CN119117850A - Elevator operation fault detection and alarm system based on artificial intelligence - Google Patents
Elevator operation fault detection and alarm system based on artificial intelligence Download PDFInfo
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- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims abstract description 149
- 230000005856 abnormality Effects 0.000 claims abstract description 70
- 230000035772 mutation Effects 0.000 claims abstract description 31
- 239000006185 dispersion Substances 0.000 claims abstract description 3
- 238000004458 analytical method Methods 0.000 claims description 3
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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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- 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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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- Maintenance And Inspection Apparatuses For Elevators (AREA)
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Abstract
The invention relates to the technical field of data processing, in particular to an elevator operation fault detection alarm system based on artificial intelligence, which comprises the steps of obtaining suspected abnormal time according to a vibration speed curve, obtaining a mutation coefficient according to variation speed difference characteristics of vibration speeds at the suspected abnormal time and other times, obtaining discrete coefficients according to dispersion degree difference characteristics of vibration speeds in three axial directions at the suspected abnormal time and other times, obtaining a stability coefficient according to distribution characteristics of vibration speeds in three axial directions at the suspected abnormal time and adjacent times, obtaining an abnormality index at the suspected abnormal time according to the mutation coefficient, the discrete coefficients and the stability coefficient, and determining the final abnormal time. According to the elevator abnormality detection method, an abnormality detection model is built according to three axial vibration speed training at the final abnormality moment, elevator abnormality detection early warning is carried out according to the abnormality detection model, and the accuracy of elevator abnormality detection is improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an elevator operation fault detection alarm system based on artificial intelligence.
Background
The long-time use of the elevator can cause the mechanical structure to generate faults such as abrasion and shaking, even accidents, especially the straight ladder in a hospital, has larger space for conveying patients, high load and shorter service life of the mechanical structure, and therefore, the elevator needs to be detected and maintained, thereby ensuring the stable operation of the elevator. When the elevator is abnormal in operation, shake is easy to generate, so that in order to rapidly analyze whether the elevator is abnormal or not, the abnormal condition of the elevator can be judged according to the vibration degree, but a straight ladder of a hospital often rapidly conveys the sickbed and a doctor of a patient to carry out emergency operation, and doctors and patients shake in the elevator, and the like, so that the elevator is subjected to shake of human factors, the numerical value of vibration data is larger, and abnormal early warning of the elevator is triggered. Therefore, the accuracy of detecting the elevator abnormality directly according to the vibration of the elevator is low.
Disclosure of Invention
In order to solve the technical problem that the accuracy of elevator abnormality detection is low directly according to the vibration of an elevator, the invention aims to provide an elevator operation fault detection alarm system based on artificial intelligence, and the adopted technical scheme is as follows:
The data acquisition module is used for respectively acquiring three axial vibration speed curves of the elevator;
The vibration analysis module is used for acquiring suspected abnormal time according to three axial vibration speed curves, acquiring mutation coefficients according to variation speed difference characteristics of the vibration speeds at the suspected abnormal time and other times, acquiring discrete coefficients according to discrete degree difference characteristics of the vibration speeds at the suspected abnormal time and other times, and acquiring stability coefficients according to distribution characteristics of the vibration speeds at the suspected abnormal time and the adjacent times;
The abnormality judgment module is used for obtaining an abnormality index of the suspected abnormality moment according to the mutation coefficient, the discrete coefficient and the stability coefficient;
The abnormality detection module is used for training and constructing an abnormality detection model according to the three axial vibration speeds at the final abnormality moment, and detecting and early warning the elevator abnormality according to the abnormality detection model.
Further, the step of obtaining suspected abnormal moments according to the three axial vibration speed curves includes:
Taking the maximum value of the vibration speeds in three axial directions at any moment as the vibration effective value at any moment; and when the vibration effective value exceeds a preset speed threshold, the random time is the suspected abnormal time.
