Elevator steel wire rope on-line detector and nondestructive detection system and method thereof

Abstract

The invention discloses an elevator steel wire rope on-line detector and a nondestructive detection system and method thereof. Belongs to the technical field of elevator equipment operation and maintenance. The on-line detector for the elevator steel wire rope comprises a main body part and a detection probe, the main body part comprises a main shell, a main control board is arranged in the main shell, and an alloy cover plate is arranged between the main control board and the main shell; a clamping mechanism is arranged outside the main shell and used for fixing the main shell to a traction machine steel beam. The detection probe is provided with a probe shell with an open single face, an amorphous wire sensor is arranged in the probe shell, and the detection face of the amorphous wire sensor faces the open face of the probe shell; the main shell is connected with the probe shell through a metal gooseneck pipe, and the main control board collects magnetic field intensity signals detected by the amorphous wire sensor. The weak magnetic excitation sensor is used as a detection element, the defects of the steel wire rope can be accurately detected, and real-time monitoring and intelligent management are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of elevator equipment operation and maintenance, and more specifically, to an on-line detector for elevator steel ropes, as well as a non-destructive detection system and method therefor. Background Art

[0002] The non-destructive detection of elevator steel ropes is a key link related to the lives and property safety of the people, and is of great significance to the safe operation of elevators. At present, the management of elevator steel ropes mainly relies on manual experience or judgment based on the service life. However, the operating environments of each elevator are different, such as differences in temperature, humidity, load weight, maintenance conditions, etc., resulting in a large number of steel ropes with remaining service lives being scrapped in advance, while steel ropes with defects are still in operation, posing potential safety hazards.

[0003] In the field of non-destructive detection, technologies such as ultrasonic detection and ray detection are widely used, but there are obvious deficiencies in the detection of elevator steel ropes. Ultrasonic detection requires a coupling agent to achieve good contact between the probe and the steel rope. However, the complex operating environment of elevator steel ropes makes it difficult for the coupling agent to stably adhere, affecting the continuity and accuracy of detection. At the same time, the complexity of the internal structure of the steel rope causes ultrasonic signals to be easily scattered and attenuated during propagation, making it difficult to accurately identify and analyze defect echo signals, and the detection ability for micro-defects and deep defects is limited. Although ray detection is applied in some industrial detection scenarios, its application in the detection of elevator steel ropes is restricted by many factors. Ray detection is harmful to the human body, and there are significant safety risks in using it in the crowded elevator operating environment. Moreover, ray detection equipment is bulky, complex to operate, and requires professional protection measures, which not only increases the detection cost but also makes it difficult to conduct effective detection in the narrow space and dynamic operating conditions of elevators.

[0004] In addition, current steel rope detection generally adopts an off-line method, and this detection method also has many drawbacks. The detection is mostly carried out when the equipment is shut down, and it is impossible to monitor the operating state of the steel rope in real time, making it difficult to discover potential defects under dynamic changes. The detection timeliness is poor and the safety risk is high. At the same time, shutdown detection will increase the equipment shutdown time, reduce the operating efficiency, increase the operating cost, and cannot meet the actual needs of safe and efficient detection of elevator steel ropes. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an on-line detector for elevator steel ropes, as well as a non-destructive detection system and method therefor. Based on a multi-level network architecture and using a weak magnetic excitation sensor as the detection element, it can accurately detect steel rope defects, realize real-time monitoring and intelligent management, solve problems such as low accuracy, high cost, and inability to monitor in real time in traditional detection methods, ensure the safe operation of elevators, reduce maintenance costs, and improve management efficiency.

[0006] On the one hand, an embodiment of the present invention provides an on-line elevator wire rope detector, comprising: a main body part and a detection probe; the main body part includes a main housing with a hollow structure, a main control board is arranged inside the main housing, and an alloy cover plate is arranged between the main control board and the main housing; a clamping mechanism is arranged outside the main housing, and the clamping mechanism is used to fix the main housing on the traction machine steel beam; the detection probe includes a probe housing with a single-sided open structure, an amorphous wire sensor is arranged inside the probe housing, and the detection surface of the amorphous wire sensor faces the open surface of the probe housing; the main housing and the probe housing are connected by a metal gooseneck tube, and the main control board is electrically connected to the amorphous wire sensor through a wire, for collecting the magnetic field intensity signal detected by the amorphous wire sensor, and adjusting the angle of the metal gooseneck tube so that the detection range of the amorphous wire sensor covers the position of the wire rope.

[0007] Further, the number of the amorphous wire sensors is two, and they are arranged side by side inside the probe housing along the length direction of the elevator wire rope.

[0008] Further, a 4G data transmission module is also arranged inside the main housing, and the 4G data transmission module is connected to the main control board for uploading the data collected by the main control board.

[0009] Further, the clamping mechanism includes an upper clamping plate, a lower clamping plate and a clamping plate shaft. The upper clamping plate and the lower clamping plate are both rotatably connected to the clamping plate shaft. A first torsion spring is concentrically installed on the clamping plate shaft, and two ends of the first torsion spring are respectively abutted against the upper clamping plate and the lower clamping plate, for realizing that when the first torsion spring is compressed, the clamping ends of the upper clamping plate and the lower clamping plate are separated from each other; a clamping claw is arranged at the clamping end of the lower clamping plate, the clamping claw and the lower clamping plate are both rotatably connected to a clamping claw shaft, and a second torsion spring is concentrically installed on the clamping claw shaft, and two ends of the second torsion spring are respectively abutted against the clamping claw and the lower clamping plate, for realizing that when the second torsion spring is compressed, the upper surface of the clamping claw is parallel to the lower surface of the clamping end of the upper clamping plate.

[0010] Further, the amorphous wire sensor includes an amorphous wire, a detection coil, a preamplifier, an analog switch, an integrator, a filter and a resistor. One end of the amorphous wire is connected to a pulse signal source, and the other end is grounded. The detection coil is wound around the periphery of the amorphous wire and connected to the input end of the preamplifier. The output end of the preamplifier is connected to the first input end of the analog switch. The second input end of the analog switch is connected to the pulse signal source. The output end of the analog switch is connected to the input end of the integrator. The output end of the integrator is connected to the input end of the filter. The resistor is connected in parallel between the input end of the preamplifier and the input end of the filter. The filter is used to output the magnetic field intensity analog signal detected by the detection coil.

