) Extracting a vibration signal: although a large amount of noise components are removed from the noise-reduced elevator acceleration signal, the initial acceleration process and the stopped deceleration process inherent in the elevator running process still remain. In order to prevent the influence of the significant acceleration change caused by the two processes on subsequent analysis, the acceleration signal needs to be subjected to de-trending processing, and a vibration signal closely related to the running performance of the elevator is further extracted.
(4) The method comprises the following steps of establishing an elevator operation health degree evaluation model, wherein the steps comprise the following substeps:
(4.1) determining an evaluation index: in order to enable the extracted vibration signals to reflect the running health degree of the elevator from multiple aspects, peak values X are respectively selected pvRoot mean square X rmsWave form factor S fAs an evaluation index. Wherein the peak value is the most intuitive expression for the vibration condition of the elevatorMany abnormal situations often occur with excessive vibration peaks; the root mean square reflects the energy value of the signal and is closely related to the comfort degree of passengers; the waveform factor can reflect the abnormal waveform of the vibration signal, is sensitive to some early mechanical faults and can play a role in early warning. The specific calculation formula is shown in (4) - (6), wherein { x i1, (i) is a discrete representation of the extracted vibration signal of length N.
X pv＝max{|x i|},(i＝1,2,...,N) (16)
Figure BDA0002226687100000031
Figure BDA0002226687100000032
(4.2) calculating an operation health degree evaluation index value, firstly dividing the extracted vibration signal of each operation process according to a time window L, and then calculating the vibration signal { x ] in each time window iAnd (4) respectively calculating and recording three evaluation indexes according to the formula in the step (4.1), wherein the length of the time window L is constant.
(4.3) determining an evaluation index threshold, firstly taking the evaluation index value in each single time window as a sample, then counting samples corresponding to vibration signals of all running processes, taking the upper bound of a 95% confidence interval of the sample value as a threshold E for each evaluation index, and finally, taking the overrun amplitude △ e of each index in each time window in each running process iCalculating and normalizing according to a formula (7) to obtain epsilon iSimultaneously recording the overrun time delta t of each time jAnd normalized according to the formula (8) to obtain tau jWherein C is the time window number of each operation process, D is the number of times of index overrun of each operation process, E is the threshold value of evaluation index, and T is the total time of the operation process.
Figure BDA0002226687100000033
Figure BDA0002226687100000041
And (4.4) determining an evaluation function to obtain an elevator running health degree score. The evaluation function comprises a real-time score S of a single operation process of each evaluation index and a health degree score S of an accumulated operation state, and the specific forms are respectively shown in formulas (9) and (10), wherein chi kMay take epsilon for the argument kOr τ kAnd M is the accumulated running times.
Figure BDA0002226687100000042
Figure BDA0002226687100000043
Wherein, the choice of the function f (χ) in the formula (9) is limited by two points, one is when χ ∈ [0,1 ]]When s is equal to [0,10 ]]I.e. limiting the value range of the scoring function to [0,10 ]](ii) a Second, satisfy f (x) 1)+f(χ 2)＜f(χ 1+χ 2) From the amplitude point of view, a single large overrun will cause more serious effects than multiple slight overruns, and from the time point of view, the effect of the overruns lasting for a long time is more obvious than the transient overruns scattered for multiple times. Therefore, the function f (x) of the amplitude angle and the time angle is selected as shown in equations (11) and (12).
Wherein C is the time window number of the operation process, D is the number of overrun times in the operation process, and the parameters a and b are adjustable parameters, and firstly the value range requirement of the function needs to be met, namely
Figure BDA0002226687100000046
Wherein the recommended value range of a is (0, 10)]。
(5) And when the system is applied on line, acquiring acceleration data in the running process in real time, and giving running health degree evaluation. The step comprises the following substeps:
and (5.1) carrying out noise reduction and trend removing operation on the acceleration signals acquired in real time in the running process by using the methods in the steps (2) and (3) to extract effective real-time vibration signals.