Further, the step of obtaining the mutation coefficient according to the variation speed difference characteristics of the vibration speeds at the suspected abnormal time and other times includes:
Calculating the change rate of the vibration effective value at any time and the adjacent previous time to obtain a change characteristic value, calculating the absolute value of the difference value of the change characteristic value at any time and the average value of the change characteristic values at all times to obtain the change difference characteristic value at any time, calculating the difference value of the change difference characteristic value at the suspected abnormal time and the average value of the change difference characteristic values at all times and normalizing to obtain the mutation coefficient at the suspected abnormal time.
Further, the step of obtaining the discrete coefficient according to the discrete degree difference characteristics of the vibration speeds of the three axial directions at the suspected abnormal time and other times includes:
Calculating the sum of the average value of the three axial vibration speeds at any moment and a preset minimum positive number to obtain an axial speed average representation value, calculating the ratio of the standard deviation of the three axial vibration speeds at any moment to the axial speed average representation value to obtain a discrete degree characteristic value, calculating the difference value of the discrete degree characteristic value at the suspected abnormal moment and the average value of the discrete degree characteristic values at all moments, and normalizing to obtain the discrete coefficient at the suspected abnormal moment.
Further, the step of obtaining the stability factor according to the distribution characteristics of the vibration speeds in the three axial directions at the suspected abnormal time and the adjacent time comprises the following steps:
The method comprises the steps of calculating Euclidean distance between a suspected abnormal moment and a vibration speed at a time adjacent to the former moment in a vibration speed curve to obtain a first distance characteristic value, calculating Euclidean distance between the suspected abnormal moment and the vibration speed at a time adjacent to the latter moment to obtain a second distance characteristic value, calculating a product of a preset first weight and the first distance characteristic value to obtain a first distance coefficient, calculating a product of a preset second weight and the second distance characteristic value to obtain a second distance coefficient, calculating a sum value of the first distance coefficient and the second distance coefficient and performing negative correlation mapping to obtain an axial stability factor of the suspected abnormal moment, and calculating a sum value of three axial stability factors of the suspected abnormal moment to obtain the stability coefficient.
Further, the step of obtaining the abnormality index at the suspected abnormality time from the mutation coefficient, the discrete coefficient, and the stability coefficient includes:
Calculating the difference value of the constant 1 and the mutation coefficient to obtain a first abnormal factor, calculating the difference value of the constant 1 and the discrete coefficient to obtain a second abnormal factor, and calculating the average value of the first abnormal factor, the second abnormal factor and the stable coefficient to obtain an abnormal index at the suspected abnormal moment.
Further, the step of obtaining the final abnormal time according to the abnormality index includes:
and when the abnormality index of the suspected abnormality time exceeds a preset abnormality threshold, the suspected abnormality time is the final abnormality time.
The invention has the following beneficial effects:
According to the method, the time of the elevator shake can be initially determined by acquiring the suspected abnormal time, and then the reason of the elevator shake can be analyzed according to the suspected abnormal time, and the obtained mutation coefficient can be analyzed according to the variation speed difference characteristics of the vibration speed caused by the elevator shake and the human factor shake, so that the reason of the elevator shake is determined. The discrete coefficient can be obtained and analyzed according to the discrete degree of the vibration speeds in different axial directions caused by abnormal elevator shake and human factor shake, so that the reason of elevator shake is determined. The obtained stability coefficient can be analyzed according to the continuous stability characteristics of the vibration speed caused by abnormal elevator shake and human factor shake, so as to determine the reason of electric shake. The obtained abnormality index can accurately analyze the reason of elevator shake according to the mutation coefficient, the discrete coefficient and the abnormality index, and determine the shake moment caused by elevator abnormality, so that the vibration speeds of the axial directions of the elevator during abnormal shake learned in the abnormality detection model are more accurate, and the detection accuracy of the elevator abnormality by the abnormality detection model is higher.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an elevator operation fault detection and alarm system based on artificial intelligence according to an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of an elevator operation fault detection alarm system based on artificial intelligence, which is provided by the invention, with reference to the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a specific scheme of an elevator operation fault detection alarm system based on artificial intelligence, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of an elevator operation fault detection and alarm system based on artificial intelligence according to an embodiment of the present invention is shown, the system includes the following modules:
The data acquisition module S1 is used for respectively acquiring three axial vibration speed curves of the elevator.