[0011] Further, the alloy cover plate is made of permalloy, the main housing is made of aluminum alloy, and the metal gooseneck tube is made of nitinol alloy.

[0012] On the one hand, an embodiment of the present invention provides an elevator non-destructive testing system including an in-line elevator wire rope detector, comprising at least one in-line elevator wire rope detector as described in any one of the above, and: a local computer with operating data receiving and processing software, where the data receiving and processing software is used to communicate with the in-line elevator wire rope detector, on the one hand, to obtain the magnetic field strength signal of the elevator wire rope collected by the in-line elevator wire rope detector, and on the other hand, to process the magnetic field strength signal of the elevator wire rope and perform defect identification based on the processed magnetic field strength signal, where the identified defects include wear, corrosion, broken wires, deformation, and internal stress changes; a cloud server with operating data receiving and management software, where the data receiving and management software is used to communicate with the data receiving and processing software, receive the magnetic field strength signal of the elevator wire rope collected by the in-line elevator wire rope detector and the defect information identified by the data receiving and processing software, and update the operation history data of the elevator equipment.

[0013] Further, the elevator non-destructive testing system further includes: a local display screen with operating display and management software, where the display and management software communicates with the data receiving and processing software and is used to graphically display the processed magnetic field strength signal.

[0014] Further, the elevator non-destructive testing system further includes a YouRenCloud 4G module installed in the elevator machine room to achieve a wireless communication connection between the cloud server and the local computer.

[0015] On the other hand, an embodiment of the present invention also provides an in-line elevator wire rope non-destructive testing method implemented based on the above elevator non-destructive testing system. The method includes the following steps: arranging at least four in-line elevator wire rope detectors along the circumferential direction of the elevator wire rope, adjusting the positions of the in-line elevator wire rope detectors so that the detection coil of the amorphous wire sensor of any in-line elevator wire rope detector is 8 - 12 mm away from the elevator wire rope vertically; extracting the magnetic field strength signal on the wire rope in real time through the amorphous wire sensor, and sending the magnetic field strength signal to the data receiving and processing software running on the local computer through the main control board of the in-line elevator wire rope detector; the data receiving and processing software processes the magnetic field strength signal, draws a magnetic field strength change curve, and performs defect identification based on the shape change of the curve; the data receiving and processing software sends the processed magnetic field strength signal and the identified defect information to the data receiving and management software running on the cloud server for storage.

[0016] Compared with the prior art, the present invention has the following advantages.

[0017] 1. This application utilizes the metal magnetic memory effect to rapidly and nondestructively detect defects and stress concentration sites in metal components and structural parts, without the need for external excitation or ultrasonic coupling media. It solves the problems of complex equipment, cumbersome operation, and high energy consumption caused by the need for excitation devices and media in traditional detection methods, achieving the effects of simple operation and low energy consumption, making the detector miniaturized and lightweight, convenient for carrying and deployment. At the same time, the real-time monitoring process does not require the elevator to stop, is compatible with elevators of different speeds and rope diameters, supports the full life cycle management of steel ropes, and significantly improves the inspection efficiency.

[0018] 2. This application uses an amorphous wire extremely weak magnetic sensor as the sensitive unit, whose sensitivity is increased by millions of times compared with the existing magnetic measurement method. It can identify internal defects in elevator steel ropes, such as wear, corrosion, broken wires, deformation, and internal stress changes, and provide specific information such as the nature, location, and degree of the defects, solving the problems of low accuracy and inability to accurately identify defects in traditional detection technologies, achieving the effect of accurate detection. At the same time, data real-time monitoring is realized through Internet of Things transmission. Based on the monitoring data, early warning of steel rope defect hazards and assessment of the expected service life can be carried out, avoiding excessive replacement of steel ropes, extending the service cycle, and formulating a scientific maintenance plan in cooperation with the elevator health file, improving the reliability and effectiveness of detection.

[0019] 3. This application adopts non-contact detection, which does not generate ray radiation and electromagnetic radiation, solving the problems that traditional detection methods may cause harm to the human body and interfere with elevator operation, achieving the effect of safe detection. In addition, it is not affected by external environments such as dust and water mist, ensuring the stability of the detection results.

[0020] 4. The system architecture constructed in this application is flexible, and the cloud server can be upgraded, capable of adapting to the growth of the number of detectors and meeting the needs of different scale applications, solving the problems that traditional detection systems are difficult to expand and adapt to diverse application scenarios, providing good technical support and expansion space for the wide application and future development of elevator steel rope detection.

[0021] In summary, on the one hand, this application does not have an excitation device, avoiding problems such as performance degradation, unstable measurement, and regular magnetization during long-term use, achieving lifelong maintenance-free and reducing maintenance costs. On the other hand, through accurate detection and assessment of the expected service life of the steel rope, it avoids economic waste caused by excessive replacement of the steel rope, reduces downtime losses, and brings significant economic benefits to elevator use and maintenance. Therefore, this application can be widely promoted in the field of elevator equipment detection. Brief Description of the Drawings

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the attached drawings required for the description of the embodiments or the prior art. Obviously, the attached drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other attached drawings can also be obtained based on these attached drawings.

[0023] Figure 1 It is a schematic structural diagram of an on-line detector for elevator steel ropes in an embodiment.

[0024] Figure 2 It is a right view of the structure of an on-line detector for elevator steel ropes in an embodiment.

[0025] Figure 3 It is a schematic structural diagram of a clamping mechanism in an embodiment.

[0026] Figure 4 It is a sectional view of the clamping mechanism in the B-B plane in an embodiment.

[0027] Figure 5 It is a sectional view of the clamping mechanism in the C-C plane in an embodiment.

[0028] Figure 6 It is a side view of the clamping mechanism in an embodiment.

