CN117105032B - Unmanned elevator fault analysis prediction system based on data analysis - Google Patents
Unmanned elevator fault analysis prediction system based on data analysis Download PDFInfo
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- 238000007405 data analysis Methods 0.000 title claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 62
- 238000012544 monitoring process Methods 0.000 claims abstract description 28
- 238000012423 maintenance Methods 0.000 claims abstract description 27
- 230000007613 environmental effect Effects 0.000 claims description 71
- 238000004364 calculation method Methods 0.000 claims description 39
- 238000001514 detection method Methods 0.000 claims description 30
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- 230000003028 elevating effect Effects 0.000 claims description 10
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Classifications
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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
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0025—Devices monitoring the operating condition of the elevator system for maintenance or repair
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B1/00—Control systems of elevators in general
- B66B1/34—Details, e.g. call counting devices, data transmission from car to control system, devices giving information to the control system
- B66B1/3415—Control system configuration and the data transmission or communication within the control system
- B66B1/3423—Control system configuration, i.e. lay-out
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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
- B66B5/0006—Monitoring devices or performance analysers
- B66B5/0018—Devices monitoring the operating condition of the elevator system
- B66B5/0031—Devices monitoring the operating condition of the elevator system for safety reasons
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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
- B66B5/02—Applications of checking, fault-correcting, or safety devices in elevators responsive to abnormal operating conditions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
Abstract
The invention belongs to the technical field of elevator analysis management, and particularly relates to a fault analysis and prediction system of an unmanned elevator based on data analysis, which comprises a server, a lifting motion monitoring module, a lifting negative influence analysis module and a fault analysis and prediction module; according to the invention, the lifting motion monitoring module is used for carrying out staged monitoring analysis on the lifting motion process so as to realize preliminary analysis feedback on the operation faults of the lifter, the lifting negative influence analysis module is used for carrying out negative influence analysis on the lifter when receiving the motion qualified signals so as to realize re-analysis feedback on the operation faults of the lifter, and the fault analysis prediction module is used for carrying out deep prediction analysis when receiving the negative influence qualified signals, so that the fault conditions of the lifter are accurately predicted through multi-step analysis and multi-factor analysis, the overhaul and maintenance of the lifter are facilitated in time, the operation of the lifter is stopped, and the safety of corresponding staff and areas is ensured.
Description
Technical Field
The invention relates to the technical field of elevator analysis and management, in particular to an unmanned elevator fault analysis and prediction system based on data analysis.
Background
The elevator represents a lifting mechanical device or a device of a platform or a semi-closed platform for carrying people or cargoes to lift on a vertical up-down channel, mainly comprises a travelling mechanism, a hydraulic mechanism, an electric control mechanism and a supporting mechanism, the traditional elevator realizes the lifting of the elevator mainly through personnel driving control, and compared with the traditional elevator, the unmanned elevator in the prior art is more advanced, and the operation of the elevator is controlled mainly through the control of a corresponding terminal by an operator so as to realize unmanned operation;
the lifting process of the unmanned lifter consists of an initial acceleration stage, a uniform motion stage and a deceleration braking stage, but the unmanned lifter in the prior art is difficult to monitor the lifting process in stages to realize accurate assessment of motion conditions, and is difficult to combine multi-factor auxiliary analysis and motion condition assessment, so that accurate prediction of operation faults of the lifter cannot be realized, and the operation of the lifter is not conveniently stopped in time by corresponding management personnel and maintenance and overhaul of the lifter are performed;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a data analysis-based unmanned elevator fault analysis prediction system, which solves the problems that in the prior art, the unmanned elevator lifting process is difficult to monitor in stages to realize accurate assessment of movement conditions, multi-factor auxiliary analysis and movement condition assessment are difficult to combine, accurate prediction of elevator operation faults cannot be realized, and the operation of an elevator is not conveniently stopped in time by corresponding management personnel and maintenance and overhaul of the elevator are performed.
In order to achieve the above purpose, the present invention provides the following technical solutions: the unmanned elevator fault analysis prediction system based on data analysis comprises a server, an elevating motion monitoring module, an elevating negative influence analysis module and a fault analysis prediction module, wherein the elevating motion monitoring module is used for monitoring an elevating motion process of an elevator, generating a motion qualified signal or a motion unqualified signal through monitoring and analysis, sending the motion unqualified signal to the fault prediction analysis module through the server, and sending the motion qualified signal to the elevating negative influence analysis module through the server;
the lifting negative influence analysis module analyzes the negative influence of the lifter when receiving the motion qualified signal, acquires noise data and shaking data of the lifter through analysis, generates a negative influence qualified signal or a negative influence unqualified signal through analysis based on the noise data and the shaking data, and sends the negative influence unqualified signal or the negative influence qualified signal to the fault analysis prediction module through the server;
the fault analysis prediction module is used for generating a fault early warning signal when a movement disqualification signal or a negative influence disqualification signal is received, sending the fault early warning signal to the elevator management end through the server, carrying out deep prediction analysis when the negative influence disqualification signal is received, obtaining a fault analysis value through the deep prediction analysis, judging whether to generate the fault early warning signal or not based on the fault analysis value, and sending the fault early warning signal to the elevator management end through the server.