In the embodiment of the invention, the implementation scene is to judge the working state of the elevator, the accuracy of elevator abnormality detection is improved, the most common situation is that the elevator shakes, the vibration speed can quantify the shaking degree of the elevator, so that three axial vibration speed curves of the elevator are respectively obtained, the three axial directions are three directions of a three-dimensional rectangular coordinate system, a vibration measuring instrument is arranged at the center position of the top of the elevator, the vibration speeds of the same axial direction and adjacent moments are simultaneously collected according to preset collection frequency, the vibration speeds of the same axial direction and adjacent moments are connected, and three different axial vibration speed curves are obtained.
The vibration analysis module S2 is used for obtaining suspected abnormal time according to three axial vibration speed curves, obtaining mutation coefficients according to variation speed difference characteristics of the vibration speeds at the suspected abnormal time and other times, obtaining discrete coefficients according to discrete degree difference characteristics of the vibration speeds at the suspected abnormal time and other times, and obtaining stability coefficients according to distribution characteristics of the vibration speeds at the suspected abnormal time and the three axial vibration speeds at adjacent times.
In the embodiment of the invention, the step of acquiring the suspected abnormal time comprises taking the maximum value of the three axial vibration speeds at any time as the vibration effective value at any time, wherein the vibration effective value represents the maximum condition of the vibration speed at the moment, and the larger the vibration effective value is, the more abnormal vibration is likely to occur. When the vibration effective value exceeds the preset speed threshold, the random moment is a suspected abnormal moment, the suspected abnormal moment represents the shaking of the elevator at the moment, but the abnormal condition of the elevator cannot be accurately judged, and the elevator needs to be further analyzed according to the vibration speeds in different axial directions.
The method comprises the steps of obtaining a mutation coefficient according to the change speed difference characteristics of the vibration speeds at the suspected abnormal moment and other moments, preferably, obtaining the mutation coefficient by calculating the change rate of the vibration effective value at any moment and the adjacent moment, wherein the change characteristic value is obtained, the first moment does not participate in calculation, the change rate is the ratio of the change amount of the vibration speed to the corresponding interval time, the method is not repeated in the prior art, and the larger the change characteristic value is, the more possible elevator shake caused by the human factor is, so that the vibration effective value at any moment is increased instantaneously. And calculating the absolute value of the difference between the variation characteristic value at any time and the average value of the variation characteristic values at all times to obtain the variation difference characteristic value at any time, wherein the variation difference characteristic value reflects the degree of difference between the vibration variation speed at any time and the average vibration variation speed, and the larger the variation difference characteristic value is, the more the vibration variation speed at any time deviates from the average level. Calculating and normalizing the difference value of the variation difference characteristic value of the suspected abnormal moment and the average value of the variation difference characteristic values of all moments to obtain a mutation coefficient of the suspected abnormal moment, wherein when the mutation coefficient is larger, the larger the variation speed of the vibration effective value of the suspected abnormal moment is larger than the average level, the more the vibration effective value is provided with mutation characteristics, the more the suspected abnormal moment is likely to be the elevator shake caused by human factors, and when the mutation coefficient is smaller, the less the variation speed of the vibration effective value of the suspected abnormal moment is indicated to be less obvious, the more likely to represent the shaking of the real abnormality of the elevator. The formula for obtaining the mutation coefficients includes:
In the formula, A mutation coefficient indicating the i-th suspected abnormal time,The normalization function is represented as a function of the normalization,A change characteristic value indicating the i-th suspected abnormal time,Represents the average value of the variation characteristic values at all times,A variation difference characteristic value indicating a suspected abnormal time, N indicating the number of all times,A variation difference characteristic value indicating the nth time,The average value of the variation difference characteristic values at all times is shown.