[0029] Figure 7 It is a schematic structural diagram of a metallic glass fiber sensor in an embodiment.

[0030] Figure 8(a) is a schematic diagram of magnetic induction lines without defects in defect detection.

[0031] Figure 8(b) is a schematic diagram of magnetic induction lines with defects in defect detection.

[0032] Figure 9 It is a schematic structural diagram of an elevator non-destructive testing system including an on-line detector for elevator steel ropes in an embodiment.

[0033] Figure 10 It is a schematic diagram of the data receiving process of the data receiving management software of the cloud server in an embodiment.

[0034] Figure 11 It is a schematic diagram of the data receiving process of the data receiving management software of the local computer in an embodiment.

[0035] Figure 12 It is a schematic diagram of the defect identification process of the data receiving management software of the local computer in an embodiment.

[0036] Figure 13 It is a schematic diagram of the detection interface displayed on the local display screen in an embodiment.

[0037] Figure 14 Schematic diagram of wire breakage and its magnetic field strength distribution of wire rope in the embodiment

[0038] In the figure: 1. Amorphous wire sensor; 100. Amorphous wire; 101. Detection coil; 102. Preamplifier; 103. Analog switch; 104. Integrator; 105. Filter; 106. Resistor; 2. Main control board; 3. Alloy cover plate; 4. Metal gooseneck tube; 5. Probe housing; 6. Main housing; 7. Clamping mechanism; 701. Claw; 702. Clamping plate shaft; 703. Upper clamping plate; 704. Lower clamping plate; 705. Claw shaft; 706. First torsion spring; 707. Second torsion spring; 8. Network port Detailed implementation manners

[0039] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention

[0040] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices

[0041] Embodiment 1 This embodiment provides an on-line detector for elevator wire ropes, as Figure 1As shown in the figure, it mainly includes a main body part and a detection probe. The main body part includes a main housing 6 with a hollow structure. Inside the main housing 6, a main control board 2 is provided. Between the main control board 2 and the main housing 6, an alloy cover plate 3 is provided. Outside the main housing 6, a clamping mechanism 7 is provided. The clamping mechanism 7 is used to fix the main housing 6 on the traction machine steel beam. The detection probe includes a probe housing 5 with a single-sided open structure. Inside the probe housing 5, an amorphous wire sensor 1 is provided. The detection surface of the amorphous wire sensor 1 faces the open surface of the probe housing 5. The main housing 6 and the probe housing 5 are connected by a metal gooseneck tube 4. The main control board 2 is electrically connected to the amorphous wire sensor 1 through a wire, and is used to collect the magnetic field intensity signal of the elevator steel wire rope detected by the amorphous wire sensor 1. By adjusting the angle of the metal gooseneck tube 4, the detection range of the amorphous wire sensor 1 covers the position of the steel wire rope.

[0042] As a preferred embodiment, as Figure 2 shown in the figure, in this embodiment, in order to improve the induction accuracy of the magnetic field intensity, the number of amorphous wire sensors 1 is set to two, and they are arranged in parallel along the length direction of the elevator steel wire rope inside the probe housing 5.

[0043] As a further preferred embodiment, in this embodiment, a 4G data transmission module is further provided inside the main housing 6. The 4G data transmission module is connected to the main control board 2 and is used to upload the data collected by the main control board 2. The 4G data transmission module realizes the wireless network connection between the on-line detector of the elevator steel wire rope and the outside. The on-line detector of the elevator steel wire rope collects the magnetic field intensity signal of the elevator steel wire rope. After being preliminarily processed by the internal circuit, it is sent to the local computer at a set rate through the 4G data transmission module. The 4G data transmission module adjusts the parameters according to the on-site network conditions to ensure stable data transmission.

[0044] As Figures 3 - 6 shown in the figure, in this application, the clamping mechanism 7 includes an upper clamping plate 703, a lower clamping plate 704 and a clamping plate shaft 702. Both the upper clamping plate 703 and the lower clamping plate 704 are rotatably connected to the clamping plate shaft 702. A first torsion spring 706 is concentrically installed on the clamping plate shaft 702. The two ends of the first torsion spring 706 are respectively abutted against the upper clamping plate 703 and the lower clamping plate 704, and are used to realize that when the first torsion spring 706 is compressed, the clamping ends of the upper clamping plate 703 and the lower clamping plate 704 are separated from each other. A clamping jaw 701 is provided at the clamping end of the lower clamping plate 704. The clamping jaw 701 and the lower clamping plate 704 are both rotatably connected to the clamping jaw shaft 705. A second torsion spring 707 is concentrically installed on the clamping jaw shaft 705. The two ends of the second torsion spring 707 are respectively abutted against the clamping jaw 701 and the lower clamping plate 704, and are used to realize that when the second torsion spring 707 is compressed, the upper surface of the clamping jaw 701 is parallel to the lower surface of the clamping end of the upper clamping plate 703. In this embodiment, 3 clamping jaws 701 are preferably arranged in parallel to improve the clamping stability of the clamping mechanism 7.

[0045] As Figure 7As shown in the figure, the amorphous wire sensor 1 includes an amorphous wire 100, a detection coil 101, a preamplifier 102, an analog switch 103, an integrator 104, a filter 105, and a resistor 106. One end of the amorphous wire 100 is connected to a pulse signal source, and the other end is grounded. The signal output frequency of the pulse signal source is in the range of 280 - 310 kHz. The detection coil 101 is wound around the periphery of the amorphous wire 100 and connected to the input end of the preamplifier 102. The output end of the preamplifier 102 is connected to the first input end of the analog switch 103. The second input end of the analog switch 103 is connected to the pulse signal source. The output end of the analog switch 103 is connected to the input end of the integrator 104. The output end of the integrator 104 is connected to the input end of the filter 105. The resistor 106 is connected in parallel between the input end of the preamplifier 102 and the input end of the filter 105. The filter 105 is used to output the analog signal of the magnetic field strength detected by the detection coil 101.

