Elevator fault detection method, device, system and computer readable storage medium

Abstract

Embodiments of the present application disclose an elevator fault detection method, device, system and computer readable storage medium, the method comprising: acquiring elevator running state data in a door opening and closing state; dividing the elevator running state data according to different running stages in one door opening and closing cycle of the elevator; extracting feature values from the elevator running state data corresponding to each running stage respectively; performing elevator fault diagnosis according to the feature values; and / or, when the elevator running state data comprises vibration data, selecting a running stage with a degradation trend according to historical vibration data of each running stage, creating a remaining life prediction model according to the historical vibration data of the selected running stage, and predicting the remaining life of the elevator running equipment according to the acquired vibration data and the remaining life prediction model. Through the embodiment scheme, timely and automatic elevator fault diagnosis is realized, manpower is saved, and interference of fault coupling is avoided.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the elevator design technology, and in particular to an elevator fault detection method, device, system and computer readable storage medium. BACKGROUND

[0002] In recent years, the installation and the number of elevators in China have increased dramatically. Although elevators have brought convenience to people's lives, accidents caused by elevator failures have also occurred, posing a great threat to people's life and property safety. More than 80% of elevator failures and more than 70% of elevator accidents are caused by problems in the door system. Under normal circumstances, elevators are regularly maintained by dedicated after-sales personnel. When an emergency occurs, maintenance personnel often cannot arrive at the scene in time to rescue and troubleshoot, and the cause of the failure cannot be located in time, delaying repair time. SUMMARY

[0003] Embodiments of the present application provide an elevator fault detection method, device, system and computer readable storage medium, which can timely and automatically diagnose elevator failures, save manpower, and avoid interference from fault coupling.

[0004] Embodiments of the present application provide an elevator fault detection method, which can include:

[0005] Obtaining elevator running state data in a door opening and closing state; the elevator running state data can include, but is not limited to, any one or more of the following: running data of the elevator and vibration data of the elevator;

[0006] Dividing the elevator running state data according to different running stages in one door opening and closing cycle of the elevator;

[0007] Extracting feature values from the elevator running state data corresponding to each running stage; performing elevator fault diagnosis according to the feature values extracted for each running stage; and / or, when the elevator running state data includes the vibration data, selecting a running stage with a degradation trend in historical vibration data according to the historical vibration data of each running stage, creating a remaining life prediction model for the running stage according to the historical vibration data of the selected running stage, and predicting the remaining life of the elevator running equipment according to the obtained vibration data and the remaining life prediction model.

[0008] In one embodiment, the running stage can include:

[0009] the following running stages in the door opening process: a door closing state stage, a first slow door opening stage, an accelerating door opening stage, a constant speed door opening stage, a decelerating door opening stage and a second slow door opening stage; and,

[0010] the following running stages in the door closing process: a door open stage, a first door closing slowly stage, an accelerating door closing stage, a uniform speed door closing stage, a decelerating door closing stage, and a second door closing slowly stage.

[0011] In an embodiment, the feature value extraction of the elevator running state data corresponding to each running stage can include:

[0012] mathematical calculation of the elevator running state data according to a preset calculation algorithm, so as to obtain the feature values;

[0013] The calculation algorithm can include any one of the following: mean value calculation, root mean square value calculation, minimum value calculation, and maximum value calculation.

[0014] In an embodiment, in the case where the elevator running state data includes the running data, the elevator fault diagnosis according to the feature values of each running stage can include:

[0015] inputting the feature values corresponding to the running data into a pre-trained fault judgment model, judging whether the elevator has a fault and the fault type of the elevator when the fault occurs through the fault judgment model, and outputting the judgment result; or,

[0016] judging whether the elevator has a fault and the fault type of the elevator when the fault occurs based on the feature values corresponding to the running data using a preset fault tree algorithm.

[0017] In an embodiment, the elevator running device includes a door opening and closing slide rail and / or a motor of the elevator; the vibration data of the elevator can include the vibration data of the door opening and closing slide rail and / or the motor of the elevator; in the case where the elevator running state data includes the vibration data of the elevator, the elevator fault diagnosis according to the extracted feature values can include:

[0018] judging whether the state of the door opening and closing slide rail and / or the motor is abnormal.

[0019] In an embodiment, the judgment of whether the state of the door opening and closing slide rail and / or the motor is abnormal can include:

[0020] comparing the feature values corresponding to the vibration data with a preset abnormal state index, and when the feature values are consistent with the abnormal state index, it can be determined that the door opening and closing slide rail and / or the motor is in an abnormal state.

[0021] In one embodiment, the operation phase with the historical vibration data having a degradation trend is selected according to the historical vibration data of each operation phase, a residual life prediction model of the operation phase is created according to the historical vibration data of the selected operation phase, and the method comprises the following steps:

[0022] The historical vibration data within a preset time length of each operation phase is obtained, and the root mean square value of the historical vibration data of each operation phase is calculated;

[0023] The root mean square value of each operation phase is analyzed according to a preset degradation trend analysis algorithm, and the operation phase with the degradation trend is selected;

[0024] The historical vibration data of the operation phase with the degradation trend is polynomially fitted to obtain the residual life prediction model corresponding to the operation phase.