The specific operation process of the lifting motion monitoring module comprises the following steps:
acquiring an initial position and an end position of the elevator when in lifting movement, performing height difference calculation on the initial position and the end position to obtain an actual lifting distance, performing distance difference calculation on the lifting distance and a preset lifting distance to obtain a distance deviation value, generating a movement disqualification signal if the distance deviation value exceeds a preset distance deviation threshold, and otherwise, performing movement subsection monitoring analysis.
The specific analysis process of the motion segment monitoring analysis is as follows:
obtaining starting time, finishing acceleration time, starting deceleration time and finishing time of the elevator when the elevator moves up and down, calculating time difference between finishing acceleration time and starting time to obtain starting time, calculating time difference between finishing time and starting deceleration time to obtain braking time, obtaining preset moving speed of the elevator in a uniform moving stage in the process of lifting up and down, calculating a ratio of the starting time to the preset moving speed to obtain starting data, and calculating a ratio of the braking time to the preset moving speed to obtain braking data;
performing difference calculation on the starting data and the median value of the preset starting data range, taking an absolute value to obtain a starting representation value, performing difference calculation on the braking data and the median value of the preset braking data range, and taking the absolute value to obtain a braking representation value; and obtaining a uniform speed representation value in a uniform speed motion stage through analysis, if the starting representation value, the braking representation value and the uniform speed representation value do not exceed corresponding preset thresholds, generating a motion qualification signal, otherwise, carrying out numerical calculation on the starting representation value, the braking representation value and the uniform speed representation value to obtain a motion failure coefficient, if the motion failure coefficient exceeds a preset motion failure coefficient threshold, generating a motion failure signal, otherwise, generating a motion qualification signal.
The analysis and acquisition method of the uniform velocity representation value comprises the following steps:
setting a plurality of uniform speed detection time points in a uniform speed movement stage of the current lifting process of the lifter, marking the uniform speed detection time points as analysis time points u, u= {1,2, …, k }, wherein k represents the number of the uniform speed detection time points and k is a positive integer; acquiring the actual movement speed of the elevator at the analysis time point u, performing difference calculation on the actual movement speed and the preset movement speed, acquiring a speed deviation coefficient by taking an absolute value, performing average calculation on all the speed deviation coefficients to acquire a speed deviation coefficient average value, and marking the speed deviation coefficient exceeding a preset speed deviation coefficient threshold value as a high deviation coefficient; and calculating the ratio of the number of the high deviation coefficients to the value k to obtain a high deviation ratio, and calculating the number of the high deviation coefficients, the high deviation ratio and the average value of the speed deviation coefficients to obtain a uniform speed representation value.
Specific analytical processes for negative impact analysis include:
obtaining noise intensity values of a plurality of detection periods in the current lifting movement process of the lifter, summing the noise intensity values, averaging to obtain a noise average value, marking the noise intensity value with the largest value as a noise height value, and carrying out numerical calculation on the noise average value and the noise height value to obtain noise data; and obtaining shake data by analysis; if the noise data exceeds a preset noise data threshold or the shaking data exceeds a preset shaking data threshold, generating a negative influence disqualification signal, otherwise, generating a negative influence qualification signal.
The analysis and acquisition method of the shaking data comprises the following steps:
acquiring transverse vibration frequency, transverse vibration amplitude, longitudinal vibration frequency, longitudinal vibration amplitude, vertical vibration frequency and vertical vibration amplitude of the lifter in the detection period, weighting and summing the transverse vibration frequency and the transverse vibration amplitude to obtain a transverse vibration value, and acquiring the longitudinal vibration value and the vertical vibration value in the same way; if the transverse vibration value, the longitudinal vibration value and the vertical vibration value all exceed the corresponding preset thresholds, marking the corresponding detection time period as a high vibration time period, if two of the transverse vibration value, the longitudinal vibration value and the vertical vibration value exceed the corresponding preset thresholds, marking the corresponding detection time period as a medium vibration time period, if one of the transverse vibration value, the longitudinal vibration value and the vertical vibration value exceeds the corresponding preset thresholds, marking the corresponding detection time period as a low vibration time period, otherwise marking the corresponding detection time period as a combined vibration time period; and carrying out numerical calculation on the number of high vibration time periods, the number of medium vibration time periods, the number of low vibration time periods and the number of combined vibration time periods in the current lifting movement process of the lifter to obtain shaking data.
The specific analysis process of the depth prediction analysis is as follows:
obtaining the last maintenance time of the elevator, calculating the time difference between the current time and the last maintenance time to obtain the actual maintenance interval duration, obtaining the average maintenance interval duration of the elevator in the historical operation process, and subtracting the average maintenance interval duration from the actual maintenance interval duration to obtain an maintenance duration exceeding value; the method comprises the steps of obtaining the total working time length and the lifting frequency of the lifter in the actual overhaul interval time length, carrying out numerical calculation on the overhaul time length exceeding value, the total working time length and the lifting frequency to obtain a fault analysis value, and judging whether to generate a fault early warning signal or not through analysis based on the fault analysis value.