[0046] As a preferred embodiment, the alloy cover plate 3 is made of permalloy, the main housing 6 is made of aluminum alloy, and the metal gooseneck tube 4 is made of nitinol. The alloy cover plate 3 is made of permalloy (magnetic permeability in the order of 10 5 magnitude), attenuating the low-frequency magnetic field of the permanent magnet motor of the traction machine (attenuating the 50 Hz interference by 40 dB) and weakening the interference of the time-varying magnetic field. The main housing 6 is made of aluminum alloy, shielding high-frequency electromagnetic interference (attenuating by 60 dB above 100 MHz) to cope with the electromagnetic environment at the detection site. In addition, the metal gooseneck tube 4 is made of nitinol (which can be bent and locked at 360°) to fix the detection probe in the detection interval of 5 - 15 mm from the steel wire rope, avoiding the eddy current interference when <5 mm. At the same time, the signal attenuation is compensated by the wavelet algorithm to ensure that the signal-to-noise ratio (SNR) > 15 dB. More preferably, the detection distance interval of the detection probe from the steel wire rope is set to 8 - 12 mm.

[0047] This application uses the metal magnetic memory effect to detect defects and stress concentration areas in metal components and structural members. The so-called metal magnetic memory effect refers to: when ferromagnetic metal parts are processed and operated, due to the combined action of load and geomagnetic field, in the stress and deformation concentration areas, there will be a directional and irreversible reorientation of magnetic domain structures with magnetostrictive properties. This irreversible change in the magnetic state will not only be retained after the working load is removed, but also be related to the maximum applied stress. This magnetic state on the surface of metal components "remembers" the positions of micro-defects or stress concentrations, that is, the so-called magnetic memory effect. When a ferromagnetic component in a geomagnetic field environment is subjected to an external load, in the stress concentration area, there will be a directional and irreversible reorientation of magnetic domain structures with magnetostrictive properties. Fixed nodes of magnetic domains will appear in this area, generating magnetic poles and forming a demagnetizing field, thus making the magnetic permeability of the ferromagnetic metal in this area the smallest and forming a leakage magnetic field on the metal surface. The tangential component Hpx of the leakage magnetic field intensity has a maximum value, while the normal component Hpy changes sign and has a zero value. This irreversible change in the magnetic state is still retained and remembered after the working load is removed.

[0048] The principle of metal magnetic memory detection is shown in Figures 8(a) and 8(b). The detection instrument made based on the basic principle of the metal magnetic memory effect can evaluate the stress concentration degree of the component and whether there are micro-defects by recording the distribution of the magnetic field intensity component perpendicular to the surface of the metal component along a certain direction. It can diagnose the stress concentration area inside the ferromagnetic metal component, that is, micro-defects, early failure and damage, etc., and prevent sudden fatigue damage. It is a new detection method born with the progress and development of magnetic sensor technology in the field of non-destructive testing.

[0049] When using an amorphous wire sensor to test a brand-new, defect-free single wire rope with a specification of 6×19 diameter 16mm, the magnetic field intensity is completely evenly distributed along the entire length direction, without obvious protrusions and depressions, as shown in Figure 8(a). When one or more wires of the wire rope are cut or broken at any position, theoretically, due to the increase in magnetic resistance at the broken wire position, a leakage magnetic field will be generated, so there will be obvious protrusions and depressions at the corresponding position of the output of the amorphous wire sensor, and the height and width of the protrusions and depressions are closely related to the number of broken wires, as shown in Figure 8(b).

[0050] Preferably, in this embodiment, the main housing 6 adopts a box structure, with a weight less than 0.5 kg, dimensions of 150 mm × 88.5 × 50 mm, and the installation interface is 4 M4 parallel screws, with a distribution size of 60 × 35 mm. This design takes into account miniaturization, lightweight, easy installation and easy measurement, ensuring stable detection in different scenarios. The on-line elevator wire rope detector is clamped by the clamping mechanism 7 at a suitable position on the elevator main beam in the machine room that does not affect the normal operation of the elevator. The probe housing 5 is parallel and close to the entire row of wire ropes at a distance of 5 - 15 mm. Within this distance, it can ensure that the internal amorphous wire sensor 1 can receive the magnetic field intensity signal generated by the wire rope due to geomagnetic induction.

[0051] The amorphous wire sensor 1 is arranged inside the probe housing 5. The main housing 6 and the probe housing 5 are connected by a metal gooseneck tube 4 (made of nickel-titanium alloy, which can be bent and locked arbitrarily at 360°). Ball heads are provided at both ends of the metal gooseneck tube 4 (which can rotate and lock at 360°). Considering the complex on-site installation environment and small space of the on-line elevator wire rope detector, the number and thickness of different elevator wire ropes, and the different spacings, the installation ground is rough and uneven, and the materials are cement and steel beams. Generally, drilling operations are not allowed. Therefore, the number of amorphous wire sensors 1 is set to 2. On the premise of ensuring the measurement accuracy and range, the size of the probe housing 5 can be greatly reduced and the flexibility can be enhanced. At the same time, the clamping mechanism 7 is used to overcome the problem of complex on-site installation environment. The amorphous wire sensor 1, as the data acquisition unit of the on-line elevator wire rope detector, is arranged inside the probe housing 5. During the actual detection process, the detection distance between the probe housing 5 and the elevator wire rope to be detected is adjusted by adjusting the metal gooseneck tube 4, and the probe housing 5 is kept parallel to the row of wire ropes to be detected, so as to comprehensively and accurately collect the data of the entire wire rope row. The structure of the probe housing 5 adopts a single-side plate structure, which can detect the wire ropes at different angles, improving the comprehensiveness and accuracy of the detection. At the same time, the weight of the detection module can be reduced, and the load of the metal hose can be reduced. The structure is simple, convenient for replacement and maintenance, and the replacement cost is reduced. The non-contact detection reduces the risk of touching the wire ropes. Compared with the previous single-wire measurement, the whole row of multiple wire ropes can be measured simultaneously now.