Furthermore, when the elevator shakes abnormally, the collected vibration speeds in three axial directions generally change, the overall stability of the elevator is reduced, and when the elevator shakes due to human factors, only one or two axial vibration speeds often change. Therefore, the vibration cause can be analyzed according to the difference characteristics of the vibration speeds in the three axial directions at the suspected abnormal time, so that the discrete coefficient is obtained according to the discrete degree difference characteristics of the vibration speeds in the three axial directions at the suspected abnormal time and other times.
Preferably, in the embodiment of the invention, the step of obtaining the discrete coefficient comprises the steps of calculating the sum of the average value of the vibration speeds of the three axial directions at any moment and a preset minimum positive number to obtain an average representation value of the axial directions, calculating the ratio of the standard deviation of the vibration speeds of the three axial directions at any moment to the average representation value of the axial directions to obtain a characteristic value of the discrete degree, wherein when the characteristic value of the discrete degree is larger, the larger the difference of the vibration speeds of the three axial directions at any moment is meant to be more discrete. Calculating and normalizing the difference between the discrete degree characteristic value of the suspected abnormal moment and the average value of the discrete degree characteristic values of all moments to obtain a discrete coefficient of the suspected abnormal moment, wherein when the discrete degree characteristic value of the suspected abnormal moment is higher than the average level of the discrete degree characteristic values of all moments, the larger the discrete coefficient is, which means that the larger the vibration speed difference of three axial directions of the suspected abnormal moment is, the more likely to be the elevator shake caused by human factors, and the smaller the discrete coefficient is, the smaller the difference of the vibration speeds of the three axial directions of the suspected abnormal moment is, and the more likely to be the shake caused by the elevator abnormality.
Further, since the vibration caused by the abnormal elevator exists for a long time and the vibration caused by the human factors is only instantaneous, the abnormal elevator causes the vibration speed values of three axial directions in a period of time to be larger, the human factors only cause the vibration speed to be larger at a certain moment, and the vibration speed at a subsequent moment can fall back, so that if the vibration speeds at adjacent moments are close in the vibration speed curve, the linear distance is shorter, and if the vibration speeds are larger in difference, the linear distance is longer, and further, the stability coefficient can be obtained according to the distribution characteristics of the vibration speeds of the three axial directions at the suspected abnormal moment and the adjacent moments.
Preferably, in the embodiment of the invention, the step of obtaining the stability factor includes calculating the euclidean distance between the suspected abnormal time and the vibration speed at the time immediately before the suspected abnormal time in the vibration speed curve to obtain a first distance characteristic value, calculating the euclidean distance between the suspected abnormal time and the vibration speed at the time immediately after the suspected abnormal time to obtain a second distance characteristic value, wherein the time at the two ends of the vibration speed curve does not participate in calculation, and when the first distance characteristic value and the second distance characteristic value are smaller, the reason that the vibration speed at the time of the suspected abnormal in the axial direction is similar to the vibration speed at the time immediately before the suspected abnormal time is more likely to be the vibration caused by the elevator abnormality. The method comprises the steps of calculating the product of a preset first weight and a first distance characteristic value to obtain a first distance coefficient, calculating the product of a preset second weight and a second distance characteristic value to obtain a second distance coefficient, wherein in the embodiment of the invention, the preset first weight is 0.4, the preset second weight is 0.6, and an implementer can determine according to implementation scenes. And when the first distance coefficient and the second distance coefficient are smaller, the larger the stability factor is, which means that the vibration speed of the suspected abnormal moment is more similar to that of the adjacent moment, and the vibration characteristic is more stable. The sum of the three axial stabilizing factors at the suspected abnormal moment is calculated to obtain a stabilizing coefficient, when the stabilizing coefficient is smaller, the smaller the stabilizing factors at the three axial stabilizing moment at the suspected abnormal moment are, the larger the difference between the vibration speeds at the suspected abnormal moment and the same axial direction at the adjacent moment is, the more probable the suspected abnormal moment is the elevator shake caused by the human factor, otherwise, when the stabilizing coefficient is larger, the more probable the elevator shake caused by the abnormal moment is. The formula for obtaining the stability factor includes:
wherein R represents a stability factor at a suspected abnormal time, M represents the number of axial directions, Indicating that a first weight is preset,Indicating that a second weight is preset,Representing a first distance characteristic value corresponding to the mth axial direction,Representing a second distance characteristic value corresponding to the mth axial direction,A first distance coefficient is represented and a second distance coefficient is represented,A second distance coefficient is represented and is used to represent,Represents an exponential function with a base of a natural constant,Representing the stability factor.