The specific analysis and judgment process based on the fault analysis value and used for judging whether to generate the fault early warning signal through analysis is as follows:
and comparing the fault analysis value with a preset fault analysis threshold value in a numerical mode, generating a fault early warning signal if the fault analysis value exceeds the preset fault analysis threshold value, and not generating the fault early warning signal if the fault analysis value does not exceed the preset fault analysis threshold value.
The server is in communication connection with the environmental hidden danger analysis module, and the environmental hidden danger analysis module is used for carrying out environmental hidden danger analysis on the elevator to generate an environmental high hidden danger signal or an environmental low hidden danger signal, sending the environmental high hidden danger signal or the environmental low hidden danger signal to the elevator management end through the server, and timely stopping operation and related operation of the elevator after the elevator management end receives the environmental high hidden danger signal.
The specific analysis process of the environmental hidden trouble analysis is as follows:
acquiring environment data of an environment where the elevator is located in a detection period, wherein the environment data comprises environment temperature data, environment humidity data, environment brightness data, environment wind power data, environment ultraviolet intensity data and environment ambiguity data, performing difference calculation on the environment temperature data and a preset environment temperature judgment value, acquiring ring temperature deviation data by taking an absolute value, and acquiring ring humidity deviation data and ring brightness deviation data by the same method; respectively comparing the ring temperature deviation data, the ring humidity deviation data, the ring brightness deviation data, the environment wind power data, the environment ultraviolet intensity data and the environment ambiguity data with corresponding preset thresholds;
marking the environmental data items exceeding the corresponding preset threshold as bad data items; carrying out difference value calculation on the numerical value of the bad data item and a corresponding preset threshold value to obtain a bad number difference value, multiplying the bad number difference value of the bad data item by a corresponding preset hidden danger threat coefficient, marking the product of the bad number difference value and the corresponding preset hidden danger threat coefficient as a bad hidden danger value, carrying out summation calculation on all the bad hidden danger values to obtain a hidden danger total value, and carrying out numerical calculation on the number of the bad data items and the hidden danger total value to obtain an environment hidden danger coefficient; if the environmental hidden danger coefficient exceeds a preset environmental hidden danger coefficient threshold, generating an environmental high hidden danger signal, otherwise, generating an environmental low hidden danger signal.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the elevator is subjected to staged monitoring analysis of the lifting movement process through the lifting movement monitoring module so as to realize preliminary analysis feedback of the operation faults of the elevator, the lifting negative influence analysis module is used for carrying out negative influence analysis on the elevator when receiving the movement qualified signals so as to realize re-analysis feedback of the operation faults of the elevator, the fault analysis prediction module is used for generating fault early warning signals when receiving the movement unqualified signals or the negative influence unqualified signals, carrying out deep prediction analysis when receiving the negative influence qualified signals, accurately predicting the fault conditions of the elevator through multi-step analysis and multi-factor analysis, and being beneficial to timely carrying out overhaul and maintenance of the elevator and stopping the operation of the elevator, ensuring the safety of corresponding staff and areas and ensuring the safe and stable operation of the elevator;
2. according to the invention, the elevator is subjected to environmental hidden danger analysis through the environmental hidden danger analysis module to generate the environmental high hidden danger signal or the environmental low hidden danger signal, the environmental high hidden danger signal or the environmental low hidden danger signal is sent to the elevator management end through the server, and the elevator management end stops the operation and related operation of the elevator in time after receiving the environmental high hidden danger signal, so that personnel injury caused by environmental influence is avoided, the risk of the elevator in the using process is reduced, and the safety of related staff is ensured.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1, the unmanned elevator fault analysis prediction system based on data analysis provided by the invention comprises a server, a lifting motion monitoring module, a lifting negative impact analysis module and a fault analysis prediction module, wherein the server is in communication connection with the lifting motion monitoring module, the lifting negative impact analysis module and the fault analysis prediction module, the lifting motion monitoring module monitors the lifting motion process of an elevator, and the specific operation process of the lifting motion monitoring module is as follows:
acquiring an initial position and an end position of the elevator in the current lifting movement, performing height difference calculation on the initial position and the end position to obtain an actual lifting distance, performing distance difference calculation on the lifting distance and a preset lifting distance to obtain a distance deviation value JP, performing numerical comparison on the distance deviation value JP and a preset distance deviation threshold value which is recorded and stored in advance, and generating a movement disqualification signal if the distance deviation value JP exceeds the preset distance deviation threshold value;