[0052] Embodiment 2 Based on Embodiment 1, this embodiment provides an elevator non-destructive testing system including an in-line elevator wire rope detector, which comprises at least one in-line elevator wire rope detector as given in Embodiment 1. In addition, it also includes a local computer and a cloud server. Among them, the local computer runs data receiving and processing software, which is used to communicate with the in-line elevator wire rope detector. On the one hand, it acquires the magnetic field intensity signals of the elevator wire rope collected by the in-line elevator wire rope detector. On the other hand, it processes the magnetic field intensity signals of the elevator wire rope, and conducts defect identification based on the processed magnetic field intensity signals. The identified defects include wear, corrosion, broken wires, deformation, and internal stress changes. The cloud server runs data receiving and management software, which is used to communicate with the data receiving and processing software, receive the magnetic field intensity signals of the elevator wire rope collected by the in-line elevator wire rope detector and the defect information identified by the data receiving and processing software, and update the operation history data of the elevator equipment.

[0053] As a preferred implementation manner, in this embodiment, 4 in-line elevator wire rope detectors are used to detect the elevator wire rope from 4 different directions respectively. Assume that when a brand-new and defect-free wire rope is subjected to non-destructive testing, its magnetic field intensity is completely uniformly distributed in the entire length direction without obvious protrusions and depressions. If one or more wires of the wire rope are cut or broken at any position, a leakage magnetic field will be generated due to the increased magnetic resistance at the broken wire. Thus, in the in-line elevator wire rope detector, the magnetic field intensity signal output by the amorphous sensor closest to the broken wire will have obvious protrusions and depressions at its corresponding position, while other sensors also have protrusions and depressions, but with relatively smaller amplitudes, and the height and width of the protrusions and depressions are closely related to the number of broken wires. The detected magnetic field intensity signals are subjected to multi-channel AD conversion through the main control board 2, and then relevant data (such as: position, magnetic field intensity of each magnetic sensor, etc.) are formed into a frame of data packet and sent to the local computer through the 4G data transmission module. The latter displays the current detection results on the screen in the form of magnetic maps, curves, histograms, pie charts, etc. after data processing, defect identification, and diagnosis.

[0054] As a preferred implementation manner, the elevator non-destructive testing system in this embodiment also includes a manned cloud 4G module, which is used to upload the data collected and identified on the local computer to the cloud server. In this application, the cloud server uses Alibaba Cloud server. The manned cloud 4G module uses USR-G780 v2 module. Specifically, the manned cloud 4G module mainly includes three parts: a 4G module host, a SIM IoT card, and a 4G module transceiver antenna. The local computer is connected to the RS485 port of the USR - G780 4G module through its network port to achieve data transfer. The USR - G780 v2 4G module can be set with different communication rates to achieve efficient data transmission and processing, and transmit the detection data to the Alibaba Cloud server.

[0055] For the technical difficulties of detecting the time-varying magnetic field from the permanent magnet motor of the elevator traction machine at the detection site, where the interference pattern of each elevator is unique, and the magnetic interference is relatively large at the moment of elevator startup. This embodiment adopts a dual anti-interference mechanism. On the one hand, the hardware shielding technology is used to cope with the electromagnetic interference environment. For example, the online elevator wire rope detector introduced in the embodiment adopts a double shielding of the main housing 6 made of aluminum alloy and the probe housing 5 + the metal cover 3 made of permalloy. On the other hand, during the data processing process, a software denoising algorithm is used to improve the data reliability. Specifically, the discrete wavelet transform (DWT) is used to decompose the magnetic field intensity signal into 3 frequency bands (approximate component A3, detail components D1 - D3), separating the low-frequency interference (such as the motor fundamental frequency) from the high-frequency defect signals (the broken wire corresponds to the D1 - D2 frequency band); the wavelet transform reconstructs the denoised signal, and the defect feature retention rate after reconstruction exceeds 92%, and the signal-to-noise ratio is increased by 15 - 20 dB. Subsequently, the wavelet packet decomposition (WPD) is performed to extract the multi-band energy features (such as the 0 - 500 Hz low-frequency band, the 1000 - 2000 Hz high-frequency band), and combined with parameters such as kurtosis and peak factor, a multi-dimensional feature vector is provided for defect classification.

[0056] In addition, for the broadband pulse interference (200 ms, 100 - 500 Hz) at the moment of elevator startup, the interference interval is marked by the wavelet time-frequency diagram, the corresponding wavelet coefficients are forced to be zero, and combined with morphological filtering (opening operation) to remove the spike noise, ensuring the defect recognition rate during the interference period and realizing the time-frequency interference suppression.

[0057] Aiming at the problems of complex working conditions and small space at the detection site, this application lightens and miniaturizes the online elevator wire rope detector while overcoming the technical difficulties of environmental temperature and equipment body heating on the detected object.

[0058] Specifically, this application's online elevator wire rope detector removes the local processing module and only retains the data acquisition and 4G transmission functions. The main control board and the 4G data transmission module are integrated. The aluminum alloy housing (thermal conductivity 237 W / m·K) and the installation channel steel form a heat conduction path. When the total power consumption is 3W, the temperature rise is <5℃ (the housing is 40℃ when the environment is 35℃), and the wavelet baseline correction algorithm is combined to eliminate the temperature drift (<0.1%FS). The extremely weak magnetic amorphous wire sensor 1 (accuracy 500 PT, power consumption 0.5W) is used to avoid the influence of the heat generated by high-power devices.

[0059] Aiming at the technical difficulties that there are many defect category patterns in the traction steel wire ropes for elevators, it is impossible to quickly identify and judge the defect categories and achieve reliable accuracy. This application sets up a high-frequency sampling and feature extraction mechanism. The sensor captures signals at a sampling rate of 5000Hz, and extracts features such as the energy ratio of each frequency band, kurtosis, and peak factor through WPD. For wire breakage, there are spike pulses in the high-frequency band (1000 - 2000Hz), and kurtosis > 5.0; for wear, the energy ratio in the low-frequency band (0 - 500Hz) > 60%, with continuous fluctuations; for strand breakage, there are sudden changes in multi-band energy, peak factor > 3.5 and accompanied by low-frequency resonance.