[0025] In one embodiment, the residual life of the elevator operation equipment can be predicted according to the obtained vibration data and the residual life prediction model, which can comprise the following steps:

[0026] The real-time vibration data obtained in the operation phase corresponding to each residual life prediction model is compared with the residual life prediction model, and the corresponding residual life is obtained;

[0027] The obtained multiple residual lives are calculated to obtain the final residual life of the elevator operation equipment.

[0028] In one embodiment, the root mean square value of each operation phase is analyzed according to a preset degradation trend analysis algorithm, and the operation phase with the degradation trend is selected, which comprises the following operations for each operation phase:

[0029] The root mean square value of the operation phase is linearly regressed to obtain a straight line, and the slope of the straight line is calculated;

[0030] The operation phase corresponding to the straight line with the slope greater than or equal to a preset slope threshold is taken as the operation phase with the degradation trend, and the operation phase corresponding to the straight line with the slope less than the slope threshold is taken as the operation phase without the degradation trend.

[0031] In one embodiment, after the elevator fault diagnosis is performed according to the characteristic value of each operation phase, the method can further comprise the following steps:

[0032] The obtained fault diagnosis result is sent to a pre-set cloud platform, so that the cloud platform can classify the fault diagnosis result, and the fault diagnosis result is sent to the operation and maintenance personnel by using a corresponding communication mode according to different classification results.

[0033] The embodiment of the present application also provides an elevator fault detection device, which can comprise a processor and a computer readable storage medium, wherein the computer readable storage medium stores instructions, and when the instructions are executed by the processor, the elevator fault detection method can be realized.

[0034] The embodiment of the present application also provides an elevator fault detection system, which can comprise a cloud platform, an edge device and a data acquisition device.

[0035] The data acquisition device can be configured to acquire elevator running state data.

[0036] In one embodiment, the data acquisition device can comprise but is not limited to any one or more of the following: a current sensor, a speed sensor, a position sensor and a vibration sensor.

[0037] The embodiment of the present application also provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the elevator fault detection method can be realized.

[0038] Compared with the related art, the embodiment of the present application can comprise: acquiring elevator running state data in a door opening and closing state; the elevator running state data can comprise but is not limited to: elevator running data, and vibration data of elevator opening and closing sliding rails and / or elevator motor; dividing the elevator running state data according to different running stages in one opening and closing cycle of the elevator; extracting feature values of the elevator running state data corresponding to each running stage respectively; performing elevator fault diagnosis according to the feature values of each running stage; and / or when the elevator running state data comprises the vibration data, selecting a running stage with a degradation trend of historical vibration data according to the historical vibration data of each running stage, creating a remaining life prediction model of the running stage according to the historical vibration data of the selected running stage, and predicting the remaining life of the elevator running equipment according to the acquired vibration data and the remaining life prediction model. Through the embodiment, timely and automatic elevator fault diagnosis is realized, manpower is saved, and interference of fault coupling is avoided.

[0039] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the present application. Other advantages of the present application can be realized and obtained by means of the schemes described in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0040] The accompanying drawings are included to provide a further understanding of the technical solutions of the present application, constitute a part of the specification and are used to explain the technical solutions of the present application together with the embodiments of the present application, and do not constitute a limitation on the technical solutions of the present application.

[0041] Figure 1 A flow chart of the elevator fault detection method of the embodiment of the present application;

[0042] Figure 2 A structural schematic diagram of the elevator fault detection system of the embodiment of the present application;

[0043] Figure 3 A schematic diagram of different operation stages of the embodiment of the present application;

[0044] Figure 4 A flow chart of the method of selecting an operation stage with historical vibration data having a degradation trend according to historical vibration data of each operation stage, and creating a residual life prediction model of the operation stage according to the historical vibration data of the selected operation stage of the embodiment of the present application;

[0045] Figure 5 A schematic diagram of a straight line corresponding to historical vibration data with a degradation trend of the embodiment of the present application;

[0046] Figure 6 A schematic diagram of a straight line corresponding to historical vibration data without a degradation trend of the embodiment of the present application;

[0047] Figure 7 A schematic diagram of a degradation curve of the embodiment of the present application;

[0048] Figure 8 A block diagram of the elevator fault detection device of the embodiment of the present application;

[0049] Figure 9 A block diagram of the elevator fault detection system of the embodiment of the present application. DETAILED DESCRIPTION

[0050] The present application describes a plurality of embodiments, but the description is exemplary rather than limiting, and it is obvious to those skilled in the art that there can be more embodiments and implementation schemes within the scope of the embodiments described in the present application. Although many possible combinations of features are shown in the drawings and discussed in the specific embodiments, many other combinations of the disclosed features are also possible. Unless specifically limited, any feature or element of any embodiment can be used in combination with any other feature or element of any other embodiment, or can replace any other feature or element of any other embodiment.