if the distance deviation value JP does not exceed a preset distance deviation threshold value, acquiring starting time, finishing acceleration time, starting deceleration time and finishing time of the current lifting movement of the lifter, calculating a time difference between the finishing acceleration time and the starting time to acquire starting time, calculating a time difference between the finishing time and the starting deceleration time to acquire braking time, acquiring a preset movement speed of a uniform movement stage of the lifter in the current lifting process, calculating a ratio of the starting time to the preset movement speed to acquire starting data, and calculating a ratio of the braking time to the preset movement speed to acquire braking data;
the method comprises the steps of retrieving a preset starting data range and a preset braking data range which are recorded and stored in advance, wherein the preset starting data range and the preset braking data range are corresponding proper data ranges of an initial acceleration stage and a final deceleration stage of the elevator; calculating the difference value between the starting data and the median value of the preset starting data range, and obtaining a starting representation value QB by taking an absolute value, wherein the larger the value of the starting representation value QB is, the larger the deviation degree of the starting acceleration process of the lifter is; calculating the difference value between the braking data and the median value of the preset braking data range, and obtaining a braking appearance value ZB by taking the absolute value, wherein the larger the value of the braking appearance value ZB is, the larger the deviation degree of the elevator in the decelerating braking process is; setting a plurality of uniform speed detection time points in a uniform speed movement stage of the current lifting process of the lifter, marking the uniform speed detection time points as analysis time points u, u= {1,2, …, k }, wherein k represents the number of the uniform speed detection time points and k is a positive integer;
acquiring the actual movement speed of the lifter at the analysis time point u, calculating the difference between the actual movement speed and the preset movement speed, and acquiring a speed deviation coefficient by taking an absolute value, wherein the larger the value of the speed deviation coefficient is, the more serious the speed deviation of the lifter at the corresponding analysis time point u is; carrying out average value calculation on all the speed deviation coefficients to obtain a speed deviation coefficient average value SJ, carrying out numerical comparison on the speed deviation coefficients and a preset speed deviation coefficient threshold value, and marking the speed deviation coefficients exceeding the preset speed deviation coefficient threshold value as high deviation coefficients; the method comprises the steps of obtaining the number of high deviation coefficients in a uniform motion stage, marking the number as PS, and calculating the ratio of the number PS of the high deviation coefficients to a numerical value k to obtain a high deviation occupation ratio GP;
the number PS of the high deviation coefficient, the high deviation occupation ratio GP and the average value SJ of the speed deviation coefficient are subjected to numerical calculation through a constant speed stage analysis formula YS=a1:PS+a2:GP+a3:SJ to obtain a constant speed representation value YS; wherein a1, a2 and a3 are preset weight coefficients with values larger than zero, a3 is more than a1 and less than a2; the numerical value of the uniform speed representation value YS is in a direct proportion relation with the quantity PS of the high deviation coefficient, the high deviation occupation ratio GP and the speed deviation coefficient average value SJ, wherein the uniform speed representation value YS is used for reflecting the motion condition of the elevator in the uniform speed motion stage, and the larger the numerical value of the uniform speed representation value YS is, the worse the motion performance of the elevator in the uniform speed motion stage in the current lifting motion process is;
respectively carrying out numerical comparison on the starting value QB, the braking value ZB and the uniform speed value YS with a preset starting performance threshold, a preset braking performance threshold and a preset uniform speed performance threshold, if the starting value QB, the braking value ZB and the uniform speed value YS do not exceed the corresponding preset thresholds, generating a motion qualification signal, otherwise carrying out numerical calculation on the starting value QB, the braking value ZB and the uniform speed value YS through a formula YX=kp1+kp2 (kp3×YS)/(kp1+kp2) to obtain a motion disqualification coefficient YX, wherein kp1, kp2 and kp3 are preset proportion coefficients with values larger than zero, and kp1 is smaller than kp 2; the motion failure coefficient YX is used for reflecting the overall failure condition of the lifting process of the lifter, the numerical value of the motion failure coefficient YX is in a direct proportion relation with the starting representation value QB, the braking representation value ZB and the uniform velocity representation value YS, and the larger the numerical value of the motion failure coefficient YX is, the worse the overall performance of the lifting process of the lifter is;
and comparing the motion unqualified coefficient YX with a preset motion unqualified coefficient threshold value in a numerical value mode, generating a motion unqualified signal if the motion unqualified coefficient YX exceeds the preset motion unqualified coefficient threshold value, and generating a motion qualified signal if the motion unqualified coefficient YX does not exceed the preset motion unqualified coefficient threshold value. The lifting motion process of the lifter is monitored through the lifting motion monitoring module, a motion qualified signal or a motion unqualified signal is generated through monitoring analysis, preliminary analysis and feedback of operation faults of the lifter are achieved, the motion unqualified signal is sent to the fault prediction analysis module through the server, and the motion qualified signal is sent to the lifting negative influence analysis module through the server.