(5.2) calculating the overrun amplitude and the overrun time of each index in the real-time operation process, and specifically comprising the following steps: firstly, respectively calculating peak values X of real-time vibration signals according to the step (4.2) pvRoot mean square X rmsWave form factor S fThree indexes are obtained, and then the threshold value E of each index determined in the modeling process of the step (4.3) and the total time T' of the actual operation process are taken as standards to calculate the normalized overrun amplitude value
Figure BDA0002226687100000047
And overrun time
Figure BDA0002226687100000048
(5.3) obtaining the over-limit amplitude
Figure BDA0002226687100000051
And overrun time
Figure BDA0002226687100000052
Respectively carrying the evaluation functions determined in the step (4.4) to obtain the real-time score of the operation process And updating the health degree score of the accumulated running state of the elevator according to the actual running times
Figure BDA0002226687100000054
Further, in the step (2.1), the wavelet is a Daubechies wavelet.
The invention has the beneficial effects that: the invention provides an elevator operation health degree evaluation method based on car acceleration signal analysis, aiming at a traction type elevator. The method fully utilizes an acceleration signal reflecting the vibration condition of the elevator, acquires the acceleration of the elevator car in the actual running process in real time, obtains a vibration signal closely related to the running health degree of the elevator by utilizing a wavelet modulus maximum denoising and trend removing method, then selects a plurality of characteristic variables as evaluation indexes, and finally determines a scoring function and combines the plurality of evaluation indexes to give quantitative evaluation results in various aspects of the running health degree of the elevator. The method realizes timely and quantitative evaluation of the comprehensive operation health degree of the elevator, can effectively master the change condition of the operation performance of the elevator, is beneficial to improving the comfort level of passengers, provides valuable reference basis for maintenance of the elevator, can prevent major safety accidents, and ensures safe and reliable operation of the elevator.
Drawings
Fig. 1 is a flow chart of an elevator operation health degree evaluation method based on car acceleration signal analysis, wherein (a) is a flow chart of an off-line modeling process, and (b) is a flow chart of an on-line evaluation process.
Fig. 2 is a flow chart of wavelet modulo maximum based signal denoising.
Fig. 3 is a schematic diagram of an example original acceleration signal (a) and an extracted vibration signal (b).
Fig. 4 is a diagram of the result of the evaluation of the overall health of the elevator in the example.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific examples.
The present invention is described by taking the historical operation data of a certain elevator in a certain cell in Hangzhou Zhejiang as an example, the elevator is additionally provided with an acceleration sensor with the sampling frequency of 100Hz, and the original operation acceleration signal is shown in figure 3 (a).
As shown in fig. 1, the invention relates to an elevator running health degree evaluation method based on car acceleration signal analysis, which comprises the following steps:
(1) selecting normal operation data of the elevator: selecting acceleration signals corresponding to the running process without any fault tags from a historical database of the elevator, and cleaning the selected abnormal values, namely performing pretreatment operations such as outlier rejection and the like to obtain pretreated acceleration signals a (t); wherein t is a time parameter.
(2) And (3) reducing noise of an acceleration signal: denoising the preprocessed acceleration signal a (t) by using a denoising method based on wavelet modulus maxima, wherein the step comprises the following substeps (figure 2):
(2.1) selecting a wavelet for performing the wavelet transform, the selected wavelet to have a first order vanishing moment. In this embodiment Daubechies wavelets are selected.
And (2.2) determining the number of the resolution scales required by the signal, wherein the resolution scales are required to ensure that the maximum scale can ensure that the number of the maximum value points of the modulus of the effective signal is more than that of the noise signal. Typically 3 to 5 decomposition scales.
(2.3) performing discrete wavelet transform shown in the formula (1) on the preprocessed acceleration signals a (t) obtained in the step (1) according to the wavelets selected in the step (2.1) and the decomposition scale number determined in the step (2.2), wherein the discrete wavelet transform is obtained by the step (1)
Figure BDA0002226687100000061
As a function of a dyadic wavelet, W a(2 nM) is a discrete wavelet coefficient, and n and m are discretization parameters of expansion and translation factors respectively.
Figure BDA0002226687100000062
(2.4) determining a modulus maximum value point corresponding to the wavelet transform coefficient on each decomposition scale: and comparing the modulus maximum value on each decomposition scale with a given threshold TS, and removing the modulus maximum value points with the amplitude smaller than TS. TS can be selected according to equation (2), where P is the signal power, K is the amplitude of the maximum modulo maximum, and J is the selected decomposition scale.