[0051] The present invention includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The presently disclosed embodiments, features and elements can also be combined with any conventional feature or element to form a unique inventive scheme defined by the claims. Any feature or element of any embodiment can also be combined with features or elements from other inventive schemes to form another unique inventive scheme defined by the claims. Therefore, it is to be understood that any feature shown and / or discussed in the present invention can be realized alone or in any appropriate combination. Thus, unless otherwise restricted by the claims or reasonable inference thereof, embodiments are not to be limited by any illustrated embodiment. Moreover, various modifications and changes can be made within the scope of the appended claims.

[0052] Furthermore, in describing representative embodiments, the specification can have presented the method and / or process as a particular sequence of steps. However, to the extent that the method or process depends on more than one step, the method or process should not be limited to the particular sequence of steps described. Other sequences of steps can be possible, and are within the scope of the present invention. Therefore, the particular order in which the steps are presented is not limiting. Moreover, the specification can present the steps of the method and / or process in a particular order, which is not limiting. The steps can be executed in any order, and are not limited to the particular order presented in the specification.

[0053] The embodiments of the present invention provide an elevator fault detection method, as shown in the accompanying drawings, the method can include steps S101-S104: Figure 1

[0054] S101, acquiring elevator running state data in a door opening and closing state; the elevator running state data can include but is not limited to any one or more of the following: elevator running data and elevator vibration data;

[0055] S102, dividing the elevator running state data according to different running stages in a door opening and closing cycle of the elevator;

[0056] S103, extracting feature values of the elevator running state data corresponding to each running stage respectively, and performing elevator fault diagnosis according to the feature values corresponding to each running stage; and / or, when the elevator running state data includes the vibration data, selecting a running stage with a degradation trend of historical vibration data according to the historical vibration data of each running stage, creating a remaining life prediction model of the running stage according to the historical vibration data of the selected running stage, and predicting the remaining life of the elevator running equipment according to the acquired vibration data and the remaining life prediction model.

[0057] ​The current elevator maintenance and repair are executed by maintenance personnel, and the maintenance personnel need to perform periodic maintenance on the elevator, which wastes a large amount of human resources. In addition, the current elevator failure often has a fault coupling interference phenomenon, that is, for different elevator failures, different running stages in the elevator running process can be present, but the external manifestations of the two failures are the same, or due to different fault types, the same fault external manifestations are exhibited in different running stages, which makes it difficult to detect the specific fault principle if the running stage is not divided in detail, thereby increasing the repair difficulty and workload, and the maintenance personnel are also easy to make a mistake, so that the elevator failure cannot solve the problem from the root.

[0058] In one embodiment, to solve the above problems, the elevator fault automatic diagnosis scheme is proposed, which can directly save human resources and labor costs without maintenance personnel on site. The automatic detection scheme can detect the elevator in real time during daily elevator operation, so as to timely predict and find the elevator failure and avoid causing serious accidents due to untimely discovery of the elevator failure. In addition, the elevator running state data is processed in stages, so that the fault judgment is more refined, the faults in different running stages are not confused with each other, and the fault coupling interference phenomenon is avoided. The embodiment of the present application will be described in detail below.

[0059] In one embodiment, the elevator fault detection system can be pre-set, as shown in Figure 2 The elevator fault detection system can include but is not limited to a data acquisition device, a communication device, an edge device and a cloud platform. The data acquisition device, the communication device and the edge device can be connected in sequence, and the edge device can communicate with the cloud platform. The communication device and the edge device can also be integrated together, and a full-featured edge device can be used to complete all functions of the communication device and the edge device.

[0060] In one embodiment, as shown in Figure 2 First, a plurality of elevator running state data can be acquired according to the data acquisition device arranged in the elevator. The elevator running state data can include but is not limited to various running data generated during the running of the elevator, and / or vibration data generated by the door opening and closing slide rail of the elevator and / or the motor (which can include but is not limited to a permanent magnet synchronous motor) of the elevator during the running of the elevator.

[0061] In one embodiment, the operation data can include, but not limited to, any one or more of the following: field current id, torque current iq, operation speed v design, real speed v, whether the door is open to position, whether the door is closed to position, and elevator positioning data, etc.

[0062] In one embodiment, for the above operation data and vibration data, the sensors arranged on the elevator can include, but not limited to, any one or more of the following: acceleration sensor, speed sensor, current sensor (such as Hall sensor), position sensor

such as limit switch, ranging device (such as radar device, infrared detection device, ultrasonic detection device, etc.), positioning device)

[0063] In one embodiment, one or more vibration sensors can be installed on the switch door guide rail and the permanent magnet synchronous motor of the elevator, so that the vibration data under the switch door state can be obtained in real time during the operation of the elevator.

[0064] In one embodiment, the vibration sensor can be directly connected with the pre-set communication device, that is, the vibration data acquisition device can be realized through the vibration signal high-speed acquisition module integrated in the communication device, and the vibration data under the switch door state can be obtained in real time through the vibration signal high-speed acquisition module.