[0060] Further preferably, this embodiment can also combine an intelligent classification model: construct a 1D-CNN convolutional neural network, including 3 convolutional layers and a fully connected layer, for processing multi-dimensional feature inputs. Model input: multi-dimensional feature data, with a shape of (number of samples, time steps, feature dimension). Model output: the original scores (logits) without softmax activation, with a shape of (number of samples, number of classes).

[0061] As Figure 9 shown, as a preferred embodiment of this application, the elevator non-destructive testing system in this embodiment mainly includes an Alibaba Cloud server with operation data receiving and management software, a local computer with operation data receiving and processing software, an on-line elevator wire rope detector, a local display screen with operation display and management software, and a manned cloud 4G module.

[0062] In this embodiment, the wire rope defect detector is installed online in the elevator shaft and conducts inspections 24 hours a day, 365 days a year. The data collected by the on-line elevator wire rope detector is transmitted to the cloud server in real time, and the cloud server centrally processes the data of multiple elevators. The defect types, quantities, and positions of each elevator are updated at any time, and the historical data of each elevator is stored in the cloud database. The system can grasp the health status and change trends of each elevator in real time, and make processing based on the current data, historical data, and trend of each elevator to ensure the safety of each elevator throughout its life cycle. The specific setting scheme is as follows.

[0063] Installation period: High-frequency sampling (5000Hz) captures manufacturing defects (such as cold-drawn cracks in steel wires), identifies signal abnormal points through wavelet analysis, and establishes an initial health record.

[0064] Operation period: Generate a health index daily (based on defect density and growth rate), use the ARIMA model to predict the remaining life, set the warning threshold to 80% of the theoretical life, and update the defect position and quantity in the cloud in real time.

[0065] Retirement period: Store the full-life data in the cloud, provide a basis for wear simulation in the design of new wire ropes, and form a closed loop of "detection - analysis - optimization".

[0066] Specifically, the data reception management software has a port management module, a data reception module, and a storage and management module. As Figure 10 shown in the schematic diagram of the process for the Alibaba Cloud server to receive data. Its main process is as follows: After starting, initial values are assigned to variables first, and then it enters a loop to wait until 5 am. After meeting the time condition, it judges whether the current time is greater than 5 am. If so, it calls a sub-function to obtain the port list. Then it judges whether a simple log file is established. If so, it writes the port list that needs to obtain data on the current day into the log file, otherwise the process ends. Next, it judges whether the length of the port list is greater than 0. If so, it enters the main loop and judges whether the current time is less than 23 pm. During this time period, it traverses the port list and judges whether each port is still in the list. If the port is still in the list, it judges the time again. If the time meets the condition, it calls a sub-function to receive and store the data of the current port until the port list traversal is completed. If the current time is not less than 23 pm or the port is not in the list, it records the log file and then exits the process. The whole process controls the data reception and storage operations on the specified ports within a specific time period through loops and conditional judgments, and records relevant logs for subsequent viewing at the same time.

[0067] Furthermore, the data reception and processing software has a data reception module, a data cleaning module, a defect detection module, and a result storage and management module. The local computer works according to specific time periods, obtains data from the Alibaba Cloud server, and after cleaning and extraction, processes it in segments and detects defects, and writes the results into the elevator file. The brief flowchart of the main program of the data reception and processing software on the local computer is shown in Figure 11 . After starting, initial values are assigned to variables, and then it enters a loop to wait until a specific time. After meeting the time condition, it obtains the current date. Next, it judges whether the sub-directory named after the date exists. If not, it creates the sub-directory; if it exists, it calls a sub-function to copy the data file from the Alibaba Cloud to the sub-directory. It obtains the file list under the current date directory, and then processes each file in the list: judges whether the file is still in the file list of the current date directory. If not, it records the log file. If the file exists, it calls a sub-function to clean and extract the current data file and save it as a mat file. It extracts the useful data data from the current mat file. It divides data by 28000 and judges whether the processed data_process is still within the data range: if it is not within the data range, it records the log file. If it is within the data range, it calls a sub-function to perform fault detection on the current data. If the detection result meets the condition, it writes the number of faults, failure rate, etc. in the detection result into the elevator file. If it does not meet the condition, it records the log file.

[0068] Data processing uses Python 3 as the programming tool. Currently, the time-domain processing method is adopted. The data is segmented by 25,000 bytes. By finding "peaks" and "valleys", parameter statistics, and matching with typical defect and interference signal curves, defects are accurately identified. In the long run, technologies such as wavelet analysis, artificial intelligence, and machine learning will be combined to achieve the industry's optimal detection from qualitative to quantitative. The flowchart of the defect detection subroutine is shown in Figure 12 . It mainly includes the following processes. First, extract the useful data data from the current mat file. Second, perform data segmentation and processing: Segment the useful data data at the boundary of 28,000 bytes to form the segmented and processed data_process. Judge whether the current data_process is still within the data processing range: If not, record the log file and return the detection results (number of defects, detection rate, etc.) to the main program, and finally end the subroutine. Third, initialize variables: Initialize variables: v_off = 2800, peak_valley_min = 1300, data_temp = [], peaks_temp = [], valleys_temp = [], index_0 = 0. Subsequently, perform data segmentation and matching: Segment the current data segment of 28,000 bytes to form data2_temp. Judge whether the current data2_temp is still within the data processing range: If not, return to continue segmenting data_process. If within the range, find peaks and valleys: Find the "peaks peaks_temp" within the current data segment, and eliminate the "peaks" through the set amplitude to generate a new "peaks" list. Find the "valleys valleys_temp" within the current data segment, and eliminate the "valleys" through the set amplitude to generate a new "valleys" list. Finally, perform matching and elimination processing. Use the typical defect curve and the set similarity to match in the "valleys" list to generate a new "valleys" list. Use the typical interference curve and the set similarity to eliminate in the "valleys" list to generate a new "valleys" list. Use the typical defect curve and the set similarity to match in the "peaks" list to generate a new "peaks" list. Use the typical interference curve and the set similarity to eliminate in the "peaks" list to generate a new "peaks" list. If there is still data to be processed, repeatedly execute the above steps to process the next data segment. The entire process uses loops and conditional judgments to segment the data, find peaks and valleys, match typical curves, and eliminate interference, and finally return the processed detection results to the main program and record the relevant log file.