(2.5) reconstructing a signal by the modulus maximum value point reserved on each decomposition scale according to a formula (3) to obtain the elevator running process acceleration signal a after noise reduction *(t)。
Figure BDA0002226687100000064
(3) Extracting a vibration signal: although a large amount of noise components are removed from the noise-reduced elevator acceleration signal, the initial acceleration process and the stopped deceleration process inherent in the elevator running process still remain. In order to prevent the influence of the significant acceleration change caused by the two processes on subsequent analysis, the acceleration signal needs to be subjected to de-trending processing, and a vibration signal closely related to the running performance of the elevator is further extracted. Considering that the vibration during the operation of the elevator is reflected in the high frequency of the signal, and the start and stop of the elevator have a long operation period, in this embodiment, the elevator acceleration signal after noise reduction is subjected to high-pass filtering to filter out the signal below 1Hz, so as to extract the high-frequency vibration signal, and the result is shown in fig. 3 (b).
(4) The method comprises the following steps of establishing an elevator operation health degree evaluation model, wherein the steps comprise the following substeps:
(4.1) determining an evaluation index: in order to enable the extracted vibration signals to reflect the running health degree of the elevator from multiple aspects, peak values X are respectively selected pvRoot mean square X rmsWave form factor S fAs an evaluation index. The peak value is the most intuitive expression of the elevator vibration condition, and the occurrence of a plurality of abnormal conditions is often accompanied by an overlarge vibration peak value; the root mean square reflects the energy value of the signal and is closely related to the comfort degree of passengers; the waveform factor can reflect the abnormal waveform of the vibration signal, is sensitive to some early mechanical faults and can play a role in early warning. The specific calculation formula is shown in (4) - (6), wherein { x i1, (i) is a discrete representation of the extracted vibration signal of length N.
X pv＝max{|x i|},(i＝1,2,...,N) (28)
Figure BDA0002226687100000071
(4.2) calculating an operation health degree evaluation index value, firstly dividing the extracted vibration signal of each operation process according to a time window L, and then calculating the vibration signal { x ] in each time window iAnd (4) respectively calculating and recording three evaluation indexes according to the formula in the step (4.1), wherein the length of a time window L is constant, and the evaluation indexes are selected within 1s in order to ensure real-time performance.
And (4.3) determining an evaluation index threshold, taking the evaluation index value in each individual time window as a sample, and then counting samples corresponding to vibration signals of all running processes. The upper bound of the 95% confidence interval of the sample values is taken as the threshold E for each evaluation index. Finally, the overrun value delta e of each index in each time window in each operation process iCalculating and normalizing according to a formula (7) to obtain epsilon iSimultaneously recording the overrun time delta t of each time jAnd normalized according to the formula (8) to obtain tau jWherein C is the time window number of each operation process, D is the number of times of index overrun of each operation process, E is the threshold value of evaluation index, and T is the total time of the operation process.
Figure BDA0002226687100000074
And (4.4) determining an evaluation function to obtain an elevator running health degree score. The evaluation function comprises a real-time score S of a single operation process of each evaluation index and a health degree score S of an accumulated operation state, and the specific forms are respectively shown in formulas (9) and (10), wherein χkMay take epsilon for the argument kOr τ kAnd M is the accumulated running times.
Figure BDA0002226687100000075
Figure BDA0002226687100000081
Wherein, the choice of the function f (χ) in the formula (9) is limited by two points, one is when χ ∈ [0,1 ]]When s is equal to [0,10 ]]I.e. limiting the value range of the scoring function to [0,10 ]](ii) a Second, satisfy f (x) 1)+f(χ 2)＜f(χ 1+χ 2) From the amplitude point of view, a single large overrun will cause more serious effects than multiple slight overruns, and from the time point of view, the effect of the overruns lasting for a long time is more obvious than the transient overruns scattered for multiple times. Therefore, the function f (x) of the amplitude angle and the time angle is selected as shown in equations (11) and (12).