The abnormality judgment module S3 is used for obtaining an abnormality index of suspected abnormal time according to the mutation coefficient, the discrete coefficient and the stability coefficient, and obtaining a final abnormal time according to the abnormality index.
The mutation coefficient, the discrete coefficient and the stability coefficient can reflect the reason of the abnormal vibration speed at the suspected abnormal moment, so that the abnormal index at the suspected abnormal moment can be obtained according to the mutation coefficient, the discrete coefficient and the stability coefficient; preferably, in the embodiment of the invention, the step of obtaining the abnormality index comprises the steps of calculating a difference value between a constant 1 and a mutation coefficient to obtain a first abnormality factor, calculating a difference value between the constant 1 and a discrete coefficient to obtain a second abnormality factor, and calculating average values of the first abnormality factor, the second abnormality factor and a stability coefficient to obtain the abnormality index at the suspected abnormality time. The smaller the abrupt coefficient is, the smaller the discrete coefficient is, the larger the stable coefficient is, the larger the abnormality index is, which means that the moment is more likely to be jitter caused by elevator abnormality, and the smaller the abnormality index is, the more likely to be jitter caused by human factors.
Further, the final abnormal time can be obtained according to the abnormal index, and specifically comprises the step that when the abnormal index of the suspected abnormal time exceeds a preset abnormal threshold value, the suspected abnormal time is the final abnormal time, in the embodiment of the invention, the preset abnormal threshold value is 0.5, and an implementer can determine according to implementation scenes by himself, and the final abnormal time is the shaking time caused by elevator abnormality.
The abnormality detection module S4 is used for training and constructing an abnormality detection model according to the three axial vibration speeds at the final abnormality moment, and detecting and early warning the elevator abnormality according to the abnormality detection model.
The final abnormal moment is determined according to the vibration speed curve, and then an abnormal detection model can be built according to three axial vibration speed training at the final abnormal moment, and it is to be noted that the building of the abnormal detection model through neural network training belongs to the prior art, specific steps are not repeated, the three axial vibration speeds corresponding to the final abnormal moment are put into the neural network model according to vibration speed curves of a plurality of different elevators in a plurality of time periods, and the vibration speed ranges of each axial direction when the elevator is abnormal are trained and identified, so that the built abnormal detection model can be detected in the operation of the elevator. Furthermore, the elevator abnormality can be detected and pre-warned according to the abnormality detection model, and when the three axial vibration speeds of the elevator meet the abnormal vibration speed range in the abnormality detection model in a continuous period of time, the detection and pre-warning are carried out, so that the accuracy of elevator abnormal fault detection is improved, and the length of the continuous period of time is determined by an implementer according to implementation scenes. The method comprises the steps of obtaining suspected abnormal moments in vibration speed curves of different axial directions of an elevator, analyzing three axial vibration speeds of the suspected abnormal moments according to characteristic differences of abnormal vibration and artificial vibration of the elevator, determining real abnormal moments, constructing an abnormal detection model according to the vibration speeds of the axial directions corresponding to the real abnormal moments, and improving accuracy of elevator abnormal fault detection through the model.