[0069] The local computer works from 9:00 am to 23:50 pm every day, obtains the daily data file from the Alibaba Cloud server, processes it in segments after cleaning and extraction, calls the defect detection algorithm, and writes the results into the elevator file.

[0070] Further, as Figure 13 shown is the detection interface of the local large screen display in this embodiment. The upper left part is the display area of the historical monitoring data of a certain elevator, and the display content includes the number of defects, test time, etc. Different elevators can be switched by setting the path. The upper right part is the statistical data area of all detected elevators, such as the total monitoring quantity, the number of broken wires, the number of rusts, etc. The lower part of the figure is the real-time monitoring curve display area of the elevator, which shows the magnetic signal curve of the wire rope detection of a certain elevator. The real-time magnetic signal curves of different elevators can be displayed by clicking the import data button. Figure 14 shows the schematic diagram of the magnetic field intensity distribution in the case of wire rope breakage in this embodiment. As shown in the figure, the magnetic field intensity signal curves around four wire ropes are given. The vertical coordinate in the figure represents the magnetic field intensity, and the horizontal coordinate represents the position of the elevator wire rope. According to the signal curves in the figure, it can be seen that the wire rope represented by the yellow dotted line has very obvious peak changes in the position range of 680 - 880. Therefore, it can be judged that this wire rope has defects, and according to the specific shape of the curve, the defect type can be further judged.

[0071] As a preferred implementation manner, the elevator non-destructive testing system may further include a local display large screen. The local display large screen runs display management software, and the display management software communicates with the data receiving software for graphically displaying the processed magnetic field intensity data.

[0072] In this embodiment, the manned cloud 4G module is installed in the elevator machine room. Aiming at the technical problems of weak signal of the manned cloud 4G module in the elevator machine room and its applicability to the high-temperature environment of the machine room, this embodiment conducts a dual anti-environmental interference design. On the one hand, the MIMO dual-antenna technology is adopted: the bit error rate in a weak signal environment (< -110dBm) is reduced by 40%, and it supports automatic gain control (AGC) and diversity reception (activated when < -105dBm). On the other hand, the process scenario is adapted. In the extreme environment where the signal in the elevator machine room (on the roof) is very weak, it can be switched to the NB-IoT mode (coverage enhanced by 20dB), and the TCP long connection + heartbeat packet mechanism is adopted, and the disconnection and reconnection time is < 5s. Temperature self-adaptation: An internal PT100 sensor is installed, and the power consumption is dynamically adjusted through the SPI interface. The transmission stability reaches 99.5% at -30℃ to 70℃.

[0073] Embodiment 3 The embodiment of the present invention further provides an online non-destructive testing method for elevator wire ropes, which is implemented based on the elevator non-destructive testing system in Embodiment 2. The method includes the following steps.

[0074] Install at least four on-line elevator wire rope detectors circumferentially along the elevator wire rope. Adjust the positions of the on-line elevator wire rope detectors so that the vertical distance between the detection coil of the amorphous wire sensor of any on-line elevator wire rope detector and the elevator wire rope is 8 - 12 mm. This distance range is the optimal detection distance interval obtained through a large number of experiments. It can not only ensure that the sensor effectively senses the magnetic field strength signal of the wire rope, but also avoid problems such as signal distortion or weakness caused by too close or too far distance, thus laying a foundation for subsequent accurate defect identification.

[0075] The magnetic field strength signal on the wire rope is extracted in real time by the amorphous wire sensor, and the magnetic field strength signal is sent to the data receiving and processing software running on the local computer through the main control board of the on-line elevator wire rope detector. Specifically, the amorphous wire sensors work in real time, closely track and accurately extract the magnetic field strength signals generated on the surface and inside of the wire rope. With their high sensitivity characteristics, these sensors can capture the weak magnetic field changes caused by defects during the operation of the wire rope. Subsequently, the main control board of the on-line elevator wire rope detector responds quickly, preliminarily integrates and encodes the collected magnetic field strength signals to ensure the integrity and stability of the signals during transmission. The processed magnetic field strength signal is immediately sent to the data receiving and processing software running on the local computer, establishing an efficient information transmission link between the detection site and the data processing center and realizing seamless data docking.

[0076] The data receiving and processing software processes the magnetic field strength signal, draws the magnetic field strength change curve, and identifies defects according to the shape change of the curve. Specifically, after receiving the magnetic field strength data, the data receiving and processing software running on the local computer immediately starts the professional data processing algorithm module. First, a series of preprocessing operations such as filtering and denoising are performed on the original magnetic field strength data to remove the influence of interference signals and extract the effective magnetic field strength information related to the true state of the wire rope. Then, according to the preset data fitting model and curve drawing algorithm, the processed data is converted into an intuitive magnetic field strength change curve, clearly showing the fluctuation of the magnetic field strength at different positions of the wire rope. By deeply analyzing the shape change of this curve and using the built-in defect feature recognition algorithm to compare and match with the known defect feature templates of various types, it is possible to accurately identify whether there are defects such as broken wires, wear, and corrosion in the wire rope, as well as the specific location and severity of the defects, providing a key basis for subsequent maintenance.

[0077] The data receiving and processing software sends the processed magnetic field intensity data and the identified defect information to the data receiving and management software running on the cloud server for storage. Specifically, after completing the processing of the magnetic field intensity data and the identification of the defect information, the data receiving and processing software sends two important pieces of information - the processed magnetic field intensity data and the identified defect information - to the data receiving and management software running on the cloud server according to a unified data format and transmission protocol. The data receiving and management software on the cloud server side is responsible for centrally receiving, classifying, storing, and long-term preserving these data. By building a cloud database, the orderly management of a large amount of detection data is realized, which facilitates users to remotely access and query historical detection data at any time, trace the operation status of the elevator wire rope throughout the process, and conduct comprehensive analysis, effectively improving the intelligent level and decision-making scientificity of elevator safety management.