[0065] In one embodiment, as shown in Figure 2 After the data acquisition device collects the elevator operation state data, the elevator operation state data can be sent to the above-mentioned communication device, which can be integrated with a signal filtering module, which can filter the input elevator operation state data and remove the interference of signal noise.

[0066] In one embodiment, after the communication device completes the filtering operation of the elevator operation state data, the elevator operation state data can be sent to the pre-set edge device, which can be integrated with a data processing algorithm, which can realize the integration and alignment of the input elevator operation state data, and can divide the elevator operation state data according to the different running stages in one switch door cycle of the elevator according to the switch door movement characteristics of the elevator, so as to obtain the corresponding elevator operation state data of each running stage.

[0067] In one embodiment, before performing the above data division operation, one switch door cycle of the elevator can be divided into different running stages in advance, as shown in Figure 3 It can be divided into s0~s11, a total of 13 segments.

[0068] In one embodiment, the running stage can include: opening door stage and closing door stage;

[0069] According to the sequence division in the opening process, the opening stage can include but is not limited to: the door is in the closed state stage s0, the first slow opening door stage s1, the accelerated opening door stage s2, the uniform speed opening door stage s3, the decelerated opening door stage s4 and the second slow opening door stage s5.

[0070] According to the sequence division in the closing process, the closing stage can include but is not limited to: the door is in the open state stage s6, the first slow closing door stage s7, the accelerated closing door stage s8, the uniform speed closing door stage s9, the decelerated closing door stage s10 and the second slow closing door stage s11.

[0071] In an embodiment, after the second slow closing door stage s11 ends, the door is completely closed, so as to enter the closed state stage s0 again.

[0072] In an embodiment, after the edge device obtains the elevator running state data, the pre-divided multiple running stages can be called to divide the elevator running state data according to the running stages, so as to obtain the elevator running state data corresponding to different running stages.

[0073] In an embodiment, the feature value extraction of the elevator running state data corresponding to each running stage can include:

[0074] According to the pre-set calculation algorithm, the mathematical calculation is performed on the elevator running state data, so as to obtain the feature value;

[0075] The calculation algorithm can include but is not limited to any one of the following: mean value calculation, root mean square value calculation, minimum value calculation and maximum value calculation.

[0076] In an embodiment, a plurality of calculation algorithms can be pre-stored in the edge device. For the elevator running state data of each running stage, one or more calculation algorithms can be used for mathematical operation to obtain the effective value of the elevator running state data of each running stage as the feature value of the elevator running of the running stage.

[0077] In an embodiment, different calculation algorithms can be selected for calculation according to different requirements, and the detailed algorithm is not limited herein.

[0078] In an embodiment, in the case that the elevator running state data includes the running data, the elevator fault diagnosis according to the feature value of each running stage can include:

[0079] input the characteristic values corresponding to the operation data into a pre-trained fault judgment model, judge whether the elevator has a fault and the fault type of the elevator when the fault occurs through the fault judgment model, and output the judgment result; or

[0080] Based on the characteristic values corresponding to the operation data, a pre-set fault tree algorithm is used to judge whether the elevator has a fault and the fault type of the elevator when the fault occurs.

[0081] In one embodiment, after obtaining the characteristic values of the operation data corresponding to each operation stage, a pre-set fault judgment model and / or a fault tree algorithm can be used for fault judgment.

[0082] In one embodiment, the fault judgment model can be obtained by pre-training a pre-created neural network structure multiple times using pre-set training data. The training data can be obtained by extracting characteristic values from a large amount of pre-collected operation data and performing data labeling. The input data of the fault judgment model can be various characteristic values of the operation data, and the output data can be the fault judgment result, for example, whether the current elevator has a fault and the fault type after the elevator has a fault.

[0083] In one embodiment, before performing the embodiment scheme of the present application, the characteristic values corresponding to the operation data of different operation stages can be analyzed in advance to obtain the fault characteristic values of different fault types when the faults occur, so that the fault characteristic values in the training data can be labeled before data training.

[0084] In one embodiment, the fault types can include but are not limited to: increased opening and closing door resistance, blocked door knife, slipping synchronous belt, broken synchronous belt, collapsed and broken following chain, etc.

[0085] In one embodiment, the fault judgment model can be multiple, and a corresponding fault judgment model can be set for each operation stage. During the operation of the elevator, the characteristic values of the operation data of each operation stage obtained by real-time detection can be input into the corresponding fault judgment model to realize accurate judgment of the elevator fault.

[0086] In one embodiment, the vibration data of the elevator can include the vibration data of the opening and closing door slide rail of the elevator and / or the motor in the elevator. In the case that the operation state data of the elevator includes the vibration data, the elevator fault diagnosis according to the characteristic values of each operation stage can include:

[0087] Judge whether the state of the opening and closing door slide rail and / or the motor is abnormal.