When the lifting negative influence analysis module receives the movement qualified signal, carrying out negative influence analysis on the lifter, obtaining noise data and shaking data of the lifter through analysis, generating a negative influence qualified signal or a negative influence unqualified signal through analysis based on the noise data and the shaking data, realizing re-analysis and feedback on the operation fault of the lifter, and sending the negative influence unqualified signal or the negative influence unqualified signal to the fault analysis prediction module through a server; the specific analytical procedure for negative impact analysis is as follows:
obtaining noise intensity values of a plurality of detection periods in the current lifting movement process (comprising a starting acceleration stage, a uniform movement stage and a deceleration braking stage) of the elevator, summing the noise intensity values, taking an average value to obtain a noise average value JZ, marking the noise intensity value with the largest value as a noise height value GZ, and carrying out numerical calculation on the noise average value JZ and the noise height value GZ through a formula ZS=b1 x JZ+b2 x GZ to obtain noise data ZS; wherein b1 and b2 are preset weight coefficients, and b1 is more than b2 is more than 1; it should be noted that, the magnitude of the noise data ZS is in a proportional relationship with the average value JZ of the noise and the height value GZ of the noise, the larger the magnitude of the average value JZ of the noise and the larger the magnitude of the height value GZ of the noise, the larger the magnitude of the noise data ZS, which indicates that the worse the noise generated by the elevator is, the greater the possibility of the elevator having a fault is;
acquiring transverse vibration frequency, transverse vibration amplitude, longitudinal vibration frequency, longitudinal vibration amplitude, vertical vibration frequency and vertical vibration amplitude of the lifter corresponding to the detection period, assigning weight values PT1 and PT2 to the vibration frequency and the vibration amplitude, multiplying the transverse vibration frequency and the weight value TP1, multiplying the transverse vibration amplitude and the weight value PT2, marking the sum of the products of the transverse vibration frequency and the transverse vibration amplitude as a transverse vibration value HZ, namely weighting and summing the transverse vibration frequency and the transverse vibration amplitude to obtain a transverse vibration value HZ, and acquiring a longitudinal vibration value ZZ and a vertical vibration value SZ in the same way;
respectively carrying out numerical comparison on a transverse vibration value HZ, a longitudinal vibration value ZZ and a vertical vibration value SZ with a preset transverse vibration threshold value, a preset longitudinal vibration threshold value and a preset vertical vibration threshold value, marking a corresponding detection period as a high vibration period if the transverse vibration value HZ, the longitudinal vibration value ZZ and the vertical vibration value SZ exceed the corresponding preset threshold values, marking a corresponding detection period as a medium vibration period if two of the transverse vibration value HZ, the longitudinal vibration value ZZ and the vertical vibration value SZ exceed the corresponding preset threshold values, marking a corresponding detection period as a low vibration period if one of the transverse vibration value HZ, the longitudinal vibration value ZZ and the vertical vibration value SZ exceeds the corresponding preset threshold values, and marking a corresponding detection period as a combined vibration period if the transverse vibration value HZ, the longitudinal vibration value ZZ and the vertical vibration value SZ exceed the corresponding preset threshold values;
the method comprises the steps of obtaining the number of high vibration periods, the number of medium vibration periods, the number of low vibration periods and the number of combined vibration periods in the current lifting movement process of the lifter through statistical analysis, marking the number of high vibration periods, the number of medium vibration periods, the number of low vibration periods and the number of combined vibration periods as GD, ZD, WD and FD respectively, and calculating the number of the high vibration periods GD, the number of medium vibration periods ZD, the number of low vibration periods WD and the number of combined vibration periods FD of the lifter in the current lifting movement process of the lifter through a formula HJ= (mu 1+ mu2 + mu 3+ WD)/3 + mu 4/(FD + 1.253) to obtain shaking data HJ; wherein mu1, mu2, mu3 and mu4 are preset weight coefficients, the values of mu1, mu2, mu3 and mu4 are all larger than zero, and mu4 is more than mu1 and mu2 is more than mu3;
it should be noted that, the larger the value of the shake data HJ is, the worse the vibration condition of the elevator during the current lifting movement is; and respectively comparing the noise data ZS and the shaking data HJ with a preset noise data threshold value and a preset shaking data threshold value which are recorded and stored in advance, generating a negative influence disqualification signal if the noise data ZS exceeds the preset noise data threshold value or the shaking data HJ exceeds the preset shaking data threshold value, and generating a negative influence qualification signal if the noise data ZS does not exceed the preset noise data threshold value or the shaking data HJ does not exceed the preset shaking data threshold value, so that the noise condition and the vibration condition generated in the current lifting movement process of the elevator are good.
The fault analysis prediction module generates a fault early warning signal when a motion disqualification signal or a negative influence disqualification signal is received, and carries out deep prediction analysis when the negative influence disqualification signal is received, a fault analysis value is obtained through the deep prediction analysis, whether the fault early warning signal is generated or not is judged based on the fault analysis value, the fault condition of the lifter is accurately predicted through multi-step analysis, the fault early warning signal is sent to the lifter management end through the server, the operation condition of the lifter is accurately known in detail by a manager, overhaul and maintenance of the lifter are carried out timely when the manager at the lifter management end receives the fault early warning signal, and the work of the lifter is stopped timely, so that the safety of corresponding workers and areas is ensured, and the safe and stable operation of the lifter is ensured; the specific analysis process of the depth prediction analysis is as follows:
obtaining the last maintenance time of the elevator, calculating the time difference between the current time and the last maintenance time to obtain the actual maintenance interval duration, obtaining the average maintenance interval duration of the elevator in the historical operation process, and subtracting the average maintenance interval duration from the actual maintenance interval duration to obtain a maintenance duration exceeding value SC; if the overhaul duration exceeding value SC is a negative number, the value is given to zero, the total working duration and the lifting frequency of the elevator in the actual overhaul interval duration are obtained and marked as WF and WP respectively, wherein the total working duration WF is a data value representing the total time duration of the elevator in a working state in the actual overhaul interval duration, and the lifting frequency WP is a data value representing the sum of the ascending operation and the descending operation of the elevator in the actual overhaul interval duration;
performing numerical calculation on the maintenance duration exceeding value SC, the total working duration WF and the lifting frequency WP through a formula GF=bp1, SC+bp2, WF+bp3 to obtain a fault analysis value GF; wherein, bp1, bp2 and bp3 are preset weight coefficients, and bp1 is more than bp2 is more than bp3 is more than 0; the value of the fault analysis value GF is in a direct proportion relation with the overhaul duration exceeding value SC, the total working duration WF and the lifting frequency WP, and the larger the value of the fault analysis value GF is, the greater the possibility that the elevator has faults is, and the more the elevator needs to be overhauled and maintained in time; and carrying out numerical comparison on the fault analysis value GF and a preset fault analysis threshold value, generating a fault early warning signal if the fault analysis value GF exceeds the preset fault analysis threshold value, and not generating the fault early warning signal if the fault analysis value GF does not exceed the preset fault analysis threshold value.
