CN117916185A - Elevator safety fault diagnosis method based on multi-source information fusion - Google Patents
Elevator safety fault diagnosis method based on multi-source information fusion Download PDFInfo
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B5/00—Applications of checking, fault-correcting, or safety devices in elevators
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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
- B66B7/00—Other common features of elevators
- B66B7/12—Checking, lubricating, or cleaning means for ropes, cables or guides
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- Maintenance And Inspection Apparatuses For Elevators (AREA)
- Lift-Guide Devices, And Elevator Ropes And Cables (AREA)
Abstract
The invention provides an elevator safety fault diagnosis method based on multi-source information fusion, and relates to the technical field of elevator fault diagnosis. Firstly, acquiring vibration signals of a lift car and a reduction box and visual signals of a lift car haulage rope in the running process of the lift, and extracting vibration signal characteristics of the lift car and the reduction box and transverse displacement signal characteristics of the lift car haulage rope after data preprocessing of the acquired signals; then, detecting abnormal vibration signals according to the vibration signal characteristics of the lift car and the reduction gearbox, obtaining an abnormal vibration signal detection result, detecting abnormal transverse displacement based on the transverse displacement signal characteristics of the lift car traction rope, and obtaining an abnormal transverse detection result; and finally, fusing the abnormal vibration signal detection result and the abnormal transverse displacement signal detection result, and judging whether emergency stop fault alarm is generated for the elevator based on the fusion result. The method utilizes the fuzzy information fusion theory to realize the real-time centralized monitoring of the running states of the elevator multiple components.
Description
Technical Field
The invention relates to the technical field of elevator fault diagnosis, in particular to an elevator safety fault diagnosis method based on multi-source information fusion.
Background
Along with the development of the urban process, in order to meet the requirement of vertical transportation in daily life of people, the elevator is increasingly widely applied, but the increase of the use amount of the elevator also causes a plurality of safety accidents and potential safety hazards. The elevator accident not only causes economic loss, but also seriously threatens the personal safety of passengers, so that the safety condition of the elevator needs to be detected more comprehensively and effectively.
At present, fault diagnosis of an elevator usually detects a single part of the elevator, normal operation of a plurality of parts cannot be guaranteed at the same time, single safety guarantee mainly depends on a mode of periodic maintenance and annual inspection, but problems of potential safety hazards of the elevator, waste of manpower and material resources and the like cannot be comprehensively and timely found exist, and therefore safety evaluation is conducted on the elevator according to online monitoring data of the elevator, and the elevator state maintenance is of great significance.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a safety fault diagnosis method for an elevator based on multi-source information fusion, which realizes the diagnosis of the safety fault of the elevator.
In order to solve the technical problems, the invention adopts the following technical scheme: a safety fault diagnosis method of an elevator based on multi-source information fusion comprises the following steps:
step 1, respectively acquiring vibration signals of a lift car and a reduction gearbox in the running process of the lift and visual signals of a lift car haulage rope;
The data acquisition part comprises a time domain signal of elevator car and reduction box vibration and an image visual signal of a traction rope, wherein the acquired time domain signal comprises a peak value, a mean value and a pulse index of the time domain signal;
Step 2, preprocessing data of collected vibration signals and vision signals of the car and the reduction gearbox, and respectively extracting vibration signal characteristics of the car and the reduction gearbox and transverse displacement signal characteristics of a car haulage rope;
The signal preprocessing part comprises two parts of time domain signal preprocessing of elevator car and reduction box vibration and image visual signal preprocessing of traction ropes, and specifically comprises the following steps:
Carrying out noise reduction treatment on the time domain signals acquired by vibration of the elevator car and the reduction gearbox by using a median filtering method, carrying out Fourier transformation on the processed time domain signals to convert the processed time domain signals into frequency domain signals, and carrying out feature extraction on the frequency domain signals by using a wavelet analysis method to obtain vibration signal features of the elevator car and the reduction gearbox;
Scanning and denoising the acquired image visual signal of the car haulage rope, calibrating the current haulage rope image information by utilizing a Zhang Zhengyou calibration method, tracking a light bar area irradiated on the steel wire rope in a camera lens by adopting a target tracking method in computer vision, and then acquiring the obtained transverse vibration displacement of the steel wire rope as the transverse displacement signal characteristic of the car haulage rope;