[0088] Since the current elevator and elevator maintenance personnel cannot predict the life and failure state of the core parts of the elevator in advance and perform maintenance as needed, when the core parts fail, it usually causes major failures of the elevator, so monitoring the core parts of the elevator is particularly important.

[0089] In one embodiment, the elevator core parts can include, but are not limited to, the door opening and closing slide rail of the elevator and the motor of the elevator (for example, can include, but not limited to, permanent magnet synchronous motor).

[0090] In one embodiment, by pre-monitoring the state and predicting the life of the door opening and closing slide rail and / or the motor according to the characteristic value corresponding to the vibration data, the failure and aging degree of the door opening and closing slide rail and the motor of the elevator can be predicted in advance, so that the maintenance personnel can maintain and replace the door opening and closing slide rail and the motor in advance, thereby greatly reducing the failure rate and accident rate.

[0091] In one embodiment, the judgment of whether the state of the door opening and closing slide rail and / or the motor is abnormal can include:

[0092] Comparing the characteristic value corresponding to the vibration data with the pre-set abnormal state index, when the characteristic value corresponds to the abnormal state index, it can be determined that the door opening and closing slide rail and / or the motor is in an abnormal state.

[0093] In one embodiment, the method can further include predicting the life of the door opening and closing slide rail and / or the motor according to the characteristic value corresponding to the vibration data.

[0094] In one embodiment, the prediction of the life of the door opening and closing slide rail and / or the motor according to the characteristic value corresponding to the vibration data can include:

[0095] Comparing the characteristic value corresponding to the vibration data with a pre-set mapping table, determining the range of the corresponding characteristic value of the vibration data in the mapping table, and taking the remaining life corresponding to the range of the corresponding characteristic value as the remaining life corresponding to the door opening and closing slide rail and / or the motor; the mapping table can include the mapping relationship between different characteristic value ranges and different remaining lives.

[0096] In one embodiment, the state monitoring and life prediction of the door opening and closing slide rail and / or the motor can be realized by using corresponding tables, such as the abnormal state monitoring table and the above-mentioned remaining life mapping table. The abnormal state monitoring table can include the corresponding relationship between different abnormal state indexes and different abnormal states.

[0097] In one embodiment, each operating stage can correspond to an abnormal state monitoring table respectively, and can correspond to a mapping table for confirming the remaining life respectively.

[0098] In one embodiment, during the operation of the elevator, the characteristic value corresponding to the vibration data of a certain operating stage obtained in real time can be compared with the abnormal state monitoring table corresponding to the operating stage, to determine whether the characteristic value corresponding to the vibration data is consistent with the abnormal state index in the abnormal state monitoring table corresponding to the operating stage. If the characteristic value corresponding to the vibration data is not consistent with the abnormal state index in the abnormal state monitoring table corresponding to the operating stage, it indicates that the switch door sliding rail and the motor in the operating stage do not have an abnormality. If the characteristic value corresponding to the vibration data is consistent with the abnormal state index in the abnormal state monitoring table corresponding to the operating stage, it indicates that the switch door sliding rail and / or the motor in the operating stage has an abnormality, and the abnormal state of the switch door sliding rail and / or the motor can be determined according to the abnormal state corresponding to the consistent abnormal state index.

[0099] In one embodiment, before the above-mentioned embodiment of the application is executed, the characteristic values corresponding to a large amount of vibration data of different operating stages can be analyzed in advance to obtain the abnormal characteristic values corresponding to different abnormal states, so that the abnormal characteristic values are taken as the abnormal state indexes in the abnormal state monitoring table.

[0100] In one embodiment, during the operation of the elevator, the characteristic value corresponding to the vibration data of a certain operating stage obtained in real time can be compared with the mapping table corresponding to the operating stage, to determine which range of the characteristic values in the mapping table corresponding to the operating stage the characteristic value corresponding to the vibration data conforms to. If it is determined that the characteristic value corresponding to the vibration data conforms to the range of the first characteristic value in the mapping table corresponding to the operating stage, the remaining life corresponding to the range of the first characteristic value can be further determined according to the mapping table, and the remaining life corresponding to the range of the first characteristic value is taken as the remaining life of the switch door sliding rail or the motor.

[0101] In one embodiment, before the above-mentioned embodiment of the application is executed, a large amount of vibration data of different operating stages can be collected and experimented in advance, so that the remaining life corresponding to the range of different characteristic values is obtained, and the mapping table is determined according to the corresponding remaining life corresponding to the range of different characteristic values.

[0102] In one embodiment, as Figure 4As shown, the selecting the operation stage with the historical vibration data having the degradation trend according to the historical vibration data of each operation stage, creating the residual life prediction model of the operation stage according to the historical vibration data of the selected operation stage, can include steps S201-S203:

[0103] S201, obtaining the historical vibration data within a preset time length of each operation stage, and calculating the root mean square value of the historical vibration data of each operation stage.

[0104] In an embodiment, the preset time length can refer to a time length before the current time, for example, the vibration data within one year (or one week, one month, etc.) counted from the current time.