Embodiment two:
as shown in fig. 2, the difference between this embodiment and embodiment 1 is that the server is communicatively connected to an environmental hidden trouble analysis module, and the environmental hidden trouble analysis module is configured to analyze the environmental hidden trouble of the elevator, and a specific analysis process of the environmental hidden trouble analysis is as follows:
acquiring environment data of an environment where the elevator is located in a detection period, wherein the environment data comprises environment temperature data, environment humidity data, environment brightness data, environment wind power data, environment ultraviolet intensity data and environment ambiguity data, and the environment temperature data, the environment humidity data, the environment brightness data, the environment wind power data and the environment ultraviolet intensity data are data values representing the environment real-time temperature, the environment real-time humidity, the environment real-time brightness, the environment real-time wind speed and the environment ultraviolet intensity; the environment ambiguity data is a data value representing the visibility of the environment where the elevator is located, and the larger the air visibility of the environment is, the more the value of the environment ambiguity data is positively correlated with the dust concentration and the smoke concentration in the environment, the smaller the value of the environment ambiguity data is;
invoking a preset environment temperature judgment value, a preset environment humidity judgment value and a preset environment brightness judgment value which are recorded and stored in advance, performing difference calculation on the environment temperature data and the preset environment temperature judgment value, taking an absolute value to obtain ring temperature deviation data WS, and obtaining ring humidity deviation data SS and ring brightness deviation data LS in the same way; respectively marking the environmental wind power data, the environmental ultraviolet intensity data and the environmental ambiguity data as FS, XS and MS, calling a preset ring temperature deviation data threshold value, a preset ring humidity deviation data threshold value, a preset environmental wind power data threshold value, a preset environmental ultraviolet intensity data threshold value and a preset environmental ambiguity data threshold value which are recorded and stored in advance, and respectively comparing the ring temperature deviation data WS, the ring humidity deviation data SS, the ring brightness deviation data LS, the environmental wind power data FS, the environmental ultraviolet intensity data XS and the environmental ambiguity data MS with corresponding preset thresholds;
marking the environmental data items exceeding the corresponding preset threshold as bad data items; carrying out difference calculation on the numerical value of the bad data item and the corresponding preset threshold value to obtain a bad number difference value of the corresponding bad data item, for example, if the environmental wind power data exceeds the preset environmental wind power data threshold value, the environmental wind power is the bad data item; the method comprises the steps of calling preset hidden danger threat coefficients of corresponding bad data items, wherein the values of the preset hidden danger threat coefficients are all larger than zero, the preset hidden danger threat coefficients are pre-recorded and stored in a server by corresponding staff, and the larger the values of the preset hidden danger threat coefficients are, the higher the risk caused by the corresponding bad data items is; multiplying the bad number difference value of the bad data item by a corresponding preset hidden danger threat coefficient, marking the product of the bad number difference value and the corresponding preset hidden danger threat coefficient as a bad hidden danger value, carrying out summation calculation on all the bad hidden danger values to obtain a hidden danger total value YZ, and obtaining the number of the bad data items and marking the number of the bad data items as BL;
the number BL of the bad data items and the total hidden danger value YZ are subjected to numerical calculation through a formula YH=eh1, BL+eh2 and YZ to obtain an environment hidden danger coefficient YH; wherein eh1 and eh2 are preset proportional coefficients with values larger than zero, and eh1 is larger than eh2; and, the numerical value of the environmental hidden danger coefficient YH is in a direct proportion relation with the number BL of bad data items and the hidden danger total value YZ, the environmental hidden danger coefficient YH is used for reflecting the degree of potential safety hazards existing in the environment where the elevator is located, the larger the numerical value of the environmental hidden danger coefficient YH is, the greater the potential safety hazards existing in the environment where the elevator is located are, the environmental hidden danger coefficient YH is compared with a preset environmental hidden danger coefficient threshold value which is recorded and stored in advance, if the environmental hidden danger coefficient YH exceeds the preset environmental hidden danger coefficient threshold value, an environmental high hidden danger signal is generated, and if the environmental hidden danger coefficient YH does not exceed the preset environmental hidden danger coefficient threshold value, an environmental low hidden danger signal is generated.
The elevator is subjected to environmental hidden danger analysis through the environmental hidden danger analysis module so as to generate an environmental high hidden danger signal or an environmental low hidden danger signal, the environmental high hidden danger signal or the environmental low hidden danger signal is sent to the elevator management end through the server, the elevator management end timely stops the operation and related operation of the elevator after receiving the environmental high hidden danger signal, personnel injury caused by environmental influence is avoided, the risk of the elevator in the use process is reduced, and the safety of related staff is ensured.