the target tracking adopts a CSRT target tracking method, an input signal is an image signal of an elevator traction rope, and an output result is an image signal after virtual marking; the image signal detection model adopted by the CSRT target tracking method uniformly uses CNN to classify and predict the frame positions;
Step 3, detecting abnormal vibration signals according to the vibration signal characteristics of the lift car and the reduction gearbox, obtaining abnormal vibration signal detection results, detecting abnormal transverse displacement based on the transverse displacement signal characteristics of the lift car traction rope, and obtaining abnormal transverse detection results;
Abnormal vibration signal detection is carried out on the vibration signals of the elevator car and the speed reducer by adopting an abnormal vibration signal detection model, and abnormal image signal detection is carried out on the image visual signals of the elevator traction rope by adopting an abnormal image state detection model;
The abnormal vibration signal detection model adopts LightGBM model, the input of the model is the vibration signal characteristics of the lift car and the reduction gearbox, and the output is the abnormal vibration signal detection result;
the abnormal image detection model adopts an R-CNN model, inputs an image signal of an elevator car haulage rope, outputs an abnormal image signal detection result, and further performs calibration and tracking on the abnormal image signal to obtain an abnormal transverse displacement signal;
Step 4, fusing the abnormal vibration signal detection result and the abnormal transverse displacement signal detection result, and judging whether emergency stop fault alarm is generated for the elevator based on the fusion result;
And carrying out fuzzy information-based fusion on the abnormal vibration signal detection result and the abnormal transverse displacement signal detection result to obtain a diagnosis result, wherein the diagnosis result is represented by the following formula:
Y＝X×RAB
wherein,
X＝[x1 x2 x3]
Wherein A is a diagnosed elevator fault point set, namely 3 fault types of deformation of an elevator car and a speed reducer, wherein the deformation of a car traction rope exceeds a safety value; b is an elevator running state set, namely three elevator running state signals of vibration signals of the elevator car and the reduction gearbox and traction rope transverse displacement signals; the element mu ij in the relation matrix R AB of A and B is the possibility of deducing the decision fault type j from the running states i of different parts of the elevator, i=1, 2,3, j=1, 2,3, X is a reliability value set judged by the running states of different parts of the elevator, x 1、x2、x3 is the reliability value endowed to the running states of different parts of the elevator according to experience or a fault database respectively, and Y obtained through fuzzy transformation is the possibility of fusing the decisions;
When the fault is inferred and decided, a rule-based method is adopted, and the basic principle is as follows:
(1) The determined decision target should have the largest membership value;
(2) The membership value of the determined decision target is larger than a set threshold value;
(3) The difference between the membership values of the determined decision target and other targets is greater than a certain threshold;
And finally, selecting the possibility set of each fault inference decision according to a certain weight to obtain an optimal fault inference result.
The beneficial effects of adopting above-mentioned technical scheme to produce lie in: according to the elevator safety fault diagnosis method based on multi-source information fusion, disclosed by the invention, aiming at the running conditions of an elevator car, a reduction gearbox and a traction rope, the real-time centralized monitoring of the running states of multiple parts of an elevator is realized by utilizing a fuzzy information fusion theory, different weights are given to fault decisions of different parts, and the speed and the accuracy of elevator fault diagnosis are improved.
Drawings
Fig. 1 is a flowchart of an elevator safety fault diagnosis method based on multi-source information fusion provided by an embodiment of the invention;
fig. 2 is a schematic process diagram of an elevator safety fault diagnosis method based on multi-source information fusion according to an embodiment of the present invention
Fig. 3 is a flowchart of obtaining a fault diagnosis result based on fuzzy information fusion according to an embodiment of the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
In this embodiment, an elevator safety fault diagnosis method based on multi-source information fusion, as shown in fig. 1 and 2, includes the following steps:
step 1, respectively acquiring vibration signals of a lift car and a reduction gearbox in the running process of the lift and visual signals of a lift car haulage rope;
The data acquisition part comprises a time domain signal of elevator car and reduction box vibration and an image visual signal of a traction rope, wherein the acquired time domain signal comprises a peak value, a mean value and a pulse index of the time domain signal;
In this embodiment, the arrangement of the monitoring points for the elevator vibration signal is: the monitoring points of the vibration states of the elevator are respectively arranged on two parts of the elevator car and the reduction gearbox, and the sensor nodes in the elevator car are mainly arranged at the central positions of three direction face walls of the elevator car, so that the vibration signals of the elevator are collected by adopting a mode of installing independent acceleration sensors outside mechanical parts of the elevator.