[0105] In an embodiment, the vibration data can include any one or more of the following: vibration speed, vibration acceleration and vibration displacement; when creating the residual life prediction model, one of the above vibration data can be used for calculation to obtain the residual life prediction model, or one residual life prediction model can be calculated for each of the obtained multiple vibration data, for example, one residual life prediction model is calculated according to the vibration speed, one residual life prediction model is calculated according to the vibration displacement, but the vibration speed and the vibration displacement cannot be mixed for calculation.

[0106] In an embodiment, the root mean square value RMS can be calculated by using the following calculation formula:

[0107]

[0108] Wherein, y refers to each historical vibration data in a group of historical vibration data; i refers to the i-th historical vibration data, i is between 0 and K, K+1 is the total number of historical vibration data, i is 0 or a positive integer, and K is a positive integer.

[0109] S202, performing degradation trend analysis on the root mean square value of each operation stage according to a preset degradation trend analysis algorithm, and selecting the operation stage with the degradation trend.

[0110] In an embodiment, in order to improve the residual life prediction accuracy, the degradation trend analysis can be performed on the root mean square value RMS corresponding to each operation stage respectively, so as to determine whether each operation stage has a degradation trend. Since the operation stage without the degradation trend cannot be used for residual life prediction, the historical vibration data of the operation stage without the degradation trend can be excluded, and only the historical vibration data of the operation stage with the degradation trend is retained.

[0111] In one embodiment, the degradation trend refers to a certain change trend of certain data over time, for example, the data gradually decreases or the data gradually increases. For example, when the historical vibration data are all vibration speed data, the degradation trend refers to that a plurality of root mean square values calculated according to a plurality of groups of vibration data in a period of time gradually increase.

[0112] In one embodiment, the degradation trend analysis on the root mean square value of each operation stage according to the preset degradation trend analysis algorithm to select the operation stage with the degradation trend can include: performing the following operations for each operation stage respectively:

[0113] performing linear regression processing on the root mean square value of the operation stage to obtain a straight line, and calculating a slope of the straight line;

[0114] the operation stage corresponding to the straight line with the slope greater than or equal to the preset slope threshold value is taken as the operation stage with the degradation trend, and the operation stage corresponding to the straight line with the slope less than the slope threshold value is taken as the operation stage without the degradation trend.

[0115] In one embodiment, the slope threshold value can be defined according to different application scenarios and requirements, and the detailed numerical value of the slope threshold value is not limited here. For example, the slope threshold value can be selected as 10°, when the slope is greater than or equal to 10°, it can be judged that the historical vibration data of the operation stage have the degradation trend, and when the slope is less than 10°, it can be judged that the historical vibration data of the operation stage are relatively stable and have no degradation trend; as shown in FIG. 1, it is a schematic diagram of a straight line corresponding to historical vibration data with a degradation trend; as shown in FIG. 2, it is a schematic diagram of a straight line corresponding to historical vibration data without a degradation trend. In FIGS. 1 and 2, the horizontal coordinate is time (Time) data (as the horizontal coordinate extends, the time value becomes larger and larger until the maximum service life of the elevator operation equipment), and the vertical coordinate is the root mean square value RMS. Figure 5 Figure 6 Figure 5 Figure 6

[0116] S203, performing polynomial fitting on the historical vibration data of the operation stage with the degradation trend to obtain a residual life prediction model corresponding to the operation stage.

[0117] In one embodiment, the historical vibration data of each operation stage with the degradation trend is respectively subjected to polynomial fitting to obtain a degradation curve corresponding to the operation stage, and the degradation curve corresponding to each operation stage is taken as the residual life prediction model corresponding to the operation stage. As shown in FIG. 3, it is a schematic diagram of a fitting degradation curve. Figure 7

[0118] ​​​​​In one embodiment, the method of predicting the remaining life of the elevator operation equipment according to the acquired vibration data and the remaining life prediction model can comprise:

[0119] comparing the real-time acquired vibration data in the operation phase corresponding to each remaining life prediction model with the remaining life prediction model, and obtaining the corresponding remaining life;

[0120] calculating the obtained multiple remaining lives to obtain the final remaining life of the elevator operation equipment.

[0121] In one embodiment, for example, if the remaining life prediction model (degradation curve) of each operation phase is created for vibration speed, for the remaining life prediction model (degradation curve) of any operation phase, the RMS value of the latest vibration speed of the operation phase acquired in real time can be compared with the ordinate of the degradation curve, the value corresponding to the ordinate of the RMS value is determined, and a straight line parallel to the horizontal axis passing through the value is obtained, the intersection of the straight line and the degradation curve is obtained, and the abscissa of the intersection is obtained, which can be determined as the current life of the elevator operation equipment (such as a permanent magnet synchronous motor). By subtracting the current life from the maximum life of the elevator operation equipment, the remaining life of the elevator operation equipment in the operation phase is obtained.

[0122] In one embodiment, when the operation phase with degradation trend includes multiple operation phases, the remaining life corresponding to each operation phase can be calculated according to the above calculation method.