In summary, the embodiment of the invention provides an elevator operation fault detection alarm system based on artificial intelligence, which comprises the steps of obtaining suspected abnormal time according to a vibration speed curve, obtaining a mutation coefficient according to variation speed difference characteristics of vibration speeds at the suspected abnormal time and other times, obtaining discrete coefficients according to dispersion degree difference characteristics of vibration speeds in three axial directions at the suspected abnormal time and other times, obtaining a stability coefficient according to distribution characteristics of vibration speeds in three axial directions at the suspected abnormal time and adjacent times, obtaining an abnormality index at the suspected abnormal time according to the mutation coefficient, the discrete coefficients and the stability coefficient, and determining final abnormal time. According to the elevator abnormality detection method, an abnormality detection model is built according to three axial vibration speed training at the final abnormality moment, elevator abnormality detection early warning is carried out according to the abnormality detection model, and the accuracy of elevator abnormality detection is improved.
It should be noted that the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (7)
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119329568A (en) * | 2024-12-19 | 2025-01-21 | 大连海天兴业科技有限公司 | A parking control method, control device and system for urban rail train |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1059645A (en) * | 1996-08-19 | 1998-03-03 | Hitachi Building Syst Co Ltd | Elevator abnormality detection device |
| CN101258087A (en) * | 2005-09-05 | 2008-09-03 | 通力股份公司 | Elevator device |
| CN108069308A (en) * | 2017-12-05 | 2018-05-25 | 暨南大学 | A kind of electric staircase failure diagnosis method based on sequential probability |
| CN109019216A (en) * | 2018-09-27 | 2018-12-18 | 上海昶屹机电科技有限公司 | Elevator faults intelligent diagnosis system and method |
| CN111170103A (en) * | 2019-12-27 | 2020-05-19 | 猫岐智能科技（上海）有限公司 | Equipment fault identification method |
| CN112320520A (en) * | 2020-11-09 | 2021-02-05 | 浙江新再灵科技股份有限公司 | Elevator abnormal vibration detection method based on residual error analysis |
| CN212893314U (en) * | 2020-07-07 | 2021-04-06 | 广东卓梅尼技术股份有限公司 | Elevator car fault determination system based on multi-axis sensor technology |
| CN115959549A (en) * | 2022-12-30 | 2023-04-14 | 北京博华信智科技股份有限公司 | Escalator fault diagnosis method based on digital twinning |
| CN117951455A (en) * | 2024-03-22 | 2024-04-30 | 汶上义桥煤矿有限责任公司 | On-line monitoring method for operation faults of scraper conveyor |
-
2024
- 2024-10-11 CN CN202411417566.4A patent/CN119117850B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1059645A (en) * | 1996-08-19 | 1998-03-03 | Hitachi Building Syst Co Ltd | Elevator abnormality detection device |
| CN101258087A (en) * | 2005-09-05 | 2008-09-03 | 通力股份公司 | Elevator device |
| CN108069308A (en) * | 2017-12-05 | 2018-05-25 | 暨南大学 | A kind of electric staircase failure diagnosis method based on sequential probability |
| CN109019216A (en) * | 2018-09-27 | 2018-12-18 | 上海昶屹机电科技有限公司 | Elevator faults intelligent diagnosis system and method |
| CN111170103A (en) * | 2019-12-27 | 2020-05-19 | 猫岐智能科技（上海）有限公司 | Equipment fault identification method |
| CN212893314U (en) * | 2020-07-07 | 2021-04-06 | 广东卓梅尼技术股份有限公司 | Elevator car fault determination system based on multi-axis sensor technology |
| CN112320520A (en) * | 2020-11-09 | 2021-02-05 | 浙江新再灵科技股份有限公司 | Elevator abnormal vibration detection method based on residual error analysis |
| CN115959549A (en) * | 2022-12-30 | 2023-04-14 | 北京博华信智科技股份有限公司 | Escalator fault diagnosis method based on digital twinning |
| CN117951455A (en) * | 2024-03-22 | 2024-04-30 | 汶上义桥煤矿有限责任公司 | On-line monitoring method for operation faults of scraper conveyor |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119329568A (en) * | 2024-12-19 | 2025-01-21 | 大连海天兴业科技有限公司 | A parking control method, control device and system for urban rail train |
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