[0078] The online non-destructive testing method for elevator wire ropes in this embodiment realizes real-time, accurate, and efficient non-destructive testing of elevator wire ropes through scientific arrangement of detectors, high-precision signal acquisition and transmission, professional data processing, and cloud data management, providing strong technical support for ensuring the safe operation of elevators.

[0079] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An on-line detector for elevator steel ropes, characterized in that Comprising: A main body part and a detection probe; the main body part includes a main housing with a hollow structure, a main control board is arranged inside the main housing, and an alloy cover plate is arranged between the main control board and the main housing; a clamping mechanism is arranged outside the main housing, and the clamping mechanism is used to fix the main housing on the traction machine steel beam; the detection probe includes a probe housing with a single-sided open structure, an amorphous wire sensor is arranged inside the probe housing, and the detection surface of the amorphous wire sensor faces the open surface of the probe housing; The main housing is connected to the probe housing through a metal gooseneck tube, and the main control board is electrically connected to the amorphous wire sensor through a wire, for collecting the magnetic field intensity signal detected by the amorphous wire sensor, and the detection range of the amorphous wire sensor covers the position of the steel wire rope by adjusting the angle of the metal gooseneck tube.

2. The on-line elevator wire rope detector according to claim 1, characterized in that, The number of the amorphous wire sensors is two, and they are arranged in parallel inside the probe housing along the length direction of the elevator steel wire rope.

3. An on-line detector for elevator steel wire ropes according to claim 1, characterized in that, A 4G data transmission module is further arranged inside the main housing, and the 4G data transmission module is connected to the main control board for uploading the data collected by the main control board.

4. An on-line detector for elevator steel ropes according to claim 1, characterized in that The clamping mechanism includes an upper clamping plate, a lower clamping plate and a clamping plate shaft. The upper clamping plate and the lower clamping plate are both rotatably connected to the clamping plate shaft. A first torsion spring is concentrically installed on the clamping plate shaft, and two ends of the first torsion spring respectively abut against the upper clamping plate and the lower clamping plate, for realizing that when the first torsion spring is compressed, the clamping ends of the upper clamping plate and the lower clamping plate are separated from each other; A clamping jaw is arranged at the clamping end of the lower clamping plate. The clamping jaw and the lower clamping plate are both rotatably connected to a clamping jaw shaft. A second torsion spring is concentrically installed on the clamping jaw shaft, and two ends of the second torsion spring respectively abut against the clamping jaw and the lower clamping plate, for realizing that when the second torsion spring is compressed, the upper surface of the clamping jaw is parallel to the lower surface of the clamping end of the upper clamping plate.

5. An on-line elevator steel wire rope detector according to claim 1, characterized in that, The amorphous wire sensor includes an amorphous wire, a detection coil, a preamplifier, an analog switch, an integrator, a filter and a resistor. One end of the amorphous wire is connected to a pulse signal source, and the other end is grounded. The detection coil is wound around the periphery of the amorphous wire and connected to the input end of the preamplifier. The output end of the preamplifier is connected to the first input end of the analog switch. The second input end of the analog switch is connected to the pulse signal source. The output end of the analog switch is connected to the input end of the integrator. The output end of the integrator is connected to the input end of the filter. The resistor is connected in parallel between the input end of the preamplifier and the input end of the filter. The filter is used to output the magnetic field intensity analog signal detected by the detection coil.

6. The on-line elevator steel wire rope detector according to claim 1, characterized in that The alloy cover plate is made of permalloy material, the main housing is made of aluminum alloy material, and the metal gooseneck tube is made of nitinol alloy material.

7. An elevator non-destructive testing system comprising an on-line detector for elevator steel wire ropes, characterized in that, Including at least one on-line detector for elevator steel wire ropes as described in any one of claims 1-6, and: A local computer running data receiving and processing software, which is used to communicate with the on-line elevator wire rope detector. On the one hand, it acquires the magnetic field intensity signal of the elevator wire rope collected by the on-line elevator wire rope detector. On the other hand, it processes the magnetic field intensity signal of the elevator wire rope and conducts defect identification based on the processed magnetic field intensity signal, where the identified defects include wear, corrosion, broken wires, deformation and internal stress changes; A cloud server running data receiving and management software, which is used to communicate with the data receiving and processing software, receive the magnetic field intensity signal of the elevator wire rope collected by the on-line elevator wire rope detector and the defect information identified by the data receiving and processing software, and update the operation history data of the elevator equipment.

8. An elevator non-destructive testing system comprising an on-line detector for elevator steel ropes according to claim 7, characterized in that, The elevator non-destructive testing system further includes: A local display screen running display management software, which communicates with the data receiving and processing software and is used to graphically display the processed magnetic field intensity signal.

9. An elevator non-destructive testing system comprising an on-line detector for elevator steel wire ropes according to claim 7, characterized in that, The elevator non-destructive testing system further includes a manned cloud 4G module, which is installed in the elevator machine room to realize the wireless communication connection between the cloud server and the local computer.

10. An online non-destructive testing method for elevator steel wire ropes, implemented based on the elevator non-destructive testing system described in claim 7, characterized in that, The method includes the following steps: Set at least four on-line elevator wire rope detectors along the circumferential direction of the elevator wire rope, and adjust the positions of the on-line elevator wire rope detectors so that the vertical distance between the detection coil of the amorphous wire sensor of any on-line elevator wire rope detector and the elevator wire rope is 8 - 12 mm; Extract the magnetic field intensity signal on the wire rope in real time through the amorphous wire sensor, and send the magnetic field intensity signal to the data receiving and processing software running on the local computer through the main control board of the on-line elevator wire rope detector; The data receiving and processing software processes the magnetic field intensity signal, draws the magnetic field intensity change curve, and conducts defect identification according to the shape change of the curve; The data receiving and processing software sends the processed magnetic field intensity signal and the identified defect information to the data receiving and management software running on the cloud server for storage.

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