[0123] In one embodiment, when the operation phase with degradation trend includes multiple operation phases, the average value of the remaining lives of the multiple operation phases with degradation trend can be calculated; the average value can be directly calculated, or the average value can be calculated after removing the maximum value and the minimum value from the multiple remaining lives to obtain the final remaining life. It is known that at present, maintenance personnel cannot know in time when the elevator fails or the parts are damaged, which can easily cause more serious accidents due to the failure of the elevator.

[0124] In one embodiment, after the elevator fault diagnosis is performed according to the extracted characteristic values, the method can further comprise:

[0125] sending the obtained fault diagnosis result to a pre-set cloud platform, so that the cloud platform can classify the fault diagnosis result, and send the fault diagnosis result to the operation and maintenance personnel according to different classification results by using a corresponding communication mode.

[0126] In one embodiment, the edge device can send the fault diagnosis related data and the fault diagnosis result to the pre-set cloud platform in real time, so as to save and further process the data through the cloud platform.

[0127] In an embodiment, the data analysis result and the fault diagnosis result can be transmitted to a cloud platform through a preset communication technology, which can include but is not limited to any existing communication technology that can be implemented, for example, can include but is not limited to 4G (fourth generation communication technology), 5G (fifth generation communication technology), and near field communication technology, etc.

[0128] In an embodiment, the cloud platform can manage and process the fault diagnosis data and the fault diagnosis result of multiple elevators in multiple areas, and can determine different elevators according to the unique identity ID of different elevators and / or the positioning information of different elevators.

[0129] In an embodiment, in the cloud platform, the fault degree of a certain elevator fault diagnosis result received can be classified, for example, mild fault, moderate fault, severe fault, etc., or different fault degrees can be graded, for example, first-level fault, second-level fault, third-level fault, etc.

[0130] In an embodiment, different notification methods can be used to notify the current fault for different types of fault degrees.

[0131] In an embodiment, the notification method can include but is not limited to: telephone, short message, email, WeChat, public number, applet, application APP, webpage information push, display screen display, loudspeaker alarm, etc.

[0132] In an embodiment, for the case of no fault or light fault, only webpage information push, display screen display, email notification, etc. can be used to notify the relevant personnel, such as property, owner and maintenance personnel, etc. In an embodiment, for the case of heavy fault, telephone, loudspeaker alarm, etc. can be used to notify the relevant personnel, so as to quickly make the relevant personnel understand the fault condition.

[0133] In an embodiment, the relevant personnel can also view the real-time data and historical data of the elevator running state data of the elevator through the mobile phone app and webpage query, etc.

[0134] In an embodiment, the embodiment scheme of the present application at least includes the following advantages: helping property personnel and maintenance personnel to obtain and locate the fault information and fault source of the elevator in advance and timely, predicting the life and fault state of the core parts of the elevator in advance and performing condition-based maintenance, saving manpower, and avoiding the interference of fault coupling.

[0135] The embodiment scheme of the present application also proposes an elevator fault detection device 1, as shown in Figure 8As shown, the elevator fault detection method can include a processor 11 and a computer readable storage medium 12, the computer readable storage medium 12 stores instructions, and when the instructions are executed by the processor 11, the elevator fault detection method can be implemented.

[0136] In one embodiment, any of the foregoing elevator fault detection methods can be applied to the device embodiment, which will not be repeated here.

[0137] The embodiment scheme of the application further provides an elevator fault detection system 2, as shown in the figure, which can include a cloud platform 21, an edge device 22 and a data acquisition device 23; the elevator fault detection device 1 can be arranged on the edge device 22. Figure 9

[0138] The data acquisition device 23 can be arranged to acquire elevator running state data.

[0139] In one embodiment, the data acquisition device 23 can include but is not limited to any one or more of the following: a current sensor, a speed sensor, a position sensor and a vibration sensor.

[0140] In one embodiment, any of the foregoing elevator fault detection methods can be applied to the system embodiment, which will not be repeated here.

[0141] The embodiment of the application further provides a computer readable storage medium, which stores a computer program, and the computer program can implement the elevator fault detection method when executed by a processor.

[0142] In one embodiment, any of the foregoing elevator fault detection methods can be applied to the computer readable storage medium embodiment, which will not be repeated here.

[0143] ​Those of ordinary skill in the art will realize and understand that all or some of the steps in the methods disclosed above and the functional modules / units in the systems and devices can be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component can have multiple functions, or one function or step can be performed by several physical components in cooperation. Some or all of the components can be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on computer-readable media, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As is well known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, it is common and well understood by those of ordinary skill in the art that communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and can include any information delivery media.