Figure BDA0002226687100000082
Figure BDA0002226687100000083
Wherein C is the time window number of the operation process, D is the number of overrun times in the operation process, and the parameters a and b are adjustable parameters, and firstly the value range requirement of the function needs to be met, namely
Figure BDA0002226687100000084
Wherein the recommended value range of a is (0, 10)]。
(5) And when the system is applied on line, acquiring acceleration data in the running process in real time, and giving running health degree evaluation. The step comprises the following substeps:
and (5.1) carrying out noise reduction and trend removing operation on the acceleration signals acquired in real time in the running process by using the methods in the steps (2) and (3) to extract effective real-time vibration signals.
(5.2) calculating respective indexes in real-time operationThe method comprises the following steps of: firstly, respectively calculating peak values X of real-time vibration signals according to the step (4.2) pvRoot mean square X rmsWave form factor S fThree indexes are obtained, and then the threshold value E of each index determined in the modeling process of the step (4.3) and the total time T' of the actual operation process are taken as standards to calculate the normalized overrun amplitude value
Figure BDA0002226687100000085
And overrun time
Figure BDA0002226687100000086
(5.3) obtaining the over-limit amplitude
Figure BDA0002226687100000087
And overrun time Respectively carrying the evaluation functions determined in the step (4.4) to obtain the real-time score of the operation process
Figure BDA0002226687100000089
And updating the health degree score of the accumulated running state of the elevator according to the actual running times
Figure BDA00022266871000000810
FIG. 4 shows the health evaluation score of the elevator in the community during operation, which includes the peak value X pvRoot mean square X rmsWave form factor S fThe scores of the three indexes at time and amplitude angles respectively are P _ T and P _ E, the scores of the peak indexes at time and amplitude angles respectively are R _ T and R _ E, the scores of the root-mean-square indexes at time and amplitude angles respectively are S _ T and S _ E, and the scores of the waveform indexes at time and amplitude angles respectively are S _ T and S _ E. Using the operation process in the figure as an example, [ P _ T P _ E R _ T R _ E S _ T S _ E ]]Scores were respectively [ 6.897.158.848.897.677.46 ]]. The evaluation of the peak value index is low, and the evaluation of the root mean square index is high, which shows that the elevator has certain overlarge vibration in the running process, but the elevator feels softAnd meanwhile, the score of the waveform factor index is lower, which indicates that the elevator possibly has early fault hidden danger and needs further observation.

Claims (2)

1. An elevator running health degree evaluation method based on car acceleration signal analysis is characterized by comprising the following steps:
(1) selecting normal operation data of the elevator: selecting an acceleration signal corresponding to the running process without any fault label from a history database of the elevator, and carrying out pretreatment operations such as abnormal value cleaning on the selected acceleration signal to obtain a pretreated acceleration signal a (t); wherein t is a time parameter.
(2) And (3) reducing noise of an acceleration signal: the preprocessed acceleration signal a (t) is denoised by a denoising method based on wavelet modulus maxima, and the method comprises the following substeps:
(2.1) selecting a wavelet for performing the wavelet transform, the selected wavelet to have a first order vanishing moment.
And (2.2) determining the number of the resolution scales required by the signal, wherein the resolution scales are required to ensure that the maximum scale can ensure that the number of the maximum value points of the modulus of the effective signal is more than that of the noise signal. Typically 3 to 5 decomposition scales.
(2.3) performing discrete wavelet transform shown in the formula (1) on the preprocessed acceleration signals a (t) obtained in the step (1) according to the wavelets selected in the step (2.1) and the decomposition scale number determined in the step (2.2), wherein the discrete wavelet transform is obtained by the step (1)
Figure FDA0002226687090000011
As a function of a dyadic wavelet, W a(2 nM) is a discrete wavelet coefficient, and n and m are discretization parameters of expansion and translation factors respectively.
Figure FDA0002226687090000012
(2.4) determining a modulus maximum point corresponding to the discrete wavelet coefficient on each decomposition scale: and comparing the modulus maximum value on each decomposition scale with a given threshold TS, and removing the modulus maximum value points with the amplitude smaller than TS. TS can be selected according to equation (2), where P is the signal power, K is the amplitude of the maximum modulo maximum, and J is the selected decomposition scale.