The image state monitoring point of the elevator car haulage rope is arranged on the elevator shaft wall, the detection method based on line structured light is utilized, an imaging system comprises a line light source and an industrial CCD camera, the line light source and the camera have relatively fixed space positions, the light plane of the line light source is perpendicular to the axis of the haulage rope, when the laser plane of the visual sensor of the linear structure is projected onto the surface of a workpiece to be measured, the laser plane can generate deformed light strips due to the change of the surface size of the workpiece, and the camera acquires images of the deformed light strips. The method adopts an independent sensor acquisition mode, so that the existing mechanical structure and control circuit of the elevator cannot be influenced, and the normal operation of the elevator cannot be influenced.
Step 2, preprocessing data of collected vibration signals and vision signals of the car and the reduction gearbox, and respectively extracting vibration signal characteristics of the car and the reduction gearbox and transverse displacement signal characteristics of a car haulage rope;
The signal preprocessing part comprises two parts of time domain signal preprocessing of elevator car and reduction box vibration and image visual signal preprocessing of traction ropes, and specifically comprises the following steps:
Carrying out noise reduction treatment on the time domain signals acquired by vibration of the elevator car and the reduction gearbox by using a median filtering method, carrying out Fourier transformation on the processed time domain signals to convert the processed time domain signals into frequency domain signals, and carrying out feature extraction on the frequency domain signals by using a wavelet analysis method to obtain vibration signal features of the elevator car and the reduction gearbox;
Scanning and denoising the acquired image visual signal of the car haulage rope, calibrating the current haulage rope image information by utilizing a Zhang Zhengyou calibration method, tracking a light bar area irradiated on the steel wire rope in a camera lens by adopting a target tracking method in computer vision, and then acquiring the obtained transverse vibration displacement of the steel wire rope as the transverse displacement signal characteristic of the car haulage rope;
The target tracking adopts a CSRT target tracking method, an input signal is an image (video) signal of an elevator traction rope, and an output result is an image (video) signal after virtual marking; the image signal detection model adopted by the CSRT target tracking method uniformly uses CNN to classify and predict the frame positions, wherein CNN is a convolution neural network with a seven-layer structure, is a feedforward neural network algorithm containing convolution calculation and having a depth structure, and the first layer is an input layer; the second layer and the third layer are all convolution layers and are used for extracting image features, namely converting the picture into feature vectors with fixed dimensions; the fourth layer, the fifth layer and the sixth layer are pooling layers and are used for carrying out feature compression and extracting main features; the seventh layer is a full-connection layer, and the original image is classified through the extracted characteristic parameters; the eighth layer is an output layer;
Step 3, detecting abnormal vibration signals according to the vibration signal characteristics of the lift car and the reduction gearbox, obtaining abnormal vibration signal detection results, detecting abnormal transverse displacement based on the transverse displacement signal characteristics of the lift car traction rope, and obtaining abnormal transverse detection results;
Abnormal vibration signal detection is carried out on the vibration signals of the elevator car and the speed reducer by adopting an abnormal vibration signal detection model, and abnormal image signal detection is carried out on the image visual signals of the elevator traction rope by adopting an abnormal image state detection model;
The abnormal vibration signal detection model adopts LightGBM model, the input of the model is the vibration signal characteristics of the lift car and the reduction gearbox, and the output is the abnormal vibration signal detection result;
the abnormal image detection model adopts an R-CNN model, inputs an image signal of an elevator car haulage rope, outputs an abnormal image signal detection result, and further performs calibration and tracking on the abnormal image signal to obtain an abnormal transverse displacement signal;
Step 4, fusing the abnormal vibration signal detection result and the abnormal transverse displacement signal detection result, and judging whether emergency stop fault alarm is generated for the elevator based on the fusion result;
In this embodiment, fault diagnosis based on fuzzy information fusion is performed on the abnormal vibration signal detection result and the abnormal lateral displacement signal detection result, as shown in fig. 3, specifically:
firstly, acquiring elevator running state data, carrying out fuzzification processing, establishing a relation matrix of running states and fault types, acquiring weight vectors of all sensors, carrying out multiplication operation on the relation matrix, further obtaining a diagnosis membership result matrix, and finally obtaining the possibility of each fused fault decision according to a fault judgment principle, wherein the possibility is shown in the following formula:
Y＝X×RAB
wherein,
X＝[x1 x2 x3]
Wherein A is a diagnosed elevator fault point set, namely 3 fault types of deformation of an elevator car and a speed reducer, wherein the deformation of a car traction rope exceeds a safety value; b is an elevator running state set, namely three elevator running state signals of vibration signals of the elevator car and the reduction gearbox and traction rope transverse displacement signals; the element mu ij in the relation matrix R AB of A and B is the possibility of deducing the decision fault type j from the running states i of different parts of the elevator, i=1, 2,3, j=1, 2,3, X is a reliability value set judged by the running states of different parts of the elevator, x 1、x2、x3 is the reliability value endowed to the running states of different parts of the elevator according to experience or a fault database respectively, and Y obtained through fuzzy transformation is the possibility of fusing the decisions;
When the fault is inferred and decided, a rule-based method is adopted, and the basic principle is as follows:
(1) The determined decision target should have the largest membership value;
(2) The membership value of the determined decision target is larger than a set threshold value;
(3) The difference between the membership values of the determined decision target and other targets is greater than a certain threshold;
And finally, selecting the possibility set of each fault inference decision according to a certain weight to obtain an optimal fault inference result.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (7)
1. A safety fault diagnosis method of an elevator based on multi-source information fusion is characterized in that: the method comprises the following steps:
step 1, respectively acquiring vibration signals of a lift car and a reduction gearbox in the running process of the lift and visual signals of a lift car haulage rope;
Step 2, preprocessing data of collected vibration signals and vision signals of the car and the reduction gearbox, and respectively extracting vibration signal characteristics of the car and the reduction gearbox and transverse displacement signal characteristics of a car haulage rope;
Step 3, detecting abnormal vibration signals according to the vibration signal characteristics of the lift car and the reduction gearbox, obtaining abnormal vibration signal detection results, detecting abnormal transverse displacement based on the transverse displacement signal characteristics of the lift car traction rope, and obtaining abnormal transverse detection results;
And 4, fusing the abnormal vibration signal detection result and the abnormal transverse displacement signal detection result, and judging whether emergency stop fault alarm is generated for the elevator based on the fusion result.
2. The elevator safety fault diagnosis method based on multi-source information fusion according to claim 1, wherein: and step 1, acquiring time domain signals of vibration of the elevator car and the reduction gearbox and image visual signals of the traction rope, wherein the acquired time domain signals comprise peak values, average values and pulse indexes of the time domain signals.
3. The elevator safety fault diagnosis method based on multi-source information fusion according to claim 2, wherein: the data preprocessing in the step 2 comprises a time domain signal preprocessing of elevator car and reduction box vibration and an image visual signal preprocessing of a traction rope, and specifically comprises the following steps:
Carrying out noise reduction treatment on the time domain signals acquired by vibration of the elevator car and the reduction gearbox by using a median filtering method, carrying out Fourier transformation on the processed time domain signals to convert the processed time domain signals into frequency domain signals, and carrying out feature extraction on the frequency domain signals by using a wavelet analysis method to obtain vibration signal features of the elevator car and the reduction gearbox;
Scanning and denoising the acquired image visual signal of the car haulage rope, calibrating the current haulage rope image information by utilizing a Zhang Zhengyou calibration method, tracking a light bar area irradiated on the steel wire rope in a camera lens by adopting a target tracking method in computer vision, and then acquiring the transverse vibration displacement of the steel wire rope as the transverse displacement signal characteristic of the car haulage rope.