Claims

1. An elevator failure detection method characterized by, The method comprises: acquiring elevator running state data in a door opening and closing state; the elevator running state data comprises any one or more of the following: elevator running data and elevator vibration data; dividing the elevator running state data according to different running stages in one door opening and closing cycle; wherein the running stages comprise: a plurality of door opening stages divided according to the sequence of the door opening process, and a plurality of door closing stages divided according to the sequence of the door closing process; extracting feature values from the elevator running state data of each running stage respectively, and performing elevator fault diagnosis according to the feature values of each running stage; and / or, when the elevator running state data comprises the vibration data, selecting a running stage with a degradation trend in historical vibration data according to the historical vibration data of each running stage, creating a residual life prediction model for the running stage according to the historical vibration data of the selected running stage, and predicting the residual life of the elevator running equipment according to the acquired vibration data and the residual life prediction model.

2. The elevator failure detection method according to claim 1, characterized by, The plurality of door opening stages comprise the following running stages: a door in a closed state stage, a first slow door opening stage, an accelerating door opening stage, a constant speed door opening stage, a decelerating door opening stage, and a second slow door opening stage; and The plurality of door closing stages comprise the following running stages: a door in an open state stage, a first slow door closing stage, an accelerating door closing stage, a constant speed door closing stage, a decelerating door closing stage, and a second slow door closing stage.

3. The elevator failure detection method according to claim 1, characterized by, The feature value extraction from the elevator running state data of each running stage comprises: calculating the elevator running state data according to a preset calculation algorithm to obtain the feature values; The calculation algorithm comprises any one of the following: root mean square value calculation, mean value calculation, maximum value calculation, and minimum value calculation.

4. The elevator fault detection method according to any one of claims 1 to 3, characterized by, When the elevator running state data comprises elevator running data, the elevator fault diagnosis according to the feature values of each running stage comprises: inputting the feature values corresponding to the running data into a pre-trained fault judgment model, judging whether the elevator is faulty and the fault type when the elevator is faulty through the fault judgment model, and outputting the judgment result; or based on the feature values corresponding to the running data, using a preset fault tree algorithm to judge whether the elevator is faulty and the fault type when the elevator is faulty.

5. The elevator fault detection method according to any one of claims 1 to 3, characterized by, The elevator running equipment comprises: elevator door opening and closing sliding rails and / or motors; the elevator vibration data comprises vibration data of the elevator door opening and closing sliding rails and / or motors; When the elevator running state data comprises the elevator vibration data, the elevator fault diagnosis according to the feature values of each running stage comprises: judging whether the state of the door opening and closing sliding rails and / or the motor is abnormal.

6. The elevator fault detection method according to claim 5, characterized by The judgment of whether the state of the door opening and closing sliding rails and / or the motor is abnormal comprises: comparing the feature values corresponding to the vibration data with a preset abnormal state index, and determining that the door opening and closing sliding rails and / or the motor are in an abnormal state when the feature values meet the abnormal state index.

7. The elevator fault detection method according to any one of claims 1 to 3, characterized by, The method comprises the following steps: obtaining historical vibration data of each running stage within a preset time length, and calculating the root mean square value of the historical vibration data of each running stage; performing degradation trend analysis on the root mean square value of each running stage according to a preset degradation trend analysis algorithm, and selecting a running stage with a degradation trend; performing polynomial fitting on the historical vibration data of the running stage with a degradation trend to obtain a corresponding residual life prediction model of the running stage.

8. The elevator fault detection method according to claim 7, characterized by The method comprises the following steps for each running stage: performing linear regression processing on the root mean square value of the running stage to obtain a straight line, and calculating the slope of the straight line; the running stage corresponding to the straight line with a slope greater than or equal to a preset slope threshold is regarded as a running stage with a degradation trend, and the running stage corresponding to the straight line with a slope less than the slope threshold is regarded as a running stage without a degradation trend.

9. The elevator fault detection method according to any one of claims 1 to 3, characterized by, The method comprises the following steps: comparing the vibration data obtained in real time in the running stage corresponding to each residual life prediction model with the residual life prediction model to obtain the corresponding residual life; calculating the obtained multiple residual lives to obtain the final residual life of the elevator running equipment.

10. The elevator fault detection method according to any one of claims 1 to 3, characterized by, After the elevator fault diagnosis according to the characteristic values of each running stage, the method further comprises the following steps: sending the fault diagnosis result to a preset cloud platform, so that the cloud platform classifies the fault diagnosis result, and sends the fault diagnosis result to the operation and maintenance personnel in a corresponding communication mode according to different classification results.

11. An elevator fault detection apparatus characterized by comprising: The device comprises a processor and a computer readable storage medium, and the computer readable storage medium stores instructions which, when executed by the processor, implement the elevator fault detection method of any one of claims 1-10.

12. An elevator fault detection system, characterized by The device comprises: a cloud platform, an edge device and a data acquisition device; the elevator fault detection device of claim 11 is arranged on the edge device; the data acquisition device is arranged to acquire elevator running state data.

13. The elevator fault detection system of claim 12, wherein, The data acquisition device comprises any one or more of the following: a current sensor, a speed sensor, a position sensor and a vibration sensor.

14. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the elevator fault detection method of any one of claims 1-10. The computer program is executed by the processor to implement the elevator fault detection method of any one of claims 1-10.

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