When the elevator motion monitoring system is used, the elevator is subjected to staged monitoring analysis of the elevator motion process through the elevator motion monitoring module so as to be beneficial to finding out motion abnormality in the elevator motion process, preliminary analysis feedback of elevator operation faults is realized, a motion unqualified signal is sent to the fault prediction analysis module, and a motion qualified signal is sent to the elevator negative influence analysis module; the lifting negative influence analysis module analyzes negative influence of the lifter to obtain noise data and shaking data when receiving the movement qualified signal, analyzes and generates a negative influence qualified signal or a negative influence unqualified signal, re-analysis feedback of the operation fault of the lifter is achieved, the fault analysis prediction module generates a fault early warning signal when receiving the movement unqualified signal or the negative influence unqualified signal, and performs deep prediction analysis to judge whether to generate the fault early warning signal or not when receiving the negative influence qualified signal, and accurately predicts the fault condition of the lifter through multi-step analysis and multi-factor analysis, so that management personnel can accurately know the operation condition of the lifter in detail, and the lifter management personnel can timely maintain the lifter and stop the operation of the lifter after receiving the fault early warning signal, so that the safety of corresponding workers and areas is ensured, and the safe and stable operation of the lifter is ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The unmanned elevator fault analysis prediction system based on data analysis is characterized by comprising a server, an elevating motion monitoring module, an elevating negative influence analysis module and a fault analysis prediction module, wherein the elevating motion monitoring module is used for monitoring an elevating motion process of an elevator, generating a motion qualified signal or a motion unqualified signal through monitoring and analysis, sending the motion unqualified signal to the fault prediction analysis module through the server, and sending the motion qualified signal to the elevating negative influence analysis module through the server;
the lifting negative influence analysis module analyzes the negative influence of the lifter when receiving the motion qualified signal, acquires noise data and shaking data of the lifter through analysis, generates a negative influence qualified signal or a negative influence unqualified signal through analysis based on the noise data and the shaking data, and sends the negative influence unqualified signal or the negative influence qualified signal to the fault analysis prediction module through the server;
the fault analysis prediction module is used for generating a fault early warning signal when a movement disqualification signal or a negative influence disqualification signal is received, sending the fault early warning signal to the elevator management end through the server, carrying out deep prediction analysis when the negative influence disqualification signal is received, acquiring a fault analysis value through the deep prediction analysis, judging whether to generate the fault early warning signal or not based on the fault analysis value, and sending the fault early warning signal to the elevator management end through the server;
the specific operation process of the lifting motion monitoring module comprises the following steps:
acquiring an initial position and an end position of the elevator when in lifting movement, performing height difference calculation on the initial position and the end position to obtain an actual lifting distance, performing distance difference calculation on the lifting distance and a preset lifting distance to obtain a distance deviation value, generating a movement disqualification signal if the distance deviation value exceeds a preset distance deviation threshold value, otherwise, performing movement subsection monitoring analysis, wherein the specific analysis process of the movement subsection monitoring analysis is as follows:
obtaining starting time, finishing acceleration time, starting deceleration time and finishing time of the elevator when the elevator moves up and down, calculating time difference between finishing acceleration time and starting time to obtain starting time, calculating time difference between finishing time and starting deceleration time to obtain braking time, obtaining preset moving speed of the elevator in a uniform moving stage in the process of lifting up and down, calculating a ratio of the starting time to the preset moving speed to obtain starting data, and calculating a ratio of the braking time to the preset moving speed to obtain braking data;
performing difference calculation on the starting data and the median value of the preset starting data range, taking an absolute value to obtain a starting representation value, performing difference calculation on the braking data and the median value of the preset braking data range, and taking the absolute value to obtain a braking representation value; and obtaining a uniform speed representation value in a uniform speed motion stage through analysis, if the starting representation value, the braking representation value and the uniform speed representation value do not exceed corresponding preset thresholds, generating a motion qualification signal, otherwise, carrying out numerical calculation on the starting representation value, the braking representation value and the uniform speed representation value to obtain a motion failure coefficient, if the motion failure coefficient exceeds a preset motion failure coefficient threshold, generating a motion failure signal, otherwise, generating a motion qualification signal;
the analysis and acquisition method of the uniform velocity representation value comprises the following steps:
setting a plurality of uniform speed detection time points in a uniform speed movement stage of the current lifting process of the lifter, marking the uniform speed detection time points as analysis time points u, u= {1,2, …, k }, wherein k represents the number of the uniform speed detection time points and k is a positive integer; acquiring the actual movement speed of the elevator at the analysis time point u, performing difference calculation on the actual movement speed and the preset movement speed, acquiring a speed deviation coefficient by taking an absolute value, performing average calculation on all the speed deviation coefficients to acquire a speed deviation coefficient average value, and marking the speed deviation coefficient exceeding a preset speed deviation coefficient threshold value as a high deviation coefficient; and calculating the ratio of the number of the high deviation coefficients to the value k to obtain a high deviation ratio, and calculating the number of the high deviation coefficients, the high deviation ratio and the average value of the speed deviation coefficients to obtain a uniform speed representation value.