Figure FDA0002226687090000013
(2.5) reconstructing a signal by the discrete wavelet coefficient corresponding to the modulus maximum value reserved on each decomposition scale according to a formula (3) to obtain the noise-reduced elevator running process acceleration signal a *(t)。
Figure FDA0002226687090000014
(3) Extracting a vibration signal: and performing trend removing processing on the elevator acceleration signal subjected to noise reduction.
(4) The method comprises the following steps of establishing an elevator operation health degree evaluation model, wherein the steps comprise the following substeps:
(4.1) determining an evaluation index: respectively selecting peak values X pvRoot mean square X rmsWave form factor S fAs an evaluation index. The specific calculation formula is shown in (4) - (6), wherein { x i1, (i) is a discrete representation of the extracted vibration signal of length N.
X pv＝max{|x i|},(i＝1,2,...,N) (4)
Figure FDA0002226687090000021
Figure FDA0002226687090000022
(4.2) calculating an operation health degree evaluation index value, firstly dividing the extracted vibration signal of each operation process according to a time window L, and then calculating the vibration signal { x ] in each time window iCalculating and recording three evaluation indexes according to the formula in the step (4.1), wherein the length of a time window LIs constant.
And (4.3) determining an evaluation index threshold, taking the evaluation index value in each individual time window as a sample, and then counting samples corresponding to vibration signals of all running processes. The upper bound of the 95% confidence interval of the sample values is taken as the threshold E for each evaluation index. Finally, the overrun value delta e of each index in each time window in each operation process iCalculating and normalizing according to a formula (7) to obtain epsilon iSimultaneously recording the overrun time delta t of each time jAnd normalized according to the formula (8) to obtain tau jWherein C is the time window number of each operation process, D is the number of times of index overrun of each operation process, E is the threshold value of evaluation index, and T is the total time of the operation process.
Figure FDA0002226687090000024
And (4.4) determining an evaluation function to obtain an elevator running health degree score. The evaluation function comprises a real-time score S of a single operation process of each evaluation index and a health degree score S of an accumulated operation state, and the specific forms are respectively shown in formulas (9) and (10):
Figure FDA0002226687090000025
wherein M is the cumulative number of runs, χ kMay take epsilon for the argument k(k ∈ {1,2, …, C }) or τ k(k ∈ {1,2, …, D }), function f (χ) k) Respectively as follows:
Figure FDA0002226687090000031
Figure FDA0002226687090000032
wherein the function f (χ) k) Two conditions should be met: one is when x is equal to [0,1 ]]When s is equal to [0,10 ]]I.e. limiting the value range of the scoring function to [0,10 ]](ii) a Second, satisfy f (x) 1)+f(χ 2)＜f(χ 1+χ 2) (ii) a The parameters a, b are adjustable parameters, adjusted according to the value range requirement of the function, i.e.
Figure FDA0002226687090000033
Wherein the recommended value range of a is (0, 10)]。
(5) And when the system is applied on line, acquiring acceleration data in the running process in real time, and giving running health degree evaluation. The step comprises the following substeps:
and (5.1) carrying out noise reduction and trend removing operation on the acceleration signals acquired in real time in the running process by using the methods in the steps (2) and (3) to extract effective real-time vibration signals.
(5.2) calculating the overrun amplitude and the overrun time of each index in the real-time operation process, and specifically comprising the following steps: firstly, respectively calculating peak values X of real-time vibration signals according to the step (4.2) pvRoot mean square X rmsWave form factor S fThree indexes are obtained, and then the threshold value E of each index determined in the modeling process of the step (4.3) and the total time T' of the actual operation process are taken as standards to calculate the normalized overrun amplitude value
Figure FDA0002226687090000034
And overrun time
(5.3) obtaining the over-limit amplitude
Figure FDA0002226687090000036
And overrun time
Figure FDA0002226687090000037
Respectively carrying the evaluation functions determined in the step (4.4) to obtain the real-time score of the operation process
Figure FDA0002226687090000038
And updating the health degree score of the accumulated running state of the elevator according to the actual running times
2. The method for evaluating the running health of an elevator based on car acceleration signal analysis according to claim 1, wherein in the step (2.1), the wavelet is Daubechies wavelet.
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