4. A method for diagnosing elevator safety faults based on multi-source information fusion as claimed in claim 3, wherein: the target tracking adopts a CSRT target tracking method, an input signal is an image signal of an elevator traction rope, and an output result is an image signal after virtual marking; the image signal detection model adopted by the CSRT target tracking method uniformly uses CNN to classify and predict the frame positions.
5. The elevator safety fault diagnosis method based on multi-source information fusion according to claim 4, wherein: step 3, carrying out abnormal vibration signal detection processing on the vibration signals of the elevator car and the speed reducer by adopting an abnormal vibration signal detection model, and carrying out abnormal image signal detection processing on the image visual signals of the elevator traction rope by adopting an abnormal image state detection model;
The abnormal vibration signal detection model adopts LightGBM model, the input of the model is the vibration signal characteristics of the lift car and the reduction gearbox, and the output is the abnormal vibration signal detection result;
The abnormal image detection model adopts an R-CNN model, inputs an image signal of an elevator car haulage rope, outputs an abnormal image signal detection result, and then calibrates and tracks the abnormal image signal to obtain an abnormal transverse displacement signal.
6. The elevator safety fault diagnosis method based on multi-source information fusion according to claim 5, wherein: and 4, obtaining a diagnosis result based on fuzzy information fusion, wherein the diagnosis result is shown in the following formula:
Y＝X×RAB
wherein,
X＝[x1 x2 x3]
Wherein A is a diagnosed elevator fault point set, namely 3 fault types of deformation of an elevator car and a speed reducer, wherein the deformation of a car traction rope exceeds a safety value; b is an elevator running state set, namely three elevator running state signals of vibration signals of the elevator car and the reduction gearbox and traction rope transverse displacement signals; the element mu ij in the relation matrix R AB of A and B is the possibility of deducing the decision fault type j from the running states i of different parts of the elevator, i=1, 2,3, j=1, 2,3, X is a reliability value set judged by the running states of different parts of the elevator, x 1、x2、x3 is the reliability value endowed to the running states of different parts of the elevator according to experience or a fault database respectively, and Y obtained through fuzzy transformation is the possibility of fusing the decisions;
And finally, selecting the possibility set of each fault inference decision according to a certain weight to obtain an optimal fault inference result.
7. The elevator safety fault diagnosis method based on multi-source information fusion according to claim 6, wherein: when the possibility of deciding the fault type j is inferred from the running states i of different parts of the elevator, a rule-based method is adopted, and the basic principle is as follows:
(1) The determined decision target should have the largest membership value;
(2) The membership value of the determined decision target is larger than a set threshold value;
(3) The difference between the membership values of the determined decision target and the other targets is greater than a certain threshold.
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| Application Number | Priority Date | Filing Date | Title |
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| PCT/CN2022/136820 WO2024119360A1 (en) | 2022-12-06 | 2022-12-06 | Elevator safety fault diagnosis method based on multi-source information fusion |
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Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| SG126045A1 (en) * | 2005-03-24 | 2006-10-30 | Inventio Ag | Elevator with vertical vibration compensation |
| JP4607078B2 (en) * | 2006-09-20 | 2011-01-05 | 三菱電機株式会社 | Elevator rope roll detection device and elevator control operation device |
| CN111836772B (en) * | 2018-03-27 | 2022-06-10 | 因温特奥股份公司 | Method and device for monitoring the properties of a lifting appliance arrangement in an elevator installation |
| CN110642109B (en) * | 2019-04-26 | 2021-03-19 | 深圳市豪视智能科技有限公司 | Vibration detection method and device for lifting equipment, server and storage medium |
| EP3848319B1 (en) * | 2020-01-07 | 2022-05-04 | KONE Corporation | Method for operating an elevator |
| CN111929014B (en) * | 2020-08-10 | 2022-12-06 | 广州广日电梯工业有限公司 | Method and device for measuring vertical vibration of traction rope |
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