2. A data analysis-based unmanned elevator malfunction analysis prediction system according to claim 1, wherein the specific analysis procedure for negative impact analysis comprises:
obtaining noise intensity values of a plurality of detection periods in the current lifting movement process of the lifter, summing the noise intensity values, averaging to obtain a noise average value, marking the noise intensity value with the largest value as a noise height value, and carrying out numerical calculation on the noise average value and the noise height value to obtain noise data; and obtaining shake data by analysis; if the noise data exceeds a preset noise data threshold or the shaking data exceeds a preset shaking data threshold, generating a negative influence disqualification signal, otherwise, generating a negative influence qualification signal.
3. The unmanned elevator fault analysis and prediction system based on data analysis according to claim 2, wherein the analysis and acquisition method of the sway data comprises the following steps:
acquiring transverse vibration frequency, transverse vibration amplitude, longitudinal vibration frequency, longitudinal vibration amplitude, vertical vibration frequency and vertical vibration amplitude of the lifter in the detection period, weighting and summing the transverse vibration frequency and the transverse vibration amplitude to obtain a transverse vibration value, and acquiring the longitudinal vibration value and the vertical vibration value in the same way; if the transverse vibration value, the longitudinal vibration value and the vertical vibration value all exceed the corresponding preset thresholds, marking the corresponding detection time period as a high vibration time period, if two of the transverse vibration value, the longitudinal vibration value and the vertical vibration value exceed the corresponding preset thresholds, marking the corresponding detection time period as a medium vibration time period, if one of the transverse vibration value, the longitudinal vibration value and the vertical vibration value exceeds the corresponding preset thresholds, marking the corresponding detection time period as a low vibration time period, otherwise marking the corresponding detection time period as a combined vibration time period; and carrying out numerical calculation on the number of high vibration time periods, the number of medium vibration time periods, the number of low vibration time periods and the number of combined vibration time periods in the current lifting movement process of the lifter to obtain shaking data.
4. The unmanned aerial vehicle fault analysis prediction system based on data analysis of claim 1, wherein the specific analysis process of the depth prediction analysis is as follows:
obtaining the last maintenance time of the elevator, calculating the time difference between the current time and the last maintenance time to obtain the actual maintenance interval duration, obtaining the average maintenance interval duration of the elevator in the historical operation process, and subtracting the average maintenance interval duration from the actual maintenance interval duration to obtain an maintenance duration exceeding value; the method comprises the steps of obtaining the total working time length and the lifting frequency of the lifter in the actual overhaul interval time length, carrying out numerical calculation on the overhaul time length exceeding value, the total working time length and the lifting frequency to obtain a fault analysis value, and judging whether to generate a fault early warning signal or not through analysis based on the fault analysis value.
5. The unmanned aerial vehicle fault analysis prediction system based on data analysis of claim 4, wherein the specific analysis determination process for determining whether to generate the fault warning signal based on the fault analysis value by analysis is as follows:
and comparing the fault analysis value with a preset fault analysis threshold value in a numerical mode, generating a fault early warning signal if the fault analysis value exceeds the preset fault analysis threshold value, and not generating the fault early warning signal if the fault analysis value does not exceed the preset fault analysis threshold value.
6. The unmanned elevator fault analysis prediction system based on data analysis of claim 1, wherein the server is in communication connection with an environmental hazard analysis module, the environmental hazard analysis module is configured to analyze the elevator for environmental hazards to generate an environmental high hazard signal or an environmental low hazard signal, send the environmental high hazard signal or the environmental low hazard signal to an elevator management terminal via the server, and stop operation and related operations of the elevator in time after the elevator management terminal receives the environmental high hazard signal.
7. The unmanned elevator fault analysis prediction system based on data analysis according to claim 6, wherein the specific analysis process of the environmental hidden trouble analysis is as follows:
acquiring environment data of an environment where the elevator is located in a detection period, wherein the environment data comprises environment temperature data, environment humidity data, environment brightness data, environment wind power data, environment ultraviolet intensity data and environment ambiguity data, performing difference calculation on the environment temperature data and a preset environment temperature judgment value, acquiring ring temperature deviation data by taking an absolute value, and acquiring ring humidity deviation data and ring brightness deviation data by the same method; respectively comparing the ring temperature deviation data, the ring humidity deviation data, the ring brightness deviation data, the environment wind power data, the environment ultraviolet intensity data and the environment ambiguity data with corresponding preset thresholds;
marking the environmental data items exceeding the corresponding preset threshold as bad data items; carrying out difference value calculation on the numerical value of the bad data item and a corresponding preset threshold value to obtain a bad number difference value, multiplying the bad number difference value of the bad data item by a corresponding preset hidden danger threat coefficient, marking the product of the bad number difference value and the corresponding preset hidden danger threat coefficient as a bad hidden danger value, carrying out summation calculation on all the bad hidden danger values to obtain a hidden danger total value, and carrying out numerical calculation on the number of the bad data items and the hidden danger total value to obtain an environment hidden danger coefficient; if the environmental hidden danger coefficient exceeds a preset environmental hidden danger coefficient threshold, generating an environmental high hidden danger signal, otherwise, generating an environmental low hidden danger